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Bolano-Díaz C, Verdú-Díaz J, Díaz-Manera J. MRI for the diagnosis of limb girdle muscular dystrophies. Curr Opin Neurol 2024; 37:536-548. [PMID: 39132784 DOI: 10.1097/wco.0000000000001305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
PURPOSE OF REVIEW In the last 30 years, there have many publications describing the pattern of muscle involvement of different neuromuscular diseases leading to an increase in the information available for diagnosis. A high degree of expertise is needed to remember all the patterns described. Some attempts to use artificial intelligence or analysing muscle MRIs have been developed. We review the main patterns of involvement in limb girdle muscular dystrophies (LGMDs) and summarize the strategies for using artificial intelligence tools in this field. RECENT FINDINGS The most frequent LGMDs have a widely described pattern of muscle involvement; however, for those rarer diseases, there is still not too much information available. patients. Most of the articles still include only pelvic and lower limbs muscles, which provide an incomplete picture of the diseases. AI tools have efficiently demonstrated to predict diagnosis of a limited number of disease with high accuracy. SUMMARY Muscle MRI continues being a useful tool supporting the diagnosis of patients with LGMD and other neuromuscular diseases. However, the huge variety of patterns described makes their use in clinics a complicated task. Artificial intelligence tools are helping in that regard and there are already some accessible machine learning algorithms that can be used by the global medical community.
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Affiliation(s)
- Carla Bolano-Díaz
- The John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - José Verdú-Díaz
- The John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Jordi Díaz-Manera
- The John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- Neuromuscular Diseases Laboratory, Insitut de Recerca de l'Hospital de la Santa Creu i Sant Pau
- Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Barcelona, Spain
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Fukuda N, Konda S, Umehara J, Hirashima M. Efficient musculoskeletal annotation using free-form deformation. Sci Rep 2024; 14:16077. [PMID: 38992241 PMCID: PMC11239816 DOI: 10.1038/s41598-024-67125-3] [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: 04/22/2024] [Accepted: 07/08/2024] [Indexed: 07/13/2024] Open
Abstract
Traditionally, constructing training datasets for automatic muscle segmentation from medical images involved skilled operators, leading to high labor costs and limited scalability. To address this issue, we developed a tool that enables efficient annotation by non-experts and assessed its effectiveness for training an automatic segmentation network. Our system allows users to deform a template three-dimensional (3D) anatomical model to fit a target magnetic-resonance image using free-form deformation with independent control points for axial, sagittal, and coronal directions. This method simplifies the annotation process by allowing non-experts to intuitively adjust the model, enabling simultaneous annotation of all muscles in the template. We evaluated the quality of the tool-assisted segmentation performed by non-experts, which achieved a Dice coefficient greater than 0.75 compared to expert segmentation, without significant errors such as mislabeling adjacent muscles or omitting musculature. An automatic segmentation network trained with datasets created using this tool demonstrated performance comparable to or superior to that of networks trained with expert-generated datasets. This innovative tool significantly reduces the time and labor costs associated with dataset creation for automatic muscle segmentation, potentially revolutionizing medical image annotation and accelerating the development of deep learning-based segmentation networks in various clinical applications.
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Affiliation(s)
- Norio Fukuda
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), 1-4 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shoji Konda
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), 1-4 Yamadaoka, Suita, Osaka, 565-0871, Japan
- Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, 1-17 Machikaneyama-Cho, Toyonaka, Osaka, 560-0043, Japan
| | - Jun Umehara
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), 1-4 Yamadaoka, Suita, Osaka, 565-0871, Japan
- Faculty of Rehabilitation, Kansai Medical University, 18-89 Uyama-Higashi, Hirakata, Osaka, 573-1136, Japan
| | - Masaya Hirashima
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), 1-4 Yamadaoka, Suita, Osaka, 565-0871, Japan.
- Graduate School of Frontier Biosciences, Osaka University, 1-3 Yamadaoka, Suita, Osaka, 565-0871, Japan.
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White MS, Ogier AC, Chenevert TL, Zucker E, Stoneback L, Michel CP, Palmieri-Smith RM, Lepley LK. Beyond weakness: Exploring intramuscular fat and quadriceps atrophy in ACLR recovery. J Orthop Res 2024. [PMID: 38824275 DOI: 10.1002/jor.25910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 04/19/2024] [Accepted: 05/16/2024] [Indexed: 06/03/2024]
Abstract
Muscle weakness following anterior cruciate ligament reconstruction (ACLR) increases the risk of posttraumatic osteoarthritis (OA). However, focusing solely on muscle weakness overlooks other aspects like muscle composition, which could hinder strength recovery. Intramuscular fat is a non-contractile element linked to joint degeneration in idiopathic OA, but its role post-ACLR has not been thoroughly investigated. To bridge this gap, we aimed to characterize quadriceps volume and intramuscular fat in participants with ACLR (male/female = 15/9, age = 22.8 ± 3.6 years, body mass index [BMI] = 23.2 ± 1.9, time since surgery = 3.3 ± 0.9 years) and in controls (male/female = 14/10, age = 22.0 ± 3.1 years, BMI = 23.3 ± 2.6) while also exploring the associations between intramuscular fat and muscle volume with isometric strength. Linear mixed effects models assessed (I) muscle volume, (II) intramuscular fat, and (III) strength between limbs (ACLR vs. contralateral vs. control). Regression analyses were run to determine if intramuscular fat or volume were associated with quadriceps strength. The ACLR limb was 8%-11% smaller than the contralateral limb (p < 0.05). No between-limb differences in intramuscular fat were observed (p 0.091-0.997). Muscle volume but not intramuscular fat was associated with strength in the ACLR and control limbs (p < 0.001-0.002). We demonstrate that intramuscular fat does not appear to be an additional source of quadriceps dysfunction following ACLR and that muscle size only explains some of the variance in muscle strength.
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Affiliation(s)
- McKenzie S White
- School of Kinesiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Augustin C Ogier
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Thomas L Chenevert
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Elizabeth Zucker
- School of Kinesiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Luke Stoneback
- School of Kinesiology, University of Michigan, Ann Arbor, Michigan, USA
| | | | | | - Lindsey K Lepley
- School of Kinesiology, University of Michigan, Ann Arbor, Michigan, USA
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Hostin MA, Ogier AC, Michel CP, Le Fur Y, Guye M, Attarian S, Fortanier E, Bellemare ME, Bendahan D. The Impact of Fatty Infiltration on MRI Segmentation of Lower Limb Muscles in Neuromuscular Diseases: A Comparative Study of Deep Learning Approaches. J Magn Reson Imaging 2023; 58:1826-1835. [PMID: 37025028 DOI: 10.1002/jmri.28708] [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: 12/09/2022] [Revised: 03/15/2023] [Accepted: 03/15/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND Deep learning methods have been shown to be useful for segmentation of lower limb muscle MRIs of healthy subjects but, have not been sufficiently evaluated on neuromuscular disease (NDM) patients. PURPOSE Evaluate the influence of fat infiltration on convolutional neural network (CNN) segmentation of MRIs from NMD patients. STUDY TYPE Retrospective study. SUBJECTS Data were collected from a hospital database of 67 patients with NMDs and 14 controls (age: 53 ± 17 years, sex: 48 M, 33 F). Ten individual muscles were segmented from the thigh and six from the calf (20 slices, 200 cm section). FIELD STRENGTH/SEQUENCE A 1.5 T. Sequences: 2D T1 -weighted fast spin echo. Fat fraction (FF): three-point Dixon 3D GRE, magnetization transfer ratio (MTR): 3D MT-prepared GRE, T2: 2D multispin-echo sequence. ASSESSMENT U-Net 2D, U-Net 3D, TransUNet, and HRNet were trained to segment thigh and leg muscles (101/11 and 95/11 training/validation images, 10-fold cross-validation). Automatic and manual segmentations were compared based on geometric criteria (Dice coefficient [DSC], outlier rate, absence rate) and reliability of measured MRI quantities (FF, MTR, T2, volume). STATISTICAL TESTS Bland-Altman plots were chosen to describe agreement between manual vs. automatic estimated FF, MTR, T2 and volume. Comparisons were made between muscle populations with an FF greater than 20% (G20+) and lower than 20% (G20-). RESULTS The CNNs achieved equivalent results, yet only HRNet recognized every muscle in the database, with a DSC of 0.91 ± 0.08, and measurement biases reaching -0.32% ± 0.92% for FF, 0.19 ± 0.77 for MTR, -0.55 ± 1.95 msec for T2, and - 0.38 ± 3.67 cm3 for volume. The performances of HRNet, between G20- and G20+ decreased significantly. DATA CONCLUSION HRNet was the most appropriate network, as it did not omit any muscle. The accuracy obtained shows that CNNs could provide fully automated methods for studying NMDs. However, the accuracy of the methods may be degraded on the most infiltrated muscles (>20%). EVIDENCE LEVEL 4. TECHNICAL EFFICACY Stage 1.
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Affiliation(s)
- Marc-Adrien Hostin
- Aix Marseille University, CNRS, CRMBM, Marseille, France
- Aix Marseille University, CNRS, LIS, Marseille, France
| | - Augustin C Ogier
- Aix Marseille University, CNRS, LIS, Marseille, France
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | | | - Yann Le Fur
- Aix Marseille University, CNRS, CRMBM, Marseille, France
| | - Maxime Guye
- APHM, Hopital Universitaire Timone, CEMEREM, Marseille, France
| | - Shahram Attarian
- Reference Center for Neuromuscular Diseases and ALS, APHM, University Hospital of Marseille/Timone University Hospital, Marseille, France
| | - Etienne Fortanier
- Reference Center for Neuromuscular Diseases and ALS, APHM, University Hospital of Marseille/Timone University Hospital, Marseille, France
| | | | - David Bendahan
- Aix Marseille University, CNRS, CRMBM, Marseille, France
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Durelle C, Delmont E, Michel C, Trabelsi A, Hostin MA, Ogier A, Bendahan D, Attarian S. Quantification of muscle involvement in familial amyloid polyneuropathy using MRI. Eur J Neurol 2023; 30:3286-3295. [PMID: 37422895 DOI: 10.1111/ene.15970] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 06/09/2023] [Accepted: 07/04/2023] [Indexed: 07/11/2023]
Abstract
BACKGROUND AND PURPOSE Transthyretin familial amyloid polyneuropathy (TTR-FAP) is a rare genetic disease with autosomal-dominant inheritance. In this study, we aimed to quantify fatty infiltration (fat fraction [FF]) and magnetization transfer ratio (MTR) in individual muscles of patients with symptomatic and asymptomatic TTR-FAP using magnetic resonance imaging. Secondarily, we aimed to assess correlations with clinical and electrophysiological variables. METHODS A total of 39 patients with a confirmed mutation in the TTR gene (25 symptomatic and 14 asymptomatic) and 14 healthy volunteers were included. A total of 16 muscles were manually delineated in the nondominant lower limb from T1-weighted anatomical images. The corresponding masks were propagated on the MTR and FF maps. Detailed neurological and electrophysiological examinations were conducted in each group. RESULTS The MTR was decreased (42.6 AU; p = 0.001) and FF was elevated (14%; p = 0.003) in the lower limbs of the symptomatic group, with preferential posterior and lateral involvement. In the asymptomatic group, elevated FF was quantified in the gastrocnemius lateralis muscle (11%; p = 0.021). FF was significantly correlated with disease duration (r = 0.49, p = 0.015), neuropathy impairment score for the lower limb (r = 0.42, p = 0.041), Overall Neuropathy Limitations Scale score (r = 0.49, p = 0.013), polyneuropathy disability score (r = 0.57, p = 0.03) and the sum of compound muscle action potential (r = 0.52, p = 0.009). MTR was strongly correlated to FF (r = 0.78, p < 0.0001), and a few muscles with an FF within the normal range had a reduced MTR. CONCLUSION These observations suggest that FF and MTR could be interesting biomarkers in TTR-FAP. In asymptomatic patients, FF in the gastrocnemius lateralis muscle could be a good indicator of the transition from an asymptomatic to a symptomatic form of the disease. MTR could be an early biomarker of muscle alterations.
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Affiliation(s)
- Clémence Durelle
- Centre de référence des maladies neuromusculaires et de la SLA, hôpitaux universitaires de Marseille, Marseille, France
| | - Emilien Delmont
- Centre de référence des maladies neuromusculaires et de la SLA, hôpitaux universitaires de Marseille, Marseille, France
| | - Constance Michel
- Centre de résonance magnétique biologique et médicale (Crmbm), Marseille, France
| | - Amira Trabelsi
- Aix-Marseille Univ, CNRS, Centrale Marseille, Institute Fresnel, Marseille, France
| | - Marc-Adrien Hostin
- Centre de résonance magnétique biologique et médicale (Crmbm), Marseille, France
| | - Augustin Ogier
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - David Bendahan
- Centre de résonance magnétique biologique et médicale (Crmbm), Marseille, France
| | - Shahram Attarian
- Centre de référence des maladies neuromusculaires et de la SLA, hôpitaux universitaires de Marseille, Marseille, France
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Engelke K, Chaudry O, Gast L, Eldib MAB, Wang L, Laredo JD, Schett G, Nagel AM. Magnetic resonance imaging techniques for the quantitative analysis of skeletal muscle: State of the art. J Orthop Translat 2023; 42:57-72. [PMID: 37654433 PMCID: PMC10465967 DOI: 10.1016/j.jot.2023.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 07/04/2023] [Accepted: 07/19/2023] [Indexed: 09/02/2023] Open
Abstract
Background Magnetic resonance imaging (MRI) is the dominant 3D imaging modality to quantify muscle properties in skeletal muscle disorders, in inherited and acquired muscle diseases, and in sarcopenia, in cachexia and frailty. Methods This review covers T1 weighted and Dixon sequences, introduces T2 mapping, diffusion tensor imaging (DTI) and non-proton MRI. Technical concepts, strengths, limitations and translational aspects of these techniques are discussed in detail. Examples of clinical applications are outlined. For comparison 31P-and 13C-MR Spectroscopy are also addressed. Results MRI technology provides a rich toolset to assess muscle deterioration. In addition to classical measures such as muscle atrophy using T1 weighted imaging and fat infiltration using Dixon sequences, parameters characterizing inflammation from T2 maps, tissue sodium using non-proton MRI techniques or concentration or fiber architecture using diffusion tensor imaging may be useful for an even earlier diagnosis of the impairment of muscle quality. Conclusion Quantitative MRI provides new options for muscle research and clinical applications. Current limitations that also impair its more widespread use in clinical trials are lack of standardization, ambiguity of image segmentation and analysis approaches, a multitude of outcome parameters without a clear strategy which ones to use and the lack of normal data.
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Affiliation(s)
- Klaus Engelke
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
- Institute of Medical Physics (IMP), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Henkestr. 91, 91052, Erlangen, Germany
- Clario Inc, Germany
| | - Oliver Chaudry
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
| | - Lena Gast
- Institute of Radiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
| | | | - Ling Wang
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, China
| | - Jean-Denis Laredo
- Service d’Imagerie Médicale, Institut Mutualiste Montsouris & B3OA, UMR CNRS 7052, Inserm U1271 Université de Paris-Cité, Paris, France
| | - Georg Schett
- Department of Medicine III, Friedrich-Alexander University of Erlangen-Nürnberg, University Hospital Erlangen, Ulmenweg 18, 91054, Erlangen, Germany
| | - Armin M. Nagel
- Institute of Radiology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), University Hospital Erlangen, Maximiliansplatz 3, 91054, Erlangen, Germany
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
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Nakanishi N, Otake Y, Hiasa Y, Gu Y, Uemura K, Takao M, Sugano N, Sato Y. Decomposition of musculoskeletal structures from radiographs using an improved CycleGAN framework. Sci Rep 2023; 13:8482. [PMID: 37231008 DOI: 10.1038/s41598-023-35075-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
This paper presents methods of decomposition of musculoskeletal structures from radiographs into multiple individual muscle and bone structures. While existing solutions require dual-energy scan for the training dataset and are mainly applied to structures with high-intensity contrast, such as bones, we focused on multiple superimposed muscles with subtle contrast in addition to bones. The decomposition problem is formulated as an image translation problem between (1) a real X-ray image and (2) multiple digitally reconstructed radiographs, each of which contains a single muscle or bone structure, and solved using unpaired training based on the CycleGAN framework. The training dataset was created via automatic computed tomography (CT) segmentation of muscle/bone regions and virtually projecting them with geometric parameters similar to the real X-ray images. Two additional features were incorporated into the CycleGAN framework to achieve a high-resolution and accurate decomposition: hierarchical learning and reconstruction loss with the gradient correlation similarity metric. Furthermore, we introduced a new diagnostic metric for muscle asymmetry directly measured from a plain X-ray image to validate the proposed method. Our simulation and real-image experiments using real X-ray and CT images of 475 patients with hip diseases suggested that each additional feature significantly enhanced the decomposition accuracy. The experiments also evaluated the accuracy of muscle volume ratio measurement, which suggested a potential application to muscle asymmetry assessment from an X-ray image for diagnostic and therapeutic assistance. The improved CycleGAN framework can be applied for investigating the decomposition of musculoskeletal structures from single radiographs.
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Affiliation(s)
- Naoki Nakanishi
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan
| | - Yoshito Otake
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.
| | - Yuta Hiasa
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.
| | - Yi Gu
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan
| | - Keisuke Uemura
- Department of Orthopaedic Medical Engineering, Osaka University Graduate School of Medicine, Suita, Osaka, 565-0871, Japan
| | - Masaki Takao
- Department of Bone and Joint Surgery, Ehime University Graduate School of Medicine, Toon, Ehime, 791-0295, Japan
| | - Nobuhiko Sugano
- Department of Orthopaedic Medical Engineering, Osaka University Graduate School of Medicine, Suita, Osaka, 565-0871, Japan
| | - Yoshinobu Sato
- Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Nara, 630-0192, Japan.
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Huysmans L, De Wel B, Claeys KG, Maes F. Automated MRI quantification of volumetric per-muscle fat fractions in the proximal leg of patients with muscular dystrophies. Front Neurol 2023; 14:1200727. [PMID: 37292137 PMCID: PMC10244517 DOI: 10.3389/fneur.2023.1200727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 05/03/2023] [Indexed: 06/10/2023] Open
Abstract
Muscular dystrophies (MD) are a class of rare genetic diseases resulting in progressive muscle weakness affecting specific muscle groups, depending on the type of disease. Disease progression is characterized by the gradual replacement of muscle tissue by fat, which can be assessed with fat-sensitive magnetic resonance imaging (MRI) and objectively evaluated by quantifying the fat fraction percentage (FF%) per muscle. Volumetric quantification of fat replacement over the full 3D extent of each muscle is more precise and potentially more sensitive than 2D quantification in few selected slices only, but it requires an accurate 3D segmentation of each muscle individually, which is time consuming when this has to be performed manually for a large number of muscles. A reliable, largely automated approach for 3D muscle segmentation is thus needed to facilitate the adoption of fat fraction quantification as a measure of MD disease progression in clinical routine practice, but this is challenging due to the variable appearance of the images and the ambiguity in the discrimination of the contours of adjacent muscles, especially when the normal image contrast is affected and diminished by the fat replacement. To deal with these challenges, we used deep learning to train AI-models to segment the muscles in the proximal leg from knee to hip in Dixon MRI images of healthy subjects as well as patients with MD. We demonstrate state-of-the-art segmentation results of all 18 muscles individually in terms of overlap (Dice score, DSC) with the manual ground truth delineation for images of cases with low fat infiltration (mean overall FF%: 11.3%; mean DSC: 95.3% per image, 84.4-97.3% per muscle) as well as with medium and high fat infiltration (mean overall FF%: 44.3%; mean DSC: 89.0% per image, 70.8-94.5% per muscle). In addition, we demonstrate that the segmentation performance is largely invariant to the field of view of the MRI scan, is generalizable to patients with different types of MD and that the manual delineation effort to create the training set can be drastically reduced without significant loss of segmentation quality by delineating only a subset of the slices.
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Affiliation(s)
- Lotte Huysmans
- Processing Speech and Images, Department of Electrical Engineering, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Bram De Wel
- Laboratory for Muscle Diseases and Neuropathies, Department of Neurosciences, KU Leuven, and Leuven Brain Institute, Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Kristl G. Claeys
- Laboratory for Muscle Diseases and Neuropathies, Department of Neurosciences, KU Leuven, and Leuven Brain Institute, Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Frederik Maes
- Processing Speech and Images, Department of Electrical Engineering, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
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Whole-muscle fat analysis identifies distal muscle end as disease initiation site in facioscapulohumeral muscular dystrophy. COMMUNICATIONS MEDICINE 2022; 2:155. [PMID: 36450865 PMCID: PMC9712512 DOI: 10.1038/s43856-022-00217-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 11/11/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Facioscapulohumeral dystrophy (FSHD) is a major muscular dystrophy characterized by asymmetric fatty replacement of muscles. We aimed to determine the initiation site and progression profile of the disease in lower extremity muscles of FSHD patients by assessing fat infiltration along their full proximo-distal axis using quantitative MRI. METHODS Nine patients underwent MRI of lower extremities to assess end-to-end muscle fat fractions (FFs) and inflammatory lesions. Seven patients underwent the same MRI ~3.5 years later. Individual muscles (n = 396) were semi-automatically segmented to calculate average FFs over all slices covering whole muscles. To assess disease progression we determined FF changes in 5 adjacent muscle segments. RESULTS We provide evidence that fat replacement commonly starts at the distal end of affected muscles where the highest FFs occur (p < 0.001). It progresses in a wave-like manner in the proximal direction at an increasing rate with the highest value (4.9 ± 2.7%/year) for muscles with baseline FFs of 30-40%. Thereafter it proceeds at a slower pace towards the proximal muscle end. In early phases of disease, inflammatory lesions preferentially occur at the distal muscle end. Compared with whole-muscle analysis, the common FF assessments using only few MR slices centrally placed in muscles are significantly biased (~50% in progression rate). CONCLUSIONS These findings identify the distal end of leg muscles as a prime location for disease initiation in FSHD and demonstrate a wave-like progression towards the proximal end, consistent with proposed disease mechanisms. End-to-end whole-muscle fat assessment is essential to properly diagnose FSHD and its progression.
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Forsting J, Rehmann R, Rohm M, Güttsches AK, Froeling M, Kan HE, Tegenthoff M, Vorgerd M, Schlaffke L. Robustness and stability of volume-based tractography in a multicenter setting. NMR IN BIOMEDICINE 2022; 35:e4707. [PMID: 35102637 DOI: 10.1002/nbm.4707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Muscle diffusion tensor imaging (mDTI)-based tractography is a promising tool with which to detect subclinical changes in muscle injuries and to evaluate pathophysiology in neuromuscular diseases. Classic region of interest (ROI)-based tractography is very time-consuming and requires an examiner with extensive experience. (Semi)automatic approaches such as volume-based tractography (VBT) can diminish this problem but its robustness and stability are unknown. The aim of the current study was to assess the performance of VBT in a multicenter setting and to evaluate semiautomatic segmentation approaches in the analysis of VBT-derived data in terms of the comparability of the outcome measures. Five traveling volunteers underwent 3-T mDTI of seven calf muscles of both legs at six different MR sites. Tract properties and diffusion metrics were calculated using VBT. Within-subject coefficients of variance (wsCVs) and intraclass correlation coefficients (ICCs) were calculated to assess the multicenter reproducibility of tract properties such as tract density (TD), mean tract length, volume and tract propagation angle, and diffusion metrics such as fractional anisotropy, mean diffusivity, axial diffusivity (λ1 ) and radial diffusivity in traveling subjects. Furthermore, 50 individual datasets from five different centers (10 datasets per center) were pooled to assess the feasibility of VBT with manual and semiautomatic segmentation. To assess the differences of tract properties and diffusion metrics between segmentation approaches an ANOVA was performed, and ICC and Bland-Altman plots were analyzed. wsCVs and ICCs showed good reproducibility of the tract properties TD and volume, as well as diffusion metrics. ANOVA showed no significant differences between manual and semiautomatic approaches. ICCs were excellent (≥ 0.992) and Bland-Altman analysis did not reveal any systemic bias between the methods. Tract properties and diffusion metrics derived from VBT showed good comparability among centers. Semiautomatic approaches revealed excellent agreement with gold standard of manual segmentation. These findings suggest that pooling data from different centers to construct a reference database for tractography results is feasible using semiautomatic segmentation approaches.
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Affiliation(s)
- Johannes Forsting
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Robert Rehmann
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Department of Neurology, Klinikum Dortmund, University Witten-Herdecke, Dortmund, Germany
| | - Marlena Rohm
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
| | - Anne-Katrin Güttsches
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
| | - Martijn Froeling
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Hermien E Kan
- C. J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Duchenne Center, Leiden, The Netherlands
| | - Martin Tegenthoff
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Matthias Vorgerd
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
| | - Lara Schlaffke
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
- Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
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11
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Cheng R, Crouzier M, Hug F, Tucker K, Juneau P, McCreedy E, Gandler W, McAuliffe MJ, Sheehan FT. Automatic quadriceps and patellae segmentation of MRI with cascaded U 2 -Net and SASSNet deep learning model. Med Phys 2022; 49:443-460. [PMID: 34755359 PMCID: PMC8758556 DOI: 10.1002/mp.15335] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 10/18/2021] [Accepted: 10/20/2021] [Indexed: 01/03/2023] Open
Abstract
PURPOSE Automatic muscle segmentation is critical for advancing our understanding of human physiology, biomechanics, and musculoskeletal pathologies, as it allows for timely exploration of large multi-dimensional image sets. Segmentation models are rarely developed/validated for the pediatric model. As such, autosegmentation is not available to explore how muscle architectural changes during development and how disease/pathology affects the developing musculoskeletal system. Thus, we aimed to develop and validate an end-to-end, fully automated, deep learning model for accurate segmentation of the rectus femoris and vastus lateral, medialis, and intermedialis using a pediatric database. METHODS We developed a two-stage cascaded deep learning model in a coarse-to-fine manner. In the first stage, the U2 -Net roughly detects the muscle subcompartment region. Then, in the second stage, the shape-aware 3D semantic segmentation method SASSNet refines the cropped target regions to generate the more finer and accurate segmentation masks. We utilized multifeature image maps in both stages to stabilize performance and validated their use with an ablation study. The second-stage SASSNet was independently run and evaluated with three different cropped region resolutions: the original image resolution, and images downsampled 2× and 4× (high, mid, and low). The relationship between image resolution and segmentation accuracy was explored. In addition, the patella was included as a comparator to past work. We evaluated segmentation accuracy using leave-one-out testing on a database of 3D MR images (0.43 × 0.43 × 2 mm) from 40 pediatric participants (age 15.3 ± 1.9 years, 55.8 ± 11.8 kg, 164.2 ± 7.9 cm, 38F/2 M). RESULTS The mid-resolution second stage produced the best results for the vastus medialis, rectus femoris, and patella (Dice similarity coefficient = 95.0%, 95.1%, 93.7%), whereas the low-resolution second stage produced the best results for the vastus lateralis and vastus intermedialis (DSC = 94.5% and 93.7%). In comparing the low- to mid-resolution cases, the vasti intermedialis, vastus medialis, rectus femoris, and patella produced significant differences (p = 0.0015, p = 0.0101, p < 0.0001, p = 0.0003) and the vasti lateralis did not (p = 0.2177). The high-resolution stage 2 had significantly lower accuracy (1.0 to 4.4 dice percentage points) compared to both the mid- and low-resolution routines (p value ranged from < 0.001 to 0.04). The one exception was the rectus femoris, where there was no difference between the low- and high-resolution cases. The ablation study demonstrated that the multifeature is more reliable than the single feature. CONCLUSIONS Our successful implementation of this two-stage segmentation pipeline provides a critical tool for expanding pediatric muscle physiology and clinical research. With a relatively small and variable dataset, our fully automatic segmentation technique produces accuracies that matched or exceeded the current state of the art. The two-stage segmentation avoids memory issues and excessive run times by using a first stage focused on cropping out unnecessary data. The excellent Dice similarity coefficients improve upon previous template-based automatic and semiautomatic methodologies targeting the leg musculature. More importantly, with a naturally variable dataset (size, shape, etc.), the proposed model demonstrates slightly improved accuracies, compared to previous neural networks methods.
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Affiliation(s)
- Ruida Cheng
- Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, NIH, Bethesda, MD, USA
| | - Marion Crouzier
- University of Nantes, Movement, Interactions, Performance, MIP, EA 4334, F-44000 Nantes, France,The University of Queensland, School of Biomedical Sciences, Brisbane
| | - François Hug
- Institut Universitaire de France (IUF), Paris, France,Université Côte d’Azur, LAMHESS, Nice, France
| | - Kylie Tucker
- The University of Queensland, School of Biomedical Sciences, Brisbane
| | - Paul Juneau
- NIH Library, Office of Research Services, National Institutes of Health, Bethesda, MD, USA
| | - Evan McCreedy
- Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, NIH, Bethesda, MD, USA
| | - William Gandler
- Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, NIH, Bethesda, MD, USA
| | - Matthew J. McAuliffe
- Scientific Application Services (SAS), Office of Scientific Computing Services (OSCS), Office of Intramural Research, Center of Information Technology, NIH, Bethesda, MD, USA
| | - Frances T. Sheehan
- Rehabilitation Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, USA
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12
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The increasing role of muscle MRI to monitor changes over time in untreated and treated muscle diseases. Curr Opin Neurol 2021; 33:611-620. [PMID: 32796278 DOI: 10.1097/wco.0000000000000851] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
PURPOSE OF REVIEW This review aims to discuss the recent results of studies published applying quantitative MRI sequences to large cohorts of patients with neuromuscular diseases. RECENT FINDINGS Quantitative MRI sequences are now available to identify and quantify changes in muscle water and fat content. These two components have been associated with acute and chronic injuries, respectively. Studies show that the increase in muscle water is not only reversible if therapies are applied successfully but can also predict fat replacement in neurodegenerative diseases. Muscle fat fraction correlates with muscle function tests and increases gradually over time in parallel with the functional decline of patients with neuromuscular diseases. There are new spectrometry-based sequences to quantify other components, such as glycogen, electrolytes or the pH of the muscle fibre, extending the applicability of MRI to the study of several processes in neuromuscular diseases. SUMMARY The latest results obtained from the study of long cohorts of patients with various neuromuscular diseases open the door to the use of this technology in clinical trials, which would make it possible to obtain a new measure for assessing the effectiveness of new treatments. The challenge is currently the popularization of these studies and their application to the monitoring of patients in the daily clinic.
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13
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Chen Y, Moiseev D, Kong WY, Bezanovski A, Li J. Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction. J Magn Reson Imaging 2021; 53:1539-1549. [PMID: 33448058 DOI: 10.1002/jmri.27508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 12/28/2020] [Accepted: 12/29/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle using MRI has emerged as a monitoring biomarker for axonal loss in patients with peripheral neuropathies. In this study, we are testing whether deep learning-based model can automate quantification of the FF in individual muscles. While individual muscle is smaller with irregular shape, manually segmented muscle MRI images have been accumulated in this lab; and make the deep learning feasible. PURPOSE To automate segmentation on muscle MRI images through deep learning for quantifying individual muscle FF in patients with peripheral neuropathies. STUDY TYPE Retrospective. SUBJECTS 24 patients and 19 healthy controls. FIELD STRENGTH/SEQUENCES 3T; Interleaved 3D GRE. ASSESSMENT A 3D U-Net model was implemented in segmenting muscle MRI images. This was enabled by leveraging a large set of manually segmented muscle MRI images. B1+ and B1- maps were used to correct image inhomogeneity. Accuracy of the automation was evaluated using Pixel Accuracy (PA), Dice Coefficient (DC) in binary masks; and Bland-Altman and Pearson correlation by comparing FF values between manual and automated methods. STATISTICAL TESTS PA and DC were reported with their median value and standard deviation. Two methods were compared using the ± 95% confidence intervals (CI) of Bland-Altman analysis and the Pearson's coefficient (r2 ). RESULTS DC values were from 0.83 ± 0.17 to 0.98 ± 0.02 in thigh and from 0.63 ± 0.18 to 0.96 ± 0.02 in calf muscles. For FF values, the overall ± 95% CI and r2 were [0.49, -0.56] and 0.989 in thigh and [0.84, -0.71] and 0.971 in the calf. DATA CONCLUSION Automated results well agreed with the manual results in quantifying FF for individual muscles. This method mitigates the formidable time consumption and intense labor in manual segmentations; and enables the use of individual muscle FF as outcome measures in upcoming longitudinal studies. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Daniel Moiseev
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Wan Yee Kong
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Alexandar Bezanovski
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Jun Li
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
- Center for Molecular Medicine and Genetics, Wayne State University School of Medicine, Detroit, Michigan, USA
- Department of Biochemistry, Microbiology and Immunology, Wayne State University School of Medicine, Detroit, Michigan, USA
- John D. Dingell VA Medical Center, Detroit, Michigan, USA
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14
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van Doorn JLM, Pennati F, Hansen HHG, van Engelen BGM, Aliverti A, Doorduin J. Respiratory muscle imaging by ultrasound and MRI in neuromuscular disorders. Eur Respir J 2021; 58:13993003.00137-2021. [PMID: 33863737 DOI: 10.1183/13993003.00137-2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/26/2021] [Indexed: 11/05/2022]
Abstract
Respiratory muscle weakness is common in neuromuscular disorders and leads to significant respiratory difficulties. Therefore, reliable and easy assessment of respiratory muscle structure and function in neuromuscular disorders is crucial. In the last decade, ultrasound and MRI emerged as promising imaging techniques to assess respiratory muscle structure and function. Respiratory muscle imaging directly measures the respiratory muscles and, in contrast to pulmonary function testing, is independent of patient effort. This makes respiratory muscle imaging suitable to use as tool in clinical respiratory management and as outcome parameter in upcoming drug trials for neuromuscular disorders, particularly in children. In this narrative review, we discuss the latest studies and technological developments in imaging of the respiratory muscles by US and MR, and its clinical application and limitations. We aim to increase understanding of respiratory muscle imaging and facilitate its use as outcome measure in daily practice and clinical trials.
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Affiliation(s)
- Jeroen L M van Doorn
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Francesca Pennati
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Hendrik H G Hansen
- Department of Medical Imaging, Medical Ultrasound Imaging Center (MUSIC), Radboud University Medical Center, Nijmegen, the Netherlands
| | - Baziel G M van Engelen
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Andrea Aliverti
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Jonne Doorduin
- Department of Neurology, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
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15
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Jourdan A, Le Troter A, Daude P, Rapacchi S, Masson C, Bège T, Bendahan D. Semiautomatic quantification of abdominal wall muscles deformations based on dynamic MRI image registration. NMR IN BIOMEDICINE 2021; 34:e4470. [PMID: 33525062 DOI: 10.1002/nbm.4470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 12/14/2020] [Indexed: 06/12/2023]
Abstract
Quantitative analysis of abdominal organs motion and deformation is crucial to better understand biomechanical alterations undermining respiratory, digestive or perineal pathophysiology. In particular, biomechanical characterization of the antero-lateral abdominal wall is central in the diagnosis of abdominal muscle deficiency. Here, we present a dedicated semiautomatic dynamic MRI postprocessing method enabling the quantification of spatial and temporal deformations of the antero-lateral abdominal wall muscles. Ten healthy participants were imaged during a controlled breathing session at the L3-L4 disc level using real-time dynamic MRI at 3 T. A coarse feature-tracking step allowed the selection of the inhalation cycle of maximum abdominal excursion. Over this image series, the described method combines (1) a supervised 2D+t segmentation procedure of the abdominal wall muscles, (2) the quantification of muscle deformations based on masks registration, and (3) the mapping of deformations within muscle subzones leveraging a dedicated automatic parcellation. The supervised 2D+t segmentation (1) provided an accurate segmentation of the abdominal wall muscles throughout maximum inhalation with a 0.95 ± 0.03 Dice similarity coefficient (DSC) value and a 2.3 ± 0.7 mm Hausdorff distance value while requiring only manual segmentation of 20% of the data. The robustness of the deformation quantification (2) was indicated by high indices of correspondence between the registered source mask and the target mask (0.98 ± 0.01 DSC value and 2.1 ± 1.5 mm Hausdorff distance value). Parcellation (3) enabled the distinction of muscle substructures that are anatomically relevant but could not be distinguished based on image contrast. The present genuine postprocessing method provides a quantitative analytical frame that could be used in further studies for a better understanding of abdominal wall deformations in physiological and pathological situations.
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Affiliation(s)
- Arthur Jourdan
- Aix-Marseille Université, Université Gustave Eiffel, LBA, Marseille, France
| | | | - Pierre Daude
- Aix Marseille Université, CNRS, CRMBM, Marseille, France
| | | | - Catherine Masson
- Aix-Marseille Université, Université Gustave Eiffel, LBA, Marseille, France
| | - Thierry Bège
- Aix-Marseille Université, Université Gustave Eiffel, LBA, Marseille, France
- Department of General Surgery, Aix Marseille Université, North Hospital, APHM, Marseille, France
| | - David Bendahan
- Aix Marseille Université, CNRS, CRMBM, Marseille, France
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16
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van der Plas E, Gutmann L, Thedens D, Shields RK, Langbehn K, Guo Z, Sonka M, Nopoulos P. Quantitative muscle MRI as a sensitive marker of early muscle pathology in myotonic dystrophy type 1. Muscle Nerve 2021; 63:553-562. [PMID: 33462896 PMCID: PMC8442354 DOI: 10.1002/mus.27174] [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: 07/28/2020] [Revised: 01/11/2021] [Accepted: 01/12/2021] [Indexed: 11/08/2022]
Abstract
BACKGROUND Quantitative muscle MRI as a sensitive marker of early muscle pathology and disease progression in adult-onset myotonic dystrophy type 1. The utility of muscle MRI as a marker of muscle pathology and disease progression in adult-onset myotonic dystrophy type 1 (DM1) was evaluated. METHODS This prospective, longitudinal study included 67 observations from 36 DM1 patients (50% female), and 92 observations from 49 healthy adults (49% female). Lower-leg 3T magnetic resonance imaging (MRI) scans were acquired. Volume and fat fraction (FF) were estimated using a three-point Dixon method, and T2-relaxometry was determined using a multi-echo spin-echo sequence. Muscles were segmented automatically. Mixed linear models were conducted to determine group differences across muscles and image modality, accounting for age, sex, and repeated observations. Differences in rate of change in volume, T2-relaxometry, and FF were also determined with mixed linear regression that included a group by elapsed time interaction. RESULTS Compared with healthy adults, DM1 patients exhibited reduced volume of the tibialis anterior, soleus, and gastrocnemius (GAS) (all, P < .05). T2-relaxometry and FF were increased across all calf muscles in DM1 compared to controls. (all, P < .01). Signs of muscle pathology, including reduced volume, and increased T2-relaxometry and FF were already noted in DM1 patients who did not exhibit clinical motor symptoms of DM1. As a group, DM1 patients exhibited a more rapid change than did controls in tibialis posterior volume (P = .05) and GAS T2-relaxometry (P = .03) and FF (P = .06). CONCLUSIONS Muscle MRI renders sensitive, early markers of muscle pathology and disease progression in DM1. T2 relaxometry may be particularly sensitive to early muscle changes related to DM1.
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Affiliation(s)
- Ellen van der Plas
- Department of Psychiatry, University of Iowa Hospital & Clinics, Iowa City, IA, USA
| | - Laurie Gutmann
- Department of Neurology, University of Iowa Hospital & Clinics, Iowa City, IA, USA
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Dan Thedens
- Department of Radiology, University of Iowa Hospital & Clinics, Iowa City, IA, USA
| | - Richard K. Shields
- Department of Physical Therapy and Rehabilitation Science, University of Iowa Hospital & Clinics, Iowa City, IA, USA
| | - Kathleen Langbehn
- Department of Psychiatry, University of Iowa Hospital & Clinics, Iowa City, IA, USA
| | - Zhihui Guo
- Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, IA, USA
| | - Milan Sonka
- Iowa Institute for Biomedical Imaging, University of Iowa, Iowa City, IA, USA
| | - Peggy Nopoulos
- Department of Psychiatry, University of Iowa Hospital & Clinics, Iowa City, IA, USA
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17
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Ogier AC, Hostin MA, Bellemare ME, Bendahan D. Overview of MR Image Segmentation Strategies in Neuromuscular Disorders. Front Neurol 2021; 12:625308. [PMID: 33841299 PMCID: PMC8027248 DOI: 10.3389/fneur.2021.625308] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 02/08/2021] [Indexed: 01/10/2023] Open
Abstract
Neuromuscular disorders are rare diseases for which few therapeutic strategies currently exist. Assessment of therapeutic strategies efficiency is limited by the lack of biomarkers sensitive to the slow progression of neuromuscular diseases (NMD). Magnetic resonance imaging (MRI) has emerged as a tool of choice for the development of qualitative scores for the study of NMD. The recent emergence of quantitative MRI has enabled to provide quantitative biomarkers more sensitive to the evaluation of pathological changes in muscle tissue. However, in order to extract these biomarkers from specific regions of interest, muscle segmentation is mandatory. The time-consuming aspect of manual segmentation has limited the evaluation of these biomarkers on large cohorts. In recent years, several methods have been proposed to make the segmentation step automatic or semi-automatic. The purpose of this study was to review these methods and discuss their reliability, reproducibility, and limitations in the context of NMD. A particular attention has been paid to recent deep learning methods, as they have emerged as an effective method of image segmentation in many other clinical contexts.
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Affiliation(s)
- Augustin C Ogier
- Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France
| | - Marc-Adrien Hostin
- Aix Marseille Univ, Université de Toulon, CNRS, LIS, Marseille, France.,Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
| | | | - David Bendahan
- Aix Marseille Univ, CNRS, CRMBM, UMR 7339, Marseille, France
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18
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Secondulfo L, Ogier AC, Monte JR, Aengevaeren VL, Bendahan D, Nederveen AJ, Strijkers GJ, Hooijmans MT. Supervised segmentation framework for evaluation of diffusion tensor imaging indices in skeletal muscle. NMR IN BIOMEDICINE 2021; 34:e4406. [PMID: 33001508 PMCID: PMC7757256 DOI: 10.1002/nbm.4406] [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: 05/27/2020] [Revised: 08/11/2020] [Accepted: 08/14/2020] [Indexed: 05/05/2023]
Abstract
Diffusion tensor imaging (DTI) is becoming a relevant diagnostic tool to understand muscle disease and map muscle recovery processes following physical activity or after injury. Segmenting all the individual leg muscles, necessary for quantification, is still a time-consuming manual process. The purpose of this study was to evaluate the impact of a supervised semi-automatic segmentation pipeline on the quantification of DTI indices in individual upper leg muscles. Longitudinally acquired MRI datasets (baseline, post-marathon and follow-up) of the upper legs of 11 subjects were used in this study. MR datasets consisted of a DTI and Dixon acquisition. Semi-automatic segmentations for the upper leg muscles were performed using a transversal propagation approach developed by Ogier et al on the out-of-phase Dixon images at baseline. These segmentations were longitudinally propagated for the post-marathon and follow-up time points. Manual segmentations were performed on the water image of the Dixon for each of the time points. Dice similarity coefficients (DSCs) were calculated to compare the manual and semi-automatic segmentations. Bland-Altman and regression analyses were performed, to evaluate the impact of the two segmentation methods on mean diffusivity (MD), fractional anisotropy (FA) and the third eigenvalue (λ3 ). The average DSC for all analyzed muscles over all time points was 0.92 ± 0.01, ranging between 0.48 and 0.99. Bland-Altman analysis showed that the 95% limits of agreement for MD, FA and λ3 ranged between 0.5% and 3.0% for the transversal propagation and between 0.7% and 3.0% for the longitudinal propagations. Similarly, regression analysis showed good correlation for MD, FA and λ3 (r = 0.99, p < 60; 0.0001). In conclusion, the supervised semi-automatic segmentation framework successfully quantified DTI indices in the upper-leg muscles compared with manual segmentation while only requiring manual input of 30% of the slices, resulting in a threefold reduction in segmentation time.
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Affiliation(s)
- Laura Secondulfo
- Department of Biomedical Engineering and Physics, Amsterdam University Medical CentersUniversity of AmsterdamThe Netherlands
| | - Augustin C. Ogier
- Aix Marseille Universite, Universite de Toulon, CNRS, LISMarseilleFrance
- Aix Marseille Universite, CNRS, CRMBMMarseilleFrance
| | - Jithsa R. Monte
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentersUniversity of AmsterdamThe Netherlands
| | - Vincent L. Aengevaeren
- Radboud Institute for Health Sciences, Department of PhysiologyRadboud University Medical CenterNijmegenThe Netherlands
| | | | - Aart J. Nederveen
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical CentersUniversity of AmsterdamThe Netherlands
| | - Gustav J. Strijkers
- Department of Biomedical Engineering and Physics, Amsterdam University Medical CentersUniversity of AmsterdamThe Netherlands
| | - Melissa T. Hooijmans
- Department of Biomedical Engineering and Physics, Amsterdam University Medical CentersUniversity of AmsterdamThe Netherlands
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19
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Ding J, Cao P, Chang HC, Gao Y, Chan SHS, Vardhanabhuti V. Deep learning-based thigh muscle segmentation for reproducible fat fraction quantification using fat-water decomposition MRI. Insights Imaging 2020; 11:128. [PMID: 33252711 PMCID: PMC7704819 DOI: 10.1186/s13244-020-00946-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 11/11/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Time-efficient and accurate whole volume thigh muscle segmentation is a major challenge in moving from qualitative assessment of thigh muscle MRI to more quantitative methods. This study developed an automated whole thigh muscle segmentation method using deep learning for reproducible fat fraction quantification on fat-water decomposition MRI. RESULTS This study was performed using a public reference database (Dataset 1, 25 scans) and a local clinical dataset (Dataset 2, 21 scans). A U-net was trained using 23 scans (16 from Dataset 1, seven from Dataset 2) to automatically segment four functional muscle groups: quadriceps femoris, sartorius, gracilis and hamstring. The segmentation accuracy was evaluated on an independent testing set (3 × 3 repeated scans in Dataset 1 and four scans in Dataset 2). The average Dice coefficients between manual and automated segmentation were > 0.85. The average percent difference (absolute) in volume was 7.57%, and the average difference (absolute) in mean fat fraction (meanFF) was 0.17%. The reproducibility in meanFF was calculated using intraclass correlation coefficients (ICCs) for the repeated scans, and automated segmentation produced overall higher ICCs than manual segmentation (0.921 vs. 0.902). A preliminary quantitative analysis was performed using two-sample t test to detect possible differences in meanFF between 14 normal and 14 abnormal (with fat infiltration) thighs in Dataset 2 using automated segmentation, and significantly higher meanFF was detected in abnormal thighs. CONCLUSIONS This automated thigh muscle segmentation exhibits excellent accuracy and higher reproducibility in fat fraction estimation compared to manual segmentation, which can be further used for quantifying fat infiltration in thigh muscles.
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Affiliation(s)
- Jie Ding
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.,Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Peng Cao
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Hing-Chiu Chang
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Yuan Gao
- Division of Neurology, Department of Medicine, Queen Mary Hospital, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Sophelia Hoi Shan Chan
- Division of Paediatric Neurology, Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
| | - Varut Vardhanabhuti
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China.
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