- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

AI-Based Evaluation of Prostate MR Imaging at a Modern Low-field 0.55 T Scanner Compared to 3 T in a Screening Cohort.

Purpose: To evaluate the effect of lower field strength on quantitative apparent-diffusion-coefficient (ADC) values, contrast of the T2-weighted MR images and the performance of an AI-based segmentation.

Materials And Methods: 25 screening clients (61.6 ± 7.

Intra-Individual Reproducibility of Automated Abdominal Organ Segmentation-Performance of TotalSegmentator Compared to Human Readers and an Independent nnU-Net Model.

The purpose of this study is to assess segmentation reproducibility of artificial intelligence-based algorithm, TotalSegmentator, across 34 anatomical structures using multiphasic abdominal CT scans comparing unenhanced, arterial, and portal venous phases in the same patients. A total of 1252 multiphasic abdominal CT scans acquired at our institution between January 1, 2012, and December 31, 2022, were retrospectively included. TotalSegmentator was used to derive volumetric measurements of 34 abdominal organs and structures from the total of 3756 CT series.

AI-enabled left atrial volumetry in coronary artery calcium scans (AI-CAC) predicts atrial fibrillation as early as one year, improves CHARGE-AF, and outperforms NT-proBNP: The multi-ethnic study of atherosclerosis.

Background: Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported.

Methods: We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC) to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA).

Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19) patients.

Background: The global coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges for healthcare systems, notably the increased demand for chest computed tomography (CT) scans, which lack automated analysis. Our study addresses this by utilizing artificial intelligence-supported automated computer analysis to investigate lung involvement distribution and extent in COVID-19 patients. Additionally, we explore the association between lung involvement and intensive care unit (ICU) admission, while also comparing computer analysis performance with expert radiologists' assessments.

AI-enabled Left Atrial Volumetry in Cardiac CT Scans Improves CHARGE-AF and Outperforms NT-ProBNP for Prediction of Atrial Fibrillation in Asymptomatic Individuals: Multi-Ethnic Study of Atherosclerosis.

Background: Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported.

Methods: We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC), taking on average 21 seconds per CAC scan, to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA).

Radiology weather forecast: A retrospective analysis of predictability of median daily polytrauma-CT occurrence based on weather data.

Objectives: Resource planning is a crucial component in hospitals, particularly in radiology departments. Since weather conditions are often described to correlate with emergency room visits, we aimed to forecast the amount of polytrauma-CTs using weather information.

Design: All polytrauma-CTs between 01/01/2011 and 12/31/2022 (n = 6638) were retrieved from the radiology information system.

Development and Evaluation of Deep Learning Models for Automated Estimation of Myelin Maturation Using Pediatric Brain MRI Scans.

Purpose: To predict the corresponding age of myelin maturation from brain MRI scans in infants and young children by using a deep learning algorithm and to build upon previously published models.

Materials And Methods: Brain MRI scans acquired between January 1, 2011, and March 17, 2021, in our institution in patients aged 0-3 years were retrospectively retrieved from the archive. An ensemble of two-dimensional (2D) and three-dimensional (3D) convolutional neural network models was trained and internally validated in 710 patients to predict myelin maturation age on the basis of radiologist-generated labels.

TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images.

Purpose: To present a deep learning segmentation model that can automatically and robustly segment all major anatomic structures on body CT images.

Materials And Methods: In this retrospective study, 1204 CT examinations (from 2012, 2016, and 2020) were used to segment 104 anatomic structures (27 organs, 59 bones, 10 muscles, and eight vessels) relevant for use cases such as organ volumetry, disease characterization, and surgical or radiation therapy planning. The CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, abnormalities, scanners, body parts, sequences, and sites).

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases.

Background: The aim of the current study was to investigate the distribution and extent of lung involvement in patients with COVID-19 with AI-supported, automated computer analysis and to assess the relationship between lung involvement and the need for intensive care unit (ICU) admission. A secondary aim was to compare the performance of computer analysis with the judgment of radiological experts.

Methods: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study.

Machine Learning for Onset Prediction of Patients with Intracerebral Hemorrhage.

Objective: Intracerebral hemorrhage (ICH) has a high mortality and long-term morbidity and thus has a significant overall health-economic impact. Outcomes are especially poor if the exact onset is unknown, but reliable imaging-based methods for onset estimation have not been established. We hypothesized that onset prediction of patients with ICH using artificial intelligence (AI) may be more accurate than human readers.

Pulmonary transit time of cardiovascular magnetic resonance perfusion scans for quantification of cardiopulmonary haemodynamics.

Aims: Pulmonary transit time (PTT) is the time blood takes to pass from the right ventricle to the left ventricle via pulmonary circulation. We aimed to quantify PTT in routine cardiovascular magnetic resonance imaging perfusion sequences. PTT may help in the diagnostic assessment and characterization of patients with unclear dyspnoea or heart failure (HF).

Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning.

Pancreatic cystic lesions (PCL) are a frequent and underreported incidental finding on CT scans and can transform into neoplasms with devastating consequences. We developed and evaluated an algorithm based on a two-step nnU-Net architecture for automated detection of PCL on CTs. A total of 543 cysts on 221 abdominal CTs were manually segmented in 3D by a radiology resident in consensus with a board-certified radiologist specialized in abdominal radiology.

White matter microstructure alterations in cortico-striatal networks are associated with parkinsonism in schizophrenia spectrum disorders.

The specific role of white matter (WM) microstructure in parkinsonism among patients with schizophrenia spectrum disorders (SSD) is largely unknown. To determine whether topographical alterations of WM microstructure contribute to parkinsonism in SSD patients, we examined healthy controls (HC, n=16) and SSD patients with and without parkinsonism, as defined by Simpson-Angus Scale total score of ≥4 (SSD-P, n=33) or <4 (SSD-nonP, n=62). We used whole brain tract-based spatial statistics (TBSS), tractometry (along tract statistics using TractSeg) and graph analytics (clustering coefficient (CCO), local betweenness centrality (BC)) to provide a framework of specific WM microstructural changes underlying parkinsonism in SSD.

Differentiating axonal loss and demyelination in chronic MS lesions: A novel approach using single streamline diffusivity analysis.

We describe a new single-streamline based approach to analyse diffusivity within chronic MS lesions. We used the proposed method to examine diffusivity profiles in 30 patients with relapsing multiple sclerosis and observed a significant increase of both RD and AD within the lesion core (0.38+/-0.

Multiparametric mapping of white matter microstructure in catatonia.

Catatonia is characterized by motor, affective and behavioral abnormalities. To date, the specific role of white matter (WM) abnormalities in schizophrenia spectrum disorders (SSD) patients with catatonia is largely unknown. In this study, diffusion magnetic resonance imaging (dMRI) data were collected from 111 right-handed SSD patients and 28 healthy controls.

Combined tract segmentation and orientation mapping for bundle-specific tractography.

While the major white matter tracts are of great interest to numerous studies in neuroscience and medicine, their manual dissection in larger cohorts from diffusion MRI tractograms is time-consuming, requires expert knowledge and is hard to reproduce. In previous work we presented tract orientation mapping (TOM) as a novel concept for bundle-specific tractography. It is based on a learned mapping from the original fiber orientation distribution function (FOD) peaks to tract specific peaks, called tract orientation maps.

Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge.

Background: Fiber tracking with diffusion-weighted MRI has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria.

Purpose: To assess the variability for an algorithm in group studies reproducibility is of critical context.

TractSeg - Fast and accurate white matter tract segmentation.

The individual course of white matter fiber tracts is an important factor for analysis of white matter characteristics in healthy and diseased brains. Diffusion-weighted MRI tractography in combination with region-based or clustering-based selection of streamlines is a unique combination of tools which enables the in-vivo delineation and analysis of anatomically well-known tracts. This, however, currently requires complex, computationally intensive processing pipelines which take a lot of time to set up.