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Wittsack HJ, Thiel TA, Valentin B, Stabinska J, Benkert T, Schimmöller L, Antoch G, Ljimani A. Presentation of microstructural diffusion components by color schemes in abdominal organs. Magn Reson Med 2024; 92:2074-2080. [PMID: 38852176 DOI: 10.1002/mrm.30183] [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: 03/20/2024] [Revised: 04/24/2024] [Accepted: 05/17/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE Development of a color scheme representation to facilitate the interpretation of tri-exponential DWI data from abdominal organs, where multi-exponential behavior is more pronounced. METHODS Multi-exponential analysis of DWI data provides information about the microstructure of the tissue under study. The tri-exponential signal analysis generates numerous parameter images that are difficult to analyze individually. Summarized color images can simplify at-a-glance analysis. A color scheme was developed in which the slow, intermediate, and fast diffusion components were each assigned to a different red, green, and blue color channel. To improve the appearance of the image, histogram equalization, gamma correction, and white balance were used, and the processing parameters were adjusted. Examples of the resulting color maps of the diffusion fractions of healthy and pathological kidney and prostate are shown. RESULTS The color maps obtained by the presented method show the merged information of the slow, intermediate, and fast diffusion components in a single view. A differentiation of the different fractions becomes clearly visible. Fast diffusion regimes, such as in the renal hilus, can be clearly distinguished from slow fractions, such as in dense tumor tissue. CONCLUSION Combining the diffusion information from tri-exponential DWI analysis into a single color image allows for simplified interpretation of the diffusion fractions. In the future, such color images may provide additional information about the microstructural nature of the tissue under study.
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Affiliation(s)
- Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Thomas Andreas Thiel
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Birte Valentin
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Julia Stabinska
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Thomas Benkert
- MR Applications Predevelopment, Siemens Healthineers AG, Forchheim, Germany
| | - Lars Schimmöller
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- Department of Diagnostic, Interventional Radiology and Nuclear Medicine, Marien Hospital Herne, University Hospital of the Ruhr-University Bochum, Herne, Germany
| | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Alexandra Ljimani
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- Center for Integrated Oncology (CIO Aachen, Bonn, Cologne, Duesseldorf), Aachen, Bonn, Cologne, Düsseldorf, Germany
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Huang HM. Calculation of intravoxel incoherent motion parameter maps using a kernelized total difference-based method. NMR IN BIOMEDICINE 2024:e5201. [PMID: 38863271 DOI: 10.1002/nbm.5201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 05/13/2024] [Accepted: 05/23/2024] [Indexed: 06/13/2024]
Abstract
Quantitative analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for many clinical applications since its development. In particular, the intravoxel incoherent motion (IVIM) model for DW-MRI has been commonly utilized in various organs. However, because of the presence of excessive noise, the IVIM parameter maps obtained from pixel-wise fitting are often unreliable. In this study, we propose a kernelized total difference-based curve-fitting method to estimate the IVIM parameters. Simulated DW-MRI data at five signal-to-noise ratios (i.e., 10, 20, 30, 50, and 100) and real abdominal DW-MRI data acquired on a 1.5-T MRI scanner with nine b-values (i.e., 0, 10, 25, 50, 100, 200, 300, 400, and 500 s/mm2) and six diffusion-encoding gradient directions were used to evaluate the performance of the proposed method. The results were compared with those obtained by three existing methods: trust-region reflective (TRR) algorithm, Bayesian probability (BP), and deep neural network (DNN). Our simulation results showed that the proposed method outperformed the other three comparing methods in terms of root-mean-square error. Moreover, the proposed method could preserve small details in the estimated IVIM parameter maps. The experimental results showed that, compared with the TRR method, the proposed method as well as the BP (and DNN) method could reduce the overestimation of the pseudodiffusion coefficient and improve the quality of IVIM parameter maps. For all studied abdominal organs except the pancreas, both the proposed method and the BP method could provide IVIM parameter estimates close to the reference values; the former had higher precision. The kernelized total difference-based curve-fitting method has the potential to improve the reliability of IVIM parametric imaging.
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Affiliation(s)
- Hsuan-Ming Huang
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei City, Taiwan
- Program for Precision Health and Intelligent Medicine, Graduate School of Advanced Technology, National Taiwan University, Taipei City, Taiwan
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Stabinska J, Zöllner HJ, Thiel TA, Wittsack HJ, Ljimani A. Image downsampling expedited adaptive least-squares (IDEAL) fitting improves intravoxel incoherent motion (IVIM) analysis in the human kidney. Magn Reson Med 2023; 89:1055-1067. [PMID: 36416075 DOI: 10.1002/mrm.29517] [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] [Received: 06/09/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/24/2022]
Abstract
PURPOSE To improve the reliability of intravoxel incoherent motion (IVIM) model parameter estimation for the DWI in the kidney using a novel image downsampling expedited adaptive least-squares (IDEAL) approach. METHODS The robustness of IDEAL was investigated using simulated DW-MRI data corrupted with different levels of Rician noise. Subsequently, the performance of the proposed method was tested by fitting bi- and triexponential IVIM model to in vivo renal DWI data acquired on a clinical 3 Tesla MRI scanner and compared to conventional approaches (fixed D* and segmented fitting). RESULTS The numerical simulations demonstrated that the IDEAL algorithm provides robust estimates of the IVIM parameters in the presence of noise (SNR of 20) as indicated by relatively low absolute percentage bias (maximal sMdPB <20%) and normalized RMSE (maximal RMSE <28%). The analysis of the in vivo data showed that the IDEAL-based IVIM parameter maps were less noisy and more visually appealing than those obtained using the fixed D* and segmented methods. Further, coefficients of variation for nearly all IVIM parameters were significantly reduced in cortex and medulla for IDEAL-based biexponential (coefficients of variation: 4%-50%) and triexponential (coefficients of variation: 7.5%-75%) IVIM modelling compared to the segmented (coefficients of variation: 4%-120%) and fixed D* (coefficients of variation: 17%-174%) methods, reflecting greater accuracy of this method. CONCLUSION The proposed fitting algorithm yields more robust IVIM parameter estimates and is less susceptible to poor SNR than the conventional fitting approaches. Thus, the IDEAL approach has the potential to improve the reliability of renal DW-MRI analysis for clinical applications.
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Affiliation(s)
- Julia Stabinska
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, 21205, USA
- Division of MR Research, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, Düsseldorf, Germany
| | - Helge J Zöllner
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, 21205, USA
- Division of MR Research, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Thomas A Thiel
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, Düsseldorf, Germany
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, Düsseldorf, Germany
| | - Alexandra Ljimani
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Dusseldorf, Düsseldorf, Germany
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Huang HM. An unsupervised convolutional neural network method for estimation of intravoxel incoherent motion parameters. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac9a1f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 10/13/2022] [Indexed: 11/07/2022]
Abstract
Abstract
Objective. Intravoxel incoherent motion (IVIM) imaging obtained by fitting a biexponential model to multiple b-value diffusion-weighted magnetic resonance imaging (DW-MRI) has been shown to be a promising tool for different clinical applications. Recently, several deep neural network (DNN) methods were proposed to generate IVIM imaging. Approach. In this study, we proposed an unsupervised convolutional neural network (CNN) method for estimation of IVIM parameters. We used both simulated and real abdominal DW-MRI data to evaluate the performance of the proposed CNN-based method, and compared the results with those obtained from a non-linear least-squares fit (TRR, trust-region reflective algorithm) and a feed-forward backward-propagation DNN-based method. Main results. The simulation results showed that both the DNN- and CNN-based methods had lower coefficients of variation than the TRR method, but the CNN-based method provided more accurate parameter estimates. The results obtained from real DW-MRI data showed that the TRR method produced many biased IVIM parameter estimates that hit the upper and lower parameter bounds. In contrast, both the DNN- and CNN-based methods yielded less biased IVIM parameter estimates. Overall, the perfusion fraction and diffusion coefficient obtained from the DNN- and CNN-based methods were close to literature values. However, compared with the CNN-based method, both the TRR and DNN-based methods tended to yield increased pseudodiffusion coefficients (55%–180%). Significance. Our preliminary results suggest that it is feasible to estimate IVIM parameters using CNN.
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Wang DJJ, Le Bihan D, Krishnamurthy R, Smith M, Ho ML. Noncontrast Pediatric Brain Perfusion: Arterial Spin Labeling and Intravoxel Incoherent Motion. Magn Reson Imaging Clin N Am 2021; 29:493-513. [PMID: 34717841 DOI: 10.1016/j.mric.2021.06.002] [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] [Indexed: 12/23/2022]
Abstract
Noncontrast magnetic resonance imaging techniques for measuring brain perfusion include arterial spin labeling (ASL) and intravoxel incoherent motion (IVIM). These techniques provide noninvasive and repeatable assessment of cerebral blood flow or cerebral blood volume without the need for intravenous contrast. This article discusses the technical aspects of ASL and IVIM with a focus on normal physiologic variations, technical parameters, and artifacts. Multiple pediatric clinical applications are presented, including tumors, stroke, vasculopathy, vascular malformations, epilepsy, migraine, trauma, and inflammation.
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Affiliation(s)
- Danny J J Wang
- USC Institute for Neuroimaging and Informatics, SHN, 2025 Zonal Avenue, Health Sciences Campus, Los Angeles, CA 90033, USA
| | - Denis Le Bihan
- NeuroSpin, Centre d'études de Saclay, Bâtiment 145, Gif-sur-Yvette 91191, France
| | - Ram Krishnamurthy
- Department of Radiology, Nationwide Children's Hospital, 700 Children's Drive - ED4, Columbus, OH 43205, USA
| | - Mark Smith
- Department of Radiology, Nationwide Children's Hospital, 700 Children's Drive - ED4, Columbus, OH 43205, USA
| | - Mai-Lan Ho
- Department of Radiology, Nationwide Children's Hospital, 700 Children's Drive - ED4, Columbus, OH 43205, USA.
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Uwano I, Kobayashi M, Setta K, Ogasawara K, Yamashita F, Mori F, Matsuda T, Sasaki M. Assessment of Impaired Cerebrovascular Reactivity in Chronic Cerebral Ischemia using Intravoxel Incoherent Motion Magnetic Resonance Imaging. J Stroke Cerebrovasc Dis 2021; 30:106107. [PMID: 34562793 DOI: 10.1016/j.jstrokecerebrovasdis.2021.106107] [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] [Received: 02/25/2021] [Revised: 08/10/2021] [Accepted: 09/04/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The severity of chronic cerebral ischemia can be assessed using cerebrovascular reactivity (CVR) to acetazolamide (ACZ) challenge, which is measured by single-photon emission computed tomography (SPECT); however, this is an invasive method. We investigated whether intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) can assess impaired CVR in preoperative patients with chronic cerebral ischemia and compared it to SPECT-CVR. METHODS Forty-seven patients with unilateral cervical carotid artery stenosis underwent diffusion-weighted MRI with 11 b-values in the range of 0-800 s/mm2 and cerebral perfusion SPECT with the ACZ challenge. The perfusion fraction (f) and diffusion coefficient (D) of the IVIM parameters were calculated using a bi-exponential model. The f and D values and these ratios of the ipsilateral middle cerebral artery territory against the contralateral side were compared with the CVR values of the affected side calculated from the SPECT data. RESULTS The IVIM-f and D values in the affected side were significantly higher than those in the unaffected side (median: 7.74% vs. 7.45%, p = 0.027; 0.816 vs. 0.801 10-3mm2/s, p < 0.001; respectively). However, there were no significant correlations between the f or D values and SPECT-CVR values in the affected side. In contrast, the f ratio showed a moderate negative correlation with the SPECT-CVR values (r = -0.40, p = 0.006) and detected impaired CVR (< 18.4%) with a sensitivity/specificity of 0.71/0.90. CONCLUSION The IVIM perfusion parameter, f, can noninvasively assess impaired CVR with high sensitivity and specificity in patients with unilateral cervical carotid artery stenosis.
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Affiliation(s)
- Ikuko Uwano
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, 1-1-1 Idai-dori, Yahaba, Iwate 028-3694, Japan.
| | - Masakazu Kobayashi
- Department of Neurosurgery, Iwate Medical University, Yahaba, Iwate, Japan
| | - Kengo Setta
- Department of Neurosurgery, Iwate Medical University, Yahaba, Iwate, Japan
| | - Kuniaki Ogasawara
- Department of Neurosurgery, Iwate Medical University, Yahaba, Iwate, Japan
| | - Fumio Yamashita
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, 1-1-1 Idai-dori, Yahaba, Iwate 028-3694, Japan
| | - Futoshi Mori
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, 1-1-1 Idai-dori, Yahaba, Iwate 028-3694, Japan
| | - Tsuyoshi Matsuda
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, 1-1-1 Idai-dori, Yahaba, Iwate 028-3694, Japan
| | - Makoto Sasaki
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, 1-1-1 Idai-dori, Yahaba, Iwate 028-3694, Japan
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Zhang Z, Vernekar D, Qian W, Kim M. Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord. BMC Med Imaging 2021; 21:16. [PMID: 33516178 PMCID: PMC7847150 DOI: 10.1186/s12880-021-00549-9] [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: 05/14/2020] [Accepted: 01/18/2021] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND To investigate the effect of using a Rician nonlocal means (NLM) filter on quantification of diffusion tensor (DT)- and diffusion kurtosis (DK)-derived metrics in various anatomical regions of the human brain and the spinal cord, when combined with a constrained linear least squares (CLLS) approach. METHODS Prospective brain data from 9 healthy subjects and retrospective spinal cord data from 5 healthy subjects from a 3 T MRI scanner were included in the study. Prior to tensor estimation, registered diffusion weighted images were denoised by an optimized blockwise NLM filter with CLLS. Mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA), were determined in anatomical structures of the brain and the spinal cord. DTI and DKI metrics, signal-to-noise ratio (SNR) and Chi-square values were quantified in distinct anatomical regions for all subjects, with and without Rician denoising. RESULTS The averaged SNR significantly increased with Rician denoising by a factor of 2 while the averaged Chi-square values significantly decreased up to 61% in the brain and up to 43% in the spinal cord after Rician NLM filtering. In the brain, the mean MK varied from 0.70 (putamen) to 1.27 (internal capsule) while AK and RK varied from 0.58 (corpus callosum) to 0.92 (cingulum) and from 0.70 (putamen) to 1.98 (corpus callosum), respectively. In the spinal cord, FA varied from 0.78 in lateral column to 0.81 in dorsal column while MD varied from 0.91 × 10-3 mm2/s (lateral) to 0.93 × 10-3 mm2/s (dorsal). RD varied from 0.34 × 10-3 mm2/s (dorsal) to 0.38 × 10-3 mm2/s (lateral) and AD varied from 1.96 × 10-3 mm2/s (lateral) to 2.11 × 10-3 mm2/s (dorsal). CONCLUSIONS Our results show a Rician denoising NLM filter incorporated with CLLS significantly increases SNR and reduces estimation errors of DT- and KT-derived metrics, providing the reliable metrics estimation with adequate SNR levels.
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Affiliation(s)
- Zhongping Zhang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China.,Philips Healthcare, Shanghai, China
| | - Dhanashree Vernekar
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China
| | - Wenshu Qian
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China.,Laboratory of Clinical Investigation, National Institute on Aging, NIH, Baltimore, USA
| | - Mina Kim
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong SAR, China. .,Department of Neuroinflammation, Faculty of Brain Sciences, UCL Queen Square Institute of Neurology, London, UK.
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Lin YC, Huang HM. Denoising of multi b-value diffusion-weighted MR images using deep image prior. ACTA ACUST UNITED AC 2020; 65:105003. [DOI: 10.1088/1361-6560/ab8105] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Huang HM. Reliable estimation of brain intravoxel incoherent motion parameters using denoised diffusion-weighted MRI. NMR IN BIOMEDICINE 2020; 33:e4249. [PMID: 31922646 DOI: 10.1002/nbm.4249] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 12/03/2019] [Accepted: 12/04/2019] [Indexed: 06/10/2023]
Abstract
In this study, we evaluate whether diffusion-weighted magnetic resonance imaging (DW-MRI) data after denoising can provide a reliable estimation of brain intravoxel incoherent motion (IVIM) perfusion parameters. Brain DW-MRI was performed in five healthy volunteers on a 3 T clinical scanner with 12 different b-values ranging from 0 to 1000 s/mm2 . DW-MRI data denoised using the proposed method were fitted with a biexponential model to extract perfusion fraction (PF), diffusion coefficient (D) and pseudo-diffusion coefficient (D*). To further evaluate the accuracy and precision of parameter estimation, IVIM parametric images obtained from one volunteer were used to resimulate the DW-MRI data using the biexponential model with the same b-values. Rician noise was added to generate DW-MRI data with various signal-to-noise ratio (SNR) levels. The experimental results showed that the denoised DW-MRI data yielded precise estimates for all IVIM parameters. We also found that IVIM parameters were significantly different between gray matter and white matter (P < 0.05), except for D* (P = 0.6). Our simulation results show that the proposed image denoising method displays good performance in estimating IVIM parameters (both bias and coefficient of variation were <12% for PF, D and D*) in the presence of different levels of simulated Rician noise (SNRb=0 = 20-40). Simulations and experiments show that brain DW-MRI data after denoising can provide a reliable estimation of IVIM parameters.
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Affiliation(s)
- Hsuan-Ming Huang
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei City, Taiwan
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10
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Effect of intravoxel incoherent motion on diffusion parameters in normal brain. Neuroimage 2019; 204:116228. [PMID: 31580945 DOI: 10.1016/j.neuroimage.2019.116228] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 08/15/2019] [Accepted: 09/25/2019] [Indexed: 02/07/2023] Open
Abstract
At very low diffusion weighting the diffusion MRI signal is affected by intravoxel incoherent motion (IVIM) caused by dephasing of magnetization due to incoherent blood flow in capillaries or other sources of microcirculation. While IVIM measurements at low diffusion weightings have been frequently used to investigate perfusion in the body as well as in malignant tissue, the effect and origin of IVIM in normal brain tissue is not completely established. We investigated the IVIM effect on the brain diffusion MRI signal in a cohort of 137 radiologically-normal patients (62 male; mean age = 50.2 ± 17.8, range = 18 to 94). We compared the diffusion tensor parameters estimated from a mono-exponential fit at b = 0 and 1000 s/mm2 versus at b = 250 and 1000 s/mm2. The asymptotic fitting method allowed for quantitative assessment of the IVIM signal fraction f* in specific brain tissue and regions. Our results show a mean (median) percent difference in the mean diffusivity of about 4.5 (4.9)% in white matter (WM), about 7.8 (8.7)% in cortical gray matter (GM), and 4.3 (4.2)% in thalamus. Corresponding perfusion fraction f* was estimated to be 0.033 (0.032) in WM, 0.066 (0.065) in cortical GM, and 0.033 (0.030) in the thalamus. The effect of f* with respect to age was found to be significant in cortical GM (Pearson correlation ρ = 0.35, p = 3*10-5) and the thalamus (Pearson correlation ρ = 0.20, p = 0.022) with an average increase in f* of 5.17*10-4/year and 3.61*10-4/year, respectively. Significant correlations between f* and age were not observed for WM, and corollary analysis revealed no effect of gender on f*. Possible origins of the IVIM effect in normal brain tissue are discussed.
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Huang HM, Lin C. A kernel-based image denoising method for improving parametric image generation. Med Image Anal 2019; 55:41-48. [PMID: 31022639 DOI: 10.1016/j.media.2019.04.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 02/20/2019] [Accepted: 04/13/2019] [Indexed: 01/12/2023]
Abstract
One of the main challenges in the pixel-wise modeling analysis is the presence of high noise levels. Wang and Qi proposed a kernel-based method for dynamic positron emission tomgraphy reconstruction. Inspired by this method, we propose a kernel-based image denoising method based on the minimization of a kernel-based lp-norm regularized problem. To solve the kernel-based image denoising problem, we used the general-threshold filtering algorithm in combination with total difference. In the present study, we investigated whether diffusion-weighted magnetic resonance imaging (DW-MRI) data denoised using the proposed method can provide improved intravoxel incoherent motion (IVIM) parametric images. We also compared the proposed method with the method using the local principal component analysis (LPCA). The simulated DW-MR magnitude images are assumed to have Rician distributed noise. Computer simulations show that the proposed image denoising method can achieve a better bias-variance trade-off than the LPCA method. Moreover, the proposed method can reduce variance while simultaneously preserving edges in the parametric images. We tested our image denoising method on in vivo DW-MRI data, and the result showed that the denoised DWI-MRI data obtained using the proposed method can substantially improve the quality of IVIM parametric images.
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Affiliation(s)
- Hsuan-Ming Huang
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, No.1, Sec. 1, Jen Ai Rd., Zhongzheng Dist., Taipei City 100, Taiwan.
| | - Chieh Lin
- Department of Nuclear Medicine, Chang Gung Memorial Hospital, No. 5 Fu-Shin Street, Kwei-Shan, Taoyuan County, Taiwan
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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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Lin C, Liu CC, Huang HM. A general-threshold filtering method for improving intravoxel incoherent motion parameter estimates. Phys Med Biol 2018; 63:175008. [PMID: 30091719 DOI: 10.1088/1361-6560/aad94b] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In this study, we present an image denoising method for diffusion-weighted magnetic resonance imaging (DW-MRI) data. Our aim is to improve the estimation of intravoxel incoherent motion (IVIM) parameters using denoised DW-MRI data. A general-threshold filtering (GTF) reconstruction via total variation minimization has been proposed to improve image quality in few-view computed tomography. Here, we applied the combination of GTF and total difference to image denoising. Voxel-wise IVIM analysis was performed using both real and simulated DW-MRI data. Using an institutional review board-approved protocol with written informed consent, DW-MRI imaging was performed at a 3 T hybrid PET/MR system in 10 patients with Hodgkin lymphoma lesions. A simulated phantom consisting of four organs (liver, pancreas, spleen and kidney) was used to generate noisy DW-MRI data according to the IVIM model at different noise levels. DW-MRI data were denoised before IVIM parameter estimation. The proposed image denoising method was compared with the image denoising method using joint rank and edge constraints (JREC). The results of simulated data show that at the lower signal-to-noise ratios the proposed image denoising method outperformed the JREC method in terms of the accuracy and precision of the IVIM parameter estimates. The experimental results also show that the proposed image denoising method could yield better parametric images than the JREC method in terms of noise reduction and edge preservation.
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Affiliation(s)
- Chieh Lin
- Department of Nuclear Medicine, Chang Gung Memorial Hospital, No. 5 Fuxing Street, Gueishan Dist., Taoyuan 33305, Taiwan
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