Quantitative MRI can detect subclinical disease progression in muscular dystrophy

Oculopharyngeal muscular dystrophy (OPMD) is a rare autosomal dominant muscular dystrophy with late onset and slow progression. The aim of this study was to compare different methods of quantitative MRI in the follow-up of OPMD to semiquantitative evaluation of MRI images and to functional parameters. We examined 8patients with genetically confirmed OPMD and 5healthy volunteers twice at an interval of 13months. Motor function measurements (MFM) were assessed. Imaging at 1.5T (Siemens Magnetom Avanto) comprised two axial slice groups at the largest diameter of thigh and calf and included T1w TSE, 2-point Dixon for muscular fat fraction (MFF) and a multi-contrast TSE sequence to calculate quantitative T2 values. T1 images were analyzed using Fischer's semiquantitative 5-point (0-4) scale. MFM and visual scores showed no significant difference over the study period. Overall T2 values increased in patients over the study period from 49.4 to 51.6ms, MFF increased from 19.2 to 20.7%. Neither T2 values nor MFF increased in controls. Changes in T2 correlated with the time interval between examinations (r 2=0.42). In this small pilot trial, it was shown that quantitative muscle MRI can detect subclinical changes in patients with OPMD. Quantitative MRI might, therefore, be a useful tool for monitoring disease progression in future therapeutic trial

Muscular involvement assessed by MRI correlates to motor function measurement values in oculopharyngeal muscular dystrophy

Oculopharyngeal muscular dystrophy (OPMD) is a progressive skeletal muscle dystrophy characterized by ptosis, dysphagia, and upper and lower extremity weakness. We examined eight genetically confirmed OPMD patients to detect a MRI pattern and correlate muscle involvement, with validated clinical evaluation methods. Physical assessment was performed using the Motor Function Measurement (MFM) scale. We imaged the lower extremities on a 1.5T scanner. Fatty replacement was graded on a 4-point visual scale. We found prominent affection of the adductor and hamstring muscles in the thigh, and soleus and gastrocnemius muscles in the lower leg. The MFM assessment showed relative mild clinical impairment, mostly affecting standing and transfers, while distal motor capacity was hardly affected. We observed a high (negative) correlation between the validated clinical scores and our visual imaging scores suggesting that quantitative and more objective muscle MRI might serve as outcome measure for clinical trials in muscular dystrophie

Validation of automated artificial intelligence segmentation of optical coherence tomography images

PURPOSE To benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera. METHODS A convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results. RESULTS The CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders' compartmentalization was higher than the mean score for intra-grader group comparison. CONCLUSION The proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders

Quantification of fat infiltration in oculopharyngeal muscular dystrophy : comparison of three MR imaging methods

To analyze and compare three quantitative MRI methods to determine the degree of muscle involvement in oculopharyngeal muscular dystrophy (OPMD)

Muscular involvement assessed by MRI correlates to motor function measurement values in oculopharyngeal muscular dystrophy

Oculopharyngeal muscular dystrophy (OPMD) is a progressive skeletal muscle dystrophy characterized by ptosis, dysphagia, and upper and lower extremity weakness. We examined eight genetically confirmed OPMD patients to detect a MRI pattern and correlate muscle involvement, with validated clinical evaluation methods. Physical assessment was performed using the Motor Function Measurement (MFM) scale. We imaged the lower extremities on a 1.5 T scanner. Fatty replacement was graded on a 4-point visual scale. We found prominent affection of the adductor and hamstring muscles in the thigh, and soleus and gastrocnemius muscles in the lower leg. The MFM assessment showed relative mild clinical impairment, mostly affecting standing and transfers, while distal motor capacity was hardly affected. We observed a high (negative) correlation between the validated clinical scores and our visual imaging scores suggesting that quantitative and more objective muscle MRI might serve as outcome measure for clinical trials in muscular dystrophies

Validation of automated artificial intelligence segmentation of optical coherence tomography images.

PurposeTo benchmark the human and machine performance of spectral-domain (SD) and swept-source (SS) optical coherence tomography (OCT) image segmentation, i.e., pixel-wise classification, for the compartments vitreous, retina, choroid, sclera.MethodsA convolutional neural network (CNN) was trained on OCT B-scan images annotated by a senior ground truth expert retina specialist to segment the posterior eye compartments. Independent benchmark data sets (30 SDOCT and 30 SSOCT) were manually segmented by three classes of graders with varying levels of ophthalmic proficiencies. Nine graders contributed to benchmark an additional 60 images in three consecutive runs. Inter-human and intra-human class agreement was measured and compared to the CNN results.ResultsThe CNN training data consisted of a total of 6210 manually segmented images derived from 2070 B-scans (1046 SDOCT and 1024 SSOCT; 630 C-Scans). The CNN segmentation revealed a high agreement with all grader groups. For all compartments and groups, the mean Intersection over Union (IOU) score of CNN compartmentalization versus group graders' compartmentalization was higher than the mean score for intra-grader group comparison.ConclusionThe proposed deep learning segmentation algorithm (CNN) for automated eye compartment segmentation in OCT B-scans (SDOCT and SSOCT) is on par with manual segmentations by human graders