Optimization and validation of low-field MP2RAGE T mapping on 0.35T MR-Linac: Toward adaptive dose painting with hypoxia biomarkers.

Background: Stereotactic MR-guided Adaptive Radiation Therapy (SMART) dose painting for hypoxia has potential to improve treatment outcomes, but clinical implementation on low-field MR-Linac faces substantial challenges due to dramatically lower signal-to-noise ratio (SNR) characteristics. While quantitative MRI and T mapping of hypoxia biomarkers show promise, T-to-noise ratio (TNR) optimization at low fields is paramount, particularly for the clinical implementation of oxygen-enhanced (OE)-MRI. The 3D Magnetization Prepared (2) Rapid Gradient Echo (MP2RAGE) sequence stands out for its ability to acquire homogeneous T-weighted contrast images with simultaneous T mapping.

Validation of dual-energy CT-based composition analysis using fresh animal tissues and composition-optimized tissue equivalent samples.

Proton therapy allows for highly conformal dose deposition, but is sensitive to range uncertainties. Several approaches currently under development measure composition-dependent secondary radiation to monitor the delivered proton range. To fully utilize these methods, an estimate of the elemental composition of the patient's tissue is often needed.

Lung sparing in MR-guided non-adaptive SBRT treatment of peripheral lung tumors.

We aim to: (1) quantify the benefits of lung sparing using non-adaptive magnetic resonance guided stereotactic body radiotherapy (MRgSBRT) with advanced motion management for peripheral lung cancers compared to conventional x-ray guided SBRT (ConvSBRT); (2) establish a practical decision-making guidance metric to assist a clinician in selecting the appropriate treatment modality.Eleven patients with peripheral lung cancer who underwent breath-hold, gated MRgSBRT on an MR-guided linear accelerator (MR linac) were studied. Four-dimensional computed tomography (4DCT)-based retrospective planning using an internal target volume (ITV) was performed to simulate ConvSBRT, which were evaluated against the original MRgSBRT plans.

Quantifying gadolinium-based nanoparticle uptake distributions in brain metastases via magnetic resonance imaging.

AGuIX, a novel gadolinium-based nanoparticle, has been deployed in a pioneering double-blinded Phase II clinical trial aiming to assess its efficacy in enhancing radiotherapy for tumor treatment. This paper moves towards this goal by analyzing AGuIX uptake patterns in 23 patients. A phantom was designed to establish the relationship between AGuIX concentration and longitudinal ( ) relaxation.

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.

Comparing Predicted Toxicities between Hypofractionated Proton and Photon Radiotherapy of Liver Cancer Patients with Different Adaptive Schemes.

With the availability of MRI linacs, online adaptive intensity modulated radiotherapy (IMRT) has become a treatment option for liver cancer patients, often combined with hypofractionation. Intensity modulated proton therapy (IMPT) has the potential to reduce the dose to healthy tissue, but it is particularly sensitive to changes in the beam path and might therefore benefit from online adaptation. This study compares the normal tissue complication probabilities (NTCPs) for liver and duodenal toxicity for adaptive and non-adaptive IMRT and IMPT treatments of liver cancer patients.

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.

Validation of an MR-based multimodal method for molecular composition and proton stopping power ratio determination using animal tissues and tissue-mimicking phantoms.

. Range uncertainty in proton therapy is an important factor limiting clinical effectiveness. Magnetic resonance imaging (MRI) can measure voxel-wise molecular composition and, when combined with kilovoltage CT (kVCT), accurately determine mean ionization potential (), electron density, and stopping power ratio (SPR).

Comparison of MR-soft tissue based versus biliary stent based alignment for image guidance in pancreatic SBRT.

Purpose: The role of biliary stents in image-guided localization for pancreatic cancer has been inconclusive. To date, stent accuracy has been largely evaluated against implanted fiducials on cone beam computed tomography. We aim to use magnetic resonance (MR) soft tissue as a direct reference to examine the geometric and dosimetric impacts of stent-based localization on the newly available MR linear accelerator.

Synthetic CT generation for MRI-guided adaptive radiotherapy in prostate cancer.

Current MRI-guided adaptive radiotherapy (MRgART) workflows require fraction-specific electron and/or mass density maps, which are created by deformable image registration (DIR) between the simulation CT images and daily MR images. Manual density overrides may also be needed where DIR-produced results are inaccurate. This approach slows the adaptive radiotherapy workflow and introduces additional dosimetric uncertainties, especially in the presence of the magnetic field.

MRI of radiation chemistry: First images and investigation of potential mechanisms.

Background: Paramagnetic species such as O and free radicals can enhance T and T relaxation times. If the change in relaxation time is sufficiently large, the contrast will be generated in magnetic resonance images. Since radiation is known to be capable of altering the concentration of O and free radicals during water radiolysis, it may be possible for radiation to induce MR signal change.

Validation of prompt gamma-ray spectroscopy for proton range verification in tissue-mimicking and porcine samples.

. Proton therapy of cancer improves dose conformality to the target and sparing of surrounding healthy tissues compared to conventional photon treatments. However, proton therapy's advantage could be even larger if proton range uncertainties were reduced.

Integrating Structure Propagation Uncertainties in the Optimization of Online Adaptive Proton Therapy Plans.

Currently, adaptive strategies require time- and resource-intensive manual structure corrections. This study compares different strategies: optimization without manual structure correction, adaptation with physician-drawn structures, and no adaptation. Strategies were compared for 16 patients with pancreas, liver, and head and neck (HN) cancer with 1-5 repeated images during treatment: 'reference adaptation', with structures drawn by a physician; 'single-DIR adaptation', using a single set of deformably propagated structures; 'multi-DIR adaptation', using robust planning with multiple deformed structure sets; 'conservative adaptation', using the intersection and union of all deformed structures; 'probabilistic adaptation', using the probability of a voxel belonging to the structure in the optimization weight; and 'no adaptation'.

Generation of synthetic megavoltage CT for MRI-only radiotherapy treatment planning using a 3D deep convolutional neural network.

Background: Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on-board anatomical visualization, localization, and adaptive dose calculation. Implementing an MR-only workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation.

Purpose: In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI-MVCT only treatment planning.

Dual-energy CT based mass density and relative stopping power estimation for proton therapy using physics-informed deep learning.

Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calculation. The aim of this study is to develop a physics-informed deep learning (PIDL) framework to derive accurate mass density and relative stopping power maps from dual-energy computed tomography (DECT) images.

Improved accuracy of relative electron density and proton stopping power ratio through CycleGAN machine learning.

. Kilovoltage computed tomography (kVCT) is the cornerstone of radiotherapy treatment planning for delineating tissues and towards dose calculation. For the former, kVCT provides excellent contrast and signal-to-noise ratio.

Patient specific distortion detection and mitigation in MR images used for stereotactic radiosurgery.

In MRI-based radiation therapy planning, mitigating patient-specific distortion with standard high bandwidth scans can result in unnecessary sacrifices of signal to noise ratio. This study investigates a technique for distortion detection and mitigation on a patient specific basis.Fast B0 mapping was performed using a previously developed technique for high-resolution, large dynamic range field mapping without the need for phase unwrapping algorithms.

Stereotactic Body Radiation Therapy and High-Dose-Rate Brachytherapy Boost in Combination With Intensity Modulated Radiation Therapy for Localized Prostate Cancer: A Single-Institution Propensity Score Matched Analysis.

Purpose: To perform a propensity-score matched analysis comparing stereotactic body radiation therapy (SBRT) boost and high-dose-rate (HDR) boost for localized prostate cancer.

Methods And Materials: A single-institution retrospective chart review was conducted of men treated with pelvic external beam radiation therapy (EBRT) and SBRT boost (21 Gy and 19 Gy in 2 fractions) to the prostate for prostate cancer. A cohort treated at the same institution with HDR brachytherapy boost (19 Gy in 2 fractions) was compared.

On the molecular relationship between Hounsfield Unit (HU), mass density, and electron density in computed tomography (CT).

Accurate determination of physical/mass and electron densities are critical to accurate spatial and dosimetric delivery of radiotherapy for photon and charged particles. In this manuscript, the biology, chemistry, and physics that underly the relationship between computed tomography (CT) Hounsfield Unit (HU), mass density, and electron density was explored. In standard radiation physics practice, quantities such as mass and electron density are typically calculated based off a single kilovoltage CT (kVCT) scan assuming a one-to-one relationship between HU and density.

The effect of time since stroke, gender, age, and lesion size on thalamus volume in chronic stroke: a pilot study.

Recent stroke studies have shown that the ipsi-lesional thalamus longitudinally and significantly decreases after stroke in the acute and subacute stages. However, additional considerations in the chronic stages of stroke require exploration including time since stroke, gender, intracortical volume, aging, and lesion volume to better characterize thalamic differences after cortical infarct. This cross-sectional retrospective study quantified the ipsilesional and contralesional thalamus volume from 69 chronic stroke subjects' anatomical MRI data (age 35-92) and related the thalamus volume to time since stroke, gender, intracortical volume, age, and lesion volume.

Improved contrast and noise of megavoltage computed tomography (MVCT) through cycle-consistent generative machine learning.

Purpose: Megavoltage computed tomography (MVCT) has been implemented on many radiation therapy treatment machines as a tomographic imaging modality that allows for three-dimensional visualization and localization of patient anatomy. Yet MVCT images exhibit lower contrast and greater noise than its kilovoltage CT (kVCT) counterpart. In this work, we sought to improve these disadvantages of MVCT images through an image-to-image-based machine learning transformation of MVCT and kVCT images.

Technical Note: A methodology for improved accuracy in stopping power estimation using MRI and CT.

Purpose: Proton therapy is becoming an increasingly popular cancer treatment modality due to the proton's physical advantage in that it deposits the majority of its energy at the distal end of its track where the tumor is located. The proton range in a material is determined from the stopping power ratio (SPR) of the material. However, SPR is typically estimated based on a computed tomography (CT) scan which can lead to range estimation errors due to the difference in x-ray and proton interactions in matter, which can preclude the ability to utilize protons to their full potential.

A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning.

Purpose: This study suggests a lifelong learning-based convolutional neural network (LL-CNN) algorithm as a superior alternative to single-task learning approaches for automatic segmentation of head and neck (OARs) organs at risk.

Methods And Materials: Lifelong learning-based convolutional neural network was trained on twelve head and neck OARs simultaneously using a multitask learning framework. Once the weights of the shared network were established, the final multitask convolutional layer was replaced by a single-task convolutional layer.