MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study.

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest.

Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning.

Objective: Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced dual-energy CT (DECT) to derive accurate patient mass density maps.

Methods: Seven tissue substitute MRI phantoms were used for validation including adipose, brain, muscle, liver, skin, spongiosa, 45% hydroxyapatite (HA) bone.

Early in vivo Radiation Damage Quantification for Pediatric Craniospinal Irradiation Using Longitudinal MRI for Intensity Modulated Proton Therapy.

Purpose: Proton vertebral body sparing craniospinal irradiation (CSI) treats the thecal sac while avoiding the anterior vertebral bodies in an effort to reduce myelosuppression and growth inhibition. However, robust treatment planning needs to compensate for proton range uncertainty, which contributes unwanted doses within the vertebral bodies. This work aimed to develop an early in vivo radiation damage quantification method using longitudinal magnetic resonance (MR) scans to quantify the dose effect during fractionated CSI.

Prenatal Diagnosis and Outcome of Congenital Corrected Transposition of the Great Arteries - A Multicenter Report of 69 Cases.

Objective:  Congenital corrected transposition of the great arteries (ccTGA) is a rare congenital cardiac anomaly which remains difficult to diagnose prenatally. We aim to investigate the natural history, associated anomalies and the outcome of patients in prenatally diagnosed ccTGA.

Method:  This was an international multicenter retrospective analysis of fetuses with a diagnosis of ccTGA from 2002 to 2017.