Nonradiology Health Care Professionals Significantly Benefit From AI Assistance in Emergency-Related Chest Radiography Interpretation.

Background: Chest radiographs (CXRs) are still of crucial importance in primary diagnostics, but their interpretation poses difficulties at times.

Research Question: Can a convolutional neural network-based artificial intelligence (AI) system that interprets CXRs add value in an emergency unit setting?

Study Design And Methods: A total of 563 CXRs acquired in the emergency unit of a major university hospital were retrospectively assessed twice by three board-certified radiologists, three radiology residents, and three emergency unit-experienced nonradiology residents (NRRs). They used a two-step reading process: (1) without AI support; and (2) with AI support providing additional images with AI overlays.

Artificial Intelligence to Assess Tracheal Tubes and Central Venous Catheters in Chest Radiographs Using an Algorithmic Approach With Adjustable Positioning Definitions.

Purpose: To develop and validate an artificial intelligence algorithm for the positioning assessment of tracheal tubes (TTs) and central venous catheters (CVCs) in supine chest radiographs (SCXRs) by using an algorithm approach allowing for adjustable definitions of intended device positioning.

Materials And Methods: Positioning quality of CVCs and TTs is evaluated by spatially correlating the respective tip positions with anatomical structures. For CVC analysis, a configurable region of interest is defined to approximate the expected region of well-positioned CVC tips from segmentations of anatomical landmarks.

Optimizing b-values schemes for diffusion MRI of the brain with segmented Intravoxel Incoherent Motion (IVIM) model.

Purpose: To define an optimal set of b-values for accurate derivation of diffusion MRI parameters in the brain with segmented Intravoxel Incoherent Motion (IVIM) model.

Methods: Simulations of diffusion signals were performed to define an optimal set of b-values targeting different perfusion regimes, by relying on an optimization procedure which minimizes the total relative error on estimated IVIM parameters computed with a segmented fitting procedure. Then, the optimal b-values set was acquired in vivo on healthy subjects and skull base chordoma patients to compare the optimized protocol with a clinical one.

Microstructural parameters from DW-MRI for tumour characterization and local recurrence prediction in particle therapy of skull-base chordoma.

Background: Quantitative imaging such as Diffusion-Weighted MRI (DW-MRI) can be exploited to non-invasively derive patient-specific tumor microstructure information for tumor characterization and local recurrence risk prediction in radiotherapy.

Purpose: To characterize tumor microstructure according to proliferative capacity and predict local recurrence through microstructural markers derived from pre-treatment conventional DW-MRI, in skull-base chordoma (SBC) patients treated with proton (PT) and carbon ion (CIRT) radiotherapy.

Methods: Forty-eight patients affected by SBC, who underwent conventional DW-MRI before treatment and were enrolled for CIRT (n = 25) or PT (n = 23), were retrospectively selected.

Impact of secondary particles on the magnetic field generated by a proton pencil beam: a finite-element analysis based on Geant4-DNA simulations.

Purpose: To investigate the static magnetic field generated by a proton pencil beam as a candidate for range verification by means of Monte Carlo simulations, thereby improving upon existing analytical calculations. We focus on the impact of statistical current fluctuations and secondary protons and electrons.

Methods: We considered a pulsed beam (10 s pulse duration) during the duty cycle with a peak beam current of 0.