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Jian T, Yang M, Wu T, Ji X, Xia S, Sun F. Diagnostic value of dynamic contrast enhancement combined with conventional MRI in differentiating benign and malignant lacrimal gland epithelial tumours. Clin Radiol 2024; 79:e345-e352. [PMID: 37953093 DOI: 10.1016/j.crad.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 11/14/2023]
Abstract
AIM To establish the diagnostic value of the quantitative parameters of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) combined with conventional MRI in differentiating of benign and malignant lacrimal gland epithelial tumours. MATERIALS AND METHODS A retrospective analysis of primary lacrimal gland epithelial tumours confirmed by histopathology was conducted. Conventional MRI features and DCE-MRI quantitative parameters were collected and subjected to analysis. The diagnostic value was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS A total of 53 patients were enrolled of which 29 had malignant, whereas 24 had benign tumours. Conventional MRI revealed statistically significant differences between benign and malignant tumours regarding maximum tumour diameter, posterior margin characteristic, bone destruction, and erosion. The Ktrans and Kep values obtained by DCE-MRI were higher in malignant than in benign tumours, with a statistically significant (p<0.001 and p=0.022). A type I time-signal intensity (TIC) curve was more frequent in benign tumours, whereas a type II TIC curve was prevalent in malignant tumours (p=0.001). ROC analysis showed that Ktrans had the best diagnostic value of the DCE-MRI parameters (area under the ROC curve [AUC] of 0.822, 75.9% sensitivity, and 83.3% specificity, p<0.001). The combination of conventional MRI and DCE-MRI factors had the best diagnostic value and balanced sensitivity and specificity (AUC of 0.948, 93.1% sensitivity, and 91.7% specificity, p<0.001). CONCLUSIONS The present findings indicate that the combination of quantitative parameters of DCE-MRI and image characteristics of conventional MRI have a high diagnostic value for the diagnosis of benign and malignant lacrimal gland epithelial tumours.
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Affiliation(s)
- T Jian
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - M Yang
- Department of Ophthalmology, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, Taizhou, China
| | - T Wu
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - X Ji
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - S Xia
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - F Sun
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China.
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2
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Li X, Huang W, Holmes JH. Dynamic Contrast-Enhanced (DCE) MRI. Magn Reson Imaging Clin N Am 2024; 32:47-61. [PMID: 38007282 DOI: 10.1016/j.mric.2023.09.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
The non-invasive dynamic contrast-enhanced MRI (DCE-MRI) method provides valuable insights into tissue perfusion and vascularity. Primarily used in oncology, DCE-MRI is typically utilized to assess morphology and contrast agent (CA) kinetics in the tissue of interest. Interpretation of the temporal signatures of DCE-MRI data includes qualitative, semi-quantitative, and quantitative approaches. Recent advances in MRI technology allow simultaneous high spatial and temporal resolutions in DCE-MRI data acquisition on most vendor platforms, enabling the more desirable approach of quantitative data analysis using pharmacokinetic (PK) modeling. Many technical factors, including signal-to-noise ratio, temporal resolution, quantifications of arterial input function and native tissue T1, and PK model selection, need to be carefully considered when performing quantitative DCE-MRI. Standardization in data acquisition and analysis is especially important in multi-center studies.
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Affiliation(s)
- Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
| | - James H Holmes
- Radiology, Biomedical Engineering, and Holden Cancer Center, University of Iowa, 169 Newton Road, Iowa City, IA 52242, USA.
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3
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LoCastro E, Paudyal R, Konar AS, LaViolette PS, Akin O, Hatzoglou V, Goh AC, Bochner BH, Rosenberg J, Wong RJ, Lee NY, Schwartz LH, Shukla-Dave A. A Quantitative Multiparametric MRI Analysis Platform for Estimation of Robust Imaging Biomarkers in Clinical Oncology. Tomography 2023; 9:2052-2066. [PMID: 37987347 PMCID: PMC10661267 DOI: 10.3390/tomography9060161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/12/2023] [Accepted: 10/18/2023] [Indexed: 11/22/2023] Open
Abstract
There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines ("MRI-QAMPER", current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER's functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test-retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.
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Affiliation(s)
- Eve LoCastro
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Amaresha Shridhar Konar
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
| | - Peter S. LaViolette
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI 53226, USA;
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Alvin C. Goh
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Bernard H. Bochner
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Jonathan Rosenberg
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Richard J. Wong
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (A.C.G.); (B.H.B.); (R.J.W.)
| | - Nancy Y. Lee
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA;
| | - Lawrence H. Schwartz
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (E.L.); (R.P.); (A.S.K.)
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; (O.A.); (V.H.); (L.H.S.)
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4
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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5
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Wang B, Pan Y, Xu S, Zhang Y, Ming Y, Chen L, Liu X, Wang C, Liu Y, Xia Y. Quantitative Cerebral Blood Volume Image Synthesis from Standard MRI Using Image-to-Image Translation for Brain Tumors. Radiology 2023; 308:e222471. [PMID: 37581504 DOI: 10.1148/radiol.222471] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Background Cerebral blood volume (CBV) maps derived from dynamic susceptibility contrast-enhanced (DSC) MRI are useful but not commonly available in clinical scenarios. Purpose To test image-to-image translation techniques for generating CBV maps from standard MRI sequences of brain tumors using the bookend technique DSC MRI as ground-truth references. Materials and Methods A total of 756 MRI examinations, including quantitative CBV maps produced from bookend DSC MRI, were included in this retrospective study. Two algorithms, the feature-consistency generative adversarial network (GAN) and three-dimensional encoder-decoder network with only mean absolute error loss, were trained to synthesize CBV maps. The performance of the two algorithms was evaluated quantitatively using the structural similarity index (SSIM) and qualitatively by two neuroradiologists using a four-point Likert scale. The clinical value of combining synthetic CBV maps and standard MRI scans of brain tumors was assessed in several clinical scenarios (tumor grading, prognosis prediction, differential diagnosis) using multicenter data sets (four external and one internal). Differences in diagnostic and predictive accuracy were tested using the z test. Results The three-dimensional encoder-decoder network with T1-weighted images, contrast-enhanced T1-weighted images, and apparent diffusion coefficient maps as the input achieved the highest synthetic performance (SSIM, 86.29% ± 4.30). The mean qualitative score of the synthesized CBV maps by neuroradiologists was 2.63. Combining synthetic CBV with standard MRI improved the accuracy of grading gliomas (standard MRI scans area under the receiver operating characteristic curve [AUC], 0.707; standard MRI scans with CBV maps AUC, 0.857; z = 15.17; P < .001), prediction of prognosis in gliomas (standard MRI scans AUC, 0.654; standard MRI scans with CBV maps AUC, 0.793; z = 9.62; P < .001), and differential diagnosis between tumor recurrence and treatment response in gliomas (standard MRI scans AUC, 0.778; standard MRI scans with CBV maps AUC, 0.853; z = 4.86; P < .001) and brain metastases (standard MRI scans AUC, 0.749; standard MRI scans with CBV maps AUC, 0.857; z = 6.13; P < .001). Conclusion GAN image-to-image translation techniques produced accurate synthetic CBV maps from standard MRI scans, which could be used for improving the clinical evaluation of brain tumors. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Branstetter in this issue.
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Affiliation(s)
- Bao Wang
- From the Department of Radiology, Qilu Hospital of Shandong University, Jinan, China (B.W.); School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China (Y.P., Y.X.); Departments of Neurosurgery (B.W., S.X., Y.L.) and Radiology (Y.Z.), Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China (Y.M., L.C., Y.L.); Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (X.L.); Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China (C.W.); and Shandong Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China (Y.L.)
| | - Yongsheng Pan
- From the Department of Radiology, Qilu Hospital of Shandong University, Jinan, China (B.W.); School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China (Y.P., Y.X.); Departments of Neurosurgery (B.W., S.X., Y.L.) and Radiology (Y.Z.), Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China (Y.M., L.C., Y.L.); Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (X.L.); Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China (C.W.); and Shandong Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China (Y.L.)
| | - Shangchen Xu
- From the Department of Radiology, Qilu Hospital of Shandong University, Jinan, China (B.W.); School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China (Y.P., Y.X.); Departments of Neurosurgery (B.W., S.X., Y.L.) and Radiology (Y.Z.), Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China (Y.M., L.C., Y.L.); Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (X.L.); Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China (C.W.); and Shandong Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China (Y.L.)
| | - Yi Zhang
- From the Department of Radiology, Qilu Hospital of Shandong University, Jinan, China (B.W.); School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China (Y.P., Y.X.); Departments of Neurosurgery (B.W., S.X., Y.L.) and Radiology (Y.Z.), Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China (Y.M., L.C., Y.L.); Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (X.L.); Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China (C.W.); and Shandong Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China (Y.L.)
| | - Yang Ming
- From the Department of Radiology, Qilu Hospital of Shandong University, Jinan, China (B.W.); School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China (Y.P., Y.X.); Departments of Neurosurgery (B.W., S.X., Y.L.) and Radiology (Y.Z.), Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China (Y.M., L.C., Y.L.); Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (X.L.); Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China (C.W.); and Shandong Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China (Y.L.)
| | - Ligang Chen
- From the Department of Radiology, Qilu Hospital of Shandong University, Jinan, China (B.W.); School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China (Y.P., Y.X.); Departments of Neurosurgery (B.W., S.X., Y.L.) and Radiology (Y.Z.), Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China (Y.M., L.C., Y.L.); Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (X.L.); Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China (C.W.); and Shandong Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China (Y.L.)
| | - Xuejun Liu
- From the Department of Radiology, Qilu Hospital of Shandong University, Jinan, China (B.W.); School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China (Y.P., Y.X.); Departments of Neurosurgery (B.W., S.X., Y.L.) and Radiology (Y.Z.), Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China (Y.M., L.C., Y.L.); Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (X.L.); Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China (C.W.); and Shandong Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China (Y.L.)
| | - Chengwei Wang
- From the Department of Radiology, Qilu Hospital of Shandong University, Jinan, China (B.W.); School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China (Y.P., Y.X.); Departments of Neurosurgery (B.W., S.X., Y.L.) and Radiology (Y.Z.), Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China (Y.M., L.C., Y.L.); Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (X.L.); Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China (C.W.); and Shandong Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China (Y.L.)
| | - Yingchao Liu
- From the Department of Radiology, Qilu Hospital of Shandong University, Jinan, China (B.W.); School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China (Y.P., Y.X.); Departments of Neurosurgery (B.W., S.X., Y.L.) and Radiology (Y.Z.), Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China (Y.M., L.C., Y.L.); Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (X.L.); Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China (C.W.); and Shandong Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China (Y.L.)
| | - Yong Xia
- From the Department of Radiology, Qilu Hospital of Shandong University, Jinan, China (B.W.); School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China (Y.P., Y.X.); Departments of Neurosurgery (B.W., S.X., Y.L.) and Radiology (Y.Z.), Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China; Department of Neurosurgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China (Y.M., L.C., Y.L.); Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (X.L.); Department of Neurosurgery, the Second Hospital of Shandong University, Jinan, China (C.W.); and Shandong Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China (Y.L.)
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Bagher-Ebadian H, Brown SL, Ghassemi MM, Nagaraja TN, Movsas B, Ewing JR, Chetty IJ. Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI. Sci Rep 2023; 13:10693. [PMID: 37394559 DOI: 10.1038/s41598-023-37723-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/27/2023] [Indexed: 07/04/2023] Open
Abstract
Here, we investigate radiomics-based characterization of tumor vascular and microenvironmental properties in an orthotopic rat brain tumor model measured using dynamic-contrast-enhanced (DCE) MRI. Thirty-two immune compromised-RNU rats implanted with human U-251N cancer cells were imaged using DCE-MRI (7Tesla, Dual-Gradient-Echo). The aim was to perform pharmacokinetic analysis using a nested model (NM) selection technique to classify brain regions according to vasculature properties considered as the source of truth. A two-dimensional convolutional-based radiomics analysis was performed on the raw-DCE-MRI of the rat brains to generate dynamic radiomics maps. The raw-DCE-MRI and respective radiomics maps were used to build 28 unsupervised Kohonen self-organizing-maps (K-SOMs). A Silhouette-Coefficient (SC), k-fold Nested-Cross-Validation (k-fold-NCV), and feature engineering analyses were performed on the K-SOMs' feature spaces to quantify the distinction power of radiomics features compared to raw-DCE-MRI for classification of different Nested Models. Results showed that eight radiomics features outperformed respective raw-DCE-MRI in prediction of the three nested models. The average percent difference in SCs between radiomics features and raw-DCE-MRI was: 29.875% ± 12.922%, p < 0.001. This work establishes an important first step toward spatiotemporal characterization of brain regions using radiomics signatures, which is fundamental toward staging of tumors and evaluation of tumor response to different treatments.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Physics, Oakland University, Rochester, MI, 48309, USA.
| | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Mohammad M Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tavarekere N Nagaraja
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - James R Ewing
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Wayne State University, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
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7
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Bagher-Ebadian H, Brown SL, Ghassemi MM, Nagaraja TN, Valadie OG, Acharya PC, Cabral G, Divine G, Knight RA, Lee IY, Xu JH, Movsas B, Chetty IJ, Ewing JR. Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models. Sci Rep 2023; 13:9672. [PMID: 37316579 PMCID: PMC10267191 DOI: 10.1038/s41598-023-36483-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 06/05/2023] [Indexed: 06/16/2023] Open
Abstract
We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, vp, Ktrans, and ve, respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches.
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Affiliation(s)
- Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA.
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Osteopathic Medicine, Michigan State University, East Lansing, MI, 48824, USA.
- Department of Physics, Oakland University, Rochester, MI, 48309, USA.
| | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Mohammad M Ghassemi
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA
| | - Tavarekere N Nagaraja
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Olivia Grahm Valadie
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Prabhu C Acharya
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
| | - Glauber Cabral
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
| | - George Divine
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Epidemiology and Biostatistics, Michigan State University, E. Lansing, MI, 48824, USA
| | - Robert A Knight
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
| | - Ian Y Lee
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Jun H Xu
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
| | - Benjamin Movsas
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Radiation Oncology, Wayne State University, Detroit, MI, 48202, USA
| | - James R Ewing
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
- Department of Physics, Oakland University, Rochester, MI, 48309, USA
- Department of Neurosurgery, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Henry Ford Health, Detroit, MI, 48202, USA
- Department of Neurology, Wayne State University, Detroit, MI, 48202, USA
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8
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Bartlett S, Nagaraja TN, Griffith B, Farmer KG, Van Harn M, Haider S, Hunt RJ, Cabral G, Knight RA, Valadie OG, Brown SL, Ewing JR, Lee IY. Persistent Peri-Ablation Blood-Brain Barrier Opening After Laser Interstitial Thermal Therapy for Brain Tumors. Cureus 2023; 15:e37397. [PMID: 37182017 PMCID: PMC10171839 DOI: 10.7759/cureus.37397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2023] [Indexed: 05/16/2023] Open
Abstract
Purpose Laser interstitial thermal therapy (LITT) is a minimally invasive, image-guided, cytoreductive procedure to treat recurrent glioblastoma. This study implemented dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) methods and employed a model selection paradigm to localize and quantify post-LITT blood-brain barrier (BBB) permeability in the ablation vicinity. Serum levels of neuron-specific enolase (NSE), a peripheral marker of increased BBB permeability, were measured. Methods Seventeen patients were enrolled in the study. Using an enzyme-linked immunosorbent assay, serum NSE was measured preoperatively, 24 hours postoperatively, and at two, eight, 12, and 16 weeks postoperatively, depending on postoperative adjuvant treatment. Of the 17 patients, four had longitudinal DCE-MRI data available, from which blood-to-brain forward volumetric transfer constant (Ktrans) data were assessed. Imaging was performed preoperatively, 24 hours postoperatively, and between two and eight weeks postoperatively. Results Serum NSE increased at 24 hours following ablation (p=0.04), peaked at two weeks, and returned to baseline by eight weeks postoperatively. Ktrans was found to be elevated in the peri-ablation periphery 24 hours after the procedure. This increase persisted for two weeks. Conclusion Following the LITT procedure, serum NSE levels and peri-ablation Ktrans estimated from DCE-MRI demonstrated increases during the first two weeks after ablation, suggesting transiently increased BBB permeability.
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Affiliation(s)
- Seamus Bartlett
- Neurosurgery, Wayne State University School of Medicine, Detroit, USA
| | | | | | | | | | - Sameah Haider
- Neurological Surgery, Henry Ford Health, Detroit, USA
| | | | | | | | | | | | | | - Ian Y Lee
- Neurosurgery, Henry Ford Health, Detroit, USA
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9
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Grahm Valadie O, Brown SL, Farmer K, Nagaraja TN, Cabral G, Shadaia S, Divine GW, Knight RA, Lee IY, Dolan J, Rusu S, Joiner MC, Ewing JR. Characterization of the Response of 9L and U-251N Orthotopic Brain Tumors to 3D Conformal Radiation Therapy. Radiat Res 2023; 199:217-228. [PMID: 36656561 PMCID: PMC10174721 DOI: 10.1667/rade-22-00048.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 12/21/2022] [Indexed: 01/20/2023]
Abstract
In a study employing MRI-guided stereotactic radiotherapy (SRS) in two orthotopic rodent brain tumor models, the radiation dose yielding 50% survival (the TCD50) was sought. Syngeneic 9L cells, or human U-251N cells, were implanted stereotactically in 136 Fischer 344 rats or 98 RNU athymic rats, respectively. At approximately 7 days after implantation for 9L, and 18 days for U-251N, rats were imaged with contrast-enhanced MRI (CE-MRI) and then irradiated using a Small Animal Radiation Research Platform (SARRP) operating at 220 kV and 13 mA with an effective energy of ∼70 keV and dose rate of ∼2.5 Gy per min. Radiation doses were delivered as single fractions. Cone-beam CT images were acquired before irradiation, and tumor volumes were defined using co-registered CE-MRI images. Treatment planning using MuriPlan software defined four non-coplanar arcs with an identical isocenter, subsequently accomplished by the SARRP. Thus, the treatment workflow emulated that of current clinical practice. The study endpoint was animal survival to 200 days. The TCD50 inferred from Kaplan-Meier survival estimation was approximately 25 Gy for 9L tumors and below 20 Gy, but within the 95% confidence interval in U-251N tumors. Cox proportional-hazards modeling did not suggest an effect of sex, with the caveat of wide confidence intervals. Having identified the radiation dose at which approximately half of a group of animals was cured, the biological parameters that accompany radiation response can be examined.
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Affiliation(s)
- O. Grahm Valadie
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan
- Department of Radiation Oncology, Wayne State University, Detroit, Michigan
| | - Stephen L. Brown
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan
- Department of Radiation Oncology, Wayne State University, Detroit, Michigan
- Department of Radiology, Michigan State University College of Human Medicine, East Lansing, Michigan
| | - Katelynn Farmer
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan
| | | | - Glauber Cabral
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan
| | - Sheldon Shadaia
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan
| | - George W. Divine
- Department of Public Health Sciences, Henry Ford Hospital, Detroit Michigan
| | - Robert A. Knight
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan
- Department of Physics, Oakland University, Rochester, Michigan
| | - Ian Y. Lee
- Department of Neurosurgery, Henry Ford Hospital, Detroit Michigan
| | - Jennifer Dolan
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan
| | - Sam Rusu
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan
| | - Michael C. Joiner
- Department of Radiation Oncology, Wayne State University, Detroit, Michigan
| | - James R. Ewing
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan
- Department of Radiology, Michigan State University College of Human Medicine, East Lansing, Michigan
- Department of Neurosurgery, Henry Ford Hospital, Detroit Michigan
- Department of Physics, Oakland University, Rochester, Michigan
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10
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Chawla S, Loevner L, Mohan S, Lin A, Sehgal CM, Poptani H. Dynamic contrast-enhanced MRI and Doppler sonography in patients with squamous cell carcinoma of head and neck treated with induction chemotherapy. JOURNAL OF CLINICAL ULTRASOUND : JCU 2022; 50:1353-1359. [PMID: 36205388 DOI: 10.1002/jcu.23361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/05/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
In view of the inherent limitations associated with performing dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in clinical settings, current study was designed to provide a proof of principle that Doppler sonography and DCE-MRI derived perfusion parameters yield similar hemodynamic information from metastatic lymph nodes in squamous cell carcinomas of head and neck (HNSCCs). Strong positive correlations between volume fraction of plasma space in tissues (Vp ) and blood volume (r = 0.72, p = 0.02) and between Vp and %area perfused (r = 0.65, p = 0.04) were observed. Additionally, a moderate positive correlation trending towards significance was obtained between volume transfer constant (Ktrans ) and %area perfused (r = 0.49, p = 0.09).
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Affiliation(s)
- Sanjeev Chawla
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laurie Loevner
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Alexander Lin
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Chandra M Sehgal
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Harish Poptani
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK
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11
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Han Z, Chen C, Xu X, Bai R, Staedtke V, Huang J, Chan KW, Xu J, Kamson DO, Wen Z, Knutsson L, van Zijl PC, Liu G. Dynamic contrast-enhanced CEST MRI using a low molecular weight dextran. NMR IN BIOMEDICINE 2022; 35:e4649. [PMID: 34779550 PMCID: PMC8828685 DOI: 10.1002/nbm.4649] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 09/27/2021] [Accepted: 10/19/2021] [Indexed: 05/25/2023]
Abstract
Natural and synthetic sugars have great potential for developing highly biocompatible and translatable chemical exchange saturation transfer (CEST) MRI contrast agents. In this study, we aimed to develop the smallest clinically available form of dextran, Dex1 (molecular weight, MW ~ 1 kDa), as a new CEST agent. We first characterized the CEST properties of Dex1 in vitro at 11.7 T and showed that the Dex1 had a detectable CEST signal at ~1.2 ppm, attributed to hydroxyl protons. In vivo CEST MRI studies were then carried out on C57BL6 mice bearing orthotopic GL261 brain tumors (n = 5) using a Bruker BioSpec 11.7 T MRI scanner. Both steady-state full Z-spectral images and single offset (1.2 ppm) dynamic dextran-enhanced (DDE) images were acquired before and after the intravenous injection of Dex1 (2 g/kg). The steady-state Z-spectral analysis showed a significantly higher CEST contrast enhancement in the tumor than in contralateral brain (∆MTRasym1.2 ppm = 0.010 ± 0.006 versus 0.002 ± 0.008, P = 0.0069) at 20 min after the injection of Dex1. Pharmacokinetic analyses of DDE were performed using the area under the curve (AUC) in the first 10 min after Dex1 injection, revealing a significantly higher uptake of Dex1 in the tumor than in brain tissue for tumor-bearing mice (AUC[0-10 min] = 21.9 ± 4.2 versus 5.3 ± 6.4%·min, P = 0.0294). In contrast, no Dex1 uptake was foundling in the brains of non-tumor-bearing mice (AUC[0-10 min] = -1.59 ± 2.43%·min). Importantly, the CEST MRI findings were consistent with the measurements obtained using DCE MRI and fluorescence microscopy, demonstrating the potential of Dex1 as a highly translatable CEST MRI contrast agent for assessing tumor hemodynamics.
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Affiliation(s)
- Zheng Han
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Chuheng Chen
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
| | - Xiang Xu
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Renyuan Bai
- Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, USA
| | - Verena Staedtke
- Department of Neurology and Neurosurgery, Johns Hopkins University, Baltimore, MD, USA
| | - Jianpan Huang
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Kannie W.Y. Chan
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, China
| | - Jiadi Xu
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - David O. Kamson
- The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, MD, USA
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Linda Knutsson
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Peter C.M. van Zijl
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Guanshu Liu
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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12
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Longitudinal Monitoring of Simulated Interstitial Fluid Pressure for Pancreatic Ductal Adenocarcinoma Patients Treated with Stereotactic Body Radiotherapy. Cancers (Basel) 2021; 13:cancers13174319. [PMID: 34503129 PMCID: PMC8430878 DOI: 10.3390/cancers13174319] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/03/2021] [Accepted: 08/19/2021] [Indexed: 11/25/2022] Open
Abstract
Simple Summary High vessel permeability, poor perfusion, low lymphatic drainage, and dense abundant stroma elevate interstitial fluid pressures (IFP) in pancreatic ductal adenocarcinoma (PDAC). The present study aims to monitor longitudinal changes in simulated tumor IFP and velocity (IFV) values using a dynamic contrast-enhanced (DCE)-MRI-based computational fluid modeling (CFM) approach in PDAC. Nine PDAC patients underwent DCE-MRI acquisition on a 3-Tesla MRI scanner at pre-treatment (TX (0)), immediately after the first fraction of stereotactic body radiotherapy (SBRT, (D1-TX)), and six weeks post-TX (D2-TX). The partial differential equation of IFP formulated from the continuity equation using the Starling Principle of fluid exchange and Darcy velocity–pressure relationship was solved in COMSOL Multiphysics software to generate IFP and IFV parametric maps using relevant tumor tissue physiological parameters. Initial results suggest that after validation, IFP and IFV can be imaging biomarkers of early response to therapy that may guide precision medicine in PDAC. Abstract The present study aims to monitor longitudinal changes in simulated tumor interstitial fluid pressure (IFP) and velocity (IFV) values using dynamic contrast-enhanced (DCE)-MRI-based computational fluid modeling (CFM) in pancreatic ductal adenocarcinoma (PDAC) patients. Nine PDAC patients underwent MRI, including DCE-MRI, on a 3-Tesla MRI scanner at pre-treatment (TX (0)), after the first fraction of stereotactic body radiotherapy (SBRT, (D1-TX)), and six weeks post-TX (D2-TX). The partial differential equation of IFP formulated from the continuity equation, incorporating the Starling Principle of fluid exchange, Darcy velocity, and volume transfer constant (Ktrans), was solved in COMSOL Multiphysics software to generate IFP and IFV maps. Tumor volume (Vt), Ktrans, IFP, and IFV values were compared (Wilcoxon and Spearman) between the time- points. D2-TX Ktrans values were significantly different from pre-TX and D1-TX (p < 0.05). The D1-TX and pre-TX mean IFV values exhibited a borderline significant difference (p = 0.08). The IFP values varying <3.0% between the three time-points were not significantly different (p > 0.05). Vt and IFP values were strongly positively correlated at pre-TX (ρ = 0.90, p = 0.005), while IFV exhibited a strong negative correlation at D1-TX (ρ = −0.74, p = 0.045). Vt, Ktrans, IFP, and IFV hold promise as imaging biomarkers of early response to therapy in PDAC.
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13
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Nagaraja TN, Elmghirbi R, Brown SL, Rey JA, Schultz L, Mukherjee A, Cabral G, Panda S, Lee IY, Sarntinoranont M, Keenan KA, Knight RA, Ewing JR. Imaging acute effects of bevacizumab on tumor vascular kinetics in a preclinical orthotopic model of U251 glioma. NMR IN BIOMEDICINE 2021; 34:e4516. [PMID: 33817893 PMCID: PMC8978145 DOI: 10.1002/nbm.4516] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 03/12/2021] [Accepted: 03/13/2021] [Indexed: 05/05/2023]
Abstract
The effect of a human vascular endothelial growth factor antibody on the vasculature of human tumor grown in rat brain was studied. Using dynamic contrast-enhanced magnetic resonance imaging, the effects of intravenous bevacizumab (Avastin; 10 mg/kg) were examined before and at postadministration times of 1, 2, 4, 8, 12 and 24 h (N = 26; 4-5 per time point) in a rat model of orthotopic, U251 glioblastoma (GBM). The commonly estimated vascular parameters for an MR contrast agent were: (i) plasma distribution volume (vp ), (ii) forward volumetric transfer constant (Ktrans ) and (iii) reverse transfer constant (kep ). In addition, extracellular distribution volume (VD ) was estimated in the tumor (VD-tumor ), tumor edge (VD-edge ) and the mostly normal tumor periphery (VD-peri ), along with tumor blood flow (TBF), peri-tumoral hydraulic conductivity (K) and interstitial flow (Flux) and tumor interstitial fluid pressure (TIFP). Studied as % changes from baseline, the 2-h post-treatment time point began showing significant decreases in vp , VD-tumor, VD-edge and VD-peri , as well as K, with these changes persisting at 4 and 8 h in vp , K, VD-tumor, -edge and -peri (t-tests; p < 0.05-0.01). Decreases in Ktrans were observed at the 2- and 4-h time points (p < 0.05), while interstitial volume fraction (ve ; = Ktrans /kep ) showed a significant decrease only at the 2-h time point (p < 0.05). Sustained decreases in Flux were observed from 2 to 24 h (p < 0.01) while TBF and TIFP showed delayed responses, increases in the former at 12 and 24 h and a decrease in the latter only at 12 h. These imaging biomarkers of tumor vascular kinetics describe the short-term temporal changes in physical spaces and fluid flows in a model of GBM after Avastin administration.
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Affiliation(s)
| | - Rasha Elmghirbi
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan, USA
- Department of Physics, Oakland University, Rochester, Michigan, USA
| | - Stephen L. Brown
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Julian A. Rey
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida, USA
| | - Lonni Schultz
- Department of Neurosurgery, Henry Ford Hospital, Detroit, Michigan, USA
| | - Abir Mukherjee
- Department of Pathology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Glauber Cabral
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Swayamprava Panda
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Ian Y. Lee
- Department of Neurosurgery, Henry Ford Hospital, Detroit, Michigan, USA
| | - Malisa Sarntinoranont
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida, USA
| | - Kelly A. Keenan
- Department of Neurosurgery, Henry Ford Hospital, Detroit, Michigan, USA
| | - Robert A. Knight
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan, USA
- Department of Physics, Oakland University, Rochester, Michigan, USA
| | - James R. Ewing
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan, USA
- Department of Physics, Oakland University, Rochester, Michigan, USA
- Department of Neurology, Wayne State University, Detroit, Michigan, USA
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14
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Bliesener Y, Lebel RM, Acharya J, Frayne R, Nayak KS. Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma. Radiology 2021; 300:410-420. [PMID: 34100683 PMCID: PMC8328086 DOI: 10.1148/radiol.2021203628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Background Advances in sub-Nyquist–sampled dynamic contrast-enhanced (DCE) MRI enable monitoring of brain tumors with millimeter resolution and whole-brain coverage. Such undersampled quantitative methods need careful characterization regarding achievable test-retest reproducibility. Purpose To demonstrate a fully automated high-resolution whole-brain DCE MRI pipeline with 30-fold sparse undersampling and estimate its reproducibility on the basis of reference regions of stable tissue types during multiple posttreatment time points by using longitudinal clinical images of high-grade glioma. Materials and Methods Two methods for sub-Nyquist–sampled DCE MRI were extended with automatic estimation of vascular input functions. Continuously acquired three-dimensional k-space data with ramped-up flip angles were partitioned to yield high-resolution, whole-brain tracer kinetic parameter maps with matched precontrast-agent T1 and M0 maps. Reproducibility was estimated in a retrospective study in participants with high-grade glioma, who underwent three consecutive standard-of-care examinations between December 2016 and April 2019. Coefficients of variation and reproducibility coefficients were reported for histogram statistics of the tracer kinetic parameters plasma volume fraction and volume transfer constant (Ktrans) on five healthy tissue types. Results The images from 13 participants (mean age ± standard deviation, 61 years ± 10; nine women) with high-grade glioma were evaluated. In healthy tissues, the protocol achieved a coefficient of variation less than 57% for median Ktrans, if Ktrans was estimated consecutively. The maximum reproducibility coefficient for median Ktrans was estimated to be at 0.06 min–1 for large or low-enhancing tissues and to be as high as 0.48 min–1 in smaller or strongly enhancing tissues. Conclusion A fully automated, sparsely sampled DCE MRI reconstruction with patient-specific vascular input function offered high spatial and temporal resolution and whole-brain coverage; in healthy tissues, the protocol estimated median volume transfer constant with maximum reproducibility coefficient of 0.06 min–1 in large, low-enhancing tissue regions and maximum reproducibility coefficient of less than 0.48 min–1 in smaller or more strongly enhancing tissue regions. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Lenkinski in this issue.
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Affiliation(s)
- Yannick Bliesener
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - R Marc Lebel
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - Jay Acharya
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - Richard Frayne
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
| | - Krishna S Nayak
- From the Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, 3740 McClintock Ave, EEB 400, Los Angeles, CA 90089-2564 (Y.B., K.S.N.); GE Healthcare, Calgary, Canada (R.M.L.); Department of Radiology, University of Calgary, Calgary, Canada (R.M.L.); Seaman Family MR Research Centre, Foothills Hospital, Calgary, Canada (R.M.L., R.F.); Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, Calif (J.A., K.S.N.); and Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada (R.F.)
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15
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Colbert CM, Thomas MA, Yan R, Radjenovic A, Finn JP, Hu P, Nguyen KL. Estimation of fractional myocardial blood volume and water exchange using ferumoxytol-enhanced magnetic resonance imaging. J Magn Reson Imaging 2021; 53:1699-1709. [PMID: 33382176 PMCID: PMC8297410 DOI: 10.1002/jmri.27494] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 01/07/2023] Open
Abstract
Fractional myocardial blood volume (fMBV) estimated using ferumoxytol-enhanced magnetic resonance imaging (MRI) (FE-MRI) has the potential to capture a hemodynamic response to myocardial hypoperfusion during contrast steady state without reliance on gadolinium chelates. Ferumoxytol has a long intravascular half-life and its use for steady-state MRI is off-label. The aim of this prospective study was to optimize and evaluate a two-compartment model for estimation of fMBV based on FE-MRI. Nine healthy swine and one swine with artificially induced single-vessel coronary stenosis underwent MRI on a 3.0 T clinical magnet. Myocardial longitudinal spin-lattice relaxation rate (R1) was measured using the 5(3)3(3)3 modified Look-Locker inversion recovery (MOLLI) sequence before and at contrast steady state following seven ferumoxytol infusions (0.125-4.0 mg/kg). fMBV and water exchange were estimated using a two-compartment model. Model-fitted fMBV was compared to simple fast-exchange fMBV approximation and percent change in pre- and postferumoxytol R1. Dose undersampling schemes were investigated to reduce acquisition duration. Variation in fMBV was assessed using one-way analysis of variance. Fast-exchange fMBV and ferumoxytol dose undersampling were evaluated using Bland-Altman analysis. Healthy normal swine showed a mean mid-ventricular fMBV of 7.2 ± 1.4% and water exchange rate of 11.3 ± 5.1 s-1 . There was intersubject variation in fMBV (p < 0.05) without segmental variation (p = 0.387). fMBV derived from eight-dose and four-dose sampling schemes had no significant bias (mean difference = 0.07, p = 0.541, limits of agreement -1.04% [-1.45, -0.62%] to 1.18% [0.77, 1.59%]). Pixel-wise fMBV in one swine model with coronary artery stenosis showed elevated fMBV in ischemic segments (apical anterior: 11.90 ± 4.00%, apical septum: 16.10 ± 5.71%) relative to remote segments (apical inferior: 9.59 ± 3.35%, apical lateral: 9.38 ± 2.35%). A two-compartment model based on FE-MRI using the MOLLI sequence may enable estimation of fMBV in studies of ischemic heart disease. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 2.
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Affiliation(s)
- Caroline M. Colbert
- Physics and Biology in Medicine Graduate Program, David
Geffen School of Medicine at UCLA
| | - Michael A. Thomas
- Division of Cardiology, David Geffen School of Medicine at
UCLA and VA Greater Los Angeles Healthcare System
| | - Ran Yan
- Bioengineering Graduate Program, Henry Samueli School of
Engineering and Applied Science at UCLA
| | - Aleksandra Radjenovic
- Institute of Cardiovascular & Medical Sciences, College
of Medical, Veterinary and Life Sciences, University of Glasgow, UK
| | - J. Paul Finn
- Physics and Biology in Medicine Graduate Program, David
Geffen School of Medicine at UCLA
- Diagnostic Cardiovascular Imaging Laboratory, Department of
Radiological Sciences, David Geffen School of Medicine at UCLA
| | - Peng Hu
- Physics and Biology in Medicine Graduate Program, David
Geffen School of Medicine at UCLA
- Bioengineering Graduate Program, Henry Samueli School of
Engineering and Applied Science at UCLA
- Diagnostic Cardiovascular Imaging Laboratory, Department of
Radiological Sciences, David Geffen School of Medicine at UCLA
| | - Kim-Lien Nguyen
- Physics and Biology in Medicine Graduate Program, David
Geffen School of Medicine at UCLA
- Division of Cardiology, David Geffen School of Medicine at
UCLA and VA Greater Los Angeles Healthcare System
- Diagnostic Cardiovascular Imaging Laboratory, Department of
Radiological Sciences, David Geffen School of Medicine at UCLA
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16
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Nagaraja TN, Lee IY. Cerebral microcirculation in glioblastoma: A major determinant of diagnosis, resection, and drug delivery. Microcirculation 2021; 28:e12679. [PMID: 33474805 DOI: 10.1111/micc.12679] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 01/12/2021] [Indexed: 12/25/2022]
Abstract
Glioblastoma (GBM) is the most common primary brain tumor with a dismal prognosis. Current standard of treatment is safe maximal tumor resection followed by chemotherapy and radiation. Altered cerebral microcirculation and elevated blood-tumor barrier (BTB) permeability in tumor periphery due to glioma-induced vascular dysregulation allow T1 contrast-enhanced visualization of resectable tumor boundaries. Newer tracers that label the tumor and its vasculature are being increasingly used for intraoperative delineation of glioma boundaries for even more precise resection. Fluorescent 5-aminolevulinic acid (5-ALA) and indocyanine green (ICG) are examples of such intraoperative tracers. Recently, magnetic resonance imaging (MRI)-based MR thermometry is being employed for laser interstitial thermal therapy (LITT) for glioma debulking. However, aggressive, fatal recurrence always occurs. Postsurgical chemotherapy is hampered by the inability of most drugs to cross the blood-brain barrier (BBB). Understanding postsurgical changes in brain microcirculation and permeability is crucial to improve chemotherapy delivery. It is important to understand whether any microcirculatory indices can differentiate between true recurrence and radiation necrosis. LITT leads to peri-ablation BBB opening that persists for several weeks. Whether it can be a conduit for chemotherapy delivery is yet to be explored. This review will address the role of cerebral microcirculation in such emerging ideas in GBM diagnosis and therapy.
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Affiliation(s)
| | - Ian Y Lee
- Department of Neurosurgery, Henry Ford Hospital, Detroit, MI, USA
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17
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Nagaraja TN, deCarvalho AC, Brown SL, Griffith B, Farmer K, Irtenkauf S, Hasselbach L, Mukherjee A, Bartlett S, Valadie OG, Cabral G, Knight RA, Lee IY, Divine GW, Ewing JR. The impact of initial tumor microenvironment on imaging phenotype. Cancer Treat Res Commun 2021; 27:100315. [PMID: 33571801 PMCID: PMC8127413 DOI: 10.1016/j.ctarc.2021.100315] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 01/13/2021] [Accepted: 01/14/2021] [Indexed: 11/13/2022]
Abstract
Models of human cancer, to be useful, must replicate human disease with high fidelity. Our focus in this study is rat xenograft brain tumors as a model of human embedded cerebral tumors. A distinguishing signature of such tumors in humans, that of contrast-enhancement on imaging, is often not present when the human cells grow in rodents, despite the xenografts having nearly identical DNA signatures to the original tumor specimen. Although contrast enhancement was uniformly evident in all the human tumors from which the xenografts’ cells were derived, we show that long-term contrast enhancement in the model tumors may be determined conditionally by the tumor microenvironment at the time of cell implantation. We demonstrate this phenomenon in one of two patient-derived orthotopic xenograft (PDOX) models using cancer stem-like cell (CSC)-enriched neurospheres from human tumor resection specimens, transplanted to groups of immune-compromised rats in the presence or absence of a collagen/fibrin scaffolding matrix, Matrigel. The rats were imaged by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and their brains were examined by histopathology. Targeted proteomics of the PDOX tumor specimens grown from CSC implanted with and without Matrigel showed that while the levels of the majority of proteins and post-translational modifications were comparable between contrast-enhancing and non-enhancing tumors, phosphorylation of Fox038 showed a differential expression. The results suggest key proteins determine contrast enhancement and suggest a path toward the development of better animal models of human glioma. Future work is needed to elucidate fully the molecular determinants of contrast-enhancement.
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Affiliation(s)
| | | | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, MI, United States; Department of Public Health, Henry Ford Hospital, Detroit, MI, United States
| | - Brent Griffith
- Department of Radiology, Henry Ford Hospital, Detroit, MI, United States
| | - Katelynn Farmer
- Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States
| | - Susan Irtenkauf
- Department of Neurosurgery, Henry Ford Hospital, Detroit, MI
| | | | - Abir Mukherjee
- Department of Pathology, Henry Ford Hospital, Detroit, MI, United States
| | - Seamus Bartlett
- Department of Neurosurgery, Henry Ford Hospital, Detroit, MI; School of Medicine, Wayne State University, Detroit, MI, United States
| | - O Grahm Valadie
- Department of Radiation Oncology, Wayne State University, Detroit, MI, United States
| | - Glauber Cabral
- Department of Neurology, Henry Ford Hospital, Detroit, MI, United States
| | - Robert A Knight
- Department of Neurology, Henry Ford Hospital, Detroit, MI, United States; Department of Physics, Oakland University, Rochester, MI, United States
| | - Ian Y Lee
- Department of Neurosurgery, Henry Ford Hospital, Detroit, MI
| | - George W Divine
- Department of Public Health, Henry Ford Hospital, Detroit, MI, United States
| | - James R Ewing
- Department of Neurosurgery, Henry Ford Hospital, Detroit, MI; Department of Neurology, Henry Ford Hospital, Detroit, MI, United States; Department of Physics, Oakland University, Rochester, MI, United States; Department of Neurology, Wayne State University, Detroit, MI, United States
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18
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Pedersen M, Irrera P, Dastrù W, Zöllner FG, Bennett KM, Beeman SC, Bretthorst GL, Garbow JR, Longo DL. Dynamic Contrast Enhancement (DCE) MRI-Derived Renal Perfusion and Filtration: Basic Concepts. Methods Mol Biol 2021; 2216:205-227. [PMID: 33476002 DOI: 10.1007/978-1-0716-0978-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Dynamic contrast-enhanced (DCE) MRI monitors the transit of contrast agents, typically gadolinium chelates, through the intrarenal regions, the renal cortex, the medulla, and the collecting system. In this way, DCE-MRI reveals the renal uptake and excretion of the contrast agent. An optimal DCE-MRI acquisition protocol involves finding a good compromise between whole-kidney coverage (i.e., 3D imaging), spatial and temporal resolution, and contrast resolution. By analyzing the enhancement of the renal tissues as a function of time, one can determine indirect measures of clinically important single-kidney parameters as the renal blood flow, glomerular filtration rate, and intrarenal blood volumes. Gadolinium-containing contrast agents may be nephrotoxic in patients suffering from severe renal dysfunction, but otherwise DCE-MRI is clearly useful for diagnosis of renal functions and for assessing treatment response and posttransplant rejection.Here we introduce the concept of renal DCE-MRI, describe the existing methods, and provide an overview of preclinical DCE-MRI applications to illustrate the utility of this technique to measure renal perfusion and glomerular filtration rate in animal models.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction is complemented by two separate publications describing the experimental procedure and data analysis.
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Affiliation(s)
- Michael Pedersen
- Department of Clinical Medicine - Comparative Medicine Lab, Aarhus University, Aarhus, Denmark
| | - Pietro Irrera
- University of Campania "Luigi Vanvitelli", Napoli, Italy
| | - Walter Dastrù
- Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Kevin M Bennett
- Washington University School of Medicine, St. Louis, MO, USA
| | - Scott C Beeman
- Washington University School of Medicine, St. Louis, MO, USA
| | | | - Joel R Garbow
- Washington University School of Medicine, St. Louis, MO, USA
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Torino, Italy.
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19
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Verheggen ICM, de Jong JJA, van Boxtel MPJ, Postma AA, Verhey FRJ, Jansen JFA, Backes WH. Permeability of the windows of the brain: feasibility of dynamic contrast-enhanced MRI of the circumventricular organs. Fluids Barriers CNS 2020; 17:66. [PMID: 33115484 PMCID: PMC7594295 DOI: 10.1186/s12987-020-00228-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 10/17/2020] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Circumventricular organs (CVOs) are small structures without a blood-brain barrier surrounding the brain ventricles that serve homeostasic functions and facilitate communication between the blood, cerebrospinal fluid and brain. Secretory CVOs release peptides and sensory CVOs regulate signal transmission. However, pathogens may enter the brain through the CVOs and trigger neuroinflammation and neurodegeneration. We investigated the feasibility of dynamic contrast-enhanced (DCE) MRI to assess the CVO permeability characteristics in vivo, and expected significant contrast uptake in these regions, due to blood-brain barrier absence. METHODS Twenty healthy, middle-aged to older males underwent brain DCE MRI. Pharmacokinetic modeling was applied to contrast concentration time-courses of CVOs, and in reference to white and gray matter. We investigated whether a significant and positive transfer from blood to brain could be measured in the CVOs, and whether this differed between secretory and sensory CVOs or from normal-appearing brain matter. RESULTS In both the secretory and sensory CVOs, the transfer constants were significantly positive, and all secretory CVOs had significantly higher transfer than each sensory CVO. The transfer constants in both the secretory and sensory CVOs were higher than in the white and gray matter. CONCLUSIONS Current measurements confirm the often-held assumption of highly permeable CVOs, of which the secretory types have the strongest blood-to-brain transfer. The current study suggests that DCE MRI could be a promising technique to further assess the function of the CVOs and how pathogens can potentially enter the brain via these structures. TRIAL REGISTRATION Netherlands Trial Register number: NL6358, date of registration: 2017-03-24.
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Affiliation(s)
- Inge C M Verheggen
- Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands.
- Alzheimer Center Limburg, Maastricht, The Netherlands.
| | - Joost J A de Jong
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Martin P J van Boxtel
- Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands
- Alzheimer Center Limburg, Maastricht, The Netherlands
| | - Alida A Postma
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Frans R J Verhey
- Department of Psychiatry and Neuropsychology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands
- Alzheimer Center Limburg, Maastricht, The Netherlands
| | - Jacobus F A Jansen
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Walter H Backes
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
- School for Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands
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20
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McHugh DJ, Lipowska‐Bhalla G, Babur M, Watson Y, Peset I, Mistry HB, Hubbard Cristinacce PL, Naish JH, Honeychurch J, Williams KJ, O'Connor JPB, Parker GJM. Diffusion model comparison identifies distinct tumor sub-regions and tracks treatment response. Magn Reson Med 2020; 84:1250-1263. [PMID: 32057115 PMCID: PMC7317874 DOI: 10.1002/mrm.28196] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 01/13/2020] [Accepted: 01/13/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE MRI biomarkers of tumor response to treatment are typically obtained from parameters derived from a model applied to pre-treatment and post-treatment data. However, as tumors are spatially and temporally heterogeneous, different models may be necessary in different tumor regions, and model suitability may change over time. This work evaluates how the suitability of two diffusion-weighted (DW) MRI models varies spatially within tumors at the voxel level and in response to radiotherapy, potentially allowing inference of qualitatively different tumor microenvironments. METHODS DW-MRI data were acquired in CT26 subcutaneous allografts before and after radiotherapy. Restricted and time-independent diffusion models were compared, with regions well-described by the former hypothesized to reflect cellular tissue, and those well-described by the latter expected to reflect necrosis or oedema. Technical and biological validation of the percentage of tissue described by the restricted diffusion microstructural model (termed %MM) was performed through simulations and histological comparison. RESULTS Spatial and radiotherapy-related variation in model suitability was observed. %MM decreased from a mean of 64% at baseline to 44% 6 days post-radiotherapy in the treated group. %MM correlated negatively with the percentage of necrosis from histology, but overestimated it due to noise. Within MM regions, microstructural parameters were sensitive to radiotherapy-induced changes. CONCLUSIONS There is spatial and radiotherapy-related variation in different models' suitability for describing diffusion in tumor tissue, suggesting the presence of different and changing tumor sub-regions. The biological and technical validation of the proposed %MM cancer imaging biomarker suggests it correlates with, but overestimates, the percentage of necrosis.
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Affiliation(s)
- Damien J. McHugh
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | - Grazyna Lipowska‐Bhalla
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | - Muhammad Babur
- Division of Pharmacy & OptometryThe University of ManchesterManchesterUK
| | - Yvonne Watson
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
| | - Isabel Peset
- Imaging and Flow CytometryCancer Research UK Manchester InstituteManchesterUK
| | - Hitesh B. Mistry
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | | | - Josephine H. Naish
- Division of Cardiovascular SciencesThe University of ManchesterManchesterUK
- Bioxydyn Ltd.ManchesterUK
| | | | - Kaye J. Williams
- Division of Pharmacy & OptometryThe University of ManchesterManchesterUK
| | - James P. B. O'Connor
- Quantitative Biomedical Imaging LaboratoryThe University of ManchesterManchesterUK
- Division of Cancer SciencesThe University of ManchesterManchesterUK
| | - Geoffrey J. M. Parker
- Bioxydyn Ltd.ManchesterUK
- Division of Neuroscience and Experimental PsychologyThe University of ManchesterManchesterUK
- Centre for Medical Image ComputingUniversity College LondonLondonUK
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21
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Dikaios N. Stochastic Gradient Langevin dynamics for joint parameterization of tracer kinetic models, input functions, and T1 relaxation-times from undersampled k-space DCE-MRI. Med Image Anal 2020; 62:101690. [DOI: 10.1016/j.media.2020.101690] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 02/16/2020] [Accepted: 03/13/2020] [Indexed: 02/04/2023]
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22
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Simeth J, Cao Y. GAN and dual-input two-compartment model-based training of a neural network for robust quantification of contrast uptake rate in gadoxetic acid-enhanced MRI. Med Phys 2020; 47:1702-1712. [PMID: 31997391 DOI: 10.1002/mp.14055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 01/14/2020] [Accepted: 01/20/2020] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Gadoxetic acid uptake rate (k1 ) obtained from dynamic, contrast-enhanced (DCE) magnetic resonance imaging (MRI) is a promising measure of regional liver function. Clinical exams are typically poorly temporally characterized, as seen in a low temporal resolution (LTR) compared to high temporal resolution (HTR) experimental acquisitions. Meanwhile, clinical demands incentivize shortening these exams. This study develops a neural network-based approach to quantitation of k1 , for increased robustness over current models such as the linearized single-input, two-compartment (LSITC) model. METHODS Thirty Liver HTR DCE MRI exams were acquired in 22 patients with at least 16 min of postcontrast data sampled at least every 13 s. A simple neural network (NN) with four hidden layers was trained on voxel-wise LTR data to predict k1 . Low temporal resolution data were created by subsampling HTR data to contain six time points, replicating the characteristics of clinical LTR data. Both the total length and the placement of points in the training data were varied considerably to encourage robustness to variation. A generative adversarial network (GAN) was used to generate arterial and portal venous inputs for use in data augmentation based on the dual-input, two-compartment, pharmacokinetic model of gadoxetic acid in the liver. The performance of the NN was compared to direct application of LSITC on both LTR and HTR data. The error was assessed when subsampling lengths from 16 to 4 min, enabling assessment of robustness to acquisition length. RESULTS For acquisition lengths of 16 min NRMSE (Normalized Root-Mean-Squared Error) in k1 was 0.60, 1.77, and 1.21, for LSITC applied to HTR data, LSITC applied to LTR data, and GAN-augmented NN applied to LTR data, respectively. As the acquisition length was shortened, errors greatly increased for LSITC approaches by several folds. For acquisitions shorter than 12 min the GAN-augmented NN approach outperformed the LSITC approach to a statistically significant extent, even with HTR data. CONCLUSIONS The study indicates that data length is significant for LSITC analysis as applied to DCE data for standard temporal sampling, and that machine learning methods, such as the implemented NN, have potential for much greater resilience to shortened acquisition time than directly fitting to the LSITC model.
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Affiliation(s)
- Josiah Simeth
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Yue Cao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.,Department of Radiology, University of Michigan, Ann Arbor, MI, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
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23
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Paudyal R, Lu Y, Hatzoglou V, Moreira A, Stambuk HE, Oh JH, Cunanan KM, Nunez DA, Mazaheri Y, Gonen M, Ho A, Fagin JA, Wong RJ, Shaha A, Tuttle RM, Shukla-Dave A. Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers. NMR IN BIOMEDICINE 2020; 33:e4166. [PMID: 31680360 PMCID: PMC7687051 DOI: 10.1002/nbm.4166] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 07/04/2019] [Accepted: 07/17/2019] [Indexed: 06/10/2023]
Abstract
The purpose of this study was to identify the optimal tracer kinetic model from T1 -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78 ± 0.29 vs. 0.34 ± 0.18 min-1 , P = 0.005). From ROC analysis, cut-off values of Ktrans , ve and vp , which discriminated between PTCs with and without ETE, were determined at 0.45 min-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (Ktrans ), 71 and 82% (ve ), and 86 and 55% (vp ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.
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Affiliation(s)
- Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Yonggang Lu
- Department of Radiology, Medical College of Wisconsin,
Milwaukee, Wisconsin, USA
| | - Vaios Hatzoglou
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Andre Moreira
- Department of Pathology, NYU Langone Medical Center, New
York, USA
| | - Hilda E. Stambuk
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Kristen M. Cunanan
- Department of Epidemiology and Biostatistics, Memorial
Sloan Kettering Cancer Center, New York, USA
| | - David Aramburu Nunez
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
| | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
- Department of Radiology, Medical College of Wisconsin,
Milwaukee, Wisconsin, USA
| | - Mithat Gonen
- Department of Epidemiology and Biostatistics, Memorial
Sloan Kettering Cancer Center, New York, USA
| | - Alan Ho
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - James A. Fagin
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Richard J. Wong
- Department of Surgery, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Ashok Shaha
- Department of Surgery, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - R. Michael Tuttle
- Department of Medicine, Memorial Sloan Kettering Cancer
Center, New York, USA
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering
Cancer Center, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer
Center, New York, USA
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24
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Meyer HJ, Hamerla G, Leifels L, Höhn AK, Surov A. Histogram analysis parameters derived from DCE-MRI in head and neck squamous cell cancer – Associations with microvessel density. Eur J Radiol 2019; 120:108669. [DOI: 10.1016/j.ejrad.2019.108669] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2018] [Revised: 09/04/2019] [Accepted: 09/10/2019] [Indexed: 01/21/2023]
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25
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Bliesener Y, Lingala SG, Haldar JP, Nayak KS. Impact of (k,t) sampling on DCE MRI tracer kinetic parameter estimation in digital reference objects. Magn Reson Med 2019; 83:1625-1639. [PMID: 31605556 PMCID: PMC6982604 DOI: 10.1002/mrm.28024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 09/06/2019] [Accepted: 09/09/2019] [Indexed: 12/12/2022]
Abstract
Purpose To evaluate the impact of (k,t) data sampling on the variance of tracer‐kinetic parameter (TK) estimation in high‐resolution whole‐brain dynamic contrast enhanced magnetic resonance imaging (DCE‐MRI) using digital reference objects. We study this in the context of TK model constraints, and in the absence of other constraints. Methods Three anatomically and physiologically realistic brain‐tumor digital reference objects were generated. Data sampling strategies included uniform and variable density; zone‐based, lattice, pseudo‐random, and pseudo‐radial; with 50‐time frames and 4‐fold to 25‐fold undersampling. In all cases, we assume a fully sampled first time frame, and prior knowledge of the arterial input function. TK parameters were estimated by indirect estimation (i.e., image‐time‐series reconstruction followed by model fitting), and direct estimation from the under‐sampled data. We evaluated methods based on the Cramér‐Rao bound and Monte‐Carlo simulations, over the range of signal‐to‐noise ratio (SNR) seen in clinical brain DCE‐MRI. Results Lattice‐based sampling provided the lowest SDs, followed by pseudo‐random, pseudo‐radial, and zone‐based. This ranking was consistent for the Patlak and extended Tofts model. Pseudo‐random sampling resulted in 19% higher averaged SD compared to lattice‐based sampling. Zone‐based sampling resulted in substantially higher SD at undersampling factors above 10. CRB analysis showed only a small difference between uniform and variable density for both lattice‐based and pseudo‐random sampling up to undersampling factors of 25. Conclusion Lattice sampling provided the lowest SDs, although the differences between sampling schemes were not substantial at low undersampling factors. The differences between lattice‐based and pseudo‐random sampling strategies with both uniform and variable density were within the range of error induced by other sources, at up to 25‐fold undersampling.
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Affiliation(s)
- Yannick Bliesener
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Sajan G Lingala
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Justin P Haldar
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California
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Kim J, Moestue SA, Bathen TF, Kim E. R2* Relaxation Affects Pharmacokinetic Analysis of Dynamic Contrast-Enhanced MRI in Cancer and Underestimates Treatment Response at 7 T. ACTA ACUST UNITED AC 2019; 5:308-319. [PMID: 31572792 PMCID: PMC6752293 DOI: 10.18383/j.tom.2019.00015] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Effective transverse relaxivity of gadolinium-based contrast agents is often neglected in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Here, we assess time and tissue dependence of R2* enhancement and its impact on pharmacokinetic parameter quantification and treatment monitoring. Multiecho DCE-MRI was performed at 7 T on mice bearing subcutaneous TOV-21G human ovarian cancer xenografts (n = 8) and on the transgenic adenocarcinoma of the mouse prostate (TRAMP) model (n = 7). Subsequently, the TOV-21G tumor-bearing mice were treated with bevacizumab and rescanned 2 days later. Pharmacokinetic analysis (extended Tofts model) was performed using either the first echo signal only (standard single-echo DCE-MRI) or the estimated signal at TE = 0 derived from exponential fitting of R2* relaxation (R2*-corrected). Neglecting R2* enhancement causes underestimation of Gd-DOTA concentration (peak enhancement underestimated by 9.4%-16% in TOV-21G tumors and 13%-20% in TRAMP prostates). Median Ktrans and ve were underestimated in every mouse (TOV-21G Ktrans: 11%-19%, TOV-21G ve: 5.3%-8.9%; TRAMP Ktrans: 8.6%-19%, TRAMP ve: 12%-21%). Bevacizumab treatment reduced Ktrans in all TOV-21G tumors after 48 hours. Treatment effect was significantly greater in all tumors after R2* correction (median change of -0.050 min-1 in R2*-corrected Ktrans vs. -0.037 min-1 in uncorrected Ktrans). R2* enhancement in DCE-MRI is both time- and tissue-dependent and may not be negligible at 7 T in tissue with high Ktrans. This has consequences for the use of Ktrans and other DCE-MRI parameters as biomarkers, because treatment effect size can be underestimated when R2* enhancement is neglected.
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Affiliation(s)
- Jana Kim
- Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,St. Olavs Hospital, Trondheim, Norway
| | - Siver A Moestue
- Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Laboratory Medicine, Women's and Children's Health, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Pharmacy, Faculty of Health Sciences, Nord University, Namsos, Norway; and
| | - Tone F Bathen
- Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,St. Olavs Hospital, Trondheim, Norway
| | - Eugene Kim
- Department of Circulation and Medical Imaging, Faculty of Medicine, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, England
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Stokes AM, Semmineh NB, Nespodzany A, Bell LC, Quarles CC. Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object. Magn Reson Med 2019; 83:109-123. [PMID: 31400035 DOI: 10.1002/mrm.27914] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 06/14/2019] [Accepted: 07/02/2019] [Indexed: 11/07/2022]
Abstract
PURPOSE Brain tumor dynamic susceptibility contrast (DSC) MRI is adversely impacted by T1 and T 2 ∗ contrast agent leakage effects that result in inaccurate hemodynamic metrics. While multi-echo acquisitions remove T1 leakage effects, there is no consensus on the optimal set of acquisition parameters. Using a computational approach, we systematically evaluated a wide range of acquisition strategies to determine the optimal multi-echo DSC-MRI perfusion protocol. METHODS Using a population-based DSC-MRI digital reference object (DRO), we assessed the influence of preload dosing (no preload and full dose preload), field strength (1.5 and 3T), pulse sequence parameters (echo time, repetition time, and flip angle), and leakage correction on relative cerebral blood volume (rCBV) and flow (rCBF) accuracy. We also compared multi-echo DSC-MRI protocols with standard single-echo protocols. RESULTS Multi-echo DSC-MRI is highly consistent across all protocols, and multi-echo rCBV (with or without use of a preload dose) had higher accuracy than single-echo rCBV. Regression analysis showed that choice of repetition time and flip angle had minimal impact on multi-echo rCBV and rCBV, indicating the potential for significant flexibility in acquisition parameters. The echo time combination had minimal impact on rCBV, though longer echo times should be avoided, particularly at higher field strengths. Leakage correction improved rCBV accuracy in all cases. Multi-echo rCBF was less biased than single-echo rCBF, although rCBF accuracy was reduced overall relative to rCBV. CONCLUSIONS Multi-echo acquisitions were more robust than single-echo, essentially decoupling both repetition time and flip angle from rCBV accuracy. Multi-echo acquisitions obviate the need for preload dosing, although leakage correction to remove residual T 2 ∗ leakage effects remains compulsory for high rCBV accuracy.
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Affiliation(s)
- Ashley M Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Natenael B Semmineh
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Ashley Nespodzany
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - Laura C Bell
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona
| | - C Chad Quarles
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona
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28
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Lee J, Carver E, Feldman A, Pantelic MV, Elshaikh M, Wen N. Volumetric and Voxel-Wise Analysis of Dominant Intraprostatic Lesions on Multiparametric MRI. Front Oncol 2019; 9:616. [PMID: 31334128 PMCID: PMC6624674 DOI: 10.3389/fonc.2019.00616] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2019] [Accepted: 06/24/2019] [Indexed: 12/11/2022] Open
Abstract
Introduction: Multiparametric MR imaging (mpMRI) has shown promising results in the diagnosis and localization of prostate cancer. Furthermore, mpMRI may play an important role in identifying the dominant intraprostatic lesion (DIL) for radiotherapy boost. We sought to investigate the level of correlation between dominant tumor foci contoured on various mpMRI sequences. Methods: mpMRI data from 90 patients with MR-guided biopsy-proven prostate cancer were obtained from the SPIE-AAPM-NCI Prostate MR Classification Challenge. Each case consisted of T2-weighted (T2W), apparent diffusion coefficient (ADC), and Ktrans images computed from dynamic contrast-enhanced sequences. All image sets were rigidly co-registered, and the dominant tumor foci were identified and contoured for each MRI sequence. Hausdorff distance (HD), mean distance to agreement (MDA), and Dice and Jaccard coefficients were calculated between the contours for each pair of MRI sequences (i.e., T2 vs. ADC, T2 vs. Ktrans, and ADC vs. Ktrans). The voxel wise spearman correlation was also obtained between these image pairs. Results: The DILs were located in the anterior fibromuscular stroma, central zone, peripheral zone, and transition zone in 35.2, 5.6, 32.4, and 25.4% of patients, respectively. Gleason grade groups 1-5 represented 29.6, 40.8, 15.5, and 14.1% of the study population, respectively (with group grades 4 and 5 analyzed together). The mean contour volumes for the T2W images, and the ADC and Ktrans maps were 2.14 ± 2.1, 2.22 ± 2.2, and 1.84 ± 1.5 mL, respectively. Ktrans values were indistinguishable between cancerous regions and the rest of prostatic regions for 19 patients. The Dice coefficient and Jaccard index were 0.74 ± 0.13, 0.60 ± 0.15 for T2W-ADC and 0.61 ± 0.16, 0.46 ± 0.16 for T2W-Ktrans. The voxel-based Spearman correlations were 0.20 ± 0.20 for T2W-ADC and 0.13 ± 0.25 for T2W-Ktrans. Conclusions: The DIL contoured on T2W images had a high level of agreement with those contoured on ADC maps, but there was little to no quantitative correlation of these results with tumor location and Gleason grade group. Technical hurdles are yet to be solved for precision radiotherapy to target the DILs based on physiological imaging. A Boolean sum volume (BSV) incorporating all available MR sequences may be reasonable in delineating the DIL boost volume.
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Affiliation(s)
- Joon Lee
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Eric Carver
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Aharon Feldman
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Milan V Pantelic
- Department of Radiology, Henry Ford Health System, Detroit, MI, United States
| | - Mohamed Elshaikh
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, United States
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29
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Lecler A, Balvay D, Cuenod C, Marais L, Zmuda M, Sadik J, Galatoire O, Farah E, El Methni J, Zuber K, Bergès O, Savatovsky J, Fournier L. Quality‐based pharmacokinetic model selection on DCE‐MRI for characterizing orbital lesions. J Magn Reson Imaging 2019; 50:1514-1525. [DOI: 10.1002/jmri.26747] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 03/15/2019] [Accepted: 03/18/2019] [Indexed: 12/18/2022] Open
Affiliation(s)
- Augustin Lecler
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
| | - Daniel Balvay
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
| | - Charles‐André Cuenod
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
- Radiology Department, Hôpital Européen Georges PompidouUniversité Paris Descartes Sorbonne Paris Cité, Assistance Publique‐Hôpitaux de Paris Paris France
| | - Louise Marais
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
| | - Mathieu Zmuda
- Department of Orbitopalpebral SurgeryFoundation Adolphe de Rothschild Hospital Paris France
| | - Jean‐Claude Sadik
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
| | - Olivier Galatoire
- Department of Orbitopalpebral SurgeryFoundation Adolphe de Rothschild Hospital Paris France
| | - Edgar Farah
- Department of Orbitopalpebral SurgeryFoundation Adolphe de Rothschild Hospital Paris France
| | - Jonathan El Methni
- MAP5, UMR CNRS 8145Université Paris Descartes Sorbonne Paris Cité France
| | - Kevin Zuber
- Department of Clinical ResearchFoundation Adolphe de Rothschild Hospital Paris France
| | - Olivier Bergès
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
| | - Julien Savatovsky
- Department of NeuroradiologyFoundation Adolphe de Rothschild Hospital Paris France
| | - Laure Fournier
- Université Paris Descartes Sorbonne Paris Cité, INSERM UMR‐S970Cardiovascular Research Center – PARCC Paris France
- Radiology Department, Hôpital Européen Georges PompidouUniversité Paris Descartes Sorbonne Paris Cité, Assistance Publique‐Hôpitaux de Paris Paris France
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30
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Magdoom KN, Delgado F, Bohórquez AC, Brown AC, Carney PR, Rinaldi C, Mareci TH, Ewing JR, Sarntinoranont M. Longitudinal evaluation of tumor microenvironment in rat focal brainstem glioma using diffusion and perfusion MRI. J Magn Reson Imaging 2018; 49:1322-1332. [PMID: 30318760 DOI: 10.1002/jmri.26315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2018] [Revised: 08/09/2018] [Accepted: 08/10/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Brainstem gliomas are aggressive and difficult to treat. Growth of these tumors may be characterized with MRI methods. PURPOSE To visualize longitudinal changes in tumor volume, vascular leakiness, and tissue microstructure in an animal model of brainstem glioma. STUDY TYPE Prospective animal model. ANIMAL MODEL Male Sprague-Dawley rats (n = 9) were imaged with 9L gliosarcoma cells infused into the pontine reticular formation of the brainstem. The MRI tumor microenvironment was studied at 3 and 10 days postimplantation of tumor cells. FIELD STRENGTH/SEQUENCE Diffusion tensor imaging (DTI) and dynamic contrast-enhanced (DCE)-MRI were performed at 4.7T using spin-echo multislice echo planar imaging and gradient echo multislice imaging, respectively. ASSESSMENT Tumor leakiness was assessed by the forward volumetric transfer constant, Ktrans , estimated from DCE-MRI data. Tumor structure was evaluated with fractional anisotropy (FA) obtained from DTI. Tumor volumes, delineated by a T1 map, T2 -weighted image, FA, and DCE signal enhancement were compared. STATISTICAL TESTS Changes in the assessed parameters within and across the groups (ie, rats 3 and 10 days post tumor cell implantation) were evaluated with Wilcoxon rank-sum tests. RESULTS Day 3 tumors were visible mainly on contrast-enhanced images, while day 10 tumors were visible in both contrast-enhanced and diffusion-weighted images. Mean Ktrans at day 10 was 41% lower than at day 3 (P = 0.23). In day 10 tumors, FA was regionally lower in the tumor compared to normal tissue (P = 0.0004), and tumor volume, segmented based on FA map, was significantly smaller (P ≤ 0.05) than that obtained from other contrasts. DATA CONCLUSION Contrast-enhanced MRI was found to be more sensitive in detecting early-stage tumor boundaries than other contrasts. Areas of the tumor outlined by DCE-MRI and DTI were significantly different. Over the observed period of tumor growth, average vessel leakiness decreased with tumor progression. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:1322-1332.
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Affiliation(s)
- Kulam Najmudeen Magdoom
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida, USA
| | - Francisco Delgado
- J. Crayton Pruitt Family Department of Biomedical Engineering, Gainesville, Florida, USA
| | - Ana C Bohórquez
- J. Crayton Pruitt Family Department of Biomedical Engineering, Gainesville, Florida, USA
| | - Alec C Brown
- Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, Florida, USA
| | - Paul R Carney
- Department of Neurology and Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Carlos Rinaldi
- J. Crayton Pruitt Family Department of Biomedical Engineering, Gainesville, Florida, USA.,Department of Chemical Engineering University of Florida, Gainesville, Florida, USA
| | - Thomas H Mareci
- J. Crayton Pruitt Family Department of Biomedical Engineering, Gainesville, Florida, USA.,Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, Florida, USA
| | - James R Ewing
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Malisa Sarntinoranont
- Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida, USA.,J. Crayton Pruitt Family Department of Biomedical Engineering, Gainesville, Florida, USA
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31
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Ding G, Chopp M, Li L, Zhang L, Davoodi-Bojd E, Li Q, Zhang Z, Jiang Q. MRI investigation of glymphatic responses to Gd-DTPA infusion rates. J Neurosci Res 2018; 96:1876-1886. [PMID: 30272825 PMCID: PMC6186187 DOI: 10.1002/jnr.24325] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2018] [Revised: 08/21/2018] [Accepted: 08/22/2018] [Indexed: 02/02/2023]
Abstract
The glymphatic system is a newly identified waste clearance pathway in brain discovered and investigated predominately using in vivo two‐photon confocal microscopy. Magnetic resonance imaging (MRI), in contrast to two‐photon confocal microscopy, provides dynamic and real‐time pictures of the glymphatic system in whole brain. We employ MRI to investigate the response of the glymphatic system to the rate of infusion of Gd‐DTPA (magnevist). Wistar rats were subjected to a surgery of inserting a tube into the cisterna magna for infusion during MRI. Three infusion rates were chosen for 20 min infusions of diluted magnevist into the cerebrospinal fluid (CSF) of rat brain. Glymphatic response was imaged using dynamic MRI 3D measurement for 5 hr. Robust correlations were found in all ventricles between the peak intensities of image enhancement and infusion rates, with additional correlations between the peak times of MRI image enhancement and infusion rates in the fourth ventricle. An infusion rate of 2.92 μL/min induced an evident accumulation of tracer in the fourth ventricle near the cisterna magna. In hippocampal tissue, image enhancements exhibited low correlation with the infusion rates. However, an infusion rate of 1.67 μL/min provided a high image enhancement, but less tracer accumulation near the cisterna magna. Contrast‐enhanced MRI provides a suitable tool for investigating image contrast infusion rate response of the glymphatic system in rat brain. Considering both T1 and T2* effects in response to the infused magnevist into CSF, the infusion rate of 1.67 μL/min appears suitable for MRI study of the glymphatic system in rat.
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Affiliation(s)
- Guangliang Ding
- Departments of Neurology, Henry Ford Hospital, Detroit, Michigan
| | - Michael Chopp
- Departments of Neurology, Henry Ford Hospital, Detroit, Michigan.,Department of Physics, Oakland University, Rochester, Michigan
| | - Lian Li
- Departments of Neurology, Henry Ford Hospital, Detroit, Michigan
| | - Li Zhang
- Departments of Neurology, Henry Ford Hospital, Detroit, Michigan
| | | | - Qingjiang Li
- Departments of Neurology, Henry Ford Hospital, Detroit, Michigan
| | - Zhenggang Zhang
- Departments of Neurology, Henry Ford Hospital, Detroit, Michigan
| | - Quan Jiang
- Departments of Neurology, Henry Ford Hospital, Detroit, Michigan.,Department of Physics, Oakland University, Rochester, Michigan
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32
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Klawer EM, van Houdt PJ, Pos FJ, Heijmink SW, van Osch MJ, van der Heide UA. Impact of contrast agent injection duration on dynamic contrast-enhanced MRI quantification in prostate cancer. NMR IN BIOMEDICINE 2018; 31:e3946. [PMID: 29974981 PMCID: PMC6175355 DOI: 10.1002/nbm.3946] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 04/12/2018] [Accepted: 04/24/2018] [Indexed: 06/08/2023]
Abstract
The volume transfer constant Ktrans , which describes the leakage of contrast agent (CA) from vasculature into tissue, is the most commonly reported quantitative parameter for dynamic contrast-enhanced (DCE-) MRI. However, the variation in reported Ktrans values between studies from different institutes is large. One of the primary sources of uncertainty is quantification of the arterial input function (AIF). The aim of this study is to determine the influence of the CA injection duration on the AIF and tracer kinetic analysis (TKA) parameters (i.e. Ktrans , kep and ve ). Thirty-one patients with prostate cancer received two DCE-MRI examinations with an injection duration of 5 s in the first examination and a prolonged injection duration in the second examination, varying between 7.5 s and 30 s. The DCE examination was carried out on a 3.0 T MRI scanner using a transversal T1 -weighted 3D spoiled gradient echo sequence (300 s duration, dynamic scan time of 2.5 s). Data of 29 of the 31 were further analysed. AIFs were determined from the phase signal in the left and right femoral arteries. Ktrans , kep and ve were estimated with the standard Tofts model for regions of healthy peripheral zone and tumour tissue. We observed a significantly smaller peak height and increased width in the AIF for injection durations of 15 s and longer. However, we did not find significant differences in Ktrans , kep or ve for the studied injection durations. The study demonstrates that the TKA parameters Ktrans , kep and ve , measured in the prostate, do not show a significant change as a function of injection duration.
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Affiliation(s)
- Edzo M.E. Klawer
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Petra J. van Houdt
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Floris J. Pos
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | | | | | - Uulke A. van der Heide
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
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33
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Ahmed Z, Levesque IR. An extended reference region model for DCE-MRI that accounts for plasma volume. NMR IN BIOMEDICINE 2018; 31:e3924. [PMID: 29745982 DOI: 10.1002/nbm.3924] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 02/20/2018] [Accepted: 02/27/2018] [Indexed: 06/08/2023]
Abstract
The reference region model (RRM) for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides pharmacokinetic parameters without requiring the arterial input function. A limitation of the RRM is that it assumes that the blood plasma volume in the tissue of interest is zero, but this is often not true in highly vascularized tissues, such as some tumours. This study proposes an extended reference region model (ERRM) to account for tissue plasma volume. Furthermore, ERRM was combined with a two-fit approach to reduce the number of fitting parameters, and this was named the constrained ERRM (CERRM). The accuracy and precision of RRM, ERRM and CERRM were evaluated in simulations covering a range of parameters, noise and temporal resolutions. These models were also compared with the extended Tofts model (ETM) on in vivo glioblastoma multiforme data. In simulations, RRM overestimated Ktrans by over 10% at vp = 0.01 under noiseless conditions. In comparison, ERRM and CERRM were both accurate, with CERRM showing better precision when noise was included. On in vivo data, CERRM provided maps that had the highest agreement with ETM, whilst also being robust at temporal resolutions as poor as 30 s. ERRM can provide pharmacokinetic parameters without an arterial input function in tissues with non-negligible vp where RRM provides inaccurate estimates. The two-fit approach, named CERRM, further improves on the accuracy and precision of ERRM.
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Affiliation(s)
- Zaki Ahmed
- Medical Physics Unit, McGill University, Montreal, QC, Canada
- Department of Physics, McGill University, Montreal, QC, Canada
| | - Ives R Levesque
- Medical Physics Unit, McGill University, Montreal, QC, Canada
- Department of Physics, McGill University, Montreal, QC, Canada
- Research Institute of the McGill University Health Centre, Montreal, QC, Canada
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34
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Quarles CC, Bell LC, Stokes AM. Imaging vascular and hemodynamic features of the brain using dynamic susceptibility contrast and dynamic contrast enhanced MRI. Neuroimage 2018; 187:32-55. [PMID: 29729392 DOI: 10.1016/j.neuroimage.2018.04.069] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 04/27/2018] [Accepted: 04/29/2018] [Indexed: 12/22/2022] Open
Abstract
In the context of neurologic disorders, dynamic susceptibility contrast (DSC) and dynamic contrast enhanced (DCE) MRI provide valuable insights into cerebral vascular function, integrity, and architecture. Even after two decades of use, these modalities continue to evolve as their biophysical and kinetic basis is better understood, with improvements in pulse sequences and accelerated imaging techniques and through application of more robust and automated data analysis strategies. Here, we systematically review each of these elements, with a focus on how their integration improves kinetic parameter accuracy and the development of new hemodynamic biomarkers that provide sub-voxel sensitivity (e.g., capillary transit time and flow heterogeneity). Regarding contrast mechanisms, we discuss the dipole-dipole interactions and susceptibility effects that give rise to simultaneous T1, T2 and T2∗ relaxation effects, including their quantification, influence on pulse sequence parameter optimization, and use in methods such as vessel size and vessel architectural imaging. The application of technologic advancements, such as parallel imaging, simultaneous multi-slice, undersampled k-space acquisitions, and sliding window strategies, enables improved spatial and/or temporal resolution of DSC and DCE acquisitions. Such acceleration techniques have also enabled the implementation of, clinically feasible, simultaneous multi-echo spin- and gradient echo acquisitions, providing more comprehensive and quantitative interrogation of T1, T2 and T2∗ changes. Characterizing these relaxation rate changes through different post-processing options allows for the quantification of hemodynamics and vascular permeability. The application of different biophysical models provides insight into traditional hemodynamic parameters (e.g., cerebral blood volume) and more advanced parameters (e.g., capillary transit time heterogeneity). We provide insight into the appropriate selection of biophysical models and the necessary post-processing steps to ensure reliable measurements while minimizing potential sources of error. We show representative examples of advanced DSC- and DCE-MRI methods applied to pathologic conditions affecting the cerebral microcirculation, including brain tumors, stroke, aging, and multiple sclerosis. The maturation and standardization of conventional DSC- and DCE-MRI techniques has enabled their increased integration into clinical practice and use in clinical trials, which has, in turn, spurred renewed interest in their technological and biophysical development, paving the way towards a more comprehensive assessment of cerebral hemodynamics.
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Affiliation(s)
- C Chad Quarles
- Division of Neuro imaging Research, Barrow Neurological Institute, 350 W. Thomas Rd, Phoenix, AZ, USA.
| | - Laura C Bell
- Division of Neuro imaging Research, Barrow Neurological Institute, 350 W. Thomas Rd, Phoenix, AZ, USA
| | - Ashley M Stokes
- Division of Neuro imaging Research, Barrow Neurological Institute, 350 W. Thomas Rd, Phoenix, AZ, USA
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35
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Elmghirbi R, Nagaraja TN, Brown SL, Keenan KA, Panda S, Cabral G, Bagher-Ebadian H, Divine GW, Lee IY, Ewing JR. Toward a noninvasive estimate of interstitial fluid pressure by dynamic contrast-enhanced MRI in a rat model of cerebral tumor. Magn Reson Med 2018. [PMID: 29524243 DOI: 10.1002/mrm.27163] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE This study demonstrates a DCE-MRI estimate of tumor interstitial fluid pressure (TIFP) and hydraulic conductivity in a rat model of glioblastoma, with validation against an invasive wick-in-needle (WIN) technique. An elevated TIFP is considered a mark of aggressiveness, and a decreased TIFP a predictor of response to therapy. METHODS The DCE-MRI studies were conducted in 36 athymic rats (controls and posttreatment animals) with implanted U251 cerebral tumors, and with TIFP measured using a WIN method. Using a model selection paradigm and a novel application of Patlak and Logan plots to DCE-MRI data, the MRI parameters required for estimating TIFP noninvasively were estimated. Two models, a fluid-mechanical model and a multivariate empirical model, were used for estimating TIFP, as verified against WIN-TIFP. RESULTS Using DCE-MRI, the mean estimated hydraulic conductivity (MRI-K) in U251 tumors was (2.3 ± 3.1) × 10-5 (mm2 /mmHg-s) in control studies. Significant positive correlations were found between WIN-TIFP and MRI-TIFP in both mechanical and empirical models. For instance, in the control group of the fluid-mechanical model, MRI-TIFP was a strong predictor of WIN-TIFP (R2 = 0.76, p < .0001). A similar result was found in the bevacizumab-treated group of the empirical model (R2 = 0.93, p = .014). CONCLUSION This research suggests that MRI dynamic studies contain enough information to noninvasively estimate TIFP in this, and possibly other, tumor models, and thus might be used to assess tumor aggressiveness and response to therapy.
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Affiliation(s)
- Rasha Elmghirbi
- Department of Physics, Oakland University, Rochester, Michigan.,Department of Neurology, Henry Ford Health System, Detroit, Michigan
| | | | - Stephen L Brown
- Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan
| | - Kelly A Keenan
- Department of Neurosurgery, Henry Ford Health System, Detroit, Michigan
| | - Swayamprava Panda
- Department of Neurology, Henry Ford Health System, Detroit, Michigan
| | - Glauber Cabral
- Department of Neurology, Henry Ford Health System, Detroit, Michigan
| | - Hassan Bagher-Ebadian
- Department of Physics, Oakland University, Rochester, Michigan.,Department of Radiation Oncology, Henry Ford Health System, Detroit, Michigan
| | - George W Divine
- Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan
| | - Ian Y Lee
- Department of Neurosurgery, Henry Ford Health System, Detroit, Michigan
| | - James R Ewing
- Department of Physics, Oakland University, Rochester, Michigan.,Department of Neurology, Henry Ford Health System, Detroit, Michigan.,Department of Neurology, Wayne State University, Detroit, Michigan
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Li X, Varallyay CG, Gahramanov S, Fu R, Rooney WD, Neuwelt EA. Pseudo-extravasation rate constant of dynamic susceptibility contrast-MRI determined from pharmacokinetic first principles. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3797. [PMID: 28885746 PMCID: PMC5870763 DOI: 10.1002/nbm.3797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 07/30/2017] [Accepted: 07/31/2017] [Indexed: 06/07/2023]
Abstract
Dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI) is widely used to obtain informative perfusion imaging biomarkers, such as the relative cerebral blood volume (rCBV). The related post-processing software packages for DSC-MRI are available from major MRI instrument manufacturers and third-party vendors. One unique aspect of DSC-MRI with low-molecular-weight gadolinium (Gd)-based contrast reagent (CR) is that CR molecules leak into the interstitium space and therefore confound the DSC signal detected. Several approaches to correct this leakage effect have been proposed throughout the years. Amongst the most popular is the Boxerman-Schmainda-Weisskoff (BSW) K2 leakage correction approach, in which the K2 pseudo-first-order rate constant quantifies the leakage. In this work, we propose a new method for the BSW leakage correction approach. Based on the pharmacokinetic interpretation of the data, the commonly adopted R2 * expression accounting for contributions from both intravascular and extravasating CR components is transformed using a method mathematically similar to Gjedde-Patlak linearization. Then, the leakage rate constant (KL ) can be determined as the slope of the linear portion of a plot of the transformed data. Using the DSC data of high-molecular-weight (~750 kDa), iron-based, intravascular Ferumoxytol (FeO), the pharmacokinetic interpretation of the new paradigm is empirically validated. The primary objective of this work is to empirically demonstrate that a linear portion often exists in the graph of the transformed data. This linear portion provides a clear definition of the Gd CR pseudo-leakage rate constant, which equals the slope derived from the linear segment. A secondary objective is to demonstrate that transformed points from the initial transient period during the CR wash-in often deviate from the linear trend of the linearized graph. The inclusion of these points will have a negative impact on the accuracy of the leakage rate constant, and even make it time dependent.
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Affiliation(s)
- Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Csanad G. Varallyay
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
- Department of Radiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Seymur Gahramanov
- Department of Neurosurgery, University of New Mexico, Albuquerque, New Mexico, USA
| | - Rongwei Fu
- School of Public Health, Oregon Health & Science University, Portland, Oregon, USA
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - William D. Rooney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA
| | - Edward A. Neuwelt
- Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
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Guo Y, Lingala SG, Bliesener Y, Lebel RM, Zhu Y, Nayak KS. Joint arterial input function and tracer kinetic parameter estimation from undersampled dynamic contrast-enhanced MRI using a model consistency constraint. Magn Reson Med 2017; 79:2804-2815. [PMID: 28905411 DOI: 10.1002/mrm.26904] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 08/11/2017] [Accepted: 08/16/2017] [Indexed: 12/13/2022]
Abstract
PURPOSE To develop and evaluate a model-based reconstruction framework for joint arterial input function (AIF) and kinetic parameter estimation from undersampled brain tumor dynamic contrast-enhanced MRI (DCE-MRI) data. METHODS The proposed method poses the tracer-kinetic (TK) model as a model consistency constraint, enabling the flexible inclusion of different TK models and TK solvers, and the joint estimation of the AIF. The proposed method is evaluated using an anatomic realistic digital reference object (DRO), and nine retrospectively down-sampled brain tumor DCE-MRI datasets. We also demonstrate application to 30-fold prospectively undersampled brain tumor DCE-MRI. RESULTS In DRO studies with up to 60-fold undersampling, the proposed method provided TK maps with low error that were comparable to fully sampled data and were demonstrated to be compatible with a third-party TK solver. In retrospective undersampling studies, this method provided patient-specific AIF with normalized root mean-squared-error (normalized by the 90th percentile value) less than 8% at up to 100-fold undersampling. In the 30-fold undersampled prospective study, the proposed method provided high-resolution whole-brain TK maps and patient-specific AIF. CONCLUSION The proposed model-based DCE-MRI reconstruction enables the use of different TK solvers with a model consistency constraint and enables joint estimation of patient-specific AIF. TK maps and patient-specific AIF with high fidelity can be reconstructed at up to 100-fold undersampling in k,t-space. Magn Reson Med 79:2804-2815, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Yi Guo
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Sajan Goud Lingala
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Yannick Bliesener
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | | | - Yinghua Zhu
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
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Li L, Chopp M, Ding G, Li Q, Mahmood A, Jiang Q. Chronic global analysis of vascular permeability and cerebral blood flow after bone marrow stromal cell treatment of traumatic brain injury in the rat: A long-term MRI study. Brain Res 2017; 1675:61-70. [PMID: 28899758 DOI: 10.1016/j.brainres.2017.09.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 08/29/2017] [Accepted: 09/04/2017] [Indexed: 12/11/2022]
Abstract
Vascular permeability and hemodynamic alteration in response to the transplantation of human bone marrow stromal cells (hMSCs) after traumatic brain injury (TBI) were longitudinally investigated in non directly injured and normal-appearing cerebral tissue using magnetic resonance imaging (MRI). Male Wistar rats (300-350g, n=30) subjected to controlled cortical impact TBI were intravenously injected with 1ml of saline (at 6-h or 1-week post-injury, n=5/group) or with hMSCs in suspension (∼3×106 hMSCs, at 6-h or 1-week post-injury, n=10/group). MRI measurements of T2-weighted imaging, cerebral blood flow (CBF) and blood-to-brain transfer constant (Ki) of gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA), and neurological behavioral estimates were performed on all animals at multiple time points up to 3-months post-injury. Our long-term imaging data show that blood-brain barrier (BBB) breakdown and hemodynamic disruption after TBI, as revealed by Ki and CBF, respectively, affect both hemispheres of the brain in a diffuse manner. Our data reveal a sensitive vascular permeability and hemodynamic reaction in response to the time-dependent transplantation of hMSCs. A more rapid reduction of Ki following cell treatment is associated with a higher level of CBF in the injured brain, and acute (6h) cell administration leads to enhanced therapeutic effects on both the recovery of vascular integrity and stabilization of cerebral perfusion compared to delayed (1w) cell engraftment. Our results indicate that cell-enhanced BBB reconstitution plays an important role in underlying the restoration of CBF in the injured brain, which in turn, contributes to the improvement of functional outcome.
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Affiliation(s)
- Lian Li
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA.
| | - Michael Chopp
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA; Department of Physics, Oakland University, Rochester, MI 48309, USA.
| | - Guangliang Ding
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA.
| | - Qingjiang Li
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA.
| | - Asim Mahmood
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI 48208, USA.
| | - Quan Jiang
- Department of Neurology, Henry Ford Health System, Detroit, MI 48202, USA.
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Reproducibility and relative stability in magnetic resonance imaging indices of tumor vascular physiology over a period of 24h in a rat 9L gliosarcoma model. Magn Reson Imaging 2017; 44:131-139. [PMID: 28887206 DOI: 10.1016/j.mri.2017.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 08/03/2017] [Accepted: 09/01/2017] [Indexed: 02/06/2023]
Abstract
PURPOSE The objective was to study temporal changes in tumor vascular physiological indices in a period of 24h in a 9L gliosarcoma rat model. METHODS Fischer-344 rats (N=14) were orthotopically implanted with 9L cells. At 2weeks post-implantation, they were imaged twice in a 24h interval using dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Data-driven model-selection-based analysis was used to segment tumor regions with varying vascular permeability characteristics. The region with the maximum number of estimable parameters of vascular kinetics was chosen for comparison across the two time points. It provided estimates of three parameters for an MR contrast agent (MRCA): i) plasma volume (vp), ii) forward volumetric transfer constant (Ktrans) and interstitial volume fraction (ve, ratio of Ktrans to reverse transfer constant, kep). In addition, MRCA extracellular distribution volume (VD) was estimated in the tumor and its borders, along with tumor blood flow (TBF) and peritumoral MRCA flux. Descriptors of parametric distributions were compared between the two times. Tumor extent was examined by hematoxylin and eosin (H&E) staining. Picrosirus red staining of secreted collagen was performed as an additional index for 9L cells. RESULTS Test-retest differences between population summaries for any parameter were not significant (paired t and Wilcoxon signed rank tests). Bland-Altman plots showed no apparent trends between the differences and averages of the test-retest measures for all indices. The intraclass correlation coefficients showed moderate to almost perfect reproducibility for all of the parameters, except vp. H&E staining showed tumor infiltration in parenchyma, perivascular space and white matter tracts. Collagen staining was observed along the outer edges of main tumor mass. CONCLUSION The data suggest the relative stability of these MR indices of tumor microenvironment over a 24h duration in this gliosarcoma model.
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Zhang J, Winters K, Reynaud O, Kim SG. Simultaneous measurement of T 1 /B 1 and pharmacokinetic model parameters using active contrast encoding (ACE)-MRI. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3737. [PMID: 28544159 PMCID: PMC5557664 DOI: 10.1002/nbm.3737] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 03/27/2017] [Accepted: 03/28/2017] [Indexed: 05/06/2023]
Abstract
The aim of this study was to assess the feasibility of combining dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) with the measurement of the radiofrequency (RF) transmit field B1 and pre-contrast longitudinal relaxation time T10 . A novel approach has been proposed to simultaneously estimate B1 and T10 from a modified DCE-MRI scan that actively encodes the washout phase of the curve with different amounts of T1 and B1 weighting using multiple flip angles and repetition times, hence referred to as active contrast encoding (ACE)-MRI. ACE-MRI aims to simultaneously measure B1 and T10 , together with contrast kinetic parameters, such as the transfer constant Ktrans , interstitial space volume fraction ve and vascular space volume fraction vp . The proposed method was tested using numerical simulations and in vivo studies with mouse models of breast cancer implanted in the flank and mammary fat pad, and glioma in the brain. In the numerical simulation study with a signal-to-noise ratio of 10, both B1 and T10 were estimated accurately with errors of 5.1 ± 3.5% and 12.3 ± 8.8% and coefficients of variation (CV) of 14.9 ± 8.6% and 15.0 ± 5.0%, respectively. Using the same ACE-MRI data, the kinetic parameters Ktrans , ve and vp were also estimated with errors of 14.2 ± 8.3% (CV = 13.5 ± 4.6%), 14.7 ± 9.9% (CV = 13.3 ± 4.5%) and 14.0 ± 9.3% (CV = 14.0 ± 4.5%), respectively. For the in vivo tumor data from 11 mice, voxel-wise comparisons between ACE-MRI and DCE-MRI methods showed that the mean differences for the five parameters were as follows: ΔKtrans = 0.006 (/min), Δve = 0.016, Δvp = 0.000, ΔB1 = -0.014 and ΔT1 = -0.085 (s), which suggests a good agreement between the two methods. When compared with separately measured B1 and T10 , and DCE-MRI estimated kinetic parameters as a reference, the mean relative errors of ACE-MRI estimation were B1 = -0.3%, T10 = -8.5%, Ktrans = 11.4%, ve = 14.5% and vp = 4.5%. This proof-of-concept study demonstrates that the proposed ACE-MRI method can be used to estimate B1 and T10 , together with contrast kinetic model parameters.
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Affiliation(s)
- Jin Zhang
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Kerryanne Winters
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Olivier Reynaud
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Sungheon Gene Kim
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
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Dehkordi ANV, Kamali-Asl A, Wen N, Mikkelsen T, Chetty IJ, Bagher-Ebadian H. DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique. NMR IN BIOMEDICINE 2017; 30:e3739. [PMID: 28543885 DOI: 10.1002/nbm.3739] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 03/28/2017] [Accepted: 03/30/2017] [Indexed: 06/07/2023]
Abstract
This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimated by the model selection (MS) technique in dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) data analysis, and patient age. DCE-MRI investigations of 33 treatment-naïve patients with GBM were studied. Using the modified Tofts model and MS technique, the following physiologically nested models were constructed: Model 1, no vascular leakage (normal tissue); Model 2, leakage without efflux; Model 3, leakage with bidirectional exchange (influx and efflux). For each patient, the PK parameters of the three models were estimated as follows: blood plasma volume (vp ) for Model 1; vp and volume transfer constant (Ktrans ) for Model 2; vp , Ktrans and rate constant (kep ) for Model 3. Using Cox regression analysis, the best combination of the estimated PK parameters, together with patient age, was identified for the design and training of ANFIS. A K-fold cross-validation (K = 33) technique was employed for training, testing and optimization of ANFIS. Given the survival time distribution, three classes of survival were determined and a confusion matrix for the correct classification fraction (CCF) of the trained ANFIS was estimated as an accuracy index of ANFIS's performance. Patient age, kep and ve (Ktrans /kep ) of Model 3, and Ktrans of Model 2, were found to be the most effective parameters for training ANFIS. The CCF of the trained ANFIS was 84.8%. High diagonal elements of the confusion matrix (81.8%, 90.1% and 81.8% for Class 1, Class 2 and Class 3, respectively), with low off-diagonal elements, strongly confirmed the robustness and high performance of the trained ANFIS for predicting the three survival classes. This study confirms that DCE-MRI PK analysis, combined with the MS technique and ANFIS, allows the construction of a DCE-MRI-based fuzzy integrated predictor for the prediction of the survival of patients with GBM.
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Affiliation(s)
- Azimeh N V Dehkordi
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran
| | - Alireza Kamali-Asl
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Tom Mikkelsen
- Department of Neurosurgery, Henry Ford Hospital, Detroit, Michigan, USA
- Ontario Brain Institute, Toronto, Ontario, Canada
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA
- Department of Physics, Oakland University, Rochester, Michigan, USA
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Lee H, Mortensen K, Sanggaard S, Koch P, Brunner H, Quistorff B, Nedergaard M, Benveniste H. Quantitative Gd-DOTA uptake from cerebrospinal fluid into rat brain using 3D VFA-SPGR at 9.4T. Magn Reson Med 2017. [PMID: 28627037 DOI: 10.1002/mrm.26779] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
PURPOSE We propose a quantitative technique to assess solute uptake into the brain parenchyma based on dynamic contrast-enhanced MRI (DCE-MRI). With this approach, a small molecular weight paramagnetic contrast agent (Gd-DOTA) is infused in the cerebral spinal fluid (CSF) and whole brain gadolinium concentration maps are derived. METHODS We implemented a 3D variable flip angle spoiled gradient echo (VFA-SPGR) longitudinal relaxation time (T1) technique, the accuracy of which was cross-validated by way of inversion recovery rapid acquisition with relaxation enhancement (IR-RARE) using phantoms. Normal Wistar rats underwent Gd-DOTA infusion into CSF via the cisterna magna and continuous MRI for approximately 130 min using T1-weighted imaging. Dynamic Gd-DOTA concentration maps were calculated and parenchymal uptake was estimated. RESULTS In the phantom study, T1 discrepancies between the VFA-SPGR and IR-RARE sequences were approximately 6% with a transmit coil inhomogeneity correction. In the in vivo study, contrast transport profiles indicated maximal parenchymal retention of approximately 19% relative to the total amount delivered into the cisterna magna. CONCLUSION Imaging strategies for accurate 3D contrast concentration mapping at 9.4T were developed and whole brain dynamic concentration maps were derived to study solute transport via the glymphatic system. The newly developed approach will enable future quantitative studies of the glymphatic system in health and disease states. Magn Reson Med 79:1568-1578, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Hedok Lee
- Department of Anesthesiology, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kristian Mortensen
- Section for Translational Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Simon Sanggaard
- Section for Translational Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Palle Koch
- Section for Translational Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hans Brunner
- Section for Translational Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bjørn Quistorff
- Department of Biomedical Sciences, University of Copenhagen, The Panum Institute, Copenhagen, Denmark
| | - Maiken Nedergaard
- Section for Translational Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Division of Glia Disease and Therapeutics, Center for Translational Neuromedicine, University of Rochester Medical School, Rochester, New York, USA
| | - Helene Benveniste
- Department of Anesthesiology, Yale School of Medicine, New Haven, Connecticut, USA
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Nejad-Davarani SP, Bagher-Ebadian H, Ewing JR, Noll DC, Mikkelsen T, Chopp M, Jiang Q. An extended vascular model for less biased estimation of permeability parameters in DCE-T1 images. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3698. [PMID: 28211961 PMCID: PMC5489235 DOI: 10.1002/nbm.3698] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2014] [Revised: 12/20/2016] [Accepted: 12/29/2016] [Indexed: 06/06/2023]
Abstract
One of the key elements in dynamic contrast enhanced (DCE) image analysis is the arterial input function (AIF). Traditionally, in DCE studies a global AIF sampled from a major artery or vein is used to estimate the vascular permeability parameters; however, not addressing dispersion and delay of the AIF at the tissue level can lead to biased estimates of these parameters. To find less biased estimates of vascular permeability parameters, a vascular model of the cerebral vascular system is proposed that considers effects of dispersion of the AIF in the vessel branches, as well as extravasation of the contrast agent (CA) to the extravascular-extracellular space. Profiles of the CA concentration were simulated for different branching levels of the vascular structure, combined with the effects of vascular leakage. To estimate the permeability parameters, the extended model was applied to these simulated signals and also to DCE-T1 (dynamic contrast enhanced T1 ) images of patients with glioblastoma multiforme tumors. The simulation study showed that, compared with the case of solving the pharmacokinetic equation with a global AIF, using the local AIF that is corrected by the vascular model can give less biased estimates of the permeability parameters (Ktrans , vp and Kb ). Applying the extended model to signals sampled from different areas of the DCE-T1 image showed that it is able to explain the CA concentration profile in both the normal areas and the tumor area, where effects of vascular leakage exist. Differences in the values of the permeability parameters estimated in these images using the local and global AIFs followed the same trend as the simulation study. These results demonstrate that the vascular model can be a useful tool for obtaining more accurate estimation of parameters in DCE studies.
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Affiliation(s)
- Siamak P. Nejad-Davarani
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
- Department of Neurology, Henry Ford Health System, Detroit, MI, USA
| | - Hassan Bagher-Ebadian
- Department of Radiation Oncology, Henry Ford Health System, Detroit, MI, USA
- Department of Physics, Oakland University, Rochester, MI, USA
| | - James R. Ewing
- Department of Neurology, Henry Ford Health System, Detroit, MI, USA
- Department of Physics, Oakland University, Rochester, MI, USA
| | - Douglas C. Noll
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Tom Mikkelsen
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI, USA
| | - Michael Chopp
- Department of Neurology, Henry Ford Health System, Detroit, MI, USA
- Department of Physics, Oakland University, Rochester, MI, USA
| | - Quan Jiang
- Department of Neurology, Henry Ford Health System, Detroit, MI, USA
- Department of Physics, Oakland University, Rochester, MI, USA
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Dehkordi ANV, Kamali-Asl A, Ewing JR, Wen N, Chetty IJ, Bagher-Ebadian H. An adaptive model for rapid and direct estimation of extravascular extracellular space in dynamic contrast enhanced MRI studies. NMR IN BIOMEDICINE 2017; 30:e3682. [PMID: 28195664 DOI: 10.1002/nbm.3682] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 11/07/2016] [Accepted: 11/15/2016] [Indexed: 06/06/2023]
Abstract
Extravascular extracellular space (ve ) is a key parameter to characterize the tissue of cerebral tumors. This study introduces an artificial neural network (ANN) as a fast, direct, and accurate estimator of ve from a time trace of the longitudinal relaxation rate, ΔR1 (R1 = 1/T1 ), in DCE-MRI studies. Using the extended Tofts equation, a set of ΔR1 profiles was simulated in the presence of eight different signal to noise ratios. A set of gain- and noise-insensitive features was generated from the simulated ΔR1 profiles and used as the ANN training set. A K-fold cross-validation method was employed for training, testing, and optimization of the ANN. The performance of the optimal ANN (12:6:1, 12 features as input vector, six neurons in hidden layer, and one output) in estimating ve at a resolution of 10% (error of ±5%) was 82%. The ANN was applied on DCE-MRI data of 26 glioblastoma patients to estimate ve in tumor regions. Its results were compared with the maximum likelihood estimation (MLE) of ve . The two techniques showed a strong agreement (r = 0.82, p < 0.0001). Results implied that the perfected ANN was less sensitive to noise and outperformed the MLE method in estimation of ve .
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Affiliation(s)
- Azimeh N V Dehkordi
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran
| | - Alireza Kamali-Asl
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran
| | - James R Ewing
- Department of Physics, Oakland University, Rochester, Michigan, USA
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Ning Wen
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Indrin J Chetty
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA
| | - Hassan Bagher-Ebadian
- Department of Physics, Oakland University, Rochester, Michigan, USA
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan, USA
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Fei B, Nieh PT, Master VA, Zhang Y, Osunkoya AO, Schuster DM. Molecular imaging and fusion targeted biopsy of the prostate. Clin Transl Imaging 2017; 5:29-43. [PMID: 28971090 PMCID: PMC5621648 DOI: 10.1007/s40336-016-0214-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2016] [Accepted: 11/03/2016] [Indexed: 01/08/2023]
Abstract
PURPOSE This paper provides a review on molecular imaging with positron emission tomography (PET) and magnetic resonance imaging (MRI) for prostate cancer detection and its applications in fusion targeted biopsy of the prostate. METHODS Literature search was performed through the PubMed database using the keywords "prostate cancer", "MRI/ultrasound fusion", "molecular imaging", and "targeted biopsy". Estimates in autopsy studies indicate that 50% of men older than 50 years of age have prostate cancer. Systematic transrectal ultrasound (TRUS) guided prostate biopsy is considered the standard method for prostate cancer detection and has a significant sampling error and a low sensitivity. Molecular imaging technology and new biopsy approaches are emerging to improve the detection of prostate cancer. RESULTS Molecular imaging with PET and MRI shows promising results in the early detection of prostate cancer. MRI/TRUS fusion targeted biopsy has become a new clinical standard for the diagnosis of prostate cancer. PET molecular image-directed, three-dimensional ultrasound-guided biopsy is a new technology that has great potential for improving prostate cancer detection rate and for distinguishing aggressive prostate cancer from indolent disease. CONCLUSION Molecular imaging and fusion targeted biopsy are active research areas in prostate cancer research.
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Affiliation(s)
- Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University School of
Medicine, 1841 Clifton Road NE, Atlanta, GA 30329, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute
of Technology, Atlanta, GA 30329, USA
- Winship Cancer Institute of Emory University, Atlanta, GA 30329, USA
| | - Peter T. Nieh
- Department of Urology, Emory University School of Medicine, Atlanta, GA
30322, USA
| | - Viraj A. Master
- Department of Urology, Emory University School of Medicine, Atlanta, GA
30322, USA
| | - Yun Zhang
- Department of Radiology and Imaging Sciences, Emory University School of
Medicine, 1841 Clifton Road NE, Atlanta, GA 30329, USA
| | - Adeboye O. Osunkoya
- Winship Cancer Institute of Emory University, Atlanta, GA 30329, USA
- Department of Urology, Emory University School of Medicine, Atlanta, GA
30322, USA
- Department of Pathology and Laboratory Medicine, Emory University School of
Medicine, Atlanta, GA 30322, USA
- Department of Pathology, Veterans Affairs Medical Center, Decatur, GA 30033,
USA
| | - David M. Schuster
- Department of Radiology and Imaging Sciences, Emory University School of
Medicine, 1841 Clifton Road NE, Atlanta, GA 30329, USA
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Elmghirbi R, Nagaraja TN, Brown SL, Panda S, Aryal MP, Keenan KA, Bagher-Ebadian H, Cabral G, Ewing JR. Acute Temporal Changes of MRI-Tracked Tumor Vascular Parameters after Combined Anti-angiogenic and Radiation Treatments in a Rat Glioma Model: Identifying Signatures of Synergism. Radiat Res 2017; 187:79-88. [PMID: 28001908 PMCID: PMC5381817 DOI: 10.1667/rr14358.1] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
In this study we used magnetic resonance imaging (MRI) biomarkers to monitor the acute temporal changes in tumor vascular physiology with the aim of identifying the vascular signatures that predict response to combined anti-angiogenic and radiation treatments. Forty-three athymic rats implanted with orthotopic U-251 glioma cells were studied for approximately 21 days after implantation. Two MRI studies were performed on each animal, pre- and post-treatment, to measure tumor vascular parameters. Two animal groups received treatment comprised of Cilengitide, an anti-angiogenic agent and radiation. The first group received a subcurative regimen of Cilengitide 1 h before irradiation, while the second group received a curative regimen of Cilengitide 8 h before irradiation. Cilengitide was given as a single dose (4 mg/kg; intraperitoneal) after the pretreatment MRI study and before receiving a 20 Gy radiation dose. After irradiation, the post-treatment MRI study was performed at selected time points: 2, 4, 8 and 12 h (n = ≥5 per time point). Significant changes in vascular parameters were observed at early time points after combined treatments in both treatment groups (1 and 8 h). The temporal changes in vascular parameters in the first group (treated 1 h before exposure) resembled a previously reported pattern associated with radiation exposure alone. Conversely, in the second group (treated 8 h before exposure), all vascular parameters showed an initial response at 2-4 h postirradiation, followed by an apparent lack of response at later time points. The signature time point to define the "synergy" of Cilengitide and radiation was 4 h postirradiation. For example, 4 h after combined treatments using a 1 h separation (which followed the subcurative regimen), tumor blood flow was significantly decreased, nearly 50% below baseline (P = 0.007), whereas 4 h after combined treatments using an 8 h separation (which followed the curative regimen), tumor blood flow was only 10% less than baseline. Comparison between the first and second groups further revealed that most other vascular parameters were maximally different 4 h after combined treatments. In conclusion, the data are consistent with the assertion that the delivery of radiation at the vascular normalization time window of Cilengitide improves radiation treatment outcome. The different vascular responses after the different delivery times of combined treatments in light of the known tumor responses under similar conditions would indicate that timing has a crucial influence on treatment outcome and long-term survival. Tracking acute changes in tumor physiology after monotherapy or combined treatments appears to aid in identifying the beneficial timing for administration, and perhaps has predictive value. Therefore, judicial timing of treatments may result in optimal treatment response.
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Affiliation(s)
- Rasha Elmghirbi
- Department of Physics, Oakland University, Rochester, Michigan
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan
| | | | - Stephen L. Brown
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan
| | | | - Madhava P. Aryal
- Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, Michigan
| | - Kelly A. Keenan
- Department of Neurosurgery, Henry Ford Hospital, Detroit, Michigan
| | - Hassan Bagher-Ebadian
- Department of Physics, Oakland University, Rochester, Michigan
- Department of Radiation Oncology, Henry Ford Hospital, Detroit, Michigan
| | - Glauber Cabral
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan
| | - James R. Ewing
- Department of Physics, Oakland University, Rochester, Michigan
- Department of Neurology, Henry Ford Hospital, Detroit, Michigan
- Department of Neurology, Wayne State University, Detroit, Michigan
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Mohammadian-Behbahani MR, Kamali-Asl AR. Artificial Neural Networks approach to pharmacokinetic model selection in DCE-MRI studies. Phys Med 2016; 32:1543-1550. [PMID: 27876537 DOI: 10.1016/j.ejmp.2016.11.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Revised: 11/05/2016] [Accepted: 11/07/2016] [Indexed: 11/25/2022] Open
Abstract
PURPOSE In pharmacokinetic analysis of Dynamic Contrast Enhanced MRI data, a descriptive physiological model should be selected properly out of a set of candidate models. Classical techniques suggested for this purpose suffer from issues like computation time and general fitting problems. This article proposes an approach based on Artificial Neural Networks (ANNs) for solving these problems. METHODS A set of three physiologically and mathematically nested models generated from the Tofts model were assumed: Model I, II and III. These models cover three possible tissue types from normal to malignant. Using 21 experimental arterial input functions and 12 levels of noise, a set of 27,216 time traces were generated. ANN was validated and optimized by the k-fold cross validation technique. An experimental dataset of 20 patients with glioblastoma was applied to ANN and the results were compared to outputs of F-test using Dice index. RESULTS Optimum neuronal architecture ([6:7:1]) and number of training epochs (50) of the ANN were determined. ANN correctly classified more than 99% of the dataset. Confusion matrices for both ANN and F-test results showed the superior performance of the ANN classifier. The average Dice index (over 20 patients) indicated a 75% similarity between model selection maps of ANN and F-test. CONCLUSIONS ANN improves the model selection process by removing the need for time-consuming, problematic fitting algorithms; as well as the need for hypothesis testing.
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Affiliation(s)
- Mohammad-Reza Mohammadian-Behbahani
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran; Department of Energy Engineering and Physics, Amir-Kabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Ali-Reza Kamali-Asl
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran.
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Li CH, Chen FH, Schellingerhout D, Lin YS, Hong JH, Liu HL. Flow versus permeability weighting in estimating the forward volumetric transfer constant (K trans) obtained by DCE-MRI with contrast agents of differing molecular sizes. Magn Reson Imaging 2016; 36:105-111. [PMID: 27989901 DOI: 10.1016/j.mri.2016.10.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 10/26/2016] [Indexed: 01/02/2023]
Abstract
PURPOSE To quantify the differential plasma flow- (Fp-) and permeability surface area product per unit mass of tissue- (PS-) weighting in forward volumetric transfer constant (Ktrans) estimates by using a low molecular (Gd-DTPA) versus high molecular (Gadomer) weight contrast agent in dynamic contrast enhanced (DCE) MRI. MATERIALS AND METHODS DCE MRI was performed using a 7T animal scanner in 14 C57BL/6J mice syngeneic for TRAMP tumors, by administering Gd-DTPA (0.9kD) in eight mice and Gadomer (35kD) in the remainder. The acquisition time was 10min with a sampling rate of one image every 2s. Pharmacokinetic modeling was performed to obtain Ktrans by using Extended Tofts model (ETM). In addition, the adiabatic approximation to the tissue homogeneity (AATH) model was employed to obtain the relative contributions of Fp and PS. RESULTS The Ktrans values derived from DCE-MRI with Gd-DTPA showed significant correlations with both PS (r2=0.64, p=0.009) and Fp (r2=0.57, p=0.016), whereas those with Gadomer were found only significantly correlated with PS (r2=0.96, p=0.0003) but not with Fp (r2=0.34, p=0.111). A voxel-based analysis showed that Ktrans approximated PS (<30% difference) in 78.3% of perfused tumor volume for Gadomer, but only 37.3% for Gd-DTPA. CONCLUSIONS The differential contributions of Fp and PS in estimating Ktrans values vary with the molecular weight of the contrast agent used. The macromolecular contrast agent resulted in Ktrans values that were much less dependent on flow. These findings support the use of macromolecular contrast agents for estimating tumor vessel permeability with DCE-MRI.
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Affiliation(s)
- Cheng-He Li
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Fang-Hsin Chen
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Radiation Biology Research Center, Institute for Radiological Research, Chang Gung University, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Radiation Oncology, Chang Gung Memorial Hospital at Linko, Taoyuan, Taiwan
| | - Dawid Schellingerhout
- Departments of Diagnostic Radiology and Cancer Systems Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Yu-Shi Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Ji-Hong Hong
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Radiation Biology Research Center, Institute for Radiological Research, Chang Gung University, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Radiation Oncology, Chang Gung Memorial Hospital at Linko, Taoyuan, Taiwan
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
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Wu Z, Cheng Z, Yi Z, Xie M, Zeng H, Lu L, Xu X, Shen J. Assessment of Nonalcoholic Fatty Liver Disease in Rats Using Quantitative Dynamic Contrast‐Enhanced MRI. J Magn Reson Imaging 2016; 45:1485-1493. [DOI: 10.1002/jmri.25455] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 08/17/2016] [Indexed: 01/14/2023] Open
Affiliation(s)
- Zhuo Wu
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhou China
| | - Zi‐Liang Cheng
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhou China
| | - Zhi‐Long Yi
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhou China
| | - Ming‐Wei Xie
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhou China
| | - Hong Zeng
- Department of Pathology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhou China
| | - Lie‐Jing Lu
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhou China
| | - Xiao Xu
- GE HealthcareChina Shanghai China
| | - Jun Shen
- Department of Radiology, Sun Yat‐Sen Memorial HospitalSun Yat‐Sen UniversityGuangzhou China
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Comparison of Cerebral Blood Volume and Plasma Volume in Untreated Intracranial Tumors. PLoS One 2016; 11:e0161807. [PMID: 27584684 PMCID: PMC5008702 DOI: 10.1371/journal.pone.0161807] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 08/14/2016] [Indexed: 02/06/2023] Open
Abstract
Purpose Plasma volume and blood volume are imaging-derived parameters that are often used to evaluation intracranial tumors. Physiologically, these parameters are directly related, but their two different methods of measurements, T1-dynamic contrast enhanced (DCE)- and T2-dynamic susceptibility contrast (DSC)-MR utilize different model assumptions and approaches. This poses the question of whether the interchangeable use of T1-DCE-MRI derived fractionated plasma volume (vp) and relative cerebral blood volume (rCBV) assessed using DSC-MRI, particularly in glioblastoma, is reliable, and if this relationship can be generalized to other types of brain tumors. Our goal was to examine the hypothetical correlation between these parameters in three most common intracranial tumor types. Methods Twenty-four newly diagnosed, treatment naïve brain tumor patients, who had undergone DCE- and DSC-MRI, were classified in three histologically proven groups: glioblastoma (n = 7), meningioma (n = 9), and intraparenchymal metastases (n = 8). The rCBV was obtained from DSC after normalization with the normal-appearing anatomically symmetrical contralateral white matter. Correlations between these parameters were evaluated using Pearson (r), Spearman's (ρ) and Kendall’s tau-b (τB) rank correlation coefficient. Results The Pearson, Spearman and Kendall’s correlation between vp with rCBV were r = 0.193, ρ = 0.253 and τB = 0.33 (p-Pearson = 0.326, p-Spearman= 0.814 and p-Kendall= 0.823) in glioblastoma, r = -0.007, ρ = 0.051 and τB = 0.135 (p-Pearson = 0.970, p-Spearman= 0.765 and p-Kendall= 0.358) in meningiomas, and r = 0.289, ρ = 0.228 and τB = 0.239 (p-Pearson = 0.109, p-Spearman= 0.210 and p-Kendall= 0.095) in metastasis. Conclusion Results indicate that no correlation exists between vp with rCBV in glioblastomas, meningiomas and intraparenchymal metastatic lesions. Consequently, these parameters, as calculated in this study, should not be used interchangeably in either research or clinical practice.
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