Importance of neural network complexity for the automatic segmentation of individual thigh muscles in MRI images from patients with neuromuscular diseases.

Objective: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.

Cyclosporine A Delays the Terminal Disease Stage in the Tfam KO Mitochondrial Myopathy Mouse Model Without Improving Mitochondrial Energy Production.

Introduction And Aims: Mitochondrial myopathies are rare genetic disorders for which no effective treatment exists. We previously showed that the pharmacological cyclophilin inhibitor cyclosporine A (CsA) extends the lifespan of fast-twitch skeletal muscle-specific mitochondrial transcription factor A knockout (Tfam KO) mice, lacking the ability to transcribe mitochondrial DNA and displaying lethal mitochondrial myopathy. Our present aim was to assess whether the positive effect of CsA was associated with improved in vivo mitochondrial energy production.

Towards a better understanding of abdominal wall biomechanics: In vivo relationship between dynamic intra-abdominal pressure and magnetic resonance imaging measurements.

Background: In vivo mechanical behaviour of the abdominal wall has been poorly characterised and important details are missing regarding the occurrence and post-operative recurrence rate of hernias which can be as high as 30 %. This study aimed to assess the correlation between abdominal wall displacement and intra-abdominal pressure, as well as abdominal compliance.

Methods: Eighteen healthy participants performed audio-guided passive (breathing) and active (coughing, Valsalva maneuver) exercises.

Quantitative muscle MRI combined with AI-based segmentation as a follow-up biomarker for ATTRv patients: A longitudinal pilot study.

Background And Objectives: Intramuscular fat fraction (FF), assessed using quantitative MRI (qMRI), has emerged as a promising biomarker for hereditary transthyretin amyloidosis (ATTRv) patients. Currently, the main drawbacks to its use in future therapeutic trials are its sensitivity to change over a short period of time and the time-consuming manual segmentation step to extract quantitative data. This pilot study aimed to demonstrate the suitability of an Artificial Intelligence-based (AI) segmentation technique to assess disease progression in a real-life cohort of ATTRv patients over 1 year.