Evaluation of MRI-only based online adaptive radiotherapy of abdominal region on MR-linac.

Purpose: A hybrid magnetic resonance linear accelerator (MRL) can perform magnetic resonance imaging (MRI) with high soft-tissue contrast to be used for online adaptive radiotherapy (oART). To obtain electron densities needed for the oART dose calculation, a computed tomography (CT) is often deformably registered to MRI. Our aim was to evaluate an MRI-only based synthetic CT (sCT) generation as an alternative to the deformed CT (dCT)-based oART in the abdominal region.

Characterizing geometric distortions of 3D sequences in clinical head MRI.

Objective: Phantoms are often used to estimate the geometric accuracy in magnetic resonance imaging (MRI). However, the distortions may differ between anatomical and phantom images. This study aimed to investigate the applicability of a phantom-based and a test-subject-based method in evaluating geometric distortion present in clinical head-imaging sequences.

Determination of acceptance criteria for geometric accuracy of magnetic resonance imaging scanners used in radiotherapy planning.

Background And Purpose: Magnetic resonance imaging is increasingly used in radiotherapy planning; yet, the performance of the utilized scanners is rarely regulated by any authority. The aim of this study was to determine the geometric accuracy of several magnetic resonance imaging scanners used for radiotherapy planning, and to establish acceptance criteria for such scanners.

Materials And Methods: The geometric accuracy of five different scanners was measured with three sequences using a commercial large-field-of-view phantom.

Automating chest radiograph imaging quality control.

Purpose: To automate diagnostic chest radiograph imaging quality control (lung inclusion at all four edges, patient rotation, and correct inspiration) using convolutional neural network models.

Methods: The data comprised of 2589 postero-anterior chest radiographs imaged in a standing position, which were divided into train, validation, and test sets. We increased the number of images for the inclusion by cropping appropriate images, and for the inclusion and the rotation by flipping the images horizontally.

Measuring geometric accuracy in magnetic resonance imaging with 3D-printed phantom and nonrigid image registration.

Objective: We aimed to develop a vendor-neutral and interaction-free quality assurance protocol for measuring geometric accuracy of head and brain magnetic resonance (MR) images. We investigated the usability of nonrigid image registration in the analysis and looked for the optimal registration parameters.

Materials And Methods: We constructed a 3D-printed phantom and imaged it with 12 MR scanners using clinical sequences.