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Ariyurek C, Koçanaoğulları A, Afacan O, Kurugol S. Motion-compensated image reconstruction for improved kidney function assessment using dynamic contrast-enhanced MRI. NMR IN BIOMEDICINE 2024; 37:e5116. [PMID: 38359842 DOI: 10.1002/nbm.5116] [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: 05/19/2023] [Revised: 12/08/2023] [Accepted: 01/15/2024] [Indexed: 02/17/2024]
Abstract
Accurately measuring renal function is crucial for pediatric patients with kidney conditions. Traditional methods have limitations, but dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides a safe and efficient approach for detailed anatomical evaluation and renal function assessment. However, motion artifacts during DCE-MRI can degrade image quality and introduce misalignments, leading to unreliable results. This study introduces a motion-compensated reconstruction technique for DCE-MRI data acquired using golden-angle radial sampling. Our proposed method achieves three key objectives: (1) identifying and removing corrupted data (outliers) using a Gaussian process model fitting with a k -space center navigator, (2) efficiently clustering the data into motion phases and performing interphase registration, and (3) utilizing a novel formulation of motion-compensated radial reconstruction. We applied the proposed motion correction (MoCo) method to DCE-MRI data affected by varying degrees of motion, including both respiratory and bulk motion. We compared the outcomes with those obtained from the conventional radial reconstruction. Our evaluation encompassed assessing the quality of images, concentration curves, and tracer kinetic model fitting, and estimating renal function. The proposed MoCo reconstruction improved the temporal signal-to-noise ratio for all subjects, with a 21.8% increase on average, while total variation values of the aorta, right, and left kidney concentration were improved for each subject, with 32.5%, 41.3%, and 42.9% increases on average, respectively. Furthermore, evaluation of tracer kinetic model fitting indicated that the median standard deviation of the estimated filtration rate (σ F T ), mean normalized root-mean-squared error (nRMSE), and chi-square goodness-of-fit of tracer kinetic model fit were decreased from 0.10 to 0.04, 0.27 to 0.24, and, 0.43 to 0.27, respectively. The proposed MoCo technique enabled more reliable renal function assessment and improved image quality for detailed anatomical evaluation in the case of bulk and respiratory motion during the acquisition of DCE-MRI.
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Affiliation(s)
- Cemre Ariyurek
- Quantitative Intelligent Imaging Lab (QUIN), Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Aziz Koçanaoğulları
- Quantitative Intelligent Imaging Lab (QUIN), Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Onur Afacan
- Quantitative Intelligent Imaging Lab (QUIN), Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Sila Kurugol
- Quantitative Intelligent Imaging Lab (QUIN), Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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Brumer I, Bauer DF, Schad LR, Zöllner FG. Synthetic Arterial Spin Labeling MRI of the Kidneys for Evaluation of Data Processing Pipeline. Diagnostics (Basel) 2022; 12:diagnostics12081854. [PMID: 36010205 PMCID: PMC9406826 DOI: 10.3390/diagnostics12081854] [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: 06/27/2022] [Revised: 07/28/2022] [Accepted: 07/29/2022] [Indexed: 11/30/2022] Open
Abstract
Accurate quantification of perfusion is crucial for diagnosis and monitoring of kidney function. Arterial spin labeling (ASL), a completely non-invasive magnetic resonance imaging technique, is a promising method for this application. However, differences in acquisition (e.g., ASL parameters, readout) and processing (e.g., registration, segmentation) between studies impede the comparison of results. To alleviate challenges arising solely from differences in processing pipelines, synthetic data are of great value. In this work, synthetic renal ASL data were generated using body models from the XCAT phantom and perfusion was added using the general kinetic model. Our in-house developed processing pipeline was then evaluated in terms of registration, quantification, and segmentation using the synthetic data. Registration performance was evaluated qualitatively with line profiles and quantitatively with mean structural similarity index measures (MSSIMs). Perfusion values obtained from the pipeline were compared to the values assumed when generating the synthetic data. Segmentation masks obtained by semi-automated procedure of the processing pipeline were compared to the original XCAT organ masks using the Dice index. Overall, the pipeline evaluation yielded good results. After registration, line profiles were smoother and, on average, MSSIMs increased by 25%. Mean perfusion values for cortex and medulla were close to the assumed perfusion of 250 mL/100 g/min and 50 mL/100 g/min, respectively. Dice indices ranged 0.80–0.93, 0.78–0.89, and 0.64–0.84 for whole kidney, cortex, and medulla, respectively. The generation of synthetic ASL data allows flexible choice of parameters and the generated data are well suited for evaluation of processing pipelines.
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Flouri D, Lesnic D, Chrysochou C, Parikh J, Thelwall P, Sheerin N, Kalra PA, Buckley DL, Sourbron SP. Motion correction of free-breathing magnetic resonance renography using model-driven registration. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 34:805-822. [PMID: 34160718 PMCID: PMC8578117 DOI: 10.1007/s10334-021-00936-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 05/24/2021] [Accepted: 06/08/2021] [Indexed: 11/29/2022]
Abstract
INTRODUCTION Model-driven registration (MDR) is a general approach to remove patient motion in quantitative imaging. In this study, we investigate whether MDR can effectively correct the motion in free-breathing MR renography (MRR). MATERIALS AND METHODS MDR was generalised to linear tracer-kinetic models and implemented using 2D or 3D free-form deformations (FFD) with multi-resolution and gradient descent optimization. MDR was evaluated using a kidney-mimicking digital reference object (DRO) and free-breathing patient data acquired at high temporal resolution in multi-slice 2D (5 patients) and 3D acquisitions (8 patients). Registration accuracy was assessed using comparison to ground truth DRO, calculating the Hausdorff distance (HD) between ground truth masks with segmentations and visual evaluation of dynamic images, signal-time courses and parametric maps (all data). RESULTS DRO data showed that the bias and precision of parameter maps after MDR are indistinguishable from motion-free data. MDR led to reduction in HD (HDunregistered = 9.98 ± 9.76, HDregistered = 1.63 ± 0.49). Visual inspection showed that MDR effectively removed motion effects in the dynamic data, leading to a clear improvement in anatomical delineation on parametric maps and a reduction in motion-induced oscillations on signal-time courses. DISCUSSION MDR provides effective motion correction of MRR in synthetic and patient data. Future work is needed to compare the performance against other more established methods.
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Affiliation(s)
- Dimitra Flouri
- Department of Applied Mathematics, University of Leeds, Leeds, UK. .,Department of Biomedical Imaging Sciences, University of Leeds, Leeds, UK. .,School of Biomedical Engineering and Imaging Sciences, Kings College London, London, UK. .,Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
| | - Daniel Lesnic
- Department of Applied Mathematics, University of Leeds, Leeds, UK
| | - Constantina Chrysochou
- Department of Renal Medicine, Salford Royal National Health Service Foundation Trust, Salford, UK
| | - Jehill Parikh
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.,Newcastle Magnetic Resonance Centre, Campus for Ageing and Vitality, University of Newcastle, Newcastle upon Tyne, UK
| | - Peter Thelwall
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.,Newcastle Magnetic Resonance Centre, Campus for Ageing and Vitality, University of Newcastle, Newcastle upon Tyne, UK
| | - Neil Sheerin
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Philip A Kalra
- Department of Renal Medicine, Salford Royal National Health Service Foundation Trust, Salford, UK
| | - David L Buckley
- Department of Biomedical Imaging Sciences, University of Leeds, Leeds, UK
| | - Steven P Sourbron
- Department of Biomedical Imaging Sciences, University of Leeds, Leeds, UK.,Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
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Coll-Font J, Afacan O, Chow JS, Lee RS, Warfield SK, Kurugol S. Modeling dynamic radial contrast enhanced MRI with linear time invariant systems for motion correction in quantitative assessment of kidney function. Med Image Anal 2021; 67:101880. [PMID: 33147561 PMCID: PMC7735437 DOI: 10.1016/j.media.2020.101880] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 09/30/2020] [Accepted: 10/05/2020] [Indexed: 12/11/2022]
Abstract
Early identification of kidney function deterioration is essential to determine which newborn patients with congenital kidney disease should be considered for surgical intervention as opposed to observation. Kidney function can be measured by fitting a tracer kinetic (TK) model onto a series of Dynamic Contrast Enhanced (DCE) MR images and estimating the filtration rate parameter from the model. Unfortunately, breathing and large bulk motion events due to patient movement in the scanner create outliers and misalignments that introduce large errors in the TK model parameter estimates even when using a motion-robust dynamic radial VIBE sequence for DCE-MR imaging. The misalignments between the series of volumes are difficult to correct using standard registration due to 1) the large differences in geometry and contrast between volumes of the dynamic sequence and 2) the requirement of fast dynamic imaging to achieve high temporal resolution and motion deteriorates image quality. These difficulties reduce the accuracy and stability of registration over the dynamic sequence. An alternative registration approach is to generate noise and motion free templates of the original data from the TK model and use them to register each volume to its contrast-matched template. However, the TK models used to characterize DCE-MRI are tissue specific, non-linear and sensitive to the same motion and sampling artifacts that hinder registration in the first place. Hence, these can only be applied to register accurately pre-segmented regions of interest, such as kidneys, and might converge to local minima under the presence of large artifacts. Here we introduce a novel linear time invariant (LTI) model to characterize DCE-MR data for different tissue types within a volume. We approximate the LTI model as a sparse sum of first order LTI functions to introduce robustness to motion and sampling artifacts. Hence, this model is well suited for registration of the entire field of view of DCE-MR data with artifacts and outliers. We incorporate this LTI model into a registration framework and evaluate it on both synthetic data and data from 20 children. For each subject, we reconstructed the sequence of DCE-MR images, detected corrupted volumes acquired during motion, aligned the sequence of volumes and recovered the corrupted volumes using the LTI model. The results show that our approach correctly aligned the volumes, provided the most stable registration in time and improved the tracer kinetic model fit.
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Affiliation(s)
- Jaume Coll-Font
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston MA 02115, USA; Harvard Medical School, 25 Shattuck St., Boston MA 02115, USA.
| | - Onur Afacan
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston MA 02115, USA; Harvard Medical School, 25 Shattuck St., Boston MA 02115, USA
| | - Jeanne S Chow
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston MA 02115, USA; Harvard Medical School, 25 Shattuck St., Boston MA 02115, USA
| | - Richard S Lee
- Harvard Medical School, 25 Shattuck St., Boston MA 02115, USA; Department of Urology, Boston Children's Hospital, 300 Longwood Ave., Boston MA 02115, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston MA 02115, USA; Harvard Medical School, 25 Shattuck St., Boston MA 02115, USA
| | - Sila Kurugol
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston MA 02115, USA; Harvard Medical School, 25 Shattuck St., Boston MA 02115, USA
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Zöllner FG, Šerifović-Trbalić A, Kabelitz G, Kociński M, Materka A, Rogelj P. Image registration in dynamic renal MRI-current status and prospects. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2019; 33:33-48. [PMID: 31598799 PMCID: PMC7210245 DOI: 10.1007/s10334-019-00782-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 09/16/2019] [Accepted: 09/25/2019] [Indexed: 12/26/2022]
Abstract
Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acquired over time for up to several minutes. Therefore, they are hampered by motion, e.g., by pulsation, peristaltic, or breathing motion. This motion can hinder subsequent image analysis to estimate hemodynamic parameters like renal blood flow or glomerular filtration rate (GFR). To overcome motion artifacts in time-resolved renal MRI, a wide range of strategies have been proposed. Renal image registration approaches could be grouped into (1) image acquisition techniques, (2) post-processing methods, or (3) a combination of image acquisition and post-processing approaches. Despite decades of progress, the translation in clinical practice is still missing. The aim of the present article is to discuss the existing literature on renal image registration techniques and show today’s limitations of the proposed techniques that hinder clinical translation. This paper includes transformation, criterion function, and search types as traditional components and emerging registration technologies based on deep learning. The current trend points towards faster registrations and more accurate results. However, a standardized evaluation of image registration in renal MRI is still missing.
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Affiliation(s)
- Frank G Zöllner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
| | | | - Gordian Kabelitz
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Marek Kociński
- Institute of Electronics, Lodz University of Technology, Lodz, Poland
| | - Andrzej Materka
- Institute of Electronics, Lodz University of Technology, Lodz, Poland
| | - Peter Rogelj
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
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de Boer A, Leiner T, Vink EE, Blankestijn PJ, van den Berg CAT. Modified dixon-based renal dynamic contrast-enhanced MRI facilitates automated registration and perfusion analysis. Magn Reson Med 2017; 80:66-76. [PMID: 29134673 PMCID: PMC5900902 DOI: 10.1002/mrm.26999] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 10/17/2017] [Accepted: 10/18/2017] [Indexed: 12/24/2022]
Abstract
Purpose Renal dynamic contrast‐enhanced (DCE) MRI provides information on renal perfusion and filtration. However, clinical implementation is hampered by challenges in postprocessing as a result of misalignment of the kidneys due to respiration. We propose to perform automated image registration using the fat‐only images derived from a modified Dixon reconstruction of a dual‐echo acquisition because these provide consistent contrast over the dynamic series. Methods DCE data of 10 hypertensive patients was used. Dual‐echo images were acquired at 1.5 T with temporal resolution of 3.9 s during contrast agent injection. Dixon fat, water, and in‐phase and opposed‐phase (OP) images were reconstructed. Postprocessing was automated. Registration was performed both to fat images and OP images for comparison. Perfusion and filtration values were extracted from a two‐compartment model fit. Results Automatic registration to fat images performed better than automatic registration to OP images with visible contrast enhancement. Median vertical misalignment of the kidneys was 14 mm prior to registration, compared to 3 mm and 5 mm with registration to fat images and OP images, respectively (P = 0.03). Mean perfusion values and MR‐based glomerular filtration rates (GFR) were 233 ± 64 mL/100 mL/min and 60 ± 36 mL/minute, respectively, based on fat‐registered images. MR‐based GFR correlated with creatinine‐based GFR (P = 0.04) for fat‐registered images. For unregistered and OP‐registered images, this correlation was not significant. Conclusion Absence of contrast changes on Dixon fat images improves registration in renal DCE MRI and enables automated postprocessing, resulting in a more accurate estimation of GFR. Magn Reson Med 80:66–76, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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Affiliation(s)
- Anneloes de Boer
- Utrecht University Medical Center, Utrecht University, Utrecht, The Netherlands
| | - Tim Leiner
- Utrecht University Medical Center, Utrecht University, Utrecht, The Netherlands
| | - Eva E Vink
- Utrecht University Medical Center, Utrecht University, Utrecht, The Netherlands
| | - Peter J Blankestijn
- Utrecht University Medical Center, Utrecht University, Utrecht, The Netherlands
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Park S, Kim EY, Sohn CH, Park J. Dynamic Contrast-Enhanced MR Angiography Exploiting Subspace Projection for Robust Angiogram Separation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:584-595. [PMID: 27810804 DOI: 10.1109/tmi.2016.2622715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Dynamic contrast-enhanced magnetic resonance angiography (DCE MRA) has been widely used as a clinical routine for diagnostic assessment of vascular morphology and hemodynamics. It requires high spatial and temporal resolution to capture rapid variation of DCE signals within a limited imaging time. Subtraction-based approaches are typically employed to selectively delineate arteries while eliminating unwanted background signals. Nevertheless, in the presence of subject motion with time, conventional subtraction approaches suffer from incomplete background suppression that impairs the detectability of arteries. In this work, we propose a novel, DCE MRA method that exploits subspace projection (SP) based angiogram separation for robust background suppression. A new, SP-based DCE signal model is introduced, in which images are decomposed into stationary background tissues, motion-induced artifacts, and DCE angiograms of interest. Constrained image reconstruction with sparsity priors is performed to project motion-induced artifacts onto the predefined subspace while extracting DCE angiograms of interest. Simulations and experimental studies validate that the proposed method outperforms existing techniques with increasing reduction factors in suppressing artifacts and noise.
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Zöllner FG, Gaa T, Zimmer F, Ong MM, Riffel P, Hausmann D, Schoenberg SO, Weis M. [Quantitative perfusion imaging in magnetic resonance imaging]. Radiologe 2016; 56:113-23. [PMID: 26796337 DOI: 10.1007/s00117-015-0068-4] [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] [Indexed: 12/17/2022]
Abstract
CLINICAL/METHODICAL ISSUE Magnetic resonance imaging (MRI) is recognized for its superior tissue contrast while being non-invasive and free of ionizing radiation. Due to the development of new scanner hardware and fast imaging techniques during the last decades, access to tissue and organ functions became possible. One of these functional imaging techniques is perfusion imaging with which tissue perfusion and capillary permeability can be determined from dynamic imaging data. STANDARD RADIOLOGICAL METHODS Perfusion imaging by MRI can be performed by two approaches, arterial spin labeling (ASL) and dynamic contrast-enhanced (DCE) MRI. While the first method uses magnetically labelled water protons in arterial blood as an endogenous tracer, the latter involves the injection of a contrast agent, usually gadolinium (Gd), as a tracer for calculating hemodynamic parameters. PERFORMANCE Studies have demonstrated the potential of perfusion MRI for diagnostics and also for therapy monitoring. ACHIEVEMENTS The utilization and application of perfusion MRI are still restricted to specialized centers, such as university hospitals. A broad application of the technique has not yet been implemented. PRACTICAL RECOMMENDATIONS The MRI perfusion technique is a valuable tool that might come broadly available after implementation of standards on European and international levels. Such efforts are being promoted by the respective professional bodies.
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Affiliation(s)
- F G Zöllner
- Computerunterstützte Klinische Medizin, Medizinische Fakultät Mannheim, Universität Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Deutschland.
| | - T Gaa
- Computerunterstützte Klinische Medizin, Medizinische Fakultät Mannheim, Universität Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Deutschland
| | - F Zimmer
- Computerunterstützte Klinische Medizin, Medizinische Fakultät Mannheim, Universität Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Deutschland
| | - M M Ong
- Institut für Klinische Radiologie und Nuklearmedizin, Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland
| | - P Riffel
- Institut für Klinische Radiologie und Nuklearmedizin, Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland
| | - D Hausmann
- Institut für Klinische Radiologie und Nuklearmedizin, Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland
| | - S O Schoenberg
- Institut für Klinische Radiologie und Nuklearmedizin, Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland
| | - M Weis
- Institut für Klinische Radiologie und Nuklearmedizin, Universitätsmedizin Mannheim, Medizinische Fakultät Mannheim, Universität Heidelberg, Mannheim, Deutschland
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Yin XX, Hadjiloucas S, Zhang Y, Su MY, Miao Y, Abbott D. Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs. Artif Intell Med 2016; 67:1-23. [PMID: 26951630 DOI: 10.1016/j.artmed.2016.01.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 12/28/2015] [Accepted: 01/16/2016] [Indexed: 12/25/2022]
Abstract
OBJECTIVE We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. METHODS Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. VALIDATION Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. RESULTS Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. CONCLUSION The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community.
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Affiliation(s)
- Xiao-Xia Yin
- Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia.
| | - Sillas Hadjiloucas
- School of Systems Engineering and Department of Bioengineering, University of Reading, Reading RG6 6AY, UK
| | - Yanchun Zhang
- Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia
| | - Min-Ying Su
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, USA
| | - Yuan Miao
- College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia
| | - Derek Abbott
- Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, South Australia, SA 5000, Australia
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Liu W, Sung K, Ruan D. Shape-based motion correction in dynamic contrast-enhanced MRI for quantitative assessment of renal function. Med Phys 2014; 41:122302. [PMID: 25471978 PMCID: PMC4240783 DOI: 10.1118/1.4900600] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To incorporate a newly developed shape-based motion estimation scheme into magnetic resonance urography (MRU) and verify its efficacy in facilitating quantitative functional analysis. METHODS The authors propose a motion compensation scheme in MRU that consists of three sequential modules: MRU image acquisition, motion compensation, and quantitative functional analysis. They designed two sets of complementary experiments to evaluate the performance of the proposed method. In the first experiment, dynamic contrast enhanced (DCE) MR images were acquired from three sedated subjects, from which clinically valid estimates were derived and served as the "ground truth." Physiologically sound motion was then simulated to synthesize image sequences influenced by respiratory motion. Quantitative assessment and comparison were performed on functional estimates of Patlak number, glomerular filtration rate, and Patlak differential renal function without and with motion compensation against the ground truth. In the second experiment, the authors acquired a temporal series of noncontrast MR images under free breathing from a healthy adult subject. The performance of the proposed method on compensating real motion was evaluated by comparing the standard deviation of the obtained temporal intensity curves before and after motion compensation. RESULTS On DCE-MR images with simulated motion, the generated relative enhancement curves exhibited large perturbations and the Patlak numbers of the left and right kidney were significantly underestimated up to 35% and 34%, respectively, compared with the ground truth. After motion compensation, the relative enhancement curves exhibited much less perturbations and Patlak estimation errors reduced within 3% and 4% for the left and right kidneys, respectively. On clinical free-breathing MR images, the temporal intensity curves exhibited significantly reduced variations after motion compensation, with standard deviation decreased from 30.3 and 38.2 to 8.3 and 11.7 within two manually selected regions of interest, respectively. CONCLUSIONS The developed motion compensation method has demonstrated its ability to facilitate quantitative MRU functional analysis, with improved accuracy of pharmacokinetic modeling and quantitative parameter estimations. Future work will consider performing more intensive clinical verifications with sophisticated pharmacokinetic models and generalizing the proposed method to other quantitative DCE analysis, such as on liver or prostate function.
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Affiliation(s)
- Wenyang Liu
- Department of Bioengineering, University of California, Los Angeles 90095
| | - Kyunghyun Sung
- Department of Bioengineering, University of California, Los Angeles 90095 and Department of Radiological Sciences, University of California, Los Angeles 90095
| | - Dan Ruan
- Department of Bioengineering, University of California, Los Angeles 90095 and Department of Radiation Oncology, University of California, Los Angeles 90095
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Assessment of Pulmonary Perfusion With Breath-Hold and Free-Breathing Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Invest Radiol 2014; 49:382-9. [DOI: 10.1097/rli.0000000000000020] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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12
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Renal perfusion in acute kidney injury with DCE-MRI: deconvolution analysis versus two-compartment filtration model. Magn Reson Imaging 2014; 32:781-5. [PMID: 24631714 DOI: 10.1016/j.mri.2014.02.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2013] [Revised: 01/08/2014] [Accepted: 02/06/2014] [Indexed: 11/23/2022]
Abstract
PURPOSE To investigate the results of different pharmacokinetic models of a quantitative analysis of renal blood flow (RBF) in acute kidney injury using deconvolution analysis and a two-compartment renal filtration model. MATERIALS AND METHODS MRI data of ten male Lewis rats were analyzed retrospectively. Six animals were subjected to unilateral acute kidney injury and underwent perfusion imaging by dynamic contrast-enhanced MRI (DCE-MRI). Renal blood flow was estimated from regions-of-interest depicting the cortex in the DCE-MRI perfusion maps. The perfusion models were compared by a paired t-test and Bland-Altman plots. RESULTS No significant difference was found between the two compartment model and the deconvolution analysis (P=0.2807). Differences between healthy and diseased kidney in the AKI model were significant for both methods (P<0.05). A Bland-Altman plot showed no systematic errors, and values were equally distributed around the mean difference between the methods lying within the range of 1.96 standard deviations. CONCLUSION Both quantification strategies could detect the kidneys that were impaired by AKI. When just aiming at RBF as a marker, a deconvolution analysis can provide similar values as the 2CFM. If functional parameters beyond RBF like glomerular filtration rate are needed, the 2CFM should be employed.
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Liu W, Ruan D. Estimating nonrigid motion from inconsistent intensity with robust shape features. Med Phys 2013; 40:121912. [PMID: 24320523 DOI: 10.1118/1.4829507] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To develop a nonrigid motion estimation method that is robust to heterogeneous intensity inconsistencies amongst the image pairs or image sequence. METHODS Intensity and contrast variations, as in dynamic contrast enhanced magnetic resonance imaging, present a considerable challenge to registration methods based on general discrepancy metrics. In this study, the authors propose and validate a novel method that is robust to such variations by utilizing shape features. The geometry of interest (GOI) is represented with a flexible zero level set, segmented via well-behaved regularized optimization. The optimization energy drives the zero level set to high image gradient regions, and regularizes it with area and curvature priors. The resulting shape exhibits high consistency even in the presence of intensity or contrast variations. Subsequently, a multiscale nonrigid registration is performed to seek a regular deformation field that minimizes shape discrepancy in the vicinity of GOIs. RESULTS To establish the working principle, realistic 2D and 3D images were subject to simulated nonrigid motion and synthetic intensity variations, so as to enable quantitative evaluation of registration performance. The proposed method was benchmarked against three alternative registration approaches, specifically, optical flow, B-spline based mutual information, and multimodality demons. When intensity consistency was satisfied, all methods had comparable registration accuracy for the GOIs. When intensities among registration pairs were inconsistent, however, the proposed method yielded pronounced improvement in registration accuracy, with an approximate fivefold reduction in mean absolute error (MAE = 2.25 mm, SD = 0.98 mm), compared to optical flow (MAE = 9.23 mm, SD = 5.36 mm), B-spline based mutual information (MAE = 9.57 mm, SD = 8.74 mm) and mutimodality demons (MAE = 10.07 mm, SD = 4.03 mm). Applying the proposed method on a real MR image sequence also provided qualitatively appealing results, demonstrating good feasibility and applicability of the proposed method. CONCLUSIONS The authors have developed a novel method to estimate the nonrigid motion of GOIs in the presence of spatial intensity and contrast variations, taking advantage of robust shape features. Quantitative analysis and qualitative evaluation demonstrated good promise of the proposed method. Further clinical assessment and validation is being performed.
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Affiliation(s)
- Wenyang Liu
- Department of Bioengineering, University of California, Los Angeles, California 90095
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