Deep-learning-based reconstruction of T2-weighted magnetic resonance imaging of the prostate accelerated by compressed sensing provides improved image quality at half the acquisition time.

Background: Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics.

Methods: In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS).

Reduced Radiation Exposure Protocol during Computer Tomography of the Left Atrium Prior to Catheter Ablation in Patients with Atrial Fibrillation.

(1) Background: Computer tomography (CT) is an imaging modality used in the pre-planning of radiofrequency catheter ablation (RFA) procedure in patients with cardiac arrhythmias. However, it is associated with a considerable ionizing radiation dose for patients. This study aims to develop and validate low-dose CT scanning protocols of the left atrium (LA) for RFA guidance.

Iterative machine learning based rotational alignment of brain 3D CT data.

The optimal rotational alignment of brain Computed Tomography (CT) images to a required standard position has a crucial importance for both automatic and manual diagnostic analysis. In this contribution, we present a novel two-step iterative approach for the automatic 3D rotational alignment of brain CT data. The angles of axial and coronal rotations are determined by an unsupervised by localisation of the Midsagittal Plane (MSP) method.

Deep-learning-based fully automatic spine centerline detection in CT data.

In this contribution, we present a fully automatic approach, that is based on two convolution neural networks (CNN) together with a spine tracing algorithm utilizing a population optimization algorithm. Based on the evaluation of 130 CT scans including heavily distorted and complicated cases, it turned out that this new combination enables fast and robust detection with almost 90% of correctly determined spinal centerlines with computing time of fewer than 20 seconds.