Learning Transversus Abdominis Activation in Older Adults with Chronic Low Back Pain Using an Ultrasound-Based Wearable: A Randomized Controlled Pilot Study.

: Chronic low back pain (CLBP) is prevalent among older adults and leads to significant functional limitations and reduced quality of life. Segmental stabilization exercises (SSEs) are commonly used to treat CLBP, but the selective activation of deep abdominal muscles during these exercises can be challenging for patients. To support muscle activation, physiotherapists use biofeedback methods such as palpation and ultrasound imaging.

Contraction assessment of abdominal muscles using automated segmentation designed for wearable ultrasound applications.

Purpose: Wearable ultrasound devices can be used to continuously monitor muscle activity. One possible application is to provide real-time feedback during physiotherapy, to show a patient whether an exercise is performed correctly. Algorithms which automatically analyze the data can be of importance to overcome the need for manual assessment and annotations and speed up evaluations especially when considering real-time video sequences.

MR-CT multi-atlas registration guided by fully automated brain structure segmentation with CNNs.

Purpose: Computed tomography (CT) is widely used to identify anomalies in brain tissues because their localization is important for diagnosis and therapy planning. Due to the insufficient soft tissue contrast of CT, the division of the brain into anatomical meaningful regions is challenging and is commonly done with magnetic resonance imaging (MRI).

Methods: We propose a multi-atlas registration approach to propagate anatomical information from a standard MRI brain atlas to CT scans.

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets.

Deep learning for ultrasound image formation is rapidly garnering research support and attention, quickly rising as the latest frontier in ultrasound image formation, with much promise to balance both image quality and display speed. Despite this promise, one challenge with identifying optimal solutions is the absence of unified evaluation methods and datasets that are not specific to a single research group. This article introduces the largest known international database of ultrasound channel data and describes the associated evaluation methods that were initially developed for the challenge on ultrasound beamforming with deep learning (CUBDL), which was offered as a component of the 2020 IEEE International Ultrasonics Symposium.

Ultrasonography Grading of Internal Carotid Artery Disease: Multiparametric German Society of Ultrasound in Medicine (DEGUM) versus Society of Radiologists in Ultrasound (SRU) Consensus Criteria.

Purpose:  We sought to determine the diagnostic agreement between the revised ultrasonography approach by the German Society of Ultrasound in Medicine (DEGUM) and the established Society of Radiologists in Ultrasound (SRU) consensus criteria for the grading of carotid artery disease.

Materials And Methods:  Post-hoc analysis of a prospective multicenter study, in which patients underwent ultrasonography and digital subtraction angiography (DSA) of carotid arteries for validation of the DEGUM approach. According to DEGUM and SRU ultrasonography criteria, carotid arteries were independently categorized into clinically relevant NASCET strata (normal, mild [1-49 %], moderate [50-69 %], severe [70-99 %], occlusion).