Single energy CT-based mass density and relative stopping power estimation for proton therapy using deep learning method.

Background: The number of patients undergoing proton therapy has increased in recent years. Current treatment planning systems (TPS) calculate dose maps using three-dimensional (3D) maps of relative stopping power (RSP) and mass density. The patient-specific maps of RSP and mass density were obtained by translating the CT number (HU) acquired using single-energy computed tomography (SECT) with appropriate conversions and coefficients.

A Couch Mounted Smartphone-based Motion Monitoring System for Radiation Therapy.

Purpose: Surface-guided radiation-therapy (SGRT) systems are being adopted into clinical practice for patient setup and motion monitoring. However, commercial systems remain cost prohibitive to resource-limited clinics around the world. Our aim is to develop and validate a smartphone-based application using LiDAR cameras (such as on recent Apple iOS devices) for facilitating SGRT in low-resource centers.

Deformable lung 4DCT image registration via landmark-driven cycle network.

Background: An automated, accurate, and efficient lung four-dimensional computed tomography (4DCT) image registration method is clinically important to quantify respiratory motion for optimal motion management.

Purpose: The purpose of this work is to develop a weakly supervised deep learning method for 4DCT lung deformable image registration (DIR).

Methods: The landmark-driven cycle network is proposed as a deep learning platform that performs DIR of individual phase datasets in a simulation 4DCT.

Data-Driven Volumetric Image Generation from Surface Structures using a Patient-Specific Deep Leaning Model.

The advent of computed tomography significantly improves patients' health regarding diagnosis, prognosis, and treatment planning and verification. However, tomographic imaging escalates concomitant radiation doses to patients, inducing potential secondary cancer by 4%. We demonstrate the feasibility of a data-driven approach to synthesize volumetric images using patients' surface images, which can be obtained from a zero-dose surface imaging system.

MRI-based prostate and dominant lesion segmentation using cascaded scoring convolutional neural network.

Purpose: Dose escalation to dominant intraprostatic lesions (DILs) is a novel treatment strategy to improve the treatment outcome of prostate radiation therapy. Treatment planning requires accurate and fast delineation of the prostate and DILs. In this study, a 3D cascaded scoring convolutional neural network is proposed to automatically segment the prostate and DILs from MRI.

Magnetic resonance imaging contrast enhancement synthesis using cascade networks with local supervision.

Purpose: Gadolinium-based contrast agents (GBCAs) are widely administrated in MR imaging for diagnostic studies and treatment planning. Although GBCAs are generally thought to be safe, various health and environmental concerns have been raised recently about their use in MR imaging. The purpose of this work is to derive synthetic contrast enhance MR images from unenhanced counterpart images, thereby eliminating the need for GBCAs, using a cascade deep learning workflow that incorporates contour information into the network.

Self-supervised learning for accelerated 3D high-resolution ultrasound imaging.

Purpose: Ultrasound (US) imaging has been widely used in diagnosis, image-guided intervention, and therapy, where high-quality three-dimensional (3D) images are highly desired from sparsely acquired two-dimensional (2D) images. This study aims to develop a deep learning-based algorithm to reconstruct high-resolution (HR) 3D US images only reliant on the acquired sparsely distributed 2D images.

Methods: We propose a self-supervised learning framework using cycle-consistent generative adversarial network (cycleGAN), where two independent cycleGAN models are trained with paired original US images and two sets of low-resolution (LR) US images, respectively.

Interplay Effect of Target Motion and Pencil-Beam Scanning in Proton Therapy for Pediatric Patients.

Purpose: To investigate the effect of interplay between spot-scanning proton beams and respiration-induced tumor motion on internal target volume coverage for pediatric patients.

Materials And Methods: Photon treatments for 10 children with representative tumor motions (1-13 mm superior-inferior) were replanned to simulate single-field uniform dose- optimized proton therapy. Static plans were designed by using average computed tomography (CT) data sets created from 4D CT data to obtain nominal dose distributions.

Clinical evaluation of the iterative metal artifact reduction algorithm for CT simulation in radiotherapy.

Purpose: To clinically evaluate an iterative metal artifact reduction (IMAR) algorithm prototype in the radiation oncology clinic setting by testing for accuracy in CT number retrieval, relative dosimetric changes in regions affected by artifacts, and improvements in anatomical and shape conspicuity of corrected images.

Methods: A phantom with known material inserts was scanned in the presence/absence of metal with different configurations of placement and sizes. The relative change in CT numbers from the reference data (CT with no metal) was analyzed.

Imaging dose in variable pitch body perfusion CT scans: an analysis using TG111 formalism.

Purpose: To investigate the variation of imaging dose with tube potential in variable pitch body CT perfusion (CTp) protocols using the TG111 dosimetric formalism.

Methods: TG111 recommendations were followed in choosing the phantom, dosimetric equipment, and methodology. Specifically, equilibrium doses (D(eq)) were measured centrally and peripherally in a long PMMA phantom.

An alternative approach to histopathological validation of PET imaging for radiation therapy image-guidance: a proof of concept.

Purpose: In radiotherapy, PET images can be used to guide the delivery of selectively escalated doses to biologically relevant tumour subvolumes. Validation of PET for such applications requires demonstration of spatial coincidence between PET tracer uptake pattern and the histopathologically confirmed target. This study introduces a novel approach to histopathological validation of PET image segmentation for radiotherapy guidance.

High-resolution radioluminescence microscopy of 18F-FDG uptake by reconstructing the β-ionization track.

Unlabelled: Radioluminescence microscopy is a new method for imaging radionuclide uptake by single live cells with a fluorescence microscope. Here, we report a particle-counting scheme that improves spatial resolution by overcoming the β-range limit.

Methods: Short frames (10 μs-1 s) were acquired using a high-gain camera coupled to a microscope to capture individual ionization tracks.

Single-fraction simulation of relative cell survival in response to uniform versus hypoxia-targeted dose escalation.

The purpose of this study was to investigate the increase in cell kill that can be achieved by tumor irradiation with heterogeneous dose distributions targeting hypoxic regions that can be visualized with non-invasive imaging. Starting with a heterogeneous distribution of microvessels, a microscopic two-dimensional model of tumor oxygenation was developed using planar simulation of oxygen diffusion. Non-invasive imaging of hypoxia was simulated taking partial volume effect into account.

Tumour microenvironment heterogeneity affects the perceived spatial concordance between the intratumoural patterns of cell proliferation and 18F-fluorothymidine uptake.

Background And Purpose: PET imaging with (18)F-fluorothymidine ((18)F-FLT) can potentially be used to identify tumour subvolumes for selective dose escalation in radiation therapy. The purpose of this study is to analyse the co-localization of intratumoural patterns of cell proliferation with (18)F-FLT tracer uptake.

Materials And Methods: Mice bearing FaDu or SQ20B xenograft tumours were injected with (18)F-FLT, and bromodeoxyuridine (proliferation marker).

Comprehensive approach to coregistration of autoradiography and microscopy images acquired from a set of sequential tissue sections.

Unlabelled: Histopathologic validation of a PET tracer requires assessment of colocalization of the tracer with its intended biologic target. Using thin tissue section autoradiography, it is possible to visualize the spatial distribution of the PET tracer uptake and compare it with the distribution of the intended biologic target (as visualized with immunohistochemistry). The purpose of this study was to develop and evaluate an objective methodology for deformable coregistration of autoradiography and microscopy images acquired from a set of sequential tissue sections.