Retrospective Clinical Evaluation of a Decision-Support Software for Adaptive Radiotherapy of Head and Neck Cancer Patients.

Purpose: This study aimed to evaluate the clinical need for an automated decision-support software platform for adaptive radiation therapy (ART) of head and neck cancer (HNC) patients.

Methods: We tested RTapp (SegAna), a new ART software platform for deciding when a treatment replan is needed, to investigate a set of 27 HNC patients' data retrospectively. For each fraction, the software estimated key components of ART such as daily dose distribution and cumulative doses received by targets and organs at risk (OARs) from daily 3D imaging in real-time.

Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy.

Electron density maps must be accurately estimated to achieve valid dose calculation in MR-only radiotherapy. The goal of this study is to assess whether two deep learning models, the conditional generative adversarial network (cGAN) and the cycle-consistent generative adversarial network (cycleGAN), can generate accurate abdominal synthetic CT (sCT) images from 0.35T MR images for MR-only liver radiotherapy.

A quantitative analysis of biomechanical lung model consistency using 5DCT datasets.

Purpose: Lung biomechanical models are important for understanding and characterizing lung anatomy and physiology. A key parameter of biomechanical modeling is the underlying tissue elasticity distribution. While human lung elasticity estimations do not have ground truths, model consistency checks can and should be employed to gauge the stability of the estimation techniques.

An adversarial machine learning framework and biomechanical model-guided approach for computing 3D lung tissue elasticity from end-expiration 3DCT.

Purpose: Lung elastography aims at measuring the lung parenchymal tissue elasticity for applications ranging from diagnostic purposes to biomechanically guided deformations. Characterizing the lung tissue elasticity requires four-dimensional (4D) lung motion as an input, which is currently estimated by deformably registering 4D computed tomography (4DCT) datasets. Since 4DCT imaging is widely used only in a radiotherapy treatment setup, there is a need to predict the elasticity distribution in the absence of 4D imaging for applications within and outside of radiotherapy domain.

Systematic feasibility analysis of performing elastography using reduced dose CT lung image pairs.

Purpose: Elastography using computer tomography (CT) is a promising methodology that can provide patient-specific regional distributions of lung biomechanical properties. The purpose of this paper is to investigate the feasibility of performing elastography using simulated lower dose CT scans.

Methods: A cohort of eight patient CT image pairs were acquired with a tube current-time product of 40 mAs for estimating baseline lung elastography results.

Feasibility of deriving a novel imaging biomarker based on patient-specific lung elasticity for characterizing the degree of COPD in lung SBRT patients.

Objective:: Lung tissue elasticity is an effective spatial representation for Chronic Obstructive Pulmonary Disease phenotypes and pathophysiology. We investigated a novel imaging biomarker based on the voxel-by-voxel distribution of lung tissue elasticity. Our approach combines imaging and biomechanical modeling to characterize tissue elasticity.

Multi-Kinect v2 Camera Based Monitoring System for Radiotherapy Patient Safety.

3D kinect camera systems are essential for real-time imaging of 3D treatment space that consists of both the patient anatomy as well as the treatment equipment setup. In this paper, we present the technical details of a 3D treatment room monitoring system that employs a scalable number of calibrated and coregistered Kinect v2 cameras. The monitoring system tracks radiation gantry and treatment couch positions, and tracks the patient and immobilization accessories.

GPU-Based Parallelized Solver for Large Scale Vascular Blood Flow Modeling and Simulations.

Cardio-vascular blood flow simulations are essential in understanding the blood flow behavior during normal and disease conditions. To date, such blood flow simulations have only been done at a macro scale level due to computational limitations. In this paper, we present a GPU based large scale solver that enables modeling the flow even in the smallest arteries.

Real Time Gabor-Domain Optical Coherence Microscopy for 3D Imaging.

Fast, robust, nondestructive 3D imaging is needed for the characterization of microscopic tissue structures across various clinical applications. A custom microelectromechanical system (MEMS)-based 2D scanner was developed to achieve, together with a multi-level GPU architecture, 55 kHz fast-axis A-scan acquisition in a Gabor-domain optical coherence microscopy (GD-OCM) custom instrument. GD-OCM yields high-definition micrometer-class volumetric images.

Systematic feasibility analysis of a quantitative elasticity estimation for breast anatomy using supine/prone patient postures.

Purpose: Breast elastography is a critical tool for improving the targeted radiotherapy treatment of breast tumors. Current breast radiotherapy imaging protocols only involve prone and supine CT scans. There is a lack of knowledge on the quantitative accuracy with which breast elasticity can be systematically measured using only prone and supine CT datasets.

Analytic Intermodel Consistent Modeling of Volumetric Human Lung Dynamics.

Human lung undergoes breathing-induced deformation in the form of inhalation and exhalation. Modeling the dynamics is numerically complicated by the lack of information on lung elastic behavior and fluid-structure interactions between air and the tissue. A mathematical method is developed to integrate deformation results from a deformable image registration (DIR) and physics-based modeling approaches in order to represent consistent volumetric lung dynamics.

Parallelized multi-graphics processing unit framework for high-speed Gabor-domain optical coherence microscopy.

Gabor-domain optical coherence microscopy (GD-OCM) is a volumetric high-resolution technique capable of acquiring three-dimensional (3-D) skin images with histological resolution. Real-time image processing is needed to enable GD-OCM imaging in a clinical setting. We present a parallelized and scalable multi-graphics processing unit (GPU) computing framework for real-time GD-OCM image processing.

4D-CT Lung registration using anatomy-based multi-level multi-resolution optical flow analysis and thin-plate splines.

Purpose: The accuracy of 4D-CT registration is limited by inconsistent Hounsfield unit (HU) values in the 4D-CT data from one respiratory phase to another and lower image contrast for lung substructures. This paper presents an optical flow and thin-plate spline (TPS)-based 4D-CT registration method to account for these limitations.

Methods: The use of unified HU values on multiple anatomy levels (e.

A client-server framework for 3D remote visualization of radiotherapy treatment space.

Radiotherapy is safely employed for treating wide variety of cancers. The radiotherapy workflow includes a precise positioning of the patient in the intended treatment position. While trained radiation therapists conduct patient positioning, consultation is occasionally required from other experts, including the radiation oncologist, dosimetrist, or medical physicist.

Multi-scale, multi-modal image integration for image-guided clinical interventions in the head and neck anatomy.

The aim of this paper is to enable model guided multi-scale and multi-modal image integration for the head and neck anatomy. The image modality used for this purpose includes multi-pose Magnetic Resonance Imaging (MRI), Mega Voltage CT, and hand-held Optical Coherence Tomography. A biomechanical model that incorporates subject-specific young's modulus and shear modulus properties is developed from multi-pose MRI, positioned in the treatment setup using Mega Voltage CT (MVCT), and actuated using multiple kinect surface cameras to mimic patient postures during Optical Coherence Microscopy (OCM) imaging.

Modeling Airflow Using Subject-Specific 4DCT-Based Deformable Volumetric Lung Models.

Lung radiotherapy is greatly benefitted when the tumor motion caused by breathing can be modeled. The aim of this paper is to present the importance of using anisotropic and subject-specific tissue elasticity for simulating the airflow inside the lungs. A computational-fluid-dynamics (CFD) based approach is presented to simulate airflow inside a subject-specific deformable lung for modeling lung tumor motion and the motion of the surrounding tissues during radiotherapy.

Computational fluid dynamics modeling of airflow inside lungs using heterogenous anisotropic lung tissue elastic properties.

The aim of this paper is to model the airflow inside lungs during breathing and its fluid-structure interaction with the lung tissues and the lung tumor using subject-specific elastic properties. The fluid-structure interaction technique simultaneously simulates flow within the airway and anisotropic deformation of the lung lobes. The three-dimensional (3D) lung geometry is reconstructed from the end-expiration 3D CT scan datasets of humans with lung cancer.

Visualization of 3D volumetric lung dynamics for real-time external beam lung radiotherapy.

This paper reports on the usage of physics-based 3D volumetric lung dynamic models for visualizing and monitoring the radiation dose deposited on the lung of a human subject during lung radiotherapy. The dynamic model of each subject is computed from a 4D Computed Tomography (4DCT) imaging acquired before the treatment. The 3D lung deformation and the radiation dose deposited are computed using Graphics Processing Units (GPU).

Generating classes of 3D virtual mandibles for AR-based medical simulation.

Introduction: Simulation and modeling represent promising tools for several application domains from engineering to forensic science and medicine. Advances in 3D imaging technology convey paradigms such as augmented reality (AR) and mixed reality inside promising simulation tools for the training industry.

Methods: Motivated by the requirement for superimposing anatomically correct 3D models on a human patient simulator (HPS) and visualizing them in an AR environment, the purpose of this research effort was to develop and validate a method for scaling a source human mandible to a target human mandible within a 2 mm root mean square (RMS) error.

Real-time simulation of 4D lung tumor radiotherapy using a breathing model.

In this paper, we present a real-time simulation and visualization framework that models a deformable surface lung model with tumor, simulates the tumor motion and predicts the amount of radiation doses that would be deposited in the moving lung tumor during the actual delivery of radiation. The model takes as input a subject-specific 4D Computed Tomography (4D CT) of lungs and computes a deformable lung surface model by estimating the deformation properties of the surface model using an inverse dynamics approach. Once computed, the deformable model is used to simulate and visualize lung tumor motion that would occur during radiation therapy accounting for variations in the breathing pattern.

Combining a breathing model and tumor-specific rigidity constraints for registration of CT-PET thoracic data.

Diagnosis and therapy planning in oncology applications often rely on the joint exploitation of two complementary imaging modalities, namely Computerized Tomography (CT) and Positron Emission Tomography (PET). While recent technical advances in combined CT/PET scanners enable 3D CT and PET data of the thoracic region to be obtained with the patient in the same global position, current image data registration methods do not account for breathing-induced anatomical changes in the thoracic region, and this remains an important limitation. This paper deals with the 3D registration of CT thoracic image volumes acquired at two different instances in the breathing cycle and PET volumes of thoracic regions.

Modeling real-time 3-d lung deformations for medical visualization.

In this paper, we propose a physics-based and physiology-based approach for modeling real-time deformations of 3-D high-resolution polygonal lung models obtained from high-resolution computed tomography (HRCT) images of normal human subjects. The physics-based deformation operator is nonsymmetric, which accounts for the heterogeneous elastic properties of the lung tissue and spatial-dynamic flow properties of the air. An iterative approach is used to estimate the deformation with the deformation operator initialized based on the regional alveolar expandability, a key physiology-based parameter.

Thoracic CT-PET registration using a 3D breathing model.

In the context of thoracic CT-PET volume registration, we present a novel method to incorporate a breathing model in a non-linear registration procedure, guaranteeing physiologically plausible deformations. The approach also accounts for the rigid motions of lung tumors during breathing. We performed a set of registration experiments on one healthy and four pathological data sets.

Simulating 3-D lung dynamics using a programmable graphics processing unit.

Medical simulations of lung dynamics promise to be effective tools for teaching and training clinical and surgical procedures related to lungs. Their effectiveness may be greatly enhanced when visualized in an augmented reality (AR) environment. However, the computational requirements of AR environments limit the availability of the central processing unit (CPU) for the lung dynamics simulation for different breathing conditions.

Distributed augmented reality with 3-D lung dynamics--a planning tool concept.

Augmented reality (AR) systems add visual information to the world by using advanced display techniques. The advances in miniaturization and reduced hardware costs make some of these systems feasible for applications in a wide set of fields. We present a potential component of the cyber infrastructure for the operating room of the future: a distributed AR-based software-hardware system that allows real-time visualization of three-dimensional (3-D) lung dynamics superimposed directly on the patient's body.