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Paoletti M, Monforte M, Barzaghi L, Tasca G, Bergsland N, Faggioli A, Solazzo F, Manco G, Bortolani S, Torchia E, Ravera B, Deligianni X, Santini F, Ballante E, Figini S, Tartaglione T, Ricci E, Pichiecchio A. Natural history of facioscapulohumeral muscular dystrophy evaluated by multiparametric quantitative MRI: a prospective cohort study. J Neurol 2025; 272:306. [PMID: 40172709 PMCID: PMC11965262 DOI: 10.1007/s00415-025-13062-8] [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: 11/26/2024] [Revised: 03/19/2025] [Accepted: 03/20/2025] [Indexed: 04/04/2025]
Abstract
BACKGROUND Facioscapulohumeral muscular dystrophy (FSHD) is a genetic disorder characterized by progressive skeletal muscle wasting. Longitudinal muscle magnetic resonance imaging (MRI) studies demonstrated that the risk of developing irreversible fatty replacement is higher in muscles showing edematous lesions. The quantification of this phenomenon is an understudied topic in FSHD and intramuscular water content can also represent a potential biomarker sensitive to the effect of investigational drugs. We applied a multiparametric quantitative muscle MRI protocol to assess disease progression quantifying fatty replacement and muscle edema over 2 years, using fat fraction (FF) and water-T2 (wT2) metrics. METHODS Thirty FSHD patients with at least one muscle showing signs of edema on conventional MRI were enrolled. FF and wT2 maps were assessed in 12 thigh and 6 leg muscles for each side, and a linear mixed model was employed to explore their variations over time. The measurements were acquired at baseline, 12, and 24 months. Quantitative MRI parameters were also correlated with clinical scales and functional assessments collected at baseline. RESULTS The average yearly increase in FF was 2 ± 0.6% at thigh level and 1.9 ± 0.7% at leg level. No significant longitudinal changes in wT2 were observed. Muscles with intermediate FF (15-30%) at baseline and those with baseline wT2 values above 41 ms showed the highest increase in fat replacement. Both FF and wT2 showed significant correlations with clinical scales and functional assessments. CONCLUSIONS Our longitudinal study identified muscles and compartments more likely to show FF increase in FSHD subjects. Multiparametric quantitative MRI metrics should be incorporated into clinical trial frameworks to explore their potential in detecting early therapeutic effects.
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Affiliation(s)
- M Paoletti
- Advanced Imaging and Artificial Intelligence, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - M Monforte
- Dipartimento di Neuroscienze, Organi di Senso e Torace, UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli, 8, 00168, Rome, Italy.
| | - L Barzaghi
- Advanced Imaging and Artificial Intelligence, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
- INFN, Group of Pavia, Pavia, Italy
- Department of Mathematics, University of Pavia, Pavia, Italy
| | - G Tasca
- John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trusts, Newcastle Upon Tyne, UK
| | - N Bergsland
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University of Buffalo, the State University of New York, Buffalo, NY, USA
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - A Faggioli
- Advanced Imaging and Artificial Intelligence, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - F Solazzo
- Advanced Imaging and Artificial Intelligence, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - G Manco
- Advanced Imaging and Artificial Intelligence, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - S Bortolani
- Dipartimento di Neuroscienze, Organi di Senso e Torace, UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli, 8, 00168, Rome, Italy
| | - E Torchia
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - B Ravera
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - X Deligianni
- Department of Radiology, University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, Basel Muscle MRI, University of Basel, Basel, Switzerland
| | - F Santini
- Department of Radiology, University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, Basel Muscle MRI, University of Basel, Basel, Switzerland
| | - E Ballante
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy
- BioData Science Center, IRCCS Mondino Foundation, Pavia, Italy
| | - S Figini
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy
- BioData Science Center, IRCCS Mondino Foundation, Pavia, Italy
| | - T Tartaglione
- Università Cattolica del Sacro Cuore, Rome, Italy
- Istituto Dermopatico Dell'Immacolata (IDI), IRCCS, Rome, Italy
| | - E Ricci
- Dipartimento di Neuroscienze, Organi di Senso e Torace, UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli, 8, 00168, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - A Pichiecchio
- Advanced Imaging and Artificial Intelligence, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
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2
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Nagy S, Kubassova O, Hafner P, Schädelin S, Schmidt S, Sinnreich M, Schröder J, Bieri O, Boesen M, Fischer D. Automated analysis of quantitative muscle MRI and its reliability in patients with Duchenne muscular dystrophy. J Neuromuscul Dis 2025:22143602251319184. [PMID: 40129140 DOI: 10.1177/22143602251319184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2025]
Abstract
BACKGROUND Quantitative muscle MRI is one of the most promising biomarkers to detect subclinical disease progression in patients with neuromuscular disorders, including Duchenne muscular dystrophy (DMD). However, its clinical application has been limited partly due to the time-intensive process of manual segmentation. OBJECTIVE We present a simple and fast automated approach to obtain quantitative measurement of thigh muscle fat fraction and investigate its reliability in patients with DMD. METHODS Clinical and radiological baseline and 6-month follow-up data of 41 ambulant patients with DMD were analysed retrospectively. Axial 2-point Dixon MR images of all thigh muscles were used to quantify mean fat fraction, while clinical outcomes were measured by the Motor Function Measure (MFM) and its D1 domain. Data obtained by automated segmentation were compared to manual segmentation and correlated with clinical outcomes. Results were also used to compare the statistical power when using automated or manual segmentation. RESULTS A mean increase of 3.55% in thigh muscle fat fraction at 6-month follow-up could be detected by both methods without any significant difference between them (p=0.437). The automated muscle segmentation method demonstrated a strong correlation with manually segmented data (Pearson's ρ = 0.97). Additionally, there was no statistically significant difference between the automated and manual segmentation methods in their association with clinical progression, as measured by the total MFM score and its D1 domain (p = 0.235 and p = 0.425, respectively). CONCLUSIONS The presented automated segmentation technique is a fast and reliable tool for assessing disease progression, particularly in the early stages of DMD. It is one of the few studies validated using manual segmentation, and with further refinement, it has the potential to become a good surrogate marker for disease progression in various neuromuscular disorders.
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Affiliation(s)
- Sara Nagy
- Division of Neuropediatrics and Developmental Medicine, University Childrens` Hospital of Basel (UKBB), University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - Patricia Hafner
- Division of Neuropediatrics and Developmental Medicine, University Childrens` Hospital of Basel (UKBB), University of Basel, Basel, Switzerland
| | | | - Simone Schmidt
- Division of Neuropediatrics and Developmental Medicine, University Childrens` Hospital of Basel (UKBB), University of Basel, Basel, Switzerland
| | - Michael Sinnreich
- Department of Neurology, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Jonas Schröder
- Department of Radiology, Division of Radiological Physics, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Oliver Bieri
- Department of Radiology, Division of Radiological Physics, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Mikael Boesen
- Image Analysis Group, London, UK
- Department of Radiology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Dirk Fischer
- Division of Neuropediatrics and Developmental Medicine, University Childrens` Hospital of Basel (UKBB), University of Basel, Basel, Switzerland
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Yan D, Li Q, Chuang YW, Lin CW, Shieh JY, Weng WC, Tsui PH. Radiomics with Ultrasound Radiofrequency Data for Improving Evaluation of Duchenne Muscular Dystrophy. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025:10.1007/s10278-025-01450-5. [PMID: 40087223 DOI: 10.1007/s10278-025-01450-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2024] [Revised: 02/11/2025] [Accepted: 02/13/2025] [Indexed: 03/17/2025]
Abstract
Duchenne muscular dystrophy (DMD) is a rare and severe genetic neuromuscular disease, characterized by rapid progression and high mortality, highlighting the need for accurate ambulatory function assessment tools. Ultrasound imaging methods have been widely used for quantitative analysis. Radiomics, which converts medical images into data, combined with machine learning (ML), offers a promising solution. This study is aimed at utilizing radiomics to analyze different stages of data generated during B-mode image processing to evaluate the ambulatory function of DMD patients. The study included 85 participants, categorized into ambulatory and non-ambulatory groups based on their functional status. Ultrasound scans were utilized to capture backscattered radiofrequency data, which were then processed to generate envelope, normalized, and B-mode images. Radiomics analysis involved the manual segmentation of grayscale images and automatic feature extraction using specialized software, followed by feature selection using the maximal relevance and minimal redundancy method. The selected features were input into five ML algorithms, with model evaluation conducted via area under the receiver operating characteristic curve (AUROC). To ensure robustness, both leave-one-out cross-validation and repeated data splitting methods were employed. Additionally, multiple ML models were constructed and tested to assess their performance. The intensity values across all image types increased as walking ability declined, with significant differences observed between the ambulatory and non-ambulatory groups (p < 0.001). These groups exhibited similar diagnostic performance levels, with AUROC values below 0.8. However, radiofrequency (RF) images outperformed other types when radiomics was applied, notably achieving an AUROC value of 0.906. Additionally, combining multiple ML algorithms yielded a higher AUROC value of 0.912 using RF images as input. Radiomics analysis of RF data surpasses conventional B-mode imaging and other ultrasound-derived images in evaluating ambulatory function in DMD. Moreover, integrating multiple machine learning models further enhances classification performance. The proposed method in this study offers a promising framework for improving the accuracy and reliability of clinical follow-up evaluations, supporting more effective management of DMD. The code is available at https://github.com/Goldenyan/radiomicsUS .
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Affiliation(s)
- Dong Yan
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin, China
| | - Ya-Wen Chuang
- Department of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Chia-Wei Lin
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jeng-Yi Shieh
- Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan
| | - Wen-Chin Weng
- Department of Pediatrics, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan
- Department of Pediatric Neurology, National Taiwan University Children's Hospital, Taipei, Taiwan
| | - Po-Hsiang Tsui
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
- Research Center for Radiation Medicine, Chang Gung University, Taoyuan, Taiwan.
- Division of Pediatric Gastroenterology, Department of Pediatrics, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
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Risi B, Caria F, Damioli S, Labella B, Lanzi G, Bugatti M, Baronchelli C, Bertella E, Giovanelli G, Ferullo L, Olivieri E, Poli L, Padovani A, Filosto M. SELENON-related myopathy as a cause of acute respiratory failure in middle age: a case report. J Med Case Rep 2025; 19:64. [PMID: 39980054 PMCID: PMC11843784 DOI: 10.1186/s13256-025-05077-6] [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: 08/30/2024] [Accepted: 01/13/2025] [Indexed: 02/22/2025] Open
Abstract
BACKGROUND SELENON-related myopathy is a rare autosomal recessive congenital neuromuscular disorder linked to defects in the selenoprotein N. The clinical onset typically occurs in infancy and axial weakness, rigid spine, and respiratory involvement are almost invariably present at early stages. CASE PRESENTATION We report the case of a 44-year-old Italian woman who underwent intubation for acute respiratory failure, followed by weaning from invasive ventilation within 6 months. Her medical history was not significant, but a detailed medical history collection revealed slight motor limitations since childhood such as slow running, difficulty climbing high steps, early muscle exhaustion, and fatigue. The neurological examination showed a waddling gait and axial and proximal limb muscle weakness without rigid spine. The right quadriceps muscle biopsy showed nonspecific myopathic abnormalities. Clinical exome sequencing revealed the presence of the two heterozygous variants c.713DupA and c.803G > A in the SELENON gene. CONCLUSION This report focused on the clinical heterogeneity of SELENON-related myopathy. While we highlight that the absence of spinal rigidity and core pathology on muscle biopsy should not exclude the diagnostic suspicion, overall we stress the importance of respiratory failure as a possible late manifestation of the disease, even in middle-aged individuals.
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Affiliation(s)
- Barbara Risi
- NeMO-Brescia Clinical Center for Neuromuscular Diseases, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Filomena Caria
- NeMO-Brescia Clinical Center for Neuromuscular Diseases, Brescia, Italy
| | - Simona Damioli
- NeMO-Brescia Clinical Center for Neuromuscular Diseases, Brescia, Italy
| | - Beatrice Labella
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Unit of Neurology, ASST Spedali Civili, Brescia, Italy
| | - Gaetana Lanzi
- Laboratory of Medical Genetics, Diagnostic Department, ASST Spedali Civili, Brescia, Italy
| | - Mattia Bugatti
- Unit of Pathological Anatomy, ASST Spedali Civili, Brescia, Italy
| | | | - Enrica Bertella
- NeMO-Brescia Clinical Center for Neuromuscular Diseases, Brescia, Italy
| | | | - Lucia Ferullo
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Unit of Neurology, ASST Spedali Civili, Brescia, Italy
| | - Emanuele Olivieri
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Unit of Neurology, ASST Spedali Civili, Brescia, Italy
| | - Loris Poli
- Unit of Neurology, ASST Spedali Civili, Brescia, Italy
| | - Alessandro Padovani
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Unit of Neurology, ASST Spedali Civili, Brescia, Italy
| | - Massimiliano Filosto
- NeMO-Brescia Clinical Center for Neuromuscular Diseases, Brescia, Italy.
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.
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Güttsches A, Forsting J, Kneifel M, Rehmann R, De Lorenzo A, Enax‐Krumova E, Froeling M, Vorgerd M, Schlaffke L. Pre- and post-skeletal muscle biopsy quantitative magnetic resonance imaging reveals correlations with histopathological findings. Eur J Neurol 2024; 31:e16479. [PMID: 39283047 PMCID: PMC11555129 DOI: 10.1111/ene.16479] [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: 03/29/2024] [Revised: 07/31/2024] [Accepted: 08/28/2024] [Indexed: 11/13/2024]
Abstract
BACKGROUND AND PURPOSE Quantitative muscle magnetic resonance imaging (MRI) is a promising non-invasive method in the diagnostic workup as well as follow-up of neuromuscular disorders. The aim of this study was to correlate quantitative MRI (qMRI) parameters to histopathological changes in skeletal muscle tissue and thus to verify the data from our pilot study. METHODS Twenty-six patients (eight females, 46.4 ± 15.1 years) were examined within 72 h before and within 24 h after a skeletal muscle biopsy using quantitative muscle MRI. Post-biopsy MRI was employed to pinpoint the exact localization of the biopsy. qMRI parameters including fat fraction, water T2 relaxation time and diffusion metrics including fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity were extracted from the localization of the biopsy and correlated with histopathological findings. Additionally, three different segmentation masks were applied to the qMRI dataset, to evaluate whether the whole muscle represents the exact biopsy location. RESULTS Fat fraction and water T2 relaxation time in qMRI correlated significantly with the fat fraction in the muscle biopsy and histopathological inflammatory markers. Fractional anisotropy correlated with the quantity of type 2 fibres, whilst mean diffusivity correlated with p62. No differences were found using different segmentation masks in qMRI. CONCLUSIONS In this follow-up study, the results from our previous study were verified regarding the correlation of qMRI parameters with histopathological features in muscle biopsies, indicating that qMRI serves as a suitable non-invasive method in the follow-up of patients with neuromuscular disorders. If post-biopsy MRI is not available, whole muscle volume can be used for histopathological correlations.
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Affiliation(s)
- Anne‐Katrin Güttsches
- Department of NeurologyBG‐University Hospital Bergmannsheil, Ruhr‐University BochumBochumGermany
- Department of Neurology, Heimer Institute for Muscle ResearchBG‐University Hospital BergmannsheilBochumGermany
| | - Johannes Forsting
- Department of NeurologyBG‐University Hospital Bergmannsheil, Ruhr‐University BochumBochumGermany
| | - Moritz Kneifel
- Department of NeurologyBG‐University Hospital Bergmannsheil, Ruhr‐University BochumBochumGermany
- Department of Neurology, Heimer Institute for Muscle ResearchBG‐University Hospital BergmannsheilBochumGermany
| | - Robert Rehmann
- Department of NeurologyBG‐University Hospital Bergmannsheil, Ruhr‐University BochumBochumGermany
| | - Alice De Lorenzo
- Department of NeurologyBG‐University Hospital Bergmannsheil, Ruhr‐University BochumBochumGermany
| | - Elena Enax‐Krumova
- Department of NeurologyBG‐University Hospital Bergmannsheil, Ruhr‐University BochumBochumGermany
| | - Martijn Froeling
- Department of RadiologyUniversity Medical Centre UtrechtUtrechtNetherlands
| | - Matthias Vorgerd
- Department of NeurologyBG‐University Hospital Bergmannsheil, Ruhr‐University BochumBochumGermany
- Department of Neurology, Heimer Institute for Muscle ResearchBG‐University Hospital BergmannsheilBochumGermany
| | - Lara Schlaffke
- Department of NeurologyBG‐University Hospital Bergmannsheil, Ruhr‐University BochumBochumGermany
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Forsting J, Wächter M, Froeling M, Rohm M, Güttsches AK, De Lorenzo A, Südkamp N, Kocabas A, Vorgerd M, Enax-Krumova E, Rehmann R, Schlaffke L. Quantitative muscle magnetic resonance imaging in limb-girdle muscular dystrophy type R1 (LGMDR1): A prospective longitudinal cohort study. NMR IN BIOMEDICINE 2024; 37:e5172. [PMID: 38794994 DOI: 10.1002/nbm.5172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 04/16/2024] [Accepted: 04/20/2024] [Indexed: 05/27/2024]
Abstract
Limb-girdle muscular dystrophy (LGMD) type R1 (LGMDR1) is the most common subtype of LGMD in Europe. Prospective longitudinal data, including clinical assessments and new biomarkers such as quantitative magnetic resonance imaging (qMRI), are needed to evaluate the natural course of the disease and therapeutic options. We evaluated eight thigh and seven leg muscles of 13 LGMDR1 patients (seven females, mean age 36.7 years, body mass index 23.9 kg/m2) and 13 healthy age- and gender-matched controls in a prospective longitudinal design over 1 year. Clinical assessment included testing for muscle strength with quick motor function measure (QMFM), gait analysis and patient questionnaires (neuromuscular symptom score, activity limitation [ACTIVLIM]). MRI scans were performed on a 3-T MRI scanner, including a Dixon-based sequence, T2 mapping and diffusion tensor imaging. The qMRI values of fat fraction (FF), water T2 relaxation time (T2), fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity were analysed. Within the clinical outcome measures, significant deterioration between baseline and follow-up was found for ACTIVLIM (p = 0.029), QMFM (p = 0.012). Analysis of qMRI parameters of the patient group revealed differences between time points for both FF and T2 when analysing all muscles (FF: p < 0.001; T2: p = 0.016). The highest increase of fat replacement was found in muscles with an FF of between 10% and 50% at baseline. T2 in muscles with low-fat replacement increased significantly. No significant differences were found for the diffusion metrics. Significant correlations between qMRI metrics and clinical assessments were found at baseline and follow-up, while only T2 changes in thigh muscles correlated with changes in ACTIVLIM over time (ρ = -0.621, p < 0.05). Clinical assessments can show deterioration of the general condition of LGMDR1 patients. qMRI measures can give additional information about underlying pathophysiology. Further research is needed to establish qMRI outcome measures for clinical trials.
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Affiliation(s)
- Johannes Forsting
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Marian Wächter
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Martijn Froeling
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - 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
| | - Alice De Lorenzo
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Nicolina Südkamp
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - Abdulhadi Kocabas
- 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
| | - Elena Enax-Krumova
- 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
| | - 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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7
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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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Peng F, Tang D, Qing W, Chen W, Li S, Guo Y, Luo G, Zhao H. Utilization of Multi-Parametric Quantitative Magnetic Resonance Imaging in the Early Diagnosis of Duchenne Muscular Dystrophy. J Magn Reson Imaging 2024; 60:1402-1413. [PMID: 38095338 DOI: 10.1002/jmri.29182] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 11/27/2023] [Accepted: 11/29/2023] [Indexed: 11/15/2024] Open
Abstract
BACKGROUND It is challenging to diagnose suspected Duchenne muscular dystrophy (DMD) patients in the very early stage of the disease. More evidence is needed to demonstrate the potential of quantitative MRI (qMRI) in precisely identifying patients before substantial physical decline occurs. PURPOSE To assess the early diagnostic performance of multi-parametric qMRI for DMD patients, and the ability to identify DMD patients with mild functional decline. STUDY TYPE Prospective. SUBJECTS One hundred and forty DMD subjects (9.0 ± 2.2 years old), 24 male healthy controls (HCs) (9.2 ± 2.5 years old). FIELD STRENGTH/SEQUENCE 3.0 T/3-point Dixon, T1-mapping, and T2-mapping. ASSESSMENT qMRI measurements (fat fraction [FF], T1, and T2) of 11 thigh muscles (rectus femoris [RF], vastus lateralis [VL], vastus intermedius, vastus medialis, gracilis, sartorius, adductor longus, adductor magnus [AM], semitendinosus, semimembranosus, biceps femoris long head [BFLH]) on the right side were conducted. NorthStar ambulatory assessment (NSAA) score used to evaluate the function of DMD patients and divided them into three subgroups: mild (76-100 score), moderate (51-75 score), and severe (0-50 score) functional decline. STATISTICAL TESTS Independent t-test, ANOVA analysis, and receiver operating characteristic (ROC) curves. A P-value <0.05 was considered statistically significant. RESULTS Compared with HCs, FF and T2 were significantly higher in the group of all DMD patients, while T1 was significantly lower. The combination of T1 and T2 in RF, VL, AM, and BFLH achieved excellent area under curve (AUCs) (0.967-0.992) in differentiating five DMD patients without abnormal fat infiltration from HCs. Overall, T2 reached higher AUCs than FF and T1 in distinguishing DMD with mild functional decline from HCs, whereas FF achieved higher AUCs than T1 and T2 in distinguishing three DMD subgroups with functional decline. DATA CONCLUSION Multi-parametric qMRI demonstrate effective diagnostic capabilities for DMD patients in the early stage of the disease, and can identify patients with mild physical decline. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Fei Peng
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
- Department of Radiology, Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
- Department of Medical Imaging center, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Deqiu Tang
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Weipeng Qing
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Wei Chen
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Shuhao Li
- Department of Medical Imaging center, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yingkun Guo
- Department of Radiology, Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Guanghua Luo
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Heng Zhao
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
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9
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Barzaghi L, Paoletti M, Monforte M, Bortolani S, Bonizzoni C, Thorsten F, Bergsland N, Santini F, Deligianni X, Tasca G, Ballante E, Figini S, Ricci E, Pichiecchio A. Muscle diffusion tensor imaging in facioscapulohumeral muscular dystrophy. Muscle Nerve 2024; 70:248-256. [PMID: 38873946 DOI: 10.1002/mus.28179] [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: 05/18/2023] [Revised: 05/16/2024] [Accepted: 05/26/2024] [Indexed: 06/15/2024]
Abstract
INTRODUCTION/AIMS Muscle diffusion tensor imaging has not yet been explored in facioscapulohumeral muscular dystrophy (FSHD). We assessed diffusivity parameters in FSHD subjects compared with healthy controls (HCs), with regard to their ability to precede any fat replacement or edema. METHODS Fat fraction (FF), water T2 (wT2), mean, radial, axial diffusivity (MD, RD, AD), and fractional anisotropy (FA) of thigh muscles were calculated in 10 FSHD subjects and 15 HCs. All parameters were compared between FSHD and controls, also exploring their gradient along the main axis of the muscle. Diffusivity parameters were tested in a subgroup analysis as predictors of disease involvement in muscle compartments with different degrees of FF and wT2 and were also correlated with clinical severity scores. RESULTS We found that MD, RD, and AD were significantly lower in FSHD subjects than in controls, whereas we failed to find a difference for FA. In contrast, we found a significant positive correlation between FF and FA and a negative correlation between MD, RD, and AD and FF. No correlation was found with wT2. In our subgroup analysis we found that muscle compartments with no significant fat replacement or edema (FF < 10% and wT2 < 41 ms) showed a reduced AD and FA compared with controls. Less involved compartments showed different diffusivity parameters than more involved compartments. DISCUSSION Our exploratory study was able to demonstrate diffusivity parameter abnormalities even in muscles with no significant fat replacement or edema. Larger cohorts are needed to confirm these preliminary findings.
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Affiliation(s)
- Leonardo Barzaghi
- Department of Mathematics, University of Pavia, Pavia, Italy
- Advanced Imaging and Artificial Intelligence Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
- INFN, Group of Pavia, Pavia, Italy
| | - Matteo Paoletti
- Advanced Imaging and Artificial Intelligence Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Mauro Monforte
- UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Sara Bortolani
- UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Chiara Bonizzoni
- Advanced Imaging and Artificial Intelligence Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Niels Bergsland
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University of Buffalo, The State University of New York, Buffalo, New York, USA
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Francesco Santini
- Department of Radiology, University Hospital Basel, Basel, Switzerland
- Basel Muscle MRI, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Xeni Deligianni
- Department of Radiology, University Hospital Basel, Basel, Switzerland
- Basel Muscle MRI, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Giorgio Tasca
- UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trusts, Newcastle upon Tyne, UK
| | - Elena Ballante
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy
- BioData Science Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Silvia Figini
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy
- BioData Science Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Enzo Ricci
- UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Anna Pichiecchio
- Advanced Imaging and Artificial Intelligence Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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10
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Zhang Y, Zou Y, Tan W, Lv C. Value of radiomics-based automatic grading of muscle edema in polymyositis/dermatomyositis based on MRI fat-suppressed T2-weighted images. Acta Radiol 2024; 65:632-640. [PMID: 38591947 DOI: 10.1177/02841851241244507] [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] [Indexed: 04/10/2024]
Abstract
BACKGROUND The precise and objective assessment of thigh muscle edema is pivotal in diagnosing and monitoring the treatment of dermatomyositis (DM) and polymyositis (PM). PURPOSE Radiomic features are extracted from fat-suppressed (FS) T2-weighted (T2W) magnetic resonance imaging (MRI) of thigh muscles to enable automatic grading of muscle edema in cases of polymyositis and dermatomyositis. MATERIAL AND METHODS A total of 241 MR images were analyzed and classified into five levels using the Stramare criteria. The correlation between muscle edema grading and T2-mapping values was assessed using Spearman's correlation. The dataset was divided into a 7:3 ratio of training (168 samples) and testing (73 samples). Thigh muscle boundaries in FS T2W images were manually delineated with 3D-Slicer. Radiomics features were extracted using Python 3.7, applying Z-score normalization, Pearson correlation analysis, and recursive feature elimination for reduction. A Naive Bayes classifier was trained, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curves and comparing sensitivity and specificity with senior doctors. RESULTS A total of 1198 radiomics parameters were extracted and reduced to 18 features for Naive Bayes modeling. In the testing set, the model achieved an area under the ROC curve of 0.97, sensitivity of 0.85, specificity of 0.98, and accuracy of 0.91. The Naive Bayes classifier demonstrated grading performance comparable to senior doctors. A significant correlation (r = 0.82, P <0.05) was observed between Stramare edema grading and T2-mapping values. CONCLUSION The Naive Bayes model, utilizing radiomics features extracted from thigh FS T2W images, accurately assesses the severity of muscle edema in cases of PM/DM.
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Affiliation(s)
- Yumei Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Yuefen Zou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Wenfeng Tan
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
| | - Chengyin Lv
- Department of Rheumatology and Immunology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China
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11
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Nava S, Conte G, Triulzi FM, Comi GP, Magri F, Velardo D, Cinnante CM. Diffusion tensor imaging reveals subclinical alterations in muscles of patients with Becker muscular dystrophy. Br J Radiol 2024; 97:947-953. [PMID: 38574384 PMCID: PMC11075994 DOI: 10.1093/bjr/tqae070] [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: 06/01/2023] [Revised: 11/21/2023] [Accepted: 03/27/2024] [Indexed: 04/06/2024] Open
Abstract
OBJECTIVES Becker muscular dystrophy (BMD) is a relatively less investigated neuromuscular disease, partially overlapping the phenotype of Duchenne dystrophy (DMD). Physiopathological and anatomical patterns are still not comprehensively known, despite recent effort in the search of early biomarkers. Aim of this study was to selectively compare normal appearing muscles of BMD with healthy controls. METHODS Among a pool of 40 BMD patients and 20 healthy controls, Sartorius and gracilis muscles were selected on the basis of a blinded clinical quantitative/qualitative evaluation, if classified as normal (0 or 1 on Mercuri scale) and subsequently segmented on diffusion tensor MRI scans with a tractographic approach. Diffusion derived parameters were extracted. RESULTS Non-parametric testing revealed significant differences between normal and normal appearing BMD derived parameters in both muscles, the difference being more evident in sartorius. Bonferroni-corrected P-values (<.05) of Mann-Whitney test could discriminate between BMD and controls for standard deviation of all diffusion parameters (mean diffusivity, fractional anisotropy, axial and radial diffusivity) in both sartorius and gracilis, while in sartorius the significant difference was found also in the average values of the same parameters (with exception of RD). CONCLUSIONS This method could identify microstructural alterations in BMD normal appearing sartorius and gracilis. ADVANCES IN KNOWLEDGE Diffusion based MRI could be able to identify possible early or subclinical microstructural alterations in dystrophic patients with BMD.
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Affiliation(s)
- Simone Nava
- Neuroradiology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, 20122 Milan, Italy
| | - Giorgio Conte
- Neuroradiology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, 20122 Milan, Italy
| | - Fabio M Triulzi
- Neuroradiology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, 20122 Milan, Italy
| | - Giacomo P Comi
- Neuromuscular and Rare Diseases Unit, Department of Neuroscience, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, 20122 Milan, Italy
- Dino Ferrari Center, Department of Pathophysiology and Transplantation, University of Milan, via Francesco Sforza 35, 20122 MilanItaly
| | - Francesca Magri
- Neuromuscular and Rare Diseases Unit, Department of Neuroscience, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, 20122 Milan, Italy
- Dino Ferrari Center, Department of Pathophysiology and Transplantation, University of Milan, via Francesco Sforza 35, 20122 MilanItaly
| | - Daniele Velardo
- Neuromuscular and Rare Diseases Unit, Department of Neuroscience, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, 20122 Milan, Italy
- Dino Ferrari Center, Department of Pathophysiology and Transplantation, University of Milan, via Francesco Sforza 35, 20122 MilanItaly
| | - Claudia M Cinnante
- Radiology Department, Istituto Auxologico Italiano IRCCS, Piazzale Brescia 20, 20149 Milan, Italy
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12
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Esteller D, Schiava M, Verdú-Díaz J, Villar-Quiles RN, Dibowski B, Venturelli N, Laforet P, Alonso-Pérez J, Olive M, Domínguez-González C, Paradas C, Vélez B, Kostera-Pruszczyk A, Kierdaszuk B, Rodolico C, Claeys K, Pál E, Malfatti E, Souvannanorath S, Alonso-Jiménez A, de Ridder W, De Smet E, Papadimas G, Papadopoulos C, Xirou S, Luo S, Muelas N, Vilchez JJ, Ramos-Fransi A, Monforte M, Tasca G, Udd B, Palmio J, Sri S, Krause S, Schoser B, Fernández-Torrón R, López de Munain A, Pegoraro E, Farrugia ME, Vorgerd M, Manousakis G, Chanson JB, Nadaj-Pakleza A, Cetin H, Badrising U, Warman-Chardon J, Bevilacqua J, Earle N, Campero M, Díaz J, Ikenaga C, Lloyd TE, Nishino I, Nishimori Y, Saito Y, Oya Y, Takahashi Y, Nishikawa A, Sasaki R, Marini-Bettolo C, Guglieri M, Straub V, Stojkovic T, Carlier RY, Díaz-Manera J. Analysis of muscle magnetic resonance imaging of a large cohort of patient with VCP-mediated disease reveals characteristic features useful for diagnosis. J Neurol 2023; 270:5849-5865. [PMID: 37603075 PMCID: PMC10632218 DOI: 10.1007/s00415-023-11862-4] [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: 05/06/2023] [Revised: 06/29/2023] [Accepted: 07/01/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND The diagnosis of patients with mutations in the VCP gene can be complicated due to their broad phenotypic spectrum including myopathy, motor neuron disease and peripheral neuropathy. Muscle MRI guides the diagnosis in neuromuscular diseases (NMDs); however, comprehensive muscle MRI features for VCP patients have not been reported so far. METHODS We collected muscle MRIs of 80 of the 255 patients who participated in the "VCP International Study" and reviewed the T1-weighted (T1w) and short tau inversion recovery (STIR) sequences. We identified a series of potential diagnostic MRI based characteristics useful for the diagnosis of VCP disease and validated them in 1089 MRIs from patients with other genetically confirmed NMDs. RESULTS Fat replacement of at least one muscle was identified in all symptomatic patients. The most common finding was the existence of patchy areas of fat replacement. Although there was a wide variability of muscles affected, we observed a common pattern characterized by the involvement of periscapular, paraspinal, gluteal and quadriceps muscles. STIR signal was enhanced in 67% of the patients, either in the muscle itself or in the surrounding fascia. We identified 10 diagnostic characteristics based on the pattern identified that allowed us to distinguish VCP disease from other neuromuscular diseases with high accuracy. CONCLUSIONS Patients with mutations in the VCP gene had common features on muscle MRI that are helpful for diagnosis purposes, including the presence of patchy fat replacement and a prominent involvement of the periscapular, paraspinal, abdominal and thigh muscles.
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Affiliation(s)
- Diana Esteller
- Neurology Department, Hospital Clinic de Barcelona, Universitat de Barcelona, Barcelona, Spain
| | - Marianela Schiava
- John Walton Muscular Dystrophy Research Centre, Newcastle University Translational and Clinical Research Institute and Newcastle Hospitals NHS Foundation Trust, Center for Life, Central Parkway, Newcastle Upon Tyne, NE13BZ, United Kingdom
| | - José Verdú-Díaz
- John Walton Muscular Dystrophy Research Centre, Newcastle University Translational and Clinical Research Institute and Newcastle Hospitals NHS Foundation Trust, Center for Life, Central Parkway, Newcastle Upon Tyne, NE13BZ, United Kingdom
| | - Rocío-Nur Villar-Quiles
- APHP, Centre de Référence des Maladies Neuromusculaires, Institut de Myologie, Centre de Recherche en Myologie, Sorbonne Université, APHP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Boris Dibowski
- Department of Radiology, Assistance Publique-Hôpitaux de Paris (AP-HP), DMU Start Imaging, Raymond Poincaré Teaching Hospital, Garches, France
| | - Nadia Venturelli
- Department of Radiology, Assistance Publique-Hôpitaux de Paris (AP-HP), DMU Start Imaging, Raymond Poincaré Teaching Hospital, Garches, France
| | - Pascal Laforet
- Département de Neurologie Hôpital Raymond-Poincaré Garches France Inserm U1179, Garches, France
| | - Jorge Alonso-Pérez
- Servicio de Neurología. Hospital Virgen de la Candelaria, Tenerife, Spain
- Neuromuscular Diseases Unit, Neurology Department, Institut d'Investigació Biomèdica Sant Pau (IIB SANT PAU), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Montse Olive
- Neuromuscular Diseases Unit, Neurology Department, Institut d'Investigació Biomèdica Sant Pau (IIB SANT PAU), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
| | - Cristina Domínguez-González
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Unidad de Enfermedades Neuromusculares, Servicio de Neurología, Instituto de Investigación imas12, Hospital 12 de Octubre, Madrid, Spain
| | - Carmen Paradas
- Unidad de Enfermedades Neuromusculares, Servicio de Neurología, Hospital Virgen del Rocio, Seville, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain
| | - Beatriz Vélez
- Unidad de Enfermedades Neuromusculares, Servicio de Neurología, Hospital Virgen del Rocio, Seville, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain
| | - Anna Kostera-Pruszczyk
- Department of Neurology, Medical University of Warsaw, ERN EURO NMD, Warsaw, Poland
- Neuromuscular Reference Centre, ERN-EURO-NMD, Warsaw, Poland
| | - Biruta Kierdaszuk
- Department of Neurology, Medical University of Warsaw, ERN EURO NMD, Warsaw, Poland
- Neuromuscular Reference Centre, ERN-EURO-NMD, Warsaw, Poland
| | - Carmelo Rodolico
- UOC di Neurologia e Malattie Neuromuscolari, AOU Policlinico "G. Martino", Rome, Italy
| | - Kristl Claeys
- Neurologie, Neuromusculair Referentiecentrum, Universitaire Ziekenhuizen, Leuven, Belgium
| | - Endre Pál
- Neurology Department, University of Pécs, Pécs, Hungary
| | - Edoardo Malfatti
- Université Paris Est, U955 INSERM, Centre de Référence de Pathologie Neuromusculaire Nord-Est-Ile-de-France, Henri Mondor Hospital, EURO-NMD, 94010, Creteil, France
| | - Sarah Souvannanorath
- Université Paris Est, U955 INSERM, Centre de Référence de Pathologie Neuromusculaire Nord-Est-Ile-de-France, Henri Mondor Hospital, EURO-NMD, 94010, Creteil, France
| | | | - Willem de Ridder
- Neurology Department, Universitary Hospital Antwerpen, Edegem, Belgium
| | - Eline De Smet
- Neurology Department, Universitary Hospital Antwerpen, Edegem, Belgium
| | - George Papadimas
- Department of Neurology, Eginition Hospital, Medical School, NKUA, ERN, EURO NMD, Athens, Greece
| | | | - Sofia Xirou
- Department of Neurology, Eginition Hospital, Medical School, NKUA, ERN, EURO NMD, Athens, Greece
| | - Sushan Luo
- Neurology Department, Huashan Hospital, Fudan University, Shangai, China
| | - Nuria Muelas
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Neuromuscular Diseases Unit, Neurology Department, Hospital Universitari I Politècnic La Fe, Valencia, Spain
- Neuromuscular and Ataxias Research Group, Instituto de Investigación Sanitaria La Fe, Valencia, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
| | - Juan J Vilchez
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain
- Neuromuscular Diseases Unit, Neurology Department, Hospital Universitari I Politècnic La Fe, Valencia, Spain
- Neuromuscular and Ataxias Research Group, Instituto de Investigación Sanitaria La Fe, Valencia, Spain
- Department of Medicine, Universitat de València, Valencia, Spain
| | - Alba Ramos-Fransi
- Unitat de Malalties Neuromusculars, Servei de Neurologia, Hospital Germans Tries I Pujol, Badalona, Spain
| | - Mauro Monforte
- UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giorgio Tasca
- John Walton Muscular Dystrophy Research Centre, Newcastle University Translational and Clinical Research Institute and Newcastle Hospitals NHS Foundation Trust, Center for Life, Central Parkway, Newcastle Upon Tyne, NE13BZ, United Kingdom
| | - Bjarne Udd
- Tampere Neuromuscular Center, Tampere University Hospital, Tampere, Finland
- Folkhalsan Genetic Institute, Helsinki University, Helsinki, Finland
| | - Johanna Palmio
- Tampere Neuromuscular Center, Tampere University Hospital, Tampere, Finland
- Folkhalsan Genetic Institute, Helsinki University, Helsinki, Finland
| | - Srtuhi Sri
- Sree Chitra Tirunal Insitute for Medical Sciences and Technology, Thiruvananthapuram, India
| | - Sabine Krause
- Department of Neurology, Friedrich-Baur-Institute, LMU Clinics, Munich, Germany
| | - Benedikt Schoser
- Department of Neurology, Friedrich-Baur-Institute, LMU Clinics, Munich, Germany
| | - Roberto Fernández-Torrón
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain
- Neurology Department, Biodonostia Health Research Institute, Donostia, Spain
| | - Adolfo López de Munain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Barcelona, Spain
- Neurology Department, Biodonostia Health Research Institute, Donostia, Spain
| | - Elena Pegoraro
- Department of Neurosciences, University of Padova, Padua, Italy
| | - Maria Elena Farrugia
- Department of Neurology, Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow, Scotland, UK
| | - Mathias Vorgerd
- Heimer Institut for Muscle Research, Klinikum Bergmannsheil Ruhr, University Bochum, Bochum, Germany
| | | | - Jean Baptiste Chanson
- Centre de Référence des Maladies Neuromusculaires Nord/Est/Ile-de-France and ERN-EURO-NMD, Neurology Department, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Aleksandra Nadaj-Pakleza
- Centre de Référence des Maladies Neuromusculaires Nord/Est/Ile-de-France and ERN-EURO-NMD, Neurology Department, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Hakan Cetin
- Neurology Department, Medical University of Vienna, Vienna, Austria
| | | | | | - Jorge Bevilacqua
- Departamento de Neurología y Neurocirugía, Hospital Clínico Universidad de Chile, Santiago de Chile, Chile
| | - Nicholas Earle
- Departamento de Neurología y Neurocirugía, Hospital Clínico Universidad de Chile, Santiago de Chile, Chile
| | - Mario Campero
- Departamento de Neurología y Neurocirugía, Hospital Clínico Universidad de Chile, Santiago de Chile, Chile
| | - Jorge Díaz
- Departamento de Neurología y Neurocirugía, Hospital Clínico Universidad de Chile, Santiago de Chile, Chile
| | - Chiseko Ikenaga
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Thomas E Lloyd
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Ichizo Nishino
- Department of Neuromuscular Research, National Institute of Neuroscience, National Center of Neurology, Tokyo, Japan
| | - Yukako Nishimori
- Department of Neuromuscular Research, National Institute of Neuroscience, National Center of Neurology, Tokyo, Japan
| | - Yoshihiko Saito
- Department of Neuromuscular Research, National Institute of Neuroscience, National Center of Neurology, Tokyo, Japan
| | - Yasushi Oya
- Department of Neurology, National Center Hospital, NCNP, Tokyo, Japan
| | - Yoshiaki Takahashi
- Department of Neurology, Kagawa Prefectural Central Hospital, Kagawa, Japan
| | | | - Ryo Sasaki
- Department of Neurology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Chiara Marini-Bettolo
- John Walton Muscular Dystrophy Research Centre, Newcastle University Translational and Clinical Research Institute and Newcastle Hospitals NHS Foundation Trust, Center for Life, Central Parkway, Newcastle Upon Tyne, NE13BZ, United Kingdom
| | - Michela Guglieri
- John Walton Muscular Dystrophy Research Centre, Newcastle University Translational and Clinical Research Institute and Newcastle Hospitals NHS Foundation Trust, Center for Life, Central Parkway, Newcastle Upon Tyne, NE13BZ, United Kingdom
| | - Volker Straub
- John Walton Muscular Dystrophy Research Centre, Newcastle University Translational and Clinical Research Institute and Newcastle Hospitals NHS Foundation Trust, Center for Life, Central Parkway, Newcastle Upon Tyne, NE13BZ, United Kingdom
| | - Tanya Stojkovic
- APHP, Centre de Référence des Maladies Neuromusculaires, Institut de Myologie, Centre de Recherche en Myologie, Sorbonne Université, APHP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Robert Y Carlier
- Department of Radiology, Assistance Publique-Hôpitaux de Paris (AP-HP), DMU Start Imaging, Raymond Poincaré Teaching Hospital, Garches, France
| | - Jordi Díaz-Manera
- John Walton Muscular Dystrophy Research Centre, Newcastle University Translational and Clinical Research Institute and Newcastle Hospitals NHS Foundation Trust, Center for Life, Central Parkway, Newcastle Upon Tyne, NE13BZ, United Kingdom.
- Neuromuscular Diseases Unit, Neurology Department, Institut d'Investigació Biomèdica Sant Pau (IIB SANT PAU), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.
- Center for Biomedical Network Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III, Madrid, Spain.
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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: 1.0] [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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14
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Rehmann R, Enax-Krumova E, Meyer-Frießem CH, Schlaffke L. Quantitative muscle MRI displays clinically relevant myostructural abnormalities in long-term ICU-survivors: a case-control study. BMC Med Imaging 2023; 23:38. [PMID: 36934222 PMCID: PMC10024415 DOI: 10.1186/s12880-023-00995-7] [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: 09/20/2022] [Accepted: 03/08/2023] [Indexed: 03/20/2023] Open
Abstract
BACKGROUND Long-term data on ICU-survivors reveal persisting sequalae and a reduced quality-of-life even after years. Major complaints are neuromuscular dysfunction due to Intensive care unit acquired weakness (ICUAW). Quantitative MRI (qMRI) protocols can quantify muscle alterations in contrast to standard qualitative MRI-protocols. METHODS Using qMRI, the aim of this study was to analyse persisting myostructural abnormalities in former ICU patients compared to controls and relate them to clinical assessments. The study was conducted as a cohort/case-control study. Nine former ICU-patients and matched controls were recruited (7 males; 54.8y ± 16.9; controls: 54.3y ± 11.1). MRI scans were performed on a 3T-MRI including a mDTI, T2 mapping and a mDixonquant sequence. Water T2 times, fat-fraction and mean values of the eigenvalue (λ1), mean diffusivity (MD), radial diffusivity (RD) and fractional anisotropy (FA) were obtained for six thigh and seven calf muscles bilaterally. Clinical assessment included strength testing, electrophysiologic studies and a questionnaire on quality-of-life (QoL). Study groups were compared using a multivariate general linear model. qMRI parameters were correlated to clinical assessments and QoL questionnaire using Pearson´s correlation. RESULTS qMRI parameters were significantly higher in the patients for fat-fraction (p < 0.001), water T2 time (p < 0.001), FA (p = 0.047), MD (p < 0.001) and RD (p < 0.001). Thighs and calves showed a different pattern with significantly higher water T2 times only in the calves. Correlation analysis showed a significant negative correlation of muscle strength (MRC sum score) with FA and T2-time. The results were related to impairment seen in QoL-questionnaires, clinical testing and electrophysiologic studies. CONCLUSION qMRI parameters show chronic next to active muscle degeneration in ICU survivors even years after ICU therapy with ongoing clinical relevance. Therefore, qMRI opens new doors to characterize and monitor muscle changes of patients with ICUAW. Further, better understanding on the underlying mechanisms of the persisting complaints could contribute the development of personalized rehabilitation programs.
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Affiliation(s)
- R Rehmann
- Department of Neurology, BG-University Hospital Bergmannsheil gGmbH, Ruhr-University Bochum, Bürkle-de-La-Camp-Platz 1, 44789, Bochum, Germany.
| | - E Enax-Krumova
- Department of Neurology, BG-University Hospital Bergmannsheil gGmbH, Ruhr-University Bochum, Bürkle-de-La-Camp-Platz 1, 44789, Bochum, Germany
| | - C H Meyer-Frießem
- Department of Anaesthesiology, Intensive Care and Pain Medicine, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bochum, Germany
| | - L Schlaffke
- Department of Neurology, BG-University Hospital Bergmannsheil gGmbH, Ruhr-University Bochum, Bürkle-de-La-Camp-Platz 1, 44789, Bochum, Germany
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15
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Vegezzi E, Cortese A, Bergsland N, Mussinelli R, Paoletti M, Solazzo F, Currò R, Ascagni L, Callegari I, Quartesan I, Lozza A, Deligianni X, Santini F, Marchioni E, Cosentino G, Alfonsi E, Tassorelli C, Bastianello S, Merlini G, Palladini G, Obici L, Pichiecchio A. Muscle quantitative MRI as a novel biomarker in hereditary transthyretin amyloidosis with polyneuropathy: a cross-sectional study. J Neurol 2023; 270:328-339. [PMID: 36064814 DOI: 10.1007/s00415-022-11336-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND The development of reproducible and sensitive outcome measures has been challenging in hereditary transthyretin (ATTRv) amyloidosis. Recently, quantification of intramuscular fat by magnetic resonance imaging (MRI) has proven as a sensitive marker in patients with other genetic neuropathies. The aim of this study was to investigate the role of muscle quantitative MRI (qMRI) as an outcome measure in ATTRv. METHODS Calf- and thigh-centered multi-echo T2-weighted spin-echo and gradient-echo sequences were obtained in patients with ATTRv amyloidosis with polyneuropathy (n = 24) and healthy controls (n = 12). Water T2 (wT2) and fat fraction (FF) were calculated. Neurological assessment was performed in all ATTRv subjects. Quantitative MRI parameters were correlated with clinical and neurophysiological measures of disease severity. RESULTS Quantitative imaging revealed significantly higher FF in lower limb muscles in patients with ATTRv amyloidosis compared to controls. In addition, wT2 was significantly higher in ATTRv patients. There was prominent involvement of the posterior compartment of the thighs. Noticeably, FF and wT2 did not exhibit a length-dependent pattern in ATTRv patients. MRI biomarkers correlated with previously validated clinical outcome measures, Polyneuropathy Disability scoring system, Neuropathy Impairment Score (NIS) and NIS-lower limb, and neurophysiological parameters of axonal damage regardless of age, sex, treatment and TTR mutation. CONCLUSIONS Muscle qMRI revealed significant difference between ATTRv and healthy controls. MRI biomarkers showed high correlation with clinical and neurophysiological measures of disease severity making qMRI as a promising tool to be further investigated in longitudinal studies to assess its role at monitoring onset, progression, and therapy efficacy for future clinical trials on this treatable condition.
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Affiliation(s)
- Elisa Vegezzi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Neuroncology and Neuroinflammation Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Andrea Cortese
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy. .,Department of Neuromuscular Disease, UCL Queen Square Institute of Neurology and The National Hospital for Neurology and Neurosurgery, London, UK.
| | - Niels Bergsland
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA.,IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Roberta Mussinelli
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
| | - Matteo Paoletti
- Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Francesca Solazzo
- Specialization School in Occupational Medicine, University of Pavia, Pavia, Italy
| | - Riccardo Currò
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Department of Neuromuscular Disease, UCL Queen Square Institute of Neurology and The National Hospital for Neurology and Neurosurgery, London, UK
| | - Lucia Ascagni
- Neuroscience Department, Meyer Children's University Hospital, University of Florence, Florence, Italy
| | - Ilaria Callegari
- Department of Biomedicine, University Hospital Basel, University of Basel, Hebelstrasse 20, 4031, Basel, Switzerland
| | - Ilaria Quartesan
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Alessandro Lozza
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
| | - Xeni Deligianni
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland.,Department of Biomedical Engineering, Basel Muscle MRI Group, University of Basel, Allschwil, Switzerland
| | - Francesco Santini
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland.,Department of Biomedical Engineering, Basel Muscle MRI Group, University of Basel, Allschwil, Switzerland
| | - Enrico Marchioni
- Neuroncology and Neuroinflammation Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Giuseppe Cosentino
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Translational Neurophysiology Research Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Enrico Alfonsi
- Translational Neurophysiology Research Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Cristina Tassorelli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Headache Science and Neurorehabilitation Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Stefano Bastianello
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Giampaolo Merlini
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy.,Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Giovanni Palladini
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy.,Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Laura Obici
- Amyloidosis Research and Treatment Center, Fondazione IRCCS Policlinico San Matteo, University of Pavia, Pavia, Italy
| | - Anna Pichiecchio
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
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16
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Colelli G, Barzaghi L, Paoletti M, Monforte M, Bergsland N, Manco G, Deligianni X, Santini F, Ricci E, Tasca G, Mira A, Figini S, Pichiecchio A. Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease. Front Neurol 2023; 14:1105276. [PMID: 36908599 PMCID: PMC9999017 DOI: 10.3389/fneur.2023.1105276] [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: 11/22/2022] [Accepted: 01/30/2023] [Indexed: 03/14/2023] Open
Abstract
Purpose Quantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms. Methods Twenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (WF1, WF2, WF3), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles. Results The combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about ± 5 pp for FF and ± 1.8 ms for wT2. Conclusion This pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence.
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Affiliation(s)
- Giulia Colelli
- Department of Mathematics, University of Pavia, Pavia, Italy.,Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy.,INFN, Group of Pavia, Pavia, Italy
| | - Leonardo Barzaghi
- Department of Mathematics, University of Pavia, Pavia, Italy.,Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Matteo Paoletti
- Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Mauro Monforte
- UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Niels Bergsland
- Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo Neuroimaging Analysis Center, University of Buffalo, The State University of New York, Buffalo, NY, United States.,IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Giulia Manco
- Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Xeni Deligianni
- Department of Radiology, University Hospital Basel, Basel, Switzerland.,Basel Muscle MRI, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Francesco Santini
- Department of Radiology, University Hospital Basel, Basel, Switzerland.,Basel Muscle MRI, Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Enzo Ricci
- UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giorgio Tasca
- UOC di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,John Walton Muscular Dystrophy Research Centre, Newcastle University and Newcastle Hospitals NHS Foundation Trusts, Newcastle upon Tyne, United Kingdom
| | - Antonietta Mira
- Data Science Lab, Università della Svizzera italiana, Lugano, Switzerland.,Department of Science and High Technology, University of Insubria, Como, Italy
| | - Silvia Figini
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy.,BioData Science Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Anna Pichiecchio
- Neuroradiology Department, Advanced Imaging and Radiomics Center, IRCCS Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
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17
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Forsting J, Rohm M, Froeling M, Güttsches AK, Südkamp N, Roos A, Vorgerd M, Schlaffke L, Rehmann R. Quantitative muscle MRI captures early muscle degeneration in calpainopathy. Sci Rep 2022; 12:19676. [PMID: 36385624 PMCID: PMC9669006 DOI: 10.1038/s41598-022-23972-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 11/08/2022] [Indexed: 11/17/2022] Open
Abstract
To evaluate differences in qMRI parameters of muscle diffusion tensor imaging (mDTI), fat-fraction (FF) and water T2 time in leg muscles of calpainopathy patients (LGMD R1/D4) compared to healthy controls, to correlate those findings to clinical parameters and to evaluate if qMRI parameters show muscle degeneration in not-yet fatty infiltrated muscles. We evaluated eight thigh and seven calf muscles of 19 calpainopathy patients and 19 healthy matched controls. MRI scans were performed on a 3T MRI including a mDTI, T2 mapping and mDixonquant sequence. Clinical assessment was done with manual muscle testing, patient questionnaires (ACTIVLIM, NSS) as well as gait analysis. Average FF was significantly different in all muscles compared to controls (p < 0.001). In muscles with less than 8% FF a significant increase of FA (p < 0.005) and decrease of RD (p < 0.004) was found in high-risk muscles of calpainopathy patients. Water T2 times were increased within the low- and intermediate-risk muscles (p ≤ 0.045) but not in high-risk muscles (p = 0.062). Clinical assessments correlated significantly with qMRI values: QMFM vs. FF: r = - 0.881, p < 0.001; QMFM versus FA: r = - 0.747, p < 0.001; QMFM versus MD: r = 0.942, p < 0.001. A good correlation of FF and diffusion metrics to clinical assessments was found. Diffusion metrics and T2 values are promising candidates to serve as sensitive early and non-invasive methods to capture early muscle degeneration in non-fat-infiltrated muscles in calpainopathies.
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Affiliation(s)
- Johannes Forsting
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bürkle-de-la-Camp-Platz 1, 44789, Bochum, Germany
| | - Marlena Rohm
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bürkle-de-la-Camp-Platz 1, 44789, 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
| | - Anne-Katrin Güttsches
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bürkle-de-la-Camp-Platz 1, 44789, Bochum, Germany
- Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
| | - Nicolina Südkamp
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bürkle-de-la-Camp-Platz 1, 44789, Bochum, Germany
- Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
| | - Andreas Roos
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bürkle-de-la-Camp-Platz 1, 44789, Bochum, Germany
- Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
- Department of Neuropediatrics, University Hospital Essen, Duisburg-Essen University, Essen, Germany
| | - Matthias Vorgerd
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bürkle-de-la-Camp-Platz 1, 44789, 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, Bürkle-de-la-Camp-Platz 1, 44789, Bochum, Germany
- Heimer Institute for Muscle Research, BG-University Hospital Bergmannsheil, Bochum, Germany
| | - Robert Rehmann
- Department of Neurology, BG-University Hospital Bergmannsheil, Ruhr-University Bochum, Bürkle-de-la-Camp-Platz 1, 44789, Bochum, Germany.
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18
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Theodorou DJ, Theodorou SJ, Saba L, Kakitsubata Y. Skeletal Muscle Disease: Imaging Findings Simplified. Cureus 2022; 14:e29655. [DOI: 10.7759/cureus.29655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/08/2022] [Indexed: 11/05/2022] Open
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19
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Albayda J, Demonceau G, Carlier PG. Muscle imaging in myositis: MRI, US, and PET. Best Pract Res Clin Rheumatol 2022; 36:101765. [PMID: 35760742 DOI: 10.1016/j.berh.2022.101765] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Imaging is an important tool in the evaluation of idiopathic inflammatory myopathies. It plays a role in diagnosis, assessment of disease activity and follow-up, and as a non-invasive biomarker. Among the different modalities, nuclear magnetic resonance imaging (MRI), ultrasound (US), and positron emission tomography (PET) may have the most clinical utility in myositis. MRI is currently the best modality to evaluate skeletal muscle and provides excellent characterization of muscle edema and fat replacement through the use of T1-weighted and T2-weighted fat suppressed/STIR sequences. Although MRI can be read qualitatively for the presence of abnormalities, a more quantitative approach using Dixon sequences and the generation of water T2 parametric maps would be preferable for follow-up. Newer protocols such as diffusion-weighted imaging, functional imaging measures, and spectroscopy may be of interest to provide further insights into myositis. Despite the advantages of MRI, image acquisition is relatively time-consuming, expensive, and not accessible to all patients. The use of US to evaluate skeletal muscle in myositis is gaining interest, especially in chronic disease, where fat replacement and fibrosis are detected readily by this modality. Although easily deployed at the bedside, it is heavily dependent on operator experience to recognize disease states. Further, systematic characterization of muscle edema by US is still needed. PET provides valuable information on muscle function at a cellular level. Fluorodeoxyglucose (FDG-PET) has been the most common application in myositis to detect pathologic uptake indicative of inflammation. The use of neurodegenerative markers is now also being utilized for inclusion body myositis. These different modalities may prove to be complementary methods for myositis evaluation.
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Affiliation(s)
- Jemima Albayda
- Division of Rheumatology, Johns Hopkins University, Baltimore, USA.
| | | | - Pierre G Carlier
- Université Paris-Saclay, CEA, DRF, Service Hospitalier Frederic Joliot, Orsay, France
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20
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Locher N, Wagner B, Balsiger F, Scheidegger O. Quantitative water T2 relaxometry in the early detection of neuromuscular diseases: a retrospective biopsy-controlled analysis. Eur Radiol 2022; 32:7910-7917. [PMID: 35596779 PMCID: PMC9668929 DOI: 10.1007/s00330-022-08862-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 03/15/2022] [Accepted: 05/01/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES To assess quantitative water T2 relaxometry for the early detection of neuromuscular diseases (NMDs) in comparison to standard qualitative MR imaging in a clinical setting. METHODS This retrospective study included 83 patients with suspected NMD who underwent multiparametric MRI at 3 T with a subsequent muscle biopsy between 2015 and 2019. Qualitative T1-weighted and T2-TIRM images were graded by two neuroradiologists to be either pathological or normal. Mean and median water T2 relaxation times (water T2) were obtained from manually drawn volumes of interests in biopsied muscle from multi-echo sequence. Histopathologic pattern of corresponding muscle biopsies was used as a reference. RESULTS In 34 patients, the T1-weighted images showed clear pathological alternations indicating late-stage fatty infiltration in NMDs. In the remaining 49 patients without late-stage changes, T2-TIRM grading achieved a sensitivity of 56.4%, and mean and median water T2 a sensitivity of 87.2% and 97.4% to detect early-stage NMDs. Receiver operating characteristic (ROC) analysis revealed an area under the curve (AUC) of 0.682, 0.715, and 0.803 for T2-TIRM, mean water T2, and median water T2, respectively. Median water T2 ranged between 36 and 42 ms depending on histopathologic pattern. CONCLUSIONS Quantitative water T2 relaxometry had a significantly higher sensitivity in detecting muscle abnormalities than subjective grading of T2-TIRM, prior to late-stage fatty infiltration signal alternations in T1-weighted images. Normal-appearing T2-TIRM does not rule out early-stage NMDs. Our findings suggest considering water T2 relaxometry complementary to T2-TIRM for early detection of NMDs in clinical diagnostic routine. KEY POINTS • Quantitative water T2 relaxometry is more sensitive than subjective assessment of fat-suppressed T2-weighted images for the early detection of neuromuscular diseases, prior to late-stage fatty infiltration signal alternations in T1-weighted images. • Normal-appearing muscles in fat-suppressed T2-weighted images do not rule out early-stage neuromuscular diseases. • Quantitative water T2 relaxometry should be considered complementary to subjectively rated fat-suppressed T2-weighted images in clinical practice.
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Affiliation(s)
- Noah Locher
- Centre for Neuromuscular Diseases, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Benedikt Wagner
- Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Fabian Balsiger
- Centre for Neuromuscular Diseases, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Olivier Scheidegger
- Centre for Neuromuscular Diseases, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
- Support Center for Advanced Neuroimaging (SCAN), Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
- Universitätsklinik für Neurologie, Inselspital, Freiburgstrasse, CH-3010, Bern, Switzerland.
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21
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Paoletti M, Diamanti L, Muzic SI, Ballante E, Solazzo F, Foppoli L, Deligianni X, Santini F, Figini S, Bergsland N, Pichiecchio A. Longitudinal Quantitative MRI Evaluation of Muscle Involvement in Amyotrophic Lateral Sclerosis. Front Neurol 2021; 12:749736. [PMID: 34899571 PMCID: PMC8651545 DOI: 10.3389/fneur.2021.749736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 10/11/2021] [Indexed: 01/05/2023] Open
Abstract
Background: Biomarkers of disease progression and outcome measures are still lacking for patients with amyotrophic lateral sclerosis (ALS). Muscle MRI can be a promising candidate to track longitudinal changes and to predict response to the therapy in clinical trials. Objective: Our aim is to apply quantitative muscle MRI in the evaluation of disease progression, focusing on thigh and leg muscles of patients with ALS, and to explore the correlation between radiological and clinical scores. Methods: We enrolled newly diagnosed patients with ALS, longitudinally scored using the ALS Functional Rating Scale-Revised (ALSFRS-R), who underwent a 3T muscle MRI protocol including a 6-point Dixon gradient-echo sequence and multi-echo turbo spin echo (TSE) T2-weighted sequence for quantification of fat fraction (FF), cross-sectional area (CSA), and water T2 (wT2). A total of 12 muscles of the thigh and six muscles of the leg were assessed by the manual drawing of 18 regions of interest (ROIs), for each side. A group of 11 age-matched healthy controls (HCs) was enrolled for comparison. Results: 15 patients (M/F 8/7; mean age 62.2 years old, range 29-79) diagnosed with possible (n = 2), probable (n = 12), or definite (n = 1) ALS were enrolled. Eleven patients presented spinal onset, whereas four of them had initial bulbar involvement. All patients performed MRI at T0, nine of them at T1, and seven of them at T2. At baseline, wT2 was significantly elevated in ALS subjects compared to HCs for several muscles of the thigh and mainly for leg muscles. By contrast, FF was elevated in few muscles, and mainly at the level of the thigh. The applied mixed effects model showed that FF increased significantly in the leg muscles over time (mainly in the triceps surae) and that wT2 decreased significantly in line with worsening in the leg subscore of ALSFRS-R, mainly at the leg level and in the anterior and medial compartment of the thigh. Conclusions: Quantitative MRI represents a non-invasive tool that is able to outline the trajectory of pathogenic modifications at the muscle level in ALS. In particular, wT2 was found to be increased early in the clinical history of ALS and also tended to decrease over time, also showing a positive correlation with leg subscore of ALSFRS-R.
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Affiliation(s)
- Matteo Paoletti
- Neuroradiology Department, Advanced Imaging and Radiomics Center, Istituto di Ricovero e Cura di Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy
| | - Luca Diamanti
- Neuro-Oncology Unit, Istituto di Ricovero e Cura di Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy
| | - Shaun I Muzic
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Department of Radiology, Fondazione Istituto di Ricovero e Cura di Carattere Scientifico (IRCCS) Policlinico San Matteo, Medical School University of Pavia, Pavia, Italy
| | - Elena Ballante
- Department of Mathematics, University of Pavia, Pavia, Italy.,BioData Science Center, Istituto di Ricovero e Cura di Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy
| | - Francesca Solazzo
- Neuroradiology Department, Advanced Imaging and Radiomics Center, Istituto di Ricovero e Cura di Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy
| | - Lia Foppoli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Xeni Deligianni
- Radiology/Division of Radiological Physics, University Hospital of Basel, Basel, Switzerland.,Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Francesco Santini
- Radiology/Division of Radiological Physics, University Hospital of Basel, Basel, Switzerland.,Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Silvia Figini
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy
| | - Niels Bergsland
- Department of Neurology, Buffalo Neuroimaging Analysis Center, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, United States.,IRCCS Fondazione Don Carlo Gnocchi Organizzazione non lucrativa di utilità sociale (ONLUS), Milan, Italy
| | - Anna Pichiecchio
- Neuroradiology Department, Advanced Imaging and Radiomics Center, Istituto di Ricovero e Cura di Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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22
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Deep learning for automatic segmentation of thigh and leg muscles. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2021; 35:467-483. [PMID: 34665370 PMCID: PMC9188532 DOI: 10.1007/s10334-021-00967-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/10/2021] [Accepted: 10/04/2021] [Indexed: 01/10/2023]
Abstract
Objective In this study we address the automatic segmentation of selected muscles of the thigh and leg through a supervised deep learning approach. Material and methods The application of quantitative imaging in neuromuscular diseases requires the availability of regions of interest (ROI) drawn on muscles to extract quantitative parameters. Up to now, manual drawing of ROIs has been considered the gold standard in clinical studies, with no clear and universally accepted standardized procedure for segmentation. Several automatic methods, based mainly on machine learning and deep learning algorithms, have recently been proposed to discriminate between skeletal muscle, bone, subcutaneous and intermuscular adipose tissue. We develop a supervised deep learning approach based on a unified framework for ROI segmentation. Results The proposed network generates segmentation maps with high accuracy, consisting in Dice Scores ranging from 0.89 to 0.95, with respect to “ground truth” manually segmented labelled images, also showing high average performance in both mild and severe cases of disease involvement (i.e. entity of fatty replacement). Discussion The presented results are promising and potentially translatable to different skeletal muscle groups and other MRI sequences with different contrast and resolution.
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23
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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: 18] [Impact Index Per Article: 4.5] [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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24
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Güttsches AK, Rehmann R, Schreiner A, Rohm M, Forsting J, Froeling M, Tegenthoff M, Vorgerd M, Schlaffke L. Quantitative Muscle-MRI Correlates with Histopathology in Skeletal Muscle Biopsies. J Neuromuscul Dis 2021; 8:669-678. [PMID: 33814461 DOI: 10.3233/jnd-210641] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Skeletal muscle biopsy is one of the gold standards in the diagnostic workup of muscle disorders. By histopathologic analysis, characteristic features like inflammatory cellular infiltrations, fat and collagen replacement of muscle tissue or structural defects of the myofibers can be detected. In the past years, novel quantitative MRI (qMRI) techniques have been developed to quantify tissue parameters, thus providing a non-invasive diagnostic tool in several myopathies. OBJECTIVE This proof-of-principle study was performed to validate the qMRI-techniques to skeletal muscle biopsy results. METHODS Ten patients who underwent skeletal muscle biopsy for diagnostic purposes were examined by qMRI. Fat fraction, water T2-time and diffusion parameters were measured in the muscle from which the biopsy was taken. The proportion of fat tissue, the severity of degenerative and inflammatory parameters and the amount of type 1- and type 2- muscle fibers were determined in all biopsy samples. The qMRI-data were then correlated to the histopathological findings. RESULTS The amount of fat tissue in skeletal muscle biopsy correlated significantly with the fat fraction derived from the Dixon sequence. The water T2-time, a parameter for tissue edema, correlated with the amount of vacuolar changes of myofibers and endomysial macrophages in the histopathologic analysis. No significant correlations were found for diffusion parameters. CONCLUSION In this proof-of-principle study, qMRI techniques were related to characteristic histopathologic features in neuromuscular disorders. The study provides the basis for further development of qMRI methods in the follow-up of patients with neuromuscular disorders, especially in the context of emerging treatment strategies.
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Affiliation(s)
- Anne-Katrin Güttsches
- Department of Neurology, Heimer Institute for Muscle Research, University Hospital Bergmannsheil, Ruhr University Bochum, Bochum, Germany
| | - Robert Rehmann
- Department of Neurology, Heimer Institute for Muscle Research, University Hospital Bergmannsheil, Ruhr University Bochum, Bochum, Germany
| | - Anja Schreiner
- Department of Neurology, Heimer Institute for Muscle Research, University Hospital Bergmannsheil, Ruhr University Bochum, Bochum, Germany
| | - Marlena Rohm
- Department of Neurology, Heimer Institute for Muscle Research, University Hospital Bergmannsheil, Ruhr University Bochum, Bochum, Germany
| | - Johannes Forsting
- Department of Neurology, Heimer Institute for Muscle Research, University Hospital Bergmannsheil, Ruhr University Bochum, Bochum, Germany
| | - Martijn Froeling
- Department of Radiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Martin Tegenthoff
- Department of Neurology, Heimer Institute for Muscle Research, University Hospital Bergmannsheil, Ruhr University Bochum, Bochum, Germany
| | - Matthias Vorgerd
- Department of Neurology, Heimer Institute for Muscle Research, University Hospital Bergmannsheil, Ruhr University Bochum, Bochum, Germany
| | - Lara Schlaffke
- Department of Neurology, Heimer Institute for Muscle Research, University Hospital Bergmannsheil, Ruhr University Bochum, Bochum, Germany
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25
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Savini G, Asteggiano C, Paoletti M, Parravicini S, Pezzotti E, Solazzo F, Muzic SI, Santini F, Deligianni X, Gardani A, Germani G, Farina LM, Bergsland N, Gandini Wheeler-Kingshott CAM, Berardinelli A, Bastianello S, Pichiecchio A. Pilot Study on Quantitative Cervical Cord and Muscular MRI in Spinal Muscular Atrophy: Promising Biomarkers of Disease Evolution and Treatment? Front Neurol 2021; 12:613834. [PMID: 33854470 PMCID: PMC8039452 DOI: 10.3389/fneur.2021.613834] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 02/15/2021] [Indexed: 12/11/2022] Open
Abstract
Introduction: Nusinersen is a recent promising therapy approved for the treatment of spinal muscular atrophy (SMA), a rare disease characterized by the degeneration of alpha motor neurons (αMN) in the spinal cord (SC) leading to progressive muscle atrophy and dysfunction. Muscle and cervical SC quantitative magnetic resonance imaging (qMRI) has never been used to monitor drug treatment in SMA. The aim of this pilot study is to investigate whether qMRI can provide useful biomarkers for monitoring treatment efficacy in SMA. Methods: Three adult SMA 3a patients under treatment with nusinersen underwent longitudinal clinical and qMRI examinations every 4 months from baseline to 21-month follow-up. The qMRI protocol aimed to quantify thigh muscle fat fraction (FF) and water-T2 (w-T2) and to characterize SC volumes and microstructure. Eleven healthy controls underwent the same SC protocol (single time point). We evaluated clinical and imaging outcomes of SMA patients longitudinally and compared SC data between groups transversally. Results: Patient motor function was stable, with only Patient 2 showing moderate improvements. Average muscle FF was already high at baseline (50%) and progressed over time (57%). w-T2 was also slightly higher than previously published data at baseline and slightly decreased over time. Cross-sectional area of the whole SC, gray matter (GM), and ventral horns (VHs) of Patients 1 and 3 were reduced compared to controls and remained stable over time, while GM and VHs areas of Patient 2 slightly increased. We found altered diffusion and magnetization transfer parameters in SC structures of SMA patients compared to controls, thus suggesting changes in tissue microstructure and myelin content. Conclusion: In this pilot study, we found a progression of FF in thigh muscles of SMA 3a patients during nusinersen therapy and a concurrent slight reduction of w-T2 over time. The SC qMRI analysis confirmed previous imaging and histopathological studies suggesting degeneration of αMN of the VHs, resulting in GM atrophy and demyelination. Our longitudinal data suggest that qMRI could represent a feasible technique for capturing microstructural changes induced by SMA in vivo and a candidate methodology for monitoring the effects of treatment, once replicated on a larger cohort.
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Affiliation(s)
- Giovanni Savini
- Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Carlo Asteggiano
- Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Matteo Paoletti
- Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Stefano Parravicini
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Elena Pezzotti
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Francesca Solazzo
- Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Shaun I. Muzic
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Francesco Santini
- Department of Radiology, Division of Radiological Physics, University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Xeni Deligianni
- Department of Radiology, Division of Radiological Physics, University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwil, Switzerland
| | - Alice Gardani
- Child Neuropsychiatry Unit, IRCCS Mondino Foundation, Pavia, Italy
| | - Giancarlo Germani
- Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Lisa M. Farina
- Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Buffalo, NY, United States
- IRCCS, Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, Russell Square, London, United Kingdom
- Brain Connectivity Research Unit, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Stefano Bastianello
- Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Anna Pichiecchio
- Advanced Imaging and Radiomics Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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26
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Nuñez-Peralta C, Montesinos P, Alonso-Jiménez A, Alonso-Pérez J, Reyes-Leiva D, Sánchez-González J, Llauger-Roselló J, Segovia S, Belmonte I, Pedrosa I, Martínez-Noguera A, Matellini-Mosca B, Walter G, Díaz-Manera J. Magnetization Transfer Ratio in Lower Limbs of Late Onset Pompe Patients Correlates With Intramuscular Fat Fraction and Muscle Function Tests. Front Neurol 2021; 12:634766. [PMID: 33796064 PMCID: PMC8009135 DOI: 10.3389/fneur.2021.634766] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 02/01/2021] [Indexed: 11/23/2022] Open
Abstract
Objectives: Magnetization transfer (MT) imaging exploits the interaction between bulk water protons and protons contained in macromolecules to induce signal changes through a special radiofrequency pulse. MT detects muscle damage in patients with neuromuscular conditions, such as limb-girdle muscular dystrophies or Charcot-Marie-Tooth disease, which are characterized by progressive fiber loss and replacement by fatty tissue. In Pompe disease, in which there is, in addition, an accumulation of glycogen inside the muscle fibers, MT has not been tested yet. Our aim is to estimate MT ratio (MTR) in the skeletal muscle of these patients and correlate it with intramuscular fat fraction (FF) and results of muscle function tests. Methods: We obtained two-point axial Dixon and Dixon-MT sequences of the right thigh on a 1.5 Teslas MRI scanner in 60 individuals, including 29 late onset Pompe disease patients, 2 patients with McArdle disease, and 29 age and sex matched healthy controls. FF and MTR were estimated. Muscle function using several muscle function tests, including quantification of muscle strength, timed test quality of life scales, conventional spirometry obtaining forced vital capacity while sitting and in the supine position, were assessed in all patients. Results: MTR was significantly lower in Pompe patients compared with controls (45.5 ± 8.5 vs. 51.7 ± 2.3, Student T-test, p < 0.05). There was a negative correlation between the MTR and FF muscles studied (correlation coefficient: −0.65, Spearman test: p < 0.05). MTR correlated with most of the muscle function test results. We analyzed if there was any difference in MTR values between Pompe patients and healthy controls in those muscles that did not have an increase in fat, a measure that could be related to the presence of glycogen in skeletal muscles, but we did not identify significant differences except in the adductor magnus muscle (48.4 ± 3.6 in Pompe vs. 51 ± 1.3 in healthy controls, Student T-test = 0.023). Conclusions: MTR is a sensitive tool to identify muscle loss in patients with Pompe disease and shows a good correlation with muscle function tests. Therefore, the MT technique can be useful in monitoring muscle degeneration in Pompe disease in clinical trials or natural history studies.
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Affiliation(s)
| | | | - Alicia Alonso-Jiménez
- Neuromuscular Reference Center, Neurology Department, University Hospital of Antwerp, Edegem, Belgium
| | - Jorge Alonso-Pérez
- Neuromuscular Disorders Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - David Reyes-Leiva
- Neuromuscular Disorders Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | | | | | - Sonia Segovia
- Neuromuscular Disorders Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain
| | - Izaskun Belmonte
- Rehabilitation Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Irene Pedrosa
- Rehabilitation Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | | | | | - Glenn Walter
- Department of Physiology and Functional Genomics, University of Florida, Gainesville, FL, United States
| | - Jordi Díaz-Manera
- Neuromuscular Disorders Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Madrid, Spain.,John Walton Muscular Dystrophy Research Center, Newcastle University, Newcastle upon Tyne, United Kingdom
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27
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Lehmann Urban D, Mohamed M, Ludolph AC, Kassubek J, Rosenbohm A. The value of qualitative muscle MRI in the diagnostic procedures of myopathies: a biopsy-controlled study in 191 patients. Ther Adv Neurol Disord 2021; 14:1756286420985256. [PMID: 33737953 PMCID: PMC7934066 DOI: 10.1177/1756286420985256] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Accepted: 12/11/2020] [Indexed: 11/17/2022] Open
Abstract
Background and aims The role of muscle magnetic resonance imaging (MRI) in the diagnostic procedures of myopathies is still controversially discussed. The current study was designed to analyze the status of qualitative muscle MRI, electromyography (EMG), and muscle biopsy in different cases of clinically suspected myopathy. Methods A total of 191 patients (male: n = 112, female: n = 79) with suspected myopathy who all received muscle MRI, EMG, and muscle biopsy for diagnostic reasons were studied, with the same location of biopsy and muscle MRI (either upper or lower extremities or paravertebral muscles). Muscle MRIs were analyzed using standard rating protocols by two different raters independently. Results Diagnostic findings according to biopsy results and genetic testing were as follow: non-inflammatory myopathy: n = 65, inflammatory myopathy (myositis): n = 51, neurogenic: n = 18, unspecific: n = 23, and normal: n = 34. The majority of patients showed myopathic changes in the EMG. Edema, atrophy, muscle fatty replacement, and contrast medium enhancement (CM uptake) in MRI were observed across all final diagnostic groups. Only 30% of patients from the myositis group (n = 15) showed CM uptake. Discussion and conclusion The study provides guidance in the definition of the impact of muscle MRI in suspected myopathy: despite being an important diagnostic tool, qualitative MRI findings could not distinguish different types of neuromuscular diagnostic groups in comparison with the gold standard histopathologic diagnosis and/or genetic testing. The results suggest that neither muscle edema nor gadolinium enhancement are able to secure a diagnosis of myositis. The current results do not support qualitative MRI as aiding in the diagnostic distinction of various myopathies. Quantitative muscle MRI is, however, useful in the diagnostic procedure of a suspected neuromuscular disease, especially with regard to assessing progression of a chronic myopathy by quantification of the degree of atrophy and fatty replacement and in exploring patterns of muscle group involvements in certain genetic myopathies.
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Affiliation(s)
| | - Mohamed Mohamed
- Department of Radiology/Neuroradiology, University and Rehabilitation Clinics Ulm, Ulm, Germany
| | | | - Jan Kassubek
- Department of Neurology, Ulm University, Ulm, Germany
| | - Angela Rosenbohm
- Department of Neurology, Ulm University, Oberer Eselsberg 45, Ulm, Germany
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28
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Sharafi A, Medina K, Zibetti MWV, Rao S, Cloos MA, Brown R, Regatte RR. Simultaneous T 1 , T 2 , and T 1ρ relaxation mapping of the lower leg muscle with MR fingerprinting. Magn Reson Med 2021; 86:372-381. [PMID: 33554369 DOI: 10.1002/mrm.28704] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 12/31/2020] [Accepted: 01/09/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE To develop a novel MR-fingerprinting (MRF) pulse sequence that is insensitive to B 1 + and B0 imperfections for simultaneous T1 , T2 , and T1ρ relaxation mapping. METHODS We implemented a totally balanced spin-lock (TB-SL) module to encode T1ρ relaxation into an existing MRF framework that encoded T1 and T2 . The spin-lock module used two 180° pulses with compensatory phases to reduce T1ρ sensitivity to B1 and B0 inhomogeneities. We compared T1ρ measured using TB-SL MRF in Bloch simulations, model agar phantoms, and in vivo experiments to those with a self-compensated spin-lock preparation module (SC-SL). The TB-SL MRF repeatability was evaluated in maps acquired in the lower leg skeletal muscle of 12 diabetic peripheral neuropathy patients, scanned two times each during visits separated by about 30 days. RESULTS The phantom relaxation times measured with TB-SL and SC-SL MRF were in good agreement with reference values in regions with low B1 inhomogeneities. Compared with SC-SL, TB-SL MRF showed in experiments greater robustness against severe B1 inhomogeneities and in Bloch simulations greater robustness against B1 and B0 . We measured with TB-SL MRF an average T1 = 950.1 ± 28.7 ms, T2 = 26.0 ± 1.2 ms, and T1ρ = 31.7 ± 3.2 ms in skeletal muscle across patients. Bland-Altman analysis demonstrated low bias between TB-SL and SC-SL MRF and between TB-SL MRF maps acquired in two visits. The coefficient of variation was less than 3% for all measurements. CONCLUSION The proposed TB-SL MRF sequence is fast and insensitive to B 1 + and B0 imperfections. It can simultaneously map T1 , T2 , T1ρ , and B 1 + in a single scan and can potentially be used to study muscle composition.
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Affiliation(s)
- Azadeh Sharafi
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Katherine Medina
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Marcelo W V Zibetti
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA
| | - Smita Rao
- Department of Physical Therapy, New York University, New York, New York, USA
| | - Martijn A Cloos
- Center of Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Ryan Brown
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.,Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA
| | - Ravinder R Regatte
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.,Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA
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29
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Felisaz PF, Colelli G, Ballante E, Solazzo F, Paoletti M, Germani G, Santini F, Deligianni X, Bergsland N, Monforte M, Tasca G, Ricci E, Bastianello S, Figini S, Pichiecchio A. Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy. Eur J Radiol 2020; 134:109460. [PMID: 33296803 DOI: 10.1016/j.ejrad.2020.109460] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/04/2020] [Accepted: 11/29/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE Quantitative MRI (qMRI) plays a crucial role for assessing disease progression and treatment response in neuromuscular disorders, but the required MRI sequences are not routinely available in every center. The aim of this study was to predict qMRI values of water T2 (wT2) and fat fraction (FF) from conventional MRI, using texture analysis and machine learning. METHOD Fourteen patients affected by Facioscapulohumeral muscular dystrophy were imaged at both thighs using conventional and quantitative MR sequences. Muscle FF and wT2 were calculated for each muscle of the thighs. Forty-seven texture features were extracted for each muscle on the images obtained with conventional MRI. Multiple machine learning regressors were trained to predict qMRI values from the texture analysis dataset. RESULTS Eight machine learning methods (linear, ridge and lasso regression, tree, random forest (RF), generalized additive model (GAM), k-nearest-neighbor (kNN) and support vector machine (SVM) provided mean absolute errors ranging from 0.110 to 0.133 for FF and 0.068 to 0.115 for wT2. The most accurate methods were RF, SVM and kNN to predict FF, and tree, RF and kNN to predict wT2. CONCLUSION This study demonstrates that it is possible to estimate with good accuracy qMRI parameters starting from texture analysis of conventional MRI.
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Affiliation(s)
- Paolo Florent Felisaz
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy; Department of Radiology, Desio Hospital, ASST Monza, Desio, Italy.
| | - Giulia Colelli
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy; Department of Mathematics, University of Pavia, Pavia, Italy
| | - Elena Ballante
- Department of Mathematics, University of Pavia, Pavia, Italy; BioData Science Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Francesca Solazzo
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy
| | - Matteo Paoletti
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy
| | - Giancarlo Germani
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy
| | - Francesco Santini
- Department of Radiology, Division of Radiological Physics, University Hospital Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Xeni Deligianni
- Department of Radiology, Division of Radiological Physics, University Hospital Basel, Basel, Switzerland; Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Niels Bergsland
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, Buffalo, NY, USA; IRCCS, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Mauro Monforte
- Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Giorgio Tasca
- Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Enzo Ricci
- Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Stefano Bastianello
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy
| | - Silvia Figini
- Department of Political and Social Sciences, University of Pavia, Pavia, PV, Italy
| | - Anna Pichiecchio
- Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, PV, Italy
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30
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Díaz-Manera J, Walter G, Straub V. Skeletal muscle magnetic resonance imaging in Pompe disease. Muscle Nerve 2020; 63:640-650. [PMID: 33155691 DOI: 10.1002/mus.27099] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 10/11/2020] [Accepted: 10/18/2020] [Indexed: 12/12/2022]
Abstract
Pompe disease is characterized by a deficiency of acid alpha-glucosidase that results in muscle weakness and a variable degree of disability. There is an approved therapy based on enzymatic replacement that has modified disease progression. Several reports describing muscle magnetic resonance imaging (MRI) features of Pompe patients have been published. Most of the studies have focused on late-onset Pompe disease (LOPD) and identified a characteristic pattern of muscle involvement useful for the diagnosis. In addition, quantitative MRI studies have shown a progressive increase in fat in skeletal muscles of LOPD over time and they are increasingly considered a good tool to monitor progression of the disease. The studies performed in infantile-onset Pompe disease patients have shown less consistent changes. Other more sophisticated muscle MRI sequences, such as diffusion tensor imaging or glycogen spectroscopy, have also been used in Pompe patients and have shown promising results.
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Affiliation(s)
- Jordi Díaz-Manera
- John Walton Muscular Dystrophy Research Center, Newcastle University Translational and Clinical Research Institute, Newcastle upon Tyne, UK.,Neuromuscular Disorders Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.,Centro de Investigación Biomédica en Enfermedades Raras, Barcelona, Spain
| | - Glenn Walter
- Department of Physiology and Functional Genomics, University of Florida, Gainesville, Florida, USA
| | - Volker Straub
- John Walton Muscular Dystrophy Research Center, Newcastle University Translational and Clinical Research Institute, Newcastle upon Tyne, UK
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Weber MA, Nagel AM, Kan HE, Wattjes MP. Quantitative Imaging in Muscle Diseases with Focus on Non-proton MRI and Other Advanced MRI Techniques. Semin Musculoskelet Radiol 2020; 24:402-412. [PMID: 32992368 DOI: 10.1055/s-0040-1712955] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The role of neuromuscular imaging in the diagnosis of inherited and acquired muscle diseases has gained clinical relevance. In particular, magnetic resonance imaging (MRI), especially whole-body applications, is increasingly being used for the diagnosis and monitoring of disease progression. In addition, they are considered as a powerful outcome measure in clinical trials. Because many muscle diseases have a distinct muscle involvement pattern, whole-body imaging can be of diagnostic value by identifying this pattern and thus narrowing the differential diagnosis and supporting the clinical diagnosis. In addition, more advanced MRI applications including non-proton MRI, diffusion tensor imaging, perfusion MRI, T2 mapping, and magnetic resonance spectroscopy provide deeper insights into muscle pathophysiology beyond the mere detection of fatty degeneration and/or muscle edema. In this review article, we present and discuss recent data on these quantitative MRI techniques in muscle diseases, with a particular focus on non-proton imaging techniques.
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Affiliation(s)
- Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Rostock, Germany
| | - Armin M Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.,Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermien E Kan
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.,Duchenne Center, The Netherlands
| | - Mike P Wattjes
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
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Balsiger F, Jungo A, Scheidegger O, Carlier PG, Reyes M, Marty B. Spatially regularized parametric map reconstruction for fast magnetic resonance fingerprinting. Med Image Anal 2020; 64:101741. [DOI: 10.1016/j.media.2020.101741] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 05/22/2020] [Accepted: 05/27/2020] [Indexed: 12/13/2022]
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Muscle MRI in two SMA patients on nusinersen treatment: A two years follow-up. J Neurol Sci 2020; 417:117067. [PMID: 32745721 PMCID: PMC7388822 DOI: 10.1016/j.jns.2020.117067] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 07/20/2020] [Accepted: 07/27/2020] [Indexed: 12/21/2022]
Abstract
Introduction The effects of nusinersen in adults with SMA rely on neuromotor function scales and qualitative assessments. There are limited clinical or imaging data on muscle changes over time. Methods Two adult SMA patients underwent clinical assessments including measures of upper and lower limb function with Revised Upper Limb Module (RULM) and Hammersmith Function Motor Scale Expanded (HFMSE); both patients were also studied with whole-body muscle MRI (T1-weighted and Diffusion Tensor Imaging/DTI sequences), at baseline and after 10 and 24 months from the beginning of treatment with nusinersen. Results After two years of treatment, HFMSE and RULM scores were stable in both patients. DTI sequences revealed an increased number, length and organization of muscle fiber tracks, and Fractional Anisotropy (FA) values showed a significant reduction after 10 and 24 months from baseline, in their corresponding maps. Discussion Muscle DTI imaging seems to play an interesting role to monitor treatment effects over time in adult SMA patients. Nusinersen treatment has created great expectations in older SMA patients having long-lasting muscular atrophy. DTI is a very sensitive technique to identify small changes in muscle architecture. DTI shows that nusinersen treatment may have a positive effect on size, length and organization of fiber tracts.
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Previtali SC, Gidaro T, Díaz-Manera J, Zambon A, Carnesecchi S, Roux-Lombard P, Spitali P, Signorelli M, Szigyarto CAK, Johansson C, Gray J, Labolle D, Porte Thomé F, Pitchforth J, Domingos J, Muntoni F. Rimeporide as a first- in-class NHE-1 inhibitor: Results of a phase Ib trial in young patients with Duchenne Muscular Dystrophy. Pharmacol Res 2020; 159:104999. [PMID: 32535224 PMCID: PMC7482441 DOI: 10.1016/j.phrs.2020.104999] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 06/02/2020] [Accepted: 06/04/2020] [Indexed: 12/23/2022]
Abstract
Rimeporide, a first-in-class sodium/proton exchanger Type 1 inhibitor (NHE-1 inhibitor) is repositioned by EspeRare for patients with Duchenne Muscular Dystrophy (DMD). Historically, NHE-1 inhibitors were developed for cardiac therapeutic interventions. There is considerable overlap in the pathophysiological mechanisms in Congestive Heart Failure (CHF) and in cardiomyopathy in DMD, therefore NHE-1 inhibition could be a promising pharmacological approach to the cardiac dysfunctions observed in DMD. Extensive preclinical data was collected in various animal models including dystrophin-deficient (mdx) mice to characterise Rimeporide’s anti-fibrotic and anti-inflammatory properties and there is evidence that NHE-1 inhibitors could play a significant role in modifying DMD cardiac and also skeletal pathologies, as the NHE-1 isoform is ubiquitous. We report here the first study with Rimeporide in DMD patients. This 4-week treatment, open label phase Ib, multiple oral ascending dose study, enrolled 20 ambulant boys with DMD (6–11 years), with outcomes including safety, pharmacokinetic (PK) and pharmacodynamic (PD) biomarkers. Rimeporide was safe and well-tolerated at all doses. PK evaluations showed that Rimeporide was well absorbed orally reaching pharmacological concentrations from the lowest dose, with exposure increasing linearly with dose and with no evidence of accumulation upon repeated dosing. Exploratory PD biomarkers showed positive effect upon a 4-week treatment, supporting its therapeutic potential in patients with DMD, primarily as a cardioprotective treatment, and provide rationale for further efficacy studies.
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Affiliation(s)
- Stefano C Previtali
- IRCCS San Raffaele Scientific Institute, Department of Neurology and INSPE, Milan, Italy
| | - Teresa Gidaro
- Institute of Myology, Hopital Trousseau, I- Motion, Paris, France
| | - Jordi Díaz-Manera
- Hospital de la Santa Creu i Sant Pau de Barcelona Servei de Neurologia, Barcelona, Spain; Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Spain
| | - Alberto Zambon
- IRCCS San Raffaele Scientific Institute, Department of Neurology and INSPE, Milan, Italy
| | | | - Pascale Roux-Lombard
- Geneva University Hospital (HUG), Immunology and Allergology Department, Geneva, Switzerland
| | | | | | | | - Camilla Johansson
- Science for Life Laboratory, Department of Protein Science, Division of Systems Biology, Solna, Sweden
| | | | | | | | - Jacqueline Pitchforth
- UCL Great Ormond Street Institute of Child Health & Great Ormond Street Hospital Dubowitz Neuromuscular Centre, London, UK
| | - Joana Domingos
- UCL Great Ormond Street Institute of Child Health & Great Ormond Street Hospital Dubowitz Neuromuscular Centre, London, UK
| | - Francesco Muntoni
- UCL Great Ormond Street Institute of Child Health & Great Ormond Street Hospital Dubowitz Neuromuscular Centre, London, UK; NIHR Great Ormond Street Hospital Biomedical Research Centre, Great Ormond Street Institute of Child Health, Great Ormond Street Hospital Trust, University College London, London, UK
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Song J, Fu J, Ma M, Pang M, Li G, Gao L, Zhang J. Lower limb muscle magnetic resonance imaging in Chinese patients with myotonic dystrophy type 1. Neurol Res 2020; 42:170-177. [PMID: 31951783 DOI: 10.1080/01616412.2020.1716494] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Objectives: Muscle magnetic resonance imaging (MRI) is a reliable noninvasion tool for detecting muscle abnormalities of myopathies. This study aimed to investigate the MRI features of lower limb muscles in Chinese patients with myotonic dystrophy type 1 (DM1) and to evaluate the correlation between clinical factors and muscle MRI.Methods: We retrospectively reviewed the medical records and lower limb muscle MRI in 24 Chinese DM1 patients. Muscular Impairment Rating Scale (MIRS) was used to assess the clinical muscular impairment. Modified Mercuri's scale was used to assess the degree of fatty infiltration. Spearman rank correlation test was used to analyze the relationship between fatty degeneration score with age, age of onset, disease duration, MIRS grading and creatinine kinase (CK) level.Results: Fatty infiltration was found in 22 of 24 DM1 patients and 8 patients were asymmetrically affected. The medial gastrocnemius was the most affected muscle, followed by soleus and tibialis anterior muscles in lower legs. At thigh level, the anterior compartment was usually the most affected region with the rectus femoris relatively spared. 79.2% of DM1 patients had edema in lower limb muscles. The total mean score of fatty infiltration correlated with MIRS grading, age and disease duration but did not correlate with the age of onset or CK level.Conclusion: Here, we found fatty infiltration present in most Chinese DM1 patients with a selective involvement pattern. There is a correlation between the total mean score of fatty infiltration and MIRS grading, age and disease duration.
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Affiliation(s)
- Jia Song
- Department of Neurology, People's Hospital of Zhengzhou University, Henan Provincial People's Hospital, Henan, China
| | - Jun Fu
- Department of Neurology, People's Hospital of Zhengzhou University, Henan Provincial People's Hospital, Henan, China
| | - Mingming Ma
- Department of Neurology, People's Hospital of Zhengzhou University, Henan Provincial People's Hospital, Henan, China
| | - Mi Pang
- Department of Neurology, People's Hospital of Zhengzhou University, Henan Provincial People's Hospital, Henan, China
| | - Gang Li
- Department of Neurology, People's Hospital of Zhengzhou University, Henan Provincial People's Hospital, Henan, China
| | - Li Gao
- Department of Radiology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Henan, China
| | - Jiewen Zhang
- Department of Neurology, People's Hospital of Zhengzhou University, Henan Provincial People's Hospital, Henan, China
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Ropars J, Gravot F, Ben Salem D, Rousseau F, Brochard S, Pons C. Muscle MRI: A biomarker of disease severity in Duchenne muscular dystrophy? A systematic review. Neurology 2019; 94:117-133. [PMID: 31892637 DOI: 10.1212/wnl.0000000000008811] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Accepted: 10/29/2019] [Indexed: 12/22/2022] Open
Abstract
OBJECTIVE To assess the evidence of a relationship between muscle MRI and disease severity in Duchenne muscular dystrophy (DMD). METHODS We conducted a systematic review of studies that analyzed correlations between MRI measurements and motor function in patients with DMD. PubMed, Cochrane, Scopus, and Web of Science were searched using relevant keywords and inclusion/exclusion criteria (January 1, 1990-January 31, 2019). We evaluated article quality using the Joanna Briggs Institute scale. Information regarding the samples included, muscles evaluated, MRI protocols and motor function tests used was collected from each article. Correlations between MRI measurements and motor function were reported exhaustively. RESULTS Seventeen of 1,629 studies identified were included. Most patients included were ambulant with a mean age of 8.9 years. Most studies evaluated lower limb muscles. Moderate to excellent correlations were found between MRI measurements and motor function. The strongest correlations were found for quantitative MRI measurements such as fat fraction or mean T2. Correlations were stronger for lower leg muscles such as soleus. One longitudinal study reported that changes in soleus mean T2 were highly correlated with changes in motor function. CONCLUSION The findings of this systematic review showed that MRI measurements can be used as biomarkers of disease severity in ambulant patients with DMD. Guidelines are proposed to help clinicians choose the most appropriate MRI measurements and muscles to evaluate. Studies exploring upper limb muscles, other stages of the disease, and sensitivity of measurements to change are needed.
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Affiliation(s)
- Juliette Ropars
- From the Department of Pediatrics (J.R., F.G.), CHU Brest, Brest, France; Neuromuscular Center (J.R., S.B., C.P), Brest, France; Laboratoire du Traitement de l'Information Médicale (J.R., D.B.S., F.R, S.B., C.P.), LaTIM INSERM UMR1101, Brest, France; Department of Radiology (D.B.S.), CHU Brest, Brest, France; Institut Mines Télécom Atlantiques (F.R), Brest, France; and Department of Pediatric Physical and Medical Rehabilitation (S.B., C.P.), Fondation ILDYS, Brest, France.
| | - France Gravot
- From the Department of Pediatrics (J.R., F.G.), CHU Brest, Brest, France; Neuromuscular Center (J.R., S.B., C.P), Brest, France; Laboratoire du Traitement de l'Information Médicale (J.R., D.B.S., F.R, S.B., C.P.), LaTIM INSERM UMR1101, Brest, France; Department of Radiology (D.B.S.), CHU Brest, Brest, France; Institut Mines Télécom Atlantiques (F.R), Brest, France; and Department of Pediatric Physical and Medical Rehabilitation (S.B., C.P.), Fondation ILDYS, Brest, France
| | - Douraied Ben Salem
- From the Department of Pediatrics (J.R., F.G.), CHU Brest, Brest, France; Neuromuscular Center (J.R., S.B., C.P), Brest, France; Laboratoire du Traitement de l'Information Médicale (J.R., D.B.S., F.R, S.B., C.P.), LaTIM INSERM UMR1101, Brest, France; Department of Radiology (D.B.S.), CHU Brest, Brest, France; Institut Mines Télécom Atlantiques (F.R), Brest, France; and Department of Pediatric Physical and Medical Rehabilitation (S.B., C.P.), Fondation ILDYS, Brest, France
| | - François Rousseau
- From the Department of Pediatrics (J.R., F.G.), CHU Brest, Brest, France; Neuromuscular Center (J.R., S.B., C.P), Brest, France; Laboratoire du Traitement de l'Information Médicale (J.R., D.B.S., F.R, S.B., C.P.), LaTIM INSERM UMR1101, Brest, France; Department of Radiology (D.B.S.), CHU Brest, Brest, France; Institut Mines Télécom Atlantiques (F.R), Brest, France; and Department of Pediatric Physical and Medical Rehabilitation (S.B., C.P.), Fondation ILDYS, Brest, France
| | - Sylvain Brochard
- From the Department of Pediatrics (J.R., F.G.), CHU Brest, Brest, France; Neuromuscular Center (J.R., S.B., C.P), Brest, France; Laboratoire du Traitement de l'Information Médicale (J.R., D.B.S., F.R, S.B., C.P.), LaTIM INSERM UMR1101, Brest, France; Department of Radiology (D.B.S.), CHU Brest, Brest, France; Institut Mines Télécom Atlantiques (F.R), Brest, France; and Department of Pediatric Physical and Medical Rehabilitation (S.B., C.P.), Fondation ILDYS, Brest, France
| | - Christelle Pons
- From the Department of Pediatrics (J.R., F.G.), CHU Brest, Brest, France; Neuromuscular Center (J.R., S.B., C.P), Brest, France; Laboratoire du Traitement de l'Information Médicale (J.R., D.B.S., F.R, S.B., C.P.), LaTIM INSERM UMR1101, Brest, France; Department of Radiology (D.B.S.), CHU Brest, Brest, France; Institut Mines Télécom Atlantiques (F.R), Brest, France; and Department of Pediatric Physical and Medical Rehabilitation (S.B., C.P.), Fondation ILDYS, Brest, France
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MRI patterns of muscle involvement in type 2 and 3 spinal muscular atrophy patients. J Neurol 2019; 267:898-912. [DOI: 10.1007/s00415-019-09646-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/05/2019] [Accepted: 11/18/2019] [Indexed: 12/17/2022]
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