Ultrasound and photoacoustic methods for anatomic and functional imaging in image guided radiation therapy

(MATERIAL and METHODS) First, we define the physical principals and optimal protocols that provide contrast when imaging with US and the transducer properties contributing to resolution limits. The US field of view (FOV) was characterized to determine the optimal settings with regard to imaging depth, focal region, with and without harmonic imaging, and artifact identification. This will allow us to determine the minimum errors expected when registering multimodal volumes (CT, US, CBCT). Next, we designed an in-house integrated US manipulator and platform to relate CT, 3D-US and linear accelerator coordinate systems. To validate our platform, an agar-based phantom with measured densities and speed-of-sound consistent with tissues surrounding the bladder was fabricated. This phantom was rotated relative to the CT and US coordinate systems and imaged with both modalities. These CT and 3D-US images were imported into the treatment planning system, where US-to-US and US-to-CT images were co-registered and the registration matrix used to re-align the phantom relative to the linear accelerator. The measured precision in the phantom setup, which is defined by the standard deviation of the transformation matrix components, was consistent with and exceeding acceptable clinical patient re-alignments (2 mm). Statistical errors from US-US registrations for different patient orientations ranged from 0.06-1.66 mm for x, y, and z translational components, and 0.00-1.05 degrees for rotational components. Statistical errors from US-CT registrations were 0.23-1.18 mm for the x, y and z translational components, and 0.08-2.52 degrees for the rotational components. The high precision in the multimodal registrations suggest the ability to use US for patient positioning when targeting abdominal structures. We are now testing this on a dog patient to obtain both inter and intra-fractional positional errors. The objective of this experiment is to confirm Hill’s equation describing the relationship between hemoglobin saturation (SaO2) and the partial pressure of dissolved oxygen (pO2). The relationship is modeled as a sigmoidal curve that is a function of two parameters – the Hill coefficient, n, and the net association constant of HbO2, K (or pO2 at 50% SaO2). The goal is to noninvasively measure SaO2 in breast tumors in mice using photoacoustic computed tomographic (PCT) imaging and compare those measurements to a gold standard for pO2 using the OxyLite probe. First, a calibration study was performed to measure the SaO2 (co-oximeter) and pO2 (Oxylite probe) in blood using Hill’s equation (P50=23.2 mmHg and n=2.26). Next, non-invasive localized measurements of SaO2 in MDA-MD-231 and MCF7 breast tumors using PCT spectroscopic methods were compared to pO 2 levels using Oxylite probe. The fitted results for MCF7 and MDA-MD-231 data resulted in a P50 of 17.2 mmHg and 20.7 mmHg and a n of 1.76 and 1.63, respectively. The lower value of the P50 is consistent with tumors being more acidic than healthy tissue. Current work applying photon fluence corrections and image artifact reduction is expected to improve the quality of the results. In summary, this study demonstrates that photoacoustic imaging can be used to monitor tumor oxygenation, and its potential use to investigate the effectiveness of radiation therapy and the ability to adapt therapeutic protocols

A review of artificial intelligence in prostate cancer detection on imaging

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care

Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image Analysis

Elasticity parameter estimation is essential for generating accurate and controlled simulation results for computer animation and medical image analysis. However, finding the optimal parameters for a particular simulation often requires iterations of simulation, assessment, and adjustment and can become a tedious process. Elasticity values are especially important in medical image analysis, since cancerous tissues tend to be stiffer. Elastography is a popular type of method for finding stiffness values by reconstructing a dense displacement field from medical images taken during the application of forces or vibrations. These methods, however, are limited by the imaging modality and the force exertion or vibration actuation mechanisms, which can be complicated for deep-seated organs. In this thesis, I present a novel method for reconstructing elasticity parameters without requiring a dense displacement field or a force exertion device. The method makes use of natural deformations within the patient and relies on surface information from segmented images taken on different days. The elasticity value of the target organ and boundary forces acting on surrounding organs are optimized with an iterative optimizer, within which the deformation is always generated by a physically-based simulator. Experimental results on real patient data are presented to show the positive correlation between recovered elasticity values and clinical prostate cancer stages. Furthermore, to resolve the performance issue arising from the high dimensionality of boundary forces, I propose to use a reduced finite element model to improve the convergence of the optimizer. To find the set of bases to represent the dimensions for forces, a statistical training based on real patient data is performed. I demonstrate the trade-off between accuracy and performance by using different numbers of bases in the optimization using synthetic data. A speedup of more than an order of magnitude is observed without sacrificing too much accuracy in recovered elasticity.Doctor of Philosoph

Organ-focused mutual information for nonrigid multimodal registration of liver CT and Gd–EOB–DTPA-enhanced MRI

Accurate detection of liver lesions is of great importance in hepatic surgery planning. Recent studies have shown that the detection rate of liver lesions is significantly higher in gadoxetic acid-enhanced magnetic resonance imaging (Gd–EOB–DTPA-enhanced MRI) than in contrast-enhanced portal-phase computed tomography (CT); however, the latter remains essential because of its high specificity, good performance in estimating liver volumes and better vessel visibility. To characterize liver lesions using both the above image modalities, we propose a multimodal nonrigid registration framework using organ-focused mutual information (OF-MI). This proposal tries to improve mutual information (MI) based registration by adding spatial information, benefiting from the availability of expert liver segmentation in clinical protocols. The incorporation of an additional information channel containing liver segmentation information was studied. A dataset of real clinical images and simulated images was used in the validation process. A Gd–EOB–DTPA-enhanced MRI simulation framework is presented. To evaluate results, warping index errors were calculated for the simulated data, and landmark-based and surface-based errors were calculated for the real data. An improvement of the registration accuracy for OF-MI as compared with MI was found for both simulated and real datasets. Statistical significance of the difference was tested and confirmed in the simulated dataset (p < 0.01)

Real-time intrafraction motion monitoring in external beam radiotherapy

© 2019 Institute of Physics and Engineering in Medicine. Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT