Surrogate-driven respiratory motion models for MRI-guided lung radiotherapy treatments

An MR-Linac integrates an MR scanner with a radiotherapy delivery system, providing non-ionizing real-time imaging of the internal anatomy before, during and after radiotherapy treatments. Due to spatio-temporal limitations of MR imaging, only high-resolution 2D cine-MR images can be acquired in real-time during MRI-guided radiotherapy (MRIgRT) to monitor the respiratory-induced motion of lung tumours and organs-at-risk. However, temporally-resolved 3D anatomical information is essential for accurate MR guidance of beam delivery and dose estimation of the actually delivered dose. Surrogate-driven respiratory motion models can estimate the 3D motion of the internal anatomy from surrogate signals, producing the required information. The overall aim of this thesis was to tailor a generalized respiratory motion modelling framework for lung MRIgRT. This framework can fit the model directly to unsorted 2D MR images sampling the 3D motion, and to surrogate signals extracted from the 2D cine-MR images acquired on an MR-Linac. It can model breath-to-breath variability and produce a motion compensated super-resolution reconstruction (MCSR) 3D image that can be deformed using the estimated motion. In this work novel MRI-derived surrogate signals were generated from 2D cine-MR images to model respiratory motion for lung cancer patients, by applying principal component analysis to the control point displacements obtained from the registration of the cine-MR images. An MR multi-slice interleaved acquisition potentially suitable for the MR-Linac was developed to generate MRI-derived surrogate signals and build accurate respiratory motion models with the generalized framework for lung cancer patients. The developed models and the MCSR images were thoroughly evaluated for lung cancer patients scanned on an MR-Linac. The results showed that respiratory motion models built with the generalized framework and minimal training data generally produced median errors within the MCSR voxel size of 2 mm, throughout the whole 3D thoracic field-of-view and over the expected lung MRIgRT treatment times

Meta-Learning Initializations for Interactive Medical Image Registration

We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable network initialization. This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application: registration of magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled transrectal ultrasound (TRUS) images. Our approach obtains comparable registration error (4.26 mm) to the best-performing non-interactive learning-based 3D-to-3D method (3.97 mm) while requiring only a fraction of the data, and occurring in real-time during acquisition. Applying sparsely sampled data to non-interactive methods yields higher registration errors (6.26 mm), demonstrating the effectiveness of interactive MR-TRUS registration, which may be applied intraoperatively given the real-time nature of the adaptation process.Comment: 11 pages, 10 figures. Paper accepted to IEEE Transactions on Medical Imaging (October 26 2022

Doctor of Philosophy

dissertationFocused ultrasound (FUS) is a promising noninvasive and radiation-free cancer therapy that selectively delivers high-intensity acoustic energy to a small target volume. This dissertation presents original research that improves the speed, safety, and efficacy of FUS therapies under magnetic resonance imaging (MRI) guidance. First, a new adaptive model-predictive controller is presented that leverages the ability of MRI to measure temperature inside the patient at near real-time speeds. The controller uses MR temperature feedback to dynamically derive and update a patient-specific thermal model, and optimizes the treatment based on the model's predictions. Treatment safety is a key element of the controller's design, and it can actively protect healthy tissue from unwanted damage. In vivo and simulation studies indicate the controller can safeguard healthy tissue and accelerate treatments by as much as 50%. Significant tradeoffs exist between treatment speed, and safety, which makes a real-time controller absolutely necessary for carrying out efficient, effective, and safe treatments while also highlighting the importance of continued research into optimal treatment planning. Next, two new methods for performing 3D MR acoustic radiation force imaging (MR-ARFI) are presented. Both techniques measure the tissue displacement induced by short bursts of focused ultrasound, and provide a safe way to visualize the ultrasound beam's location. In some scenarios, ARFI is a necessity for proper targeting since traditional MR thermometry cannot measure temperature in fat. The first technique for performing 3D ARFI introduces a novel unbalanced bipolar motion encoding gradient. The results demonstrate that this technique is safe, and that 3D displacement maps can be attained time-efficiently even in organs that contain fat, such as breast. The second technique measures 3D ARFI simultaneously with temperature monitoring. This method uses a multi-contrast gradient recalled echo sequence which makes multiple readings of the data without increasing scan time. This improves the signal to noise ratio and makes it possible to separate the effects of tissue heating vs displacement. Both of the 3D MR-ARFI techniques complement the presented controllersince proper positioning of the focal spot is critical to achieving fast and safe treatments

UV Exposed Optical Fibers with Frequency Domain Reflectometry for Device Tracking in Intra-Arterial Procedures

Shape tracking of medical devices using strain sensing properties in optical fibers has seen increased attention in recent years. In this paper, we propose a novel guidance system for intra-arterial procedures using a distributed strain sensing device based on optical frequency domain reflectometry (OFDR) to track the shape of a catheter. Tracking enhancement is provided by exposing a fiber triplet to a focused ultraviolet beam, producing high scattering properties. Contrary to typical quasi-distributed strain sensors, we propose a truly distributed strain sensing approach, which allows to reconstruct a fiber triplet in real-time. A 3D roadmap of the hepatic anatomy integrated with a 4D MR imaging sequence allows to navigate the catheter within the pre-interventional anatomy, and map the blood flow velocities in the arterial tree. We employed Riemannian anisotropic heat kernels to map the sensed data to the pre-interventional model. Experiments in synthetic phantoms and an in vivo model are presented. Results show that the tracking accuracy is suitable for interventional tracking applications, with a mean 3D shape reconstruction errors of 1.6 +/- 0.3 mm. This study demonstrates the promising potential of MR-compatible UV-exposed OFDR optical fibers for non-ionizing device guidance in intra-arterial procedures

Automated Quantification of Mitral Regurgitation by Three Dimensional Real Time Full Volume Color Doppler Transthoracic Echocardiography: A Validation with Cardiac Magnetic Resonance Imaging and Comparison with Two Dimensional Quantitative Methods

BACKGROUND: Accurate assessment of mitral regurgitation (MR) severity is crucial for clinical decision-making and optimizing patient outcomes. Recent advances in real-time three dimensional (3D) echocardiography provide the option of real-time full volume color Doppler echocardiography (FVCD) measurements. This makes it practical to quantify MR by subtracting aortic stroke volume from the volume of mitral inflow in an automated manner. METHODS: Thirty-two patients with more than a moderate degree of MR assessed by transthoracic echocardiography (TTE) were consecutively enrolled during this study. MR volume was measured by 1) two dimensional (2D) Doppler TTE, using the proximal isovelocity surface area (PISA) and the volumetric quantification methods (VM). Then, 2) real time 3D-FVCD was subsequently obtained, and dedicated software was used to quantify the MR volume. MR volume was also measured using 3) phase contrast cardiac magnetic resonance imaging (PC-CMR). In each patient, all these measurements were obtained within the same day. Automated MR quantification was feasible in 30 of 32 patients. RESULTS: The mean regurgitant volume quantified by 2D-PISA, 2D-VM, 3D-FVCD, and PC-CMR was 72.1 ± 27.7, 79.9 ± 36.9, 69.9 ± 31.5, and 64.2 ± 30.7 mL, respectively (p = 0.304). There was an excellent correlation between the MR volume measured by PC-CMR and 3D-FVCD (r = 0.85, 95% CI 0.70-0.93, p < 0.001). Compared with PC-CMR, Bland-Altman analysis for 3D-FVCD showed a good agreement (2 standard deviations: 34.3 mL) than did 2D-PISA or 2D-VM (60.0 and 62.8 mL, respectively). CONCLUSION: Automated quantification of MR with 3D-FVCD is feasible and accurate. It is a promising tool for the real-time 3D echocardiographic assessment of patients with MR.ope

Magnetic Resonance Thermometry at 7T for Real-Time Monitoring and Correction of Ultrasound Induced Mild Hyperthermia

While Magnetic Resonance Thermometry (MRT) has been extensively utilized for non-invasive temperature measurement, there is limited data on the use of high field (≥7T) scanners for this purpose. MR-guided Focused Ultrasound (MRgFUS) is a promising non-invasive method for localized hyperthermia and drug delivery. MRT based on the temperature sensitivity of the proton resonance frequency (PRF) has been implemented in both a tissue phantom and in vivo in a mouse Met-1 tumor model, using partial parallel imaging (PPI) to speed acquisition. An MRgFUS system capable of delivering a controlled 3D acoustic dose during real time MRT with proportional, integral, and derivative (PID) feedback control was developed and validated. Real-time MRT was validated in a tofu phantom with fluoroptic temperature measurements, and acoustic heating simulations were in good agreement with MR temperature maps. In an in vivo Met-1 mouse tumor, the real-time PID feedback control is capable of maintaining the desired temperature with high accuracy. We found that real time MR control of hyperthermia is feasible at high field, and k-space based PPI techniques may be implemented for increasing temporal resolution while maintaining temperature accuracy on the order of 1°C

Contrast-enhanced ultrasound tracking of helical propellers with acoustic phase analysis and comparison with color Doppler

Medical microrobots (MRs) hold the potential to radically transform several interventional procedures. However, to guarantee therapy success when operating in hard-to-reach body districts, a precise and robust imaging strategy is required for monitoring and controlling MRs in real-time. Ultrasound (US) may represent a powerful technology, but MRs' visibility with US needs to be improved, especially when targeting echogenic tissues. In this context, motions of MRs have been exploited to enhance their contrast, e.g., by Doppler imaging. To exploit a more selective contrast-enhancement mechanism, in this study, we analyze in detail the characteristic motions of one of the most widely adopted MR concepts, i.e., the helical propeller, with a particular focus on its interactions with the backscattered US waves. We combine a kinematic analysis of the propeller 3D motion with an US acoustic phase analysis (APA) performed on the raw radio frequency US data in order to improve imaging and tracking in bio-mimicking environments. We validated our US-APA approach in diverse scenarios, aimed at simulating realistic in vivo conditions, and compared the results to those obtained with standard US Doppler. Overall, our technique provided a precise and stable feedback to visualize and track helical propellers in echogenic tissues (chicken breast), tissue-mimicking phantoms with bifurcated lumina, and in the presence of different motion disturbances (e.g., physiological flows and tissue motions), where standard Doppler showed poor performance. Furthermore, the proposed US-APA technique allowed for real-time estimation of MR velocity, where standard Doppler failed

Improved 3D MR Image Acquisition and Processing in Congenital Heart Disease

Congenital heart disease (CHD) is the most common type of birth defect, affecting about 1% of the population. MRI is an essential tool in the assessment of CHD, including diagnosis, intervention planning and follow-up. Three-dimensional MRI can provide particularly rich visualization and information. However, it is often complicated by long scan times, cardiorespiratory motion, injection of contrast agents, and complex and time-consuming postprocessing. This thesis comprises four pieces of work that attempt to respond to some of these challenges. The first piece of work aims to enable fast acquisition of 3D time-resolved cardiac imaging during free breathing. Rapid imaging was achieved using an efficient spiral sequence and a sparse parallel imaging reconstruction. The feasibility of this approach was demonstrated on a population of 10 patients with CHD, and areas of improvement were identified. The second piece of work is an integrated software tool designed to simplify and accelerate the development of machine learning (ML) applications in MRI research. It also exploits the strengths of recently developed ML libraries for efficient MR image reconstruction and processing. The third piece of work aims to reduce contrast dose in contrast-enhanced MR angiography (MRA). This would reduce risks and costs associated with contrast agents. A deep learning-based contrast enhancement technique was developed and shown to improve image quality in real low-dose MRA in a population of 40 children and adults with CHD. The fourth and final piece of work aims to simplify the creation of computational models for hemodynamic assessment of the great arteries. A deep learning technique for 3D segmentation of the aorta and the pulmonary arteries was developed and shown to enable accurate calculation of clinically relevant biomarkers in a population of 10 patients with CHD