Inspiration for the Future: The Role of Inspiratory Muscle Training in Cystic Fibrosis

Cystic fibrosis (CF) is an inherited, multi-system, life-limiting disease characterized by a progressive decline in lung function, which accounts for the majority of CF-related morbidity and mortality. Inspiratory muscle training (IMT) has been proposed as a rehabilitative strategy to treat respiratory impairments associated with CF. However, despite evidence of therapeutic benefits in healthy and other clinical populations, the routine application of IMT in CF can neither be supported nor refuted due to the paucity of methodologically rigorous research. Specifically, the interpretation of available studies regarding the efficacy of IMT in CF is hampered by methodological threats to internal and external validity. As such, it is important to highlight the inherent risk of bias that differences in patient characteristics, IMT protocols, and outcome measurements present when synthesizing this literature prior to making final clinical judgments. Future studies are required to identify the characteristics of individuals who may respond to IMT and determine whether the controlled application of IMT can elicit meaningful improvements in physiological and patient-centered clinical outcomes. Given the equivocal evidence regarding its efficacy, IMT should be utilized on a case-by-case basis with sound clinical reasoning, rather than simply dismissed, until a rigorous evidence-based consensus has been reached

Probabilistic multi-shape segmentation of knee extensor and flexor muscles

Abstract. Patients with chronic obstructive pulmonary disease (COPD) often exhibit skeletal muscle weakness in lower limbs. Analysis of the shapes and sizes of these muscles can lead to more effective therapy. Unfortunately, segmenting these muscles from one another is a challenging task due to a lack of image information in many areas. We present a fully automatic segmentation method that overcomes the inherent difficulties of this problem to accurately segment the different muscles. Our method enforces a multi-region shape prior on the segmentation to ensure feasibility and provides an energy minimizing probabilistic segmentation that indicates areas of uncertainty. Our experiments on 3D MRI datasets yield an average Dice similarity coefficient of 0.92 ± 0.03 with the ground truth.

Test-retest reliability of automated whole body and compartmental muscle volume measurements on a wide bore 3T MR system

To measure the test-retest reproducibility of an automated system for quantifying whole body and compartmental muscle volumes using wide bore 3 T MRI