"Triple low" free-breathing CTPA protocol for patients with dyspnoea.

AIM: To assess the performance of a "triple-low" free-breathing protocol for computed tomography pulmonary angiography (CTPA) evaluated on patients with dyspnoea and suspected pulmonary embolism and discuss its application in routine clinical practice for the study of the pulmonary parenchyma and vasculature. MATERIAL AND METHODS: This study was conducted on a selected group of dyspnoeic patients referred for CTPA. The protocol was designed using fast free-breathing acquisition and a small, fixed volume (35 ml) of contrast agent in order to achieve a low-exposure dose. For each examination, radiodensity of the pulmonary trunk and ascending aorta, and the dose-length product (DLP) were recorded. A qualitative analysis was performed of pulmonary arterial enhancement and the pulmonary parenchyma. RESULTS: This study included 134 patients. Contrast enhancement of the pulmonary arteries (409 ± 159 HU) was systematically >250 HU. The duration of acquisition ranged from 0.9 to 1.3 seconds for free-breathing imaging. The mean DLP was in the range of low-dose chest CT acquisitions (145 ± 73 mGy·cm). The analysis was deemed optimal in 90% (120/134) of cases for the pulmonary parenchyma. Sixty-nine per cent (92/134) of cases demonstrated homogeneous enhancement of the pulmonary arteries to the subsegmental level. Only 6% (8/134) of examinations were considered uninterpretable. CONCLUSION: The present "triple-low" CTPA protocol allows convenient analysis of the pulmonary parenchyma and arteries without hindrance by respiratory motion artefacts in dyspnoeic patients.

Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment.

PURPOSE: The purpose of this study was to build and train a deep convolutional neural networks (CNN) algorithm to segment muscular body mass (MBM) to predict muscular surface from a two-dimensional axial computed tomography (CT) slice through L3 vertebra. MATERIALS AND METHODS: An ensemble of 15 deep learning models with a two-dimensional U-net architecture with a 4-level depth and 18 initial filters were trained to segment MBM. The muscular surface values were computed from the predicted masks and corrected with the algorithm's estimated bias. Resulting mask prediction and surface prediction were assessed using Dice similarity coefficient (DSC) and root mean squared error (RMSE) scores respectively using ground truth masks as standards of reference. RESULTS: A total of 1025 individual CT slices were used for training and validation and 500 additional axial CT slices were used for testing. The obtained mean DSC and RMSE on the test set were 0.97 and 3.7 cm2 respectively. CONCLUSION: Deep learning methods using convolutional neural networks algorithm enable a robust and automated extraction of CT derived MBM for sarcopenia assessment, which could be implemented in a clinical workflow.

Three artificial intelligence data challenges based on CT and MRI.

PURPOSE: The second edition of the artificial intelligence (AI) data challenge was organized by the French Society of Radiology with the aim to: (i), work on relevant public health issues; (ii), build large, multicentre, high quality databases; and (iii), include three-dimensional (3D) information and prognostic questions. MATERIALS AND METHODS: Relevant clinical questions were proposed by French subspecialty colleges of radiology. Their feasibility was assessed by experts in the field of AI. A dedicated platform was set up for inclusion centers to safely upload their anonymized examinations in compliance with general data protection regulation. The quality of the database was checked by experts weekly with annotations performed by radiologists. Multidisciplinary teams competed between September 11th and October 13th 2019. RESULTS: Three questions were selected using different imaging and evaluation modalities, including: pulmonary nodule detection and classification from 3D computed tomography (CT), prediction of expanded disability status scale in multiple sclerosis using 3D magnetic resonance imaging (MRI) and segmentation of muscular surface for sarcopenia estimation from two-dimensional CT. A total of 4347 examinations were gathered of which only 6% were excluded. Three independent databases from 24 individual centers were created. A total of 143 participants were split into 20 multidisciplinary teams. CONCLUSION: Three data challenges with over 1200 general data protection regulation compliant CT or MRI examinations each were organized. Future challenges should be made with more complex situations combining histopathological or genetic information to resemble real life situations faced by radiologists in routine practice.

Smooth muscle of the male pelvic floor: An anatomic study.

Knowledge of the anatomy of the male pelvic floor is important to avoid damaging the pelvic floor muscles during surgery. We set out to explore the structure and innervation of the smooth muscle (SM) of the whole pelvic floor using male fetuses. We removed en-bloc the entire pelvis of three male fetuses. The specimens were serially sectioned before being stained with Masson's trichrome and hematoxylin and eosin, and immunostained for SMs, and somatic, adrenergic, sensory and nitrergic nerve fibers. Slides were digitized for three-dimensional reconstruction. We individualized a middle compartment that contains SM cells. This compartment is in close relation with the levator ani muscle (LAM), rectum, and urethra. We describe a posterior part of the middle compartment posterior to the rectal wall and an anterior part anterior to the rectal wall. The anterior part is split into (1) a centro-levator area of SM cells localized between the right and left LAM, (2) an endo-levator area that upholsters the internal aspect of the LAM, and (3) an infra-levator area below the LAM. All these areas are innervated by autonomic nerves coming from the inferior hypogastric plexus. The core and the infra-levator area receive the cavernous nerve and nerves supplying the urethra. We thus demonstrate that these muscular structures are smooth and under autonomic influence. These findings are relevant for the pelvic surgeon, and especially the urologist, during radical prostatectomy, abdominoperineal resection and intersphincteric resection. Clin. Anat., 2019. © 2019 Wiley Periodicals, Inc.

Anatomy of the median nerve and its clinical applications.

The median nerve is a mixed sensory and motor nerve. It is classically described as the nerve of pronation, of thumb, index finger, middle finger and wrist flexion, of thumb antepulsion and opposition, as well as the nerve of sensation for the palmar aspect of the first three fingers. It takes its name from its middle position at the end of the brachial plexus and the forearm. During its course from its origin at the brachial plexus to its terminal branches, it runs through various narrow passages where it could be compressed, such as the carpal tunnel or the pronator teres. The objective of this review is to summarize the current knowledge on the median nerve's anatomy: anatomical variations (branches, median-ulnar communicating branches), fascicular microanatomy, vascularization, anatomy of compression sites, embryology, ultrasonographic anatomy. The links between its anatomy and clinical, surgical or diagnostic applications are emphasized throughout this review.

Pronation and supination of the hand: Anatomy and biomechanics.

Proper functioning of the hand relies on its capacity to rotate and point the palm upward (i.e. supination) or downward (i.e. pronation) when standing up with the elbow in 90° flexion. Hand rotation is possible because of forearm rotation and also rotation of the whole upper limb at the shoulder. Two distinct mechanisms contribute to hand rotation: one in which the ulna is immobile and another in which the ulna is mobile. In this review, we first summarize how evolution of the human species has led to the progressive development of specific forearm anatomy that allows for pronation and supination. Then we analyze how the three joints of the forearm (i.e. proximal, middle and distal radioulnar joints), in association with the characteristic shape of both forearm bones, allow the forearm to rotate around a single axis. Lastly, we describe the neuromuscular anatomy that controls these complex rotational movements. The anatomical and biomechanical points developed in this paper are analyzed while considering clinical applications.