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Hooijmans MT, Lockard CA, Zhou X, Coolbaugh C, Pineda Guzman R, Kersh ME, Damon BM. A registration strategy to characterize DTI-observed changes in skeletal muscle architecture due to passive shortening. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.11.589123. [PMID: 38645028 PMCID: PMC11030449 DOI: 10.1101/2024.04.11.589123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Skeletal muscle architecture is a key determinant of muscle function. Architectural properties such as fascicle length, pennation angle, and curvature can be characterized using Diffusion Tensor Imaging (DTI), but acquiring these data during a contraction is not currently feasible. However, an image registration-based strategy may be able to convert muscle architectural properties observed at rest to their contracted state. As an initial step toward this long-term objective, the aim of this study was to determine if an image registration strategy could be used to convert the whole-muscle average architectural properties observed in the extended joint position to those of a flexed position, following passive rotation. DTI and high-resolution fat/water scans were acquired in the lower leg of seven healthy participants on a 3T MR system in +20° (plantarflexion) and -10° (dorsiflexion) foot positions. The diffusion and anatomical images from the two positions were used to propagate DTI fiber-tracts from seed points along a mesh representation of the aponeurosis of fiber insertion. The -10° and +20° anatomical images were registered and the displacement fields were used to transform the mesh and fiber-tracts from the +20° to the -10° position. Student's paired t-tests were used to compare the mean architectural parameters between the original and transformed fiber-tracts. The whole-muscle average fiber-tract length, pennation angle, curvature, and physiological cross-sectional areas estimates did not differ significantly. DTI fiber-tracts in plantarflexion can be transformed to dorsiflexion position without significantly affecting the average architectural characteristics of the fiber-tracts. In the future, a similar approach could be used to evaluate muscle architecture in a contracted state.
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Affiliation(s)
- Melissa T. Hooijmans
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Carle Clinical Imaging Research Program, Stephens Family Clinical Research Institute, Carle Health, Urbana, IL, United States of America
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Carly A. Lockard
- Carle Clinical Imaging Research Program, Stephens Family Clinical Research Institute, Carle Health, Urbana, IL, United States of America
| | - Xingyu Zhou
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Carle Clinical Imaging Research Program, Stephens Family Clinical Research Institute, Carle Health, Urbana, IL, United States of America
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Crystal Coolbaugh
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Roberto Pineda Guzman
- Carle Clinical Imaging Research Program, Stephens Family Clinical Research Institute, Carle Health, Urbana, IL, United States of America
| | - Mariana E. Kersh
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America
- Department of Biomedical and Translational Sciences, Carle-Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America
| | - Bruce M. Damon
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America
- Carle Clinical Imaging Research Program, Stephens Family Clinical Research Institute, Carle Health, Urbana, IL, United States of America
- Department of Biomedical and Translational Sciences, Carle-Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America
- Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States of America
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America
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Santos L, Hsu HY, Nelson RR, Sullivan B, Shin J, Fung M, Lebel MR, Jambawalikar S, Jaramillo D. Impact of Deep Learning Denoising Algorithm on Diffusion Tensor Imaging of the Growth Plate on Different Spatial Resolutions. Tomography 2024; 10:504-519. [PMID: 38668397 PMCID: PMC11054892 DOI: 10.3390/tomography10040039] [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: 02/21/2024] [Revised: 03/25/2024] [Accepted: 03/29/2024] [Indexed: 04/29/2024] Open
Abstract
To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. A General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) was employed to acquire two DTI (diffusion tensor imaging) sequences of the left knee on each child at 3T: an in-plane 2.0 × 2.0 mm2 with section thickness of 3.0 mm and a 2 mm3 isovolumetric voxel; neither had an intersection gap. For image acquisition, a multi-band DTI with a fat-suppressed single-shot spin-echo echo-planar sequence (20 non-collinear directions; b-values of 0 and 600 s/mm2) was utilized. The MR vendor-provided a commercially available DL model which was applied with 75% noise reduction settings to the same subject DTI sequences at different spatial resolutions. We compared DTI tract metrics from both DL-reconstructed scans and non-denoised scans for the femur and tibia at each spatial resolution. Differences were evaluated using Wilcoxon-signed ranked test and Bland-Altman plots. When comparing DL versus non-denoised diffusion metrics in femur and tibia using the 2 mm × 2 mm × 3 mm voxel dimension, there were no significant differences between tract count (p = 0.1, p = 0.14) tract volume (p = 0.1, p = 0.29) or tibial tract length (p = 0.16); femur tract length exhibited a significant difference (p < 0.01). All diffusion metrics (tract count, volume, length, and fractional anisotropy (FA)) derived from the DL-reconstructed scans, were significantly different from the non-denoised scan DTI metrics in both the femur and tibial physes using the 2 mm3 voxel size (p < 0.001). DL reconstruction resulted in a significant decrease in femorotibial FA for both voxel dimensions (p < 0.01). Leveraging denoising algorithms could address the drawbacks of lower signal-to-noise ratios (SNRs) associated with smaller voxel volumes and capitalize on their better spatial resolutions, allowing for more accurate quantification of diffusion metrics.
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Affiliation(s)
- Laura Santos
- Radiology Department, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Hao-Yun Hsu
- Radiology Department, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Ronald R. Nelson
- Radiology Department, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Brendan Sullivan
- Radiology Department, Columbia University Irving Medical Center, New York, NY 10032, USA
| | | | | | | | - Sachin Jambawalikar
- Radiology Department, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Diego Jaramillo
- Radiology Department, Columbia University Irving Medical Center, New York, NY 10032, USA
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Chianca V, Albano D, Rizzo S, Maas M, Sconfienza LM, Del Grande F. Inter-vendor and inter-observer reliability of diffusion tensor imaging in the musculoskeletal system: a multiscanner MR study. Insights Imaging 2023; 14:32. [PMID: 36757529 PMCID: PMC9911574 DOI: 10.1186/s13244-023-01374-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/09/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND To evaluate the inter-observer and inter-vendor reliability of diffusion tensor imaging parameters in the musculoskeletal system. METHODS This prospective study included six healthy volunteers three men (mean age: 42; range: 31-52 years) and three women (mean age: 36; range: 30-44 years). Each subject was scanned using different 3 Tesla magnetic resonance scanners from three different vendors at three different sites bilaterally. First, the intra-class correlation coefficient was used to determine between-observers agreement for overall measurements and clinical sites. Next, between-group comparisons were made through the nonparametric Friedman's test. Finally, the Bland-Altman method was used to determine agreement among the three scanner measurements, comparing them two by two. RESULTS A total of 792 measurement were calculated. ICC reported high levels of agreement between the two observers. ICC related to MD, FA, and RD measurements ranged from 0.88 (95% CI 0.85-0.90) to 0.95 (95% CI 0.94-0.96), from 0.85 (95% CI 0.81-0.88) to 0.95 (95% CI 0.93-0.96), and from 0.89 (0.85-0.90) to 0.92 (0.90-0.94). No statistically significant inter-vendor differences were observed. The Bland-Altmann method confirmed a high correlation between parameter values. CONCLUSION An excellent inter-observer and inter-vendor reliability was found in our study.
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Affiliation(s)
- Vito Chianca
- Clinica di Radiologia EOC IIMSI, Lugano, Switzerland. .,Ospedale Evangelico Betania, Via Argine 604, 80147, Naples, Italy.
| | - Domenico Albano
- grid.417776.4IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | | | - Mario Maas
- grid.7177.60000000084992262Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands ,Amsterdam Movement Sciences Research Institute, Amsterdam, The Netherlands
| | - Luca Maria Sconfienza
- grid.417776.4IRCCS Istituto Ortopedico Galeazzi, Milan, Italy ,grid.4708.b0000 0004 1757 2822Department of Biomedical Sciences for Health, University of Milano, Milan, Italy
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Martín-Noguerol T, Barousse R, Wessell DE, Rossi I, Luna A. A handbook for beginners in skeletal muscle diffusion tensor imaging: physical basis and technical adjustments. Eur Radiol 2022; 32:7623-7631. [PMID: 35554647 DOI: 10.1007/s00330-022-08837-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 01/03/2023]
Abstract
Magnetic resonance imaging (MRI) of skeletal muscle is routinely performed using morphological sequences to acquire anatomical information. Recently, there is an increasing interest in applying advanced MRI techniques that provide pathophysiologic information for skeletal muscle evaluation to complement standard morphologic information. Among these advanced techniques, diffusion tensor imaging (DTI) has emerged as a potential tool to explore muscle microstructure. DTI can noninvasively assess the movement of water molecules in well-organized tissues with anisotropic diffusion, such as skeletal muscle. The acquisition of DTI studies for skeletal muscle assessment requires specific technical adjustments. Besides, knowledge of DTI physical basis and skeletal muscle physiopathology facilitates the evaluation of this advanced sequence and both image and parameter interpretation. Parameters derived from DTI provide a quantitative assessment of muscle microstructure with potential to become imaging biomarkers of normal and pathological skeletal muscle. KEY POINTS: • Diffusion tensor imaging (DTI) allows to evaluate the three-dimensional movement of water molecules inside biological tissues. • The skeletal muscle structure makes it suitable for being evaluated with DTI. • Several technical adjustments have to be considered for obtaining robust and reproducible DTI studies for skeletal muscle assessment, minimizing potential artifacts.
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Affiliation(s)
- Teodoro Martín-Noguerol
- MRI Section, Radiology Department, SERCOSA, HT Médica, Carmelo Torres 2, 23007, Jaén, Spain.
| | | | | | | | - Antonio Luna
- MRI Section, Radiology Department, SERCOSA, HT Médica, Carmelo Torres 2, 23007, Jaén, Spain
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Rahman N, Xu K, Omer M, Budde MD, Brown A, Baron CA. Test-retest reproducibility of in vivo oscillating gradient and microscopic anisotropy diffusion MRI in mice at 9.4 Tesla. PLoS One 2021; 16:e0255711. [PMID: 34739479 PMCID: PMC8570471 DOI: 10.1371/journal.pone.0255711] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 10/22/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND AND PURPOSE Microstructure imaging with advanced diffusion MRI (dMRI) techniques have shown increased sensitivity and specificity to microstructural changes in various disease and injury models. Oscillating gradient spin echo (OGSE) dMRI, implemented by varying the oscillating gradient frequency, and microscopic anisotropy (μA) dMRI, implemented via tensor valued diffusion encoding, may provide additional insight by increasing sensitivity to smaller spatial scales and disentangling fiber orientation dispersion from true microstructural changes, respectively. The aims of this study were to characterize the test-retest reproducibility of in vivo OGSE and μA dMRI metrics in the mouse brain at 9.4 Tesla and provide estimates of required sample sizes for future investigations. METHODS Twelve adult C57Bl/6 mice were scanned twice (5 days apart). Each imaging session consisted of multifrequency OGSE and μA dMRI protocols. Metrics investigated included μA, linear diffusion kurtosis, isotropic diffusion kurtosis, and the diffusion dispersion rate (Λ), which explores the power-law frequency dependence of mean diffusivity. The dMRI metric maps were analyzed with mean region-of-interest (ROI) and whole brain voxel-wise analysis. Bland-Altman plots and coefficients of variation (CV) were used to assess the reproducibility of OGSE and μA metrics. Furthermore, we estimated sample sizes required to detect a variety of effect sizes. RESULTS Bland-Altman plots showed negligible biases between test and retest sessions. ROI-based CVs revealed high reproducibility for most metrics (CVs < 15%). Voxel-wise CV maps revealed high reproducibility for μA (CVs ~ 10%), but low reproducibility for OGSE metrics (CVs ~ 50%). CONCLUSION Most of the μA dMRI metrics are reproducible in both ROI-based and voxel-wise analysis, while the OGSE dMRI metrics are only reproducible in ROI-based analysis. Given feasible sample sizes (10-15), μA metrics and OGSE metrics may provide sensitivity to subtle microstructural changes (4-8%) and moderate changes (> 6%), respectively.
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Affiliation(s)
- Naila Rahman
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Kathy Xu
- Translational Neuroscience Group, Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Mohammad Omer
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
| | - Matthew D. Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, United States of America
| | - Arthur Brown
- Translational Neuroscience Group, Robarts Research Institute, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
- Department of Anatomy and Cell Biology, University of Western Ontario, London, Ontario, Canada
| | - Corey A. Baron
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Ontario, Canada
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