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de Jong JJA, Jansen JFA, Vergoossen LWM, Schram MT, Stehouwer CDA, Wildberger JE, Linden DEJ, Backes WH. Effect of Magnetic Resonance Image Quality on Structural and Functional Brain Connectivity: The Maastricht Study. Brain Sci 2024; 14:62. [PMID: 38248277 PMCID: PMC10813868 DOI: 10.3390/brainsci14010062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/27/2023] [Accepted: 01/04/2024] [Indexed: 01/23/2024] Open
Abstract
In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into brain connectivity. However, biases in MRI data acquisition and processing can impact brain connectivity measures and their associations with demographic and clinical variables. This study, conducted with 5110 participants from The Maastricht Study, explored the relationship between brain connectivity and various image quality metrics (e.g., signal-to-noise ratio, head motion, and atlas-template mismatches) that were obtained from dMRI and rs-fMRI scans. Results revealed that in particular increased head motion (R2 up to 0.169, p < 0.001) and reduced signal-to-noise ratio (R2 up to 0.013, p < 0.001) negatively impacted structural and functional brain connectivity, respectively. These image quality metrics significantly affected associations of overall brain connectivity with age (up to -59%), sex (up to -25%), and body mass index (BMI) (up to +14%). Associations with diabetes status, educational level, history of cardiovascular disease, and white matter hyperintensities were generally less affected. This emphasizes the potential confounding effects of image quality in large population-based neuroimaging studies on brain connectivity and underscores the importance of accounting for it.
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Affiliation(s)
- Joost J. A. de Jong
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Jacobus F. A. Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Laura W. M. Vergoossen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Miranda T. Schram
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
| | - Coen D. A. Stehouwer
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
| | - Joachim E. Wildberger
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - David E. J. Linden
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Walter H. Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
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2
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Gundogdu B, Pittman JM, Chatterjee A, Szasz T, Lee G, Giurcanu M, Medved M, Engelmann R, Guo X, Yousuf A, Antic T, Devaraj A, Fan X, Oto A, Karczmar GS. Directional and inter-acquisition variability in diffusion-weighted imaging and editing for restricted diffusion. Magn Reson Med 2022; 88:2298-2310. [PMID: 35861268 PMCID: PMC9545544 DOI: 10.1002/mrm.29385] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 06/15/2022] [Accepted: 06/21/2022] [Indexed: 11/23/2022]
Abstract
Purpose To evaluate and quantify inter‐directional and inter‐acquisition variation in diffusion‐weighted imaging (DWI) and emphasize signals that report restricted diffusion to enhance cancer conspicuity, while reducing the effects of local microscopic motion and magnetic field fluctuations. Methods Ten patients with biopsy‐proven prostate cancer were studied under an Institutional Review Board‐approved protocol. Individual acquisitions of DWI signal intensities were reconstructed to calculate inter‐acquisition distributions and their statistics, which were compared for healthy versus cancer tissue. A method was proposed to detect and filter the acquisitions affected by motion‐induced signal loss. First, signals that reflect restricted diffusion were separated from the acquisitions that suffer from signal loss, likely due to microscopic motion, by imposing a cutoff value. Furthermore, corrected apparent diffusion coefficient maps were calculated by employing a weighted sum of the multiple acquisitions, instead of conventional averaging. These weights were calculated by applying a soft‐max function to the set of acquisitions per‐voxel, making the analysis immune to acquisitions with significant signal loss, even if the number of such acquisitions is high. Results Inter‐acquisition variation is much larger than the Rician noise variance, local spatial variations, and the estimates of diffusion anisotropy based on the current data, as well as the published values of anisotropy. The proposed method increases the contrast for cancers and yields a sensitivity of 98.8% with a false positive rate of 3.9%. Conclusion Motion‐induced signal loss makes conventional signal‐averaging suboptimal and can obscure signals from areas with restricted diffusion. Filtering or weighting individual acquisitions prior to image analysis can overcome this problem. Click here for author‐reader discussions
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Affiliation(s)
- Batuhan Gundogdu
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Jay M Pittman
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | | | - Teodora Szasz
- Research Computing Center, University of Chicago, Chicago, Illinois, USA
| | - Grace Lee
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Mihai Giurcanu
- Department of Public Health Sciences, University of Chicago, Illinois, USA
| | - Milica Medved
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Roger Engelmann
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Xiaodong Guo
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Ambereen Yousuf
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, Illinois, USA
| | - Ajit Devaraj
- Philips Research North America, Cambridge, Massachusetts, USA
| | - Xiaobing Fan
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
| | - Aytekin Oto
- Department of Radiology, University of Chicago, Chicago, Illinois, USA
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Discrete Shearlets as a Sparsifying Transform in Low-Rank Plus Sparse Decomposition for Undersampled (k, t)-Space MR Data. J Imaging 2022; 8:jimaging8020029. [PMID: 35200731 PMCID: PMC8878450 DOI: 10.3390/jimaging8020029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 01/19/2022] [Accepted: 01/27/2022] [Indexed: 11/17/2022] Open
Abstract
The discrete shearlet transformation accurately represents the discontinuities and edges occurring in magnetic resonance imaging, providing an excellent option of a sparsifying transform. In the present paper, we examine the use of discrete shearlets over other sparsifying transforms in a low-rank plus sparse decomposition problem, denoted by L+S. The proposed algorithm is evaluated on simulated dynamic contrast enhanced (DCE) and small bowel data. For the small bowel, eight subjects were scanned; the sequence was run first on breath-holding and subsequently on free-breathing, without changing the anatomical position of the subject. The reconstruction performance of the proposed algorithm was evaluated against k-t FOCUSS. L+S decomposition, using discrete shearlets as sparsifying transforms, successfully separated the low-rank (background and periodic motion) from the sparse component (enhancement or bowel motility) for both DCE and small bowel data. Motion estimated from low-rank of DCE data is closer to ground truth deformations than motion estimated from L and S. Motility metrics derived from the S component of free-breathing data were not significantly different from the ones from breath-holding data up to four-fold undersampling, indicating that bowel (rapid/random) motility is isolated in S. Our work strongly supports the use of discrete shearlets as a sparsifying transform in a L+S decomposition for undersampled MR data.
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Rodríguez-Soto AE, Andreassen MMS, Fang LK, Conlin CC, Park HH, Ahn GS, Bartsch H, Kuperman J, Vidić I, Ojeda-Fournier H, Wallace AM, Hahn M, Seibert TM, Jerome NP, Østlie A, Bathen TF, Goa PE, Rakow-Penner R, Dale AM. Characterization of the diffusion signal of breast tissues using multi-exponential models. Magn Reson Med 2021; 87:1938-1951. [PMID: 34904726 DOI: 10.1002/mrm.29090] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 10/12/2021] [Accepted: 11/01/2021] [Indexed: 12/15/2022]
Abstract
PURPOSE Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues. METHODS The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging. RESULTS A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D1,3 = 0, D2,3 = 1.5 × 10-3 , and D3,3 = 10.8 × 10-3 mm2 /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent. CONCLUSION Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.
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Affiliation(s)
- Ana E Rodríguez-Soto
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Maren M Sjaastad Andreassen
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Lauren K Fang
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Christopher C Conlin
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Helen H Park
- School of Medicine, University of California San Diego, La Jolla, California, USA
| | - Grace S Ahn
- School of Medicine, University of California San Diego, La Jolla, California, USA
| | - Hauke Bartsch
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Joshua Kuperman
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Igor Vidić
- Department of Physics, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Haydee Ojeda-Fournier
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Anne M Wallace
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Michael Hahn
- Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Tyler M Seibert
- Department of Radiation Oncology, University of California San Diego, La Jolla, California, USA.,Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Neil Peter Jerome
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | - Agnes Østlie
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | - Tone Frost Bathen
- Department of Circulation and Medical Imaging, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | - Pål Erik Goa
- Department of Physics, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim, Norway
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California San Diego, La Jolla, California, USA.,Department of Bioengineering, University of California San Diego, La Jolla, California, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, California, USA
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Rahbek S, Madsen KH, Lundell H, Mahmood F, Hanson LG. Data-driven separation of MRI signal components for tissue characterization. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2021; 333:107103. [PMID: 34801822 DOI: 10.1016/j.jmr.2021.107103] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 10/14/2021] [Accepted: 11/02/2021] [Indexed: 06/13/2023]
Abstract
PURPOSE MRI can be utilized for quantitative characterization of tissue. To assess e.g. water fractions or diffusion coefficients for compartments in the brain, a decomposition of the signal is necessary. Imposing standard models carries the risk of estimating biased parameters if model assumptions are violated. This work introduces a data-driven multicomponent analysis, the monotonous slope non-negative matrix factorization (msNMF), tailored to extract data features expected in MR signals. METHODS The msNMF was implemented by extending the standard NMF with monotonicity constraints on the signal profiles and their first derivatives. The method was validated using simulated data, and subsequently applied to both ex vivo DWI data and in vivo relaxometry data. Reproducibility of the method was tested using the latter. RESULTS The msNMF recovered the multi-exponential signals in the simulated data and showed superiority to standard NMF (based on the explained variance, area under the ROC curve, and coefficient of variation). Diffusion components extracted from the DWI data reflected the cell density of the underlying tissue. The relaxometry analysis resulted in estimates of edema water fractions (EWF) highly correlated with published results, and demonstrated acceptable reproducibility. CONCLUSION The msNMF can robustly separate MR signals into components with relation to the underlying tissue composition, and may potentially be useful for e.g. tumor tissue characterization.
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Affiliation(s)
- Sofie Rahbek
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
| | - Kristoffer H Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby 2800, Denmark
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650, Denmark
| | - Faisal Mahmood
- Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense C 5000, Denmark; Department of Clinical Research, University of Southern Denmark, Odense 5000, Denmark
| | - Lars G Hanson
- Department of Health Technology, Technical University of Denmark, Kgs. Lyngby 2800, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650, Denmark.
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Quantification of T1, T2 relaxation times from Magnetic Resonance Fingerprinting radially undersampled data using analytical transformations. Magn Reson Imaging 2021; 80:81-89. [PMID: 33932541 DOI: 10.1016/j.mri.2021.04.013] [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: 03/17/2021] [Revised: 04/16/2021] [Accepted: 04/25/2021] [Indexed: 01/03/2023]
Abstract
Quantitative magnetic resonance imaging (MRI) estimates magnetic parameters related to tissue, such as T1, T2 relaxation times and proton density. MR fingerprinting (MRF) is a new concept that uses pseudo-random, incoherent measurements to create a unique fingerprint for each tissue type to quantify magnet parameters. This paper aims to enhance MRF performance by investigating (i) the most suitable acquisition trajectory, and (ii) analytical transformations, suitable for radial acquisitions. Highly undersampled MRF brain (k, t)-space data have been simulated and non-linearly reconstructed to exploit the low-rank property of dynamic imaging. Based on our findings, the radial trajectory is the most suitable for MRF compared to Cartesian and spiral acquisitions. Perhaps this is due to the fact that its aliasing artifacts are more noise-like, and that unlike spiral trajectories, it can use analytical transformations that do not require re-gridding. One such analytical algorithm is the spline reconstruction technique (SRT) that is based on a novel numerical implementation of an analytic representation of the inverse Radon transform. Here, for the first time, this algorithm is applied to MR radial data. Reconstructions using SRT were compared to the ones using filtered back-projection. SRT provided images of higher contrast, lower bias, which resulted in more accurate T1, T2 values.
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Dikaios N. Deep learning magnetic resonance spectroscopy fingerprints of brain tumours using quantum mechanically synthesised data. NMR IN BIOMEDICINE 2021; 34:e4479. [PMID: 33448078 DOI: 10.1002/nbm.4479] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 11/24/2020] [Accepted: 01/05/2021] [Indexed: 06/12/2023]
Abstract
Metabolic fingerprints are valuable biomarkers for diseases that are associated with metabolic disorders. 1H magnetic resonance spectroscopy (MRS) is a unique noninvasive diagnostic tool that can depict the metabolic fingerprint based solely on the proton signal of different molecules present in the tissue. However, its performance is severely hindered by low SNR, field inhomogeneities and overlapping spectra of metabolites, which affect the quantification of metabolites. Consequently, MRS is rarely included in routine clinical protocols and has not been proven in multi-institutional trials. This work proposes an alternative approach, where instead of quantifying metabolites' concentration, deep learning (DL) is used to model the complex nonlinear relationship between diseases and their spectroscopic metabolic fingerprint (pattern). DL requires large training datasets, acquired (ideally) with the same protocol/scanner, which are very rarely available. To overcome this limitation, a novel method is proposed that can quantum mechanically synthesise MRS data for any scanner/acquisition protocol. The proposed methodology is applied to the challenging clinical problem of differentiating metastasis from glioblastoma brain tumours on data acquired across multiple institutions. DL algorithms were trained on the augmented synthetic spectra and tested on two independent datasets acquired by different scanners, achieving a receiver operating characteristic area under the curve of up to 0.96 and 0.97, respectively.
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Affiliation(s)
- Nikolaos Dikaios
- Mathematics Research Center, Academy of Athens, Athens, Greece
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
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8
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Ullrich T, Kohli MD, Ohliger MA, Magudia K, Arora SS, Barrett T, Bittencourt LK, Margolis DJ, Schimmöller L, Turkbey B, Westphalen AC. Quality Comparison of 3 Tesla multiparametric MRI of the prostate using a flexible surface receiver coil versus conventional surface coil plus endorectal coil setup. Abdom Radiol (NY) 2020; 45:4260-4270. [PMID: 32696213 PMCID: PMC7716937 DOI: 10.1007/s00261-020-02641-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 06/21/2020] [Accepted: 07/04/2020] [Indexed: 11/29/2022]
Abstract
PURPOSE To subjectively and quantitatively compare the quality of 3 Tesla magnetic resonance imaging of the prostate acquired with a novel flexible surface coil (FSC) and with a conventional endorectal coil (ERC). METHODS Six radiologists independently reviewed 200 pairs of axial, high-resolution T2-weighted and diffusion-weighted image data sets, each containing one examination acquired with the FSC and one with the ERC, respectively. Readers selected their preferred examination from each pair and assessed every single examination using six quality criteria on 4-point scales. Signal-to-noise ratios were measured and compared. RESULTS Two readers preferred FSC acquisition (36.5-45%) over ERC acquisition (13.5-15%) for both sequences combined, and four readers preferred ERC acquisition (41-46%). Analysis of pooled responses for both sequences from all readers shows no significant preference for FSC or ERC. Analysis of the individual sequences revealed a pooled preference for the FSC in T2WI (38.7% vs 17.8%) and for the ERC in DWI (50.9% vs 19.6%). Patients' weight was the only weak predictor of a preference for the ERC acquisition (p = 0.04). SNR and CNR were significantly higher in the ERC acquisitions (p<0.001) except CNR differentiating tumor lesions from benign prostate (p=0.1). CONCLUSION Although readers have strong individual preferences, comparable subjective image quality can be obtained for prostate MRI with an ERC and the novel FSC. ERC imaging might be particularly valuable for sequences with inherently lower SNR as DWI and larger patients whereas the FSC is generally preferred in T2WI. FSC imaging generates a lower SNR than with an ERC.
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Affiliation(s)
- T Ullrich
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany.
| | - M D Kohli
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - M A Ohliger
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - K Magudia
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - S S Arora
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - T Barrett
- Department of Radiology, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- CamPARI Prostate Cancer Group, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
| | - L K Bittencourt
- DASA Company, São Paulo, Brazil
- Department of Radiology, Fluminense Federal University (UFF), Niterói, Rio De Janeiro, Brazil
| | - D J Margolis
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - L Schimmöller
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, 40225, Dusseldorf, Germany
| | - B Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - A C Westphalen
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
- Department of Urology, University of California, San Francisco, CA, USA
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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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10
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Zhong X, Dale BM, Nickel MD, Kannengiesser SAR, Kiefer B, Bashir M. Improved accuracy of apparent diffusion coefficient quantification using a fully automatic noise bias compensation method: Preliminary evaluation in prostate diffusion weighted imaging. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 305:22-30. [PMID: 31158792 DOI: 10.1016/j.jmr.2019.05.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 05/11/2019] [Accepted: 05/20/2019] [Indexed: 06/09/2023]
Abstract
Noise in diffusion magnetic resonance imaging can introduce bias in apparent diffusion coefficient (ADC) quantification. Previous studies proposed methods that are site-specific techniques as research tools with limited availability and typically require manual intervention, not completely ready to use in the clinical environment. The purpose of this study was to develop a fully automatic computational method to correct noise bias in ADC quantification and perform a preliminary evaluation in the clinical prostate diffusion weighted imaging (DWI). Using a pseudo replica approach for the noise map calculation as well as a direct mapping and a stepwise Chebychev polynomial modelling approach for the ADC fitting, a fully automatic noise-bias-compensated ADC calculation method was proposed and implemented both on the scanner and offline. The proposed method was validated in a computer simulation and a standardized diffusion phantom with ground-truth values. Two in vivo studies were performed to evaluate the proposed method in the clinical environment. The first in vivo study performed acquisitions using a clinically routine prostate DWI protocol on 29 subjects to evaluate the consistency between simulated and empirical results. In the second in vivo study, prostate ADC values of 14 subjects were compared between data acquired with external coils only and reconstructed with the proposed method vs. acquired with external combined with endorectal coils and reconstructed with the conventional method. In statistical analyses, p < 0.05 was regarded as significantly different. In the computer simulation, the proposed method showed smaller error percentage than the other methods and was significantly different (p < 2.2 × 10-16). With low signal-to-noise ratio (SNR), the conventional method underestimated ADC values compared to the ground truth values of the diffusion phantom, while the results of the proposed method were more consistent with the ground truth values. Statistical analyses showed no significant differences between measured and simulated results in the first in vivo study (p = 0.5618). Data from the second in vivo study showed that agreement between ADC measured with external coils only and combined coils was improved for the proposed method (mean bias: 0.04 × 10-3 mm2/s, 95% confidence interval (CI) = [-0.01, 0.09] × 10-3 mm2/s, p = 0.187), compared to the conventional method (mean bias: -0.12 × 10-3 mm2/s, 95% CI = [-0.17, -0.06] × 10-3 mm2/s, p < 0.0001). The proposed method compensates noise bias in low-SNR diffusion-weighted acquisitions and results show improved ADC quantification accuracy in the prostate. This method may be suitable for both clinical imaging and research utilizing ADC quantification.
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Affiliation(s)
- Xiaodong Zhong
- MR R&D Collaborations, Siemens Healthcare, Los Angeles, CA, United States.
| | - Brian M Dale
- MR R&D Collaborations, Siemens Healthcare, Cary, NC, United States
| | - Marcel D Nickel
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Berthold Kiefer
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
| | - Mustafa Bashir
- Department of Radiology, Duke University Medical Center, Durham, NC, United States; Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, NC, United States
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Ahn JH, Kim SH, Son JH, Jo SJ. Added value of diffusion-weighted imaging for evaluation of extramural venous invasion in patients with primary rectal cancer. Br J Radiol 2019; 92:20180821. [PMID: 30698998 DOI: 10.1259/bjr.20180821] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
OBJECTIVE: To evaluate the added value of diffusion-weighted imaging (DWI) to T 2 weighted imaging (T 2WI) for detection of extramural venous invasion (EMVI) in patients with primary rectal cancer. METHODS: 79 patients (50 men, 29 females, mean age 67.4 years, range 37-87 years) who had undergone rectal MRI and subsequently received surgical resection were included. The rectal MRI consisted of T 2WI in three planes and axial DWI (b-values, 0, 1000 s mm-2). Two radiologists blinded to the pathologic results independently reviewed the T 2WI first, and then the combined T 2WI and DWI 4 weeks later. They recorded their confidence scores for EMVI on a 5-point scale (0: definitely negative and 4: definitely positive). The diagnostic performance of each reading session for each reader was compared by pairwise comparison of receiver operating characteristic curves. The area under the ROC curve (AUC) was considered as the diagnostic performance. The result of a histopathological examination served as the reference standard for EMVI. RESULTS: For both readers, the diagnostic performance was not significantly different between the two image sets (for reader 1, AUC, 0.828 and 0.825, p = 0.9426 and for reader 2, AUC, 0.723 and 0.726, p = 0.9244, respectively). CONCLUSION: There was no added value of DWI to T2WI for detection of EMVI in patients with primary rectal cancer. ADVANCES IN KNOWLEDGE: High-resolution T2WI alone is sufficient to assess EMVI and a supplementary DWI has no added value in patients with primary rectal cancer.
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Affiliation(s)
- Ju Hee Ahn
- 1 Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital , Busan , Korea
| | - Seung Ho Kim
- 1 Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital , Busan , Korea
| | - Jung Hee Son
- 1 Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital , Busan , Korea
| | - Sung Jae Jo
- 1 Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital , Busan , Korea
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12
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Bonet‐Carne E, Johnston E, Daducci A, Jacobs JG, Freeman A, Atkinson D, Hawkes DJ, Punwani S, Alexander DC, Panagiotaki E. VERDICT-AMICO: Ultrafast fitting algorithm for non-invasive prostate microstructure characterization. NMR IN BIOMEDICINE 2019; 32:e4019. [PMID: 30378195 PMCID: PMC6492114 DOI: 10.1002/nbm.4019] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 08/30/2018] [Accepted: 09/01/2018] [Indexed: 05/10/2023]
Abstract
VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumours) estimates and maps microstructural features of cancerous tissue non-invasively using diffusion MRI. The main purpose of this study is to address the high computational time of microstructural model fitting for prostate diagnosis, while retaining utility in terms of tumour conspicuity and repeatability. In this work, we adapt the accelerated microstructure imaging via convex optimization (AMICO) framework to linearize the estimation of VERDICT parameters for the prostate gland. We compare the original non-linear fitting of VERDICT with the linear fitting, quantifying accuracy with synthetic data, and computational time and reliability (performance and precision) in eight patients. We also assess the repeatability (scan-rescan) of the parameters. Comparison of the original VERDICT fitting versus VERDICT-AMICO showed that the linearized fitting (1) is more accurate in simulation for a signal-to-noise ratio of 20 dB; (2) reduces the processing time by three orders of magnitude, from 6.55 seconds/voxel to 1.78 milliseconds/voxel; (3) estimates parameters more precisely; (4) produces similar parametric maps and (5) produces similar estimated parameters with a high Pearson correlation between implementations, r2 > 0.7. The VERDICT-AMICO estimates also show high levels of repeatability. Finally, we demonstrate that VERDICT-AMICO can estimate an extra diffusivity parameter without losing tumour conspicuity and retains the fitting advantages. VERDICT-AMICO provides microstructural maps for prostate cancer characterization in seconds.
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Affiliation(s)
- Elisenda Bonet‐Carne
- UCL Centre for Medical ImagingLondonUK
- Department of Computer ScienceUCL Centre for Medical Image ComputingLondonUK
| | | | - Alessandro Daducci
- Computer Science DepartmentUniversity of VeronaItaly
- Radiology DepartmentCentre Hospitalier Universitaire Vaudois (CHUV)Switzerland
| | - Joseph G. Jacobs
- Department of Computer ScienceUCL Centre for Medical Image ComputingLondonUK
| | | | | | - David J. Hawkes
- Department of Medical PhysicsUCL Centre for Medical Imaging ComputingLondonUK
| | | | - Daniel C. Alexander
- Department of Computer ScienceUCL Centre for Medical Image ComputingLondonUK
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13
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Payabvash S. Quantitative diffusion magnetic resonance imaging in head and neck tumors. Quant Imaging Med Surg 2018; 8:1052-1065. [PMID: 30598882 DOI: 10.21037/qims.2018.10.14] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In patients with head and neck cancer, conventional anatomical magnetic resonance imaging (MRI) scans are commonly used for identification of primary lesion, assessment of structural distortion, and presence of metastatic lymph nodes. However, quantitative analysis of diffusion MRI can provide added value to structural and anatomical evaluation of head and neck tumors (HNT), by differentiation of primary malignant process, prognostic prediction, and treatment monitoring. In this article, we will review the applications of quantitative diffusion MRI in identification of primary malignant tissue, differentiation of tumor pathology, prediction of molecular phenotype, monitoring of treatment response, and evaluation of posttreatment changes in patient with HNT.
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Affiliation(s)
- Seyedmehdi Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
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14
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Sanz-Estébanez S, Pieciak T, Alberola-López C, Aja-Fernández S. Robust estimation of the apparent diffusion coefficient invariant to acquisition noise and physiological motion. Magn Reson Imaging 2018; 53:123-133. [DOI: 10.1016/j.mri.2018.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2018] [Revised: 07/07/2018] [Accepted: 07/14/2018] [Indexed: 10/28/2022]
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15
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Qin Y, Yu X, Hou J, Hu Y, Li F, Wen L, Lu Q, Fu Y, Liu S. Predicting chemoradiotherapy response of nasopharyngeal carcinoma using texture features based on intravoxel incoherent motion diffusion-weighted imaging. Medicine (Baltimore) 2018; 97:e11676. [PMID: 30045324 PMCID: PMC6078652 DOI: 10.1097/md.0000000000011676] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The aim of the study was to investigative the utility of gray-level co-occurrence matrix (GLCM) texture analysis based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the early response to chemoradiotherapy for nasopharyngeal carcinoma (NPC).Baseline IVIM-DWI was performed on 81 patients with NPC receiving chemoradiotherapy in a prospective nested case-control study. The patients were categorized into the residue (n = 11) and nonresidue (n = 70) groups, according to whether there was local residual lesion or not at the end of chemoradiotherapy. The pretreatment tumor volume and the values of IVIM-DWI parameters (apparent diffusion coefficient [ADC], D, D, and f) and GLCM features based on IVIM-DWI were compared between the 2 groups. Receiver operating characteristic (ROC) curves in univariate and multivariate logistic regression analysis were generated to determine significant indicator of treatment response.The nonresidue group had lower tumor volume, ADC, D, CorrelatADC, CorrelatD, InvDfMomADC, InvDfMomD and InvDfMomD values, together with higher ContrastD, Contrastf, SumAvergADC, SumAvergD, and SumAvergD values, than the residue group (all P < .05). Based on ROC curve in univariate analysis, the area under the curve (AUC) values for individual GLCM features in the prediction of the treatment response ranged from 0.635 to 0.879, with sensitivities from 54.55% to 100.00% and specificities from 52.86% to 85.71%. Multivariate logistic regression analysis demonstrated D (P = .026), InvDfMomADC (P = .033) and SumAvergD (P = .015) as the independent predictors for identifying NPC without residue, with an AUC value of 0.977, a sensitivity of 90.91% and a specificity of 95.71%.Pretreatment GLCM features based on IVIM-DWI, especially on the diffusion-related maps, may have the potential to predict the early response to chemoradiotherapy for NPC.
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Affiliation(s)
| | | | - Jing Hou
- Department of Diagnostic Radiology
| | | | | | - Lu Wen
- Department of Diagnostic Radiology
| | - Qiang Lu
- Department of Diagnostic Radiology
| | - Yi Fu
- Department of Medical Service, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University and Hunan Cancer Hospital, Changsha, Hunan, China
| | - Siye Liu
- Department of Diagnostic Radiology
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16
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Pieciak T, Aja-Fernandez S, Vegas-Sanchez-Ferrero G. Non-Stationary Rician Noise Estimation in Parallel MRI Using a Single Image: A Variance-Stabilizing Approach. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2017; 39:2015-2029. [PMID: 27845653 DOI: 10.1109/tpami.2016.2625789] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Parallel magnetic resonance imaging (pMRI) techniques have gained a great importance both in research and clinical communities recently since they considerably accelerate the image acquisition process. However, the image reconstruction algorithms needed to correct the subsampling artifacts affect the nature of noise, i.e., it becomes non-stationary. Some methods have been proposed in the literature dealing with the non-stationary noise in pMRI. However, their performance depends on information not usually available such as multiple acquisitions, receiver noise matrices, sensitivity coil profiles, reconstruction coefficients, or even biophysical models of the data. Besides, some methods show an undesirable granular pattern on the estimates as a side effect of local estimation. Finally, some methods make strong assumptions that just hold in the case of high signal-to-noise ratio (SNR), which limits their usability in real scenarios. We propose a new automatic noise estimation technique for non-stationary Rician noise that overcomes the aforementioned drawbacks. Its effectiveness is due to the derivation of a variance-stabilizing transformation designed to deal with any SNR. The method was compared to the main state-of-the-art methods in synthetic and real scenarios. Numerical results confirm the robustness of the method and its better performance for the whole range of SNRs.
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17
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Noij DP, Martens RM, Marcus JT, de Bree R, Leemans CR, Castelijns JA, de Jong MC, de Graaf P. Intravoxel incoherent motion magnetic resonance imaging in head and neck cancer: A systematic review of the diagnostic and prognostic value. Oral Oncol 2017; 68:81-91. [DOI: 10.1016/j.oraloncology.2017.03.016] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2016] [Revised: 03/12/2017] [Accepted: 03/25/2017] [Indexed: 12/20/2022]
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18
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Dikaios N, Atkinson D, Tudisca C, Purpura P, Forster M, Ahmed H, Beale T, Emberton M, Punwani S. A comparison of Bayesian and non-linear regression methods for robust estimation of pharmacokinetics in DCE-MRI and how it affects cancer diagnosis. Comput Med Imaging Graph 2017; 56:1-10. [PMID: 28192761 DOI: 10.1016/j.compmedimag.2017.01.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2016] [Revised: 11/16/2016] [Accepted: 01/26/2017] [Indexed: 11/23/2022]
Abstract
The aim of this work is to compare Bayesian Inference for nonlinear models with commonly used traditional non-linear regression (NR) algorithms for estimating tracer kinetics in Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The algorithms are compared in terms of accuracy, and reproducibility under different initialization settings. Further it is investigated how a more robust estimation of tracer kinetics affects cancer diagnosis. The derived tracer kinetics from the Bayesian algorithm were validated against traditional NR algorithms (i.e. Levenberg-Marquardt, simplex) in terms of accuracy on a digital DCE phantom and in terms of goodness-of-fit (Kolmogorov-Smirnov test) on ROI-based concentration time courses from two different patient cohorts. The first cohort consisted of 76 men, 20 of whom had significant peripheral zone prostate cancer (any cancer-core-length (CCL) with Gleason>3+3 or any-grade with CCL>=4mm) following transperineal template prostate mapping biopsy. The second cohort consisted of 9 healthy volunteers and 24 patients with head and neck squamous cell carcinoma. The diagnostic ability of the derived tracer kinetics was assessed with receiver operating characteristic area under curve (ROC AUC) analysis. The Bayesian algorithm accurately recovered the ground-truth tracer kinetics for the digital DCE phantom consistently improving the Structural Similarity Index (SSIM) across the 50 different initializations compared to NR. For optimized initialization, Bayesian did not improve significantly the fitting accuracy on both patient cohorts, and it only significantly improved the ve ROC AUC on the HN population from ROC AUC=0.56 for the simplex to ROC AUC=0.76. For both cohorts, the values and the diagnostic ability of tracer kinetic parameters estimated with the Bayesian algorithm weren't affected by their initialization. To conclude, the Bayesian algorithm led to a more accurate and reproducible quantification of tracer kinetic parameters in DCE-MRI, improving their ROC-AUC and decreasing their dependence on initialization settings.
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Affiliation(s)
- Nikolaos Dikaios
- Centre for Vision, Speech and Signal Processing, University of Surrey, United Kingdom.
| | - David Atkinson
- Centre for Medical Imaging, University College London, United Kingdom
| | - Chiara Tudisca
- Centre for Medical Imaging, University College London, United Kingdom
| | - Pierpaolo Purpura
- Centre for Medical Imaging, University College London, United Kingdom
| | - Martin Forster
- Department of Head and Neck Oncology, University College London Hospital, United Kingdom; Cancer Institute, University College London, United Kingdom
| | - Hashim Ahmed
- Department of Urology, University College London, London NW1 2 PG, United Kingdom
| | - Timothy Beale
- Department of Head and Neck Oncology, University College London Hospital, United Kingdom
| | - Mark Emberton
- Department of Urology, University College London, London NW1 2 PG, United Kingdom
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, United Kingdom; Department of Head and Neck Oncology, University College London Hospital, United Kingdom
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19
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Chiu TW, Liu YJ, Chang HC, Lee YH, Lee JC, Hsu K, Wang CW, Yang JM, Hsu HH, Juan CJ. Evaluating Instantaneous Perfusion Responses of Parotid Glands to Gustatory Stimulation Using High-Temporal-Resolution Echo-Planar Diffusion-Weighted Imaging. AJNR Am J Neuroradiol 2016; 37:1909-1915. [PMID: 27339952 DOI: 10.3174/ajnr.a4852] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Accepted: 04/03/2016] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Parotid glands secrete and empty saliva into the oral cavity rapidly after gustatory stimulation. However, the role of the temporal resolution of DWI in investigating parotid gland function remains uncertain. Our aim was to design a high-temporal-resolution echo-planar DWI pulse sequence and to evaluate the instantaneous MR perfusion responses of the parotid glands to gustatory stimulation. MATERIALS AND METHODS This prospective study enrolled 21 healthy volunteers (M/F = 2:1; mean age, 45.2 ± 12.9 years). All participants underwent echo-planar DWI (total scan time, 304 seconds; temporal resolution, 4 s/scan) on a 1.5T MR imaging scanner. T2WI (b = 0 s/mm2) and DWI (b = 200 s/mm2) were qualitatively assessed. Signal intensity of the parotid glands on T2WI, DWI, and ADC was quantitatively analyzed. One-way ANOVA with post hoc group comparisons with Bonferroni correction was used for statistical analysis. P < .05 was statistically significant. RESULTS Almost perfect interobserver agreement was achieved (κ ≥ 0.656). The parotid glands had magnetic susceptibility artifacts in 14.3% (3 of 21) of volunteers during swallowing on DWI but were free from perceptible artifacts at the baseline and at the end of scans on all images. Increased ADC and reduced signal intensity of the parotid glands on T2WI and DWI occurred immediately after oral administration of lemon juice. Maximal signal change of ADC (24.8% ± 10.8%) was significantly higher than that of T2WI (-10.1% ± 5.2%, P < .001). The recovery ratio of ADC (100.71% ± 42.34%) was also significantly higher than that of T2WI (22.36% ± 15.54%, P < .001). CONCLUSIONS Instantaneous parotid perfusion responses to gustatory stimulation can be quantified by ADC by using high-temporal-resolution echo-planar DWI.
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Affiliation(s)
- T-W Chiu
- From the Departments of Radiology (T.-W.C., C.-W.W., H.-H.H., C.-J.J.)
- Department of Medicine (T.-W.C.), Taipei Medical University, Taipei, Taiwan
| | - Y-J Liu
- Department of Medicine (T.-W.C.), Taipei Medical University, Taipei, Taiwan
| | - H-C Chang
- Department of Diagnostic Radiology (H.-C.C.), The University of Hong Kong, Hong Kong
| | - Y-H Lee
- Department of Medicine (T.-W.C.), Taipei Medical University, Taipei, Taiwan
| | - J-C Lee
- Department of Otolaryngology-Head and Neck Surgery (J.-C.L.), Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
- Department of Biological Science and Technology (J.-C.L., J.-M.Y.), Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - K Hsu
- Dentistry (K.H.), National Defense Medical Center, Taipei, Taiwan
| | - C-W Wang
- From the Departments of Radiology (T.-W.C., C.-W.W., H.-H.H., C.-J.J.)
- Department of Radiology (C.-W.W., H.-H.H., C.-J.J.), Tri-Service General Hospital, Taipei, Taiwan
| | - J-M Yang
- Department of Biological Science and Technology (J.-C.L., J.-M.Y.), Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan
| | - H-H Hsu
- From the Departments of Radiology (T.-W.C., C.-W.W., H.-H.H., C.-J.J.)
- Department of Radiology (C.-W.W., H.-H.H., C.-J.J.), Tri-Service General Hospital, Taipei, Taiwan
| | - C-J Juan
- From the Departments of Radiology (T.-W.C., C.-W.W., H.-H.H., C.-J.J.)
- Department of Radiology (C.-W.W., H.-H.H., C.-J.J.), Tri-Service General Hospital, Taipei, Taiwan
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