Temporal Interpolation via Motion Field Prediction

Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high contrast 4D MR imaging during free breathing and provides in-vivo observations for treatment planning and guidance. Navigator slices are vital for retrospective stacking of 2D data slices in this method. However, they also prolong the acquisition sessions. Temporal interpolation of navigator slices an be used to reduce the number of navigator acquisitions without degrading specificity in stacking. In this work, we propose a convolutional neural network (CNN) based method for temporal interpolation via motion field prediction. The proposed formulation incorporates the prior knowledge that a motion field underlies changes in the image intensities over time. Previous approaches that interpolate directly in the intensity space are prone to produce blurry images or even remove structures in the images. Our method avoids such problems and faithfully preserves the information in the image. Further, an important advantage of our formulation is that it provides an unsupervised estimation of bi-directional motion fields. We show that these motion fields can be used to halve the number of registrations required during 4D reconstruction, thus substantially reducing the reconstruction time.Comment: Submitted to 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, The Netherland

Investigation of time-resolved volumetric MRI to enhance MR-guided radiotherapy of moving lung tumors

In photon radiotherapy of lung cancer, respiratory-induced motion introduces systematic and statistical uncertainties in treatment planning and dose delivery. By integrating magnetic resonance imaging (MRI) in the treatment planning process in MR-guided radiotherapy (MRgRT), uncertainties in target volume definition can be reduced with respect to state-of-the-art X-ray-based approaches. Furthermore, MR-guided linear accelerators (MR-Linacs) offer dose delivery with enhanced accuracy and precision through daily treatment plan adaptation and gated beam delivery based on real-time MRI. Today, the potential of MRgRT of moving targets is, however, not fully exploited due to the lack of time-resolved four-dimensional MRI (4D-MRI) in clinical practice. Therefore, the aim of this thesis was to develop and experimentally validate new methods for motion characterization and estimation with 4D-MRI for MRgRT of lung cancer. Different concepts were investigated for all phases of the clinical workflow - treatment planning, beam delivery, and post-treatment analysis. Firstly, a novel internal target volume (ITV) definition method based on the probability-of-presence of moving tumors derived from real-time 4D-MRI was developed. The ability of the ITVs to prospectively account for changes occurring over the course of several weeks was assessed in retrospective geometric analyses of lung cancer patient data. Higher robustness of the probabilistic 4D-MRI-based ITVs against interfractional changes was observed compared to conventional target volumes defined with four-dimensional computed tomography (4D-CT). The study demonstrated that motion characterization over extended times enabled by real-time 4D-MRI can reduce systematic and statistical uncertainties associated with today’s standard workflow. Secondly, experimental validation of a published motion estimation method - the propagation method - was conducted with a porcine lung phantom under realistic patient-like conditions. Estimated 4D-MRIs with a temporal resolution of 3.65 Hz were created based on orthogonal 2D cine MRI acquired at the scanner unit of an MR-Linac. A comparison of these datasets with ground truth respiratory-correlated 4D-MRIs in geometric analyses showed that the propagation method can generate geometrically accurate estimated 4D-MRIs. These could decrease target localization errors and enable 3D motion monitoring during beam delivery at the MR-Linac in the future. Lastly, the propagation method was extended to create continuous time-resolved estimated synthetic CTs (tresCTs). The proposed method was experimentally tested with the porcine lung phantom, successively imaged at a CT scanner and an MR-Linac. A high agreement of the images and corresponding dose distributions of the tresCTs and measured ground truth 4D-CTs was found in geometric and dosimetric analyses. The tresCTs could be used for post-treatment time-resolved reconstruction of the delivered dose to guide treatment adaptations in the future. These studies represent important steps towards a clinical application of time-resolved 4D-MRI methods for enhanced MRgRT of lung tumors in the near future

Advanced Medical Image Registration Methods for Quantitative Imaging and Multi-Channel Images

This thesis proposes advanced medical image registration methods for applications that can be grouped in two broad themes. The first theme focuses on registration techniques increasing the reliability of _quantitative measurements_ extracted from sets of medical images. The second theme that is considered in this thesis is the registration of _multi-channel_ images

A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image

Dynamic medical imaging is usually limited in application due to the large radiation doses and longer image scanning and reconstruction times. Existing methods attempt to reduce the dynamic sequence by interpolating the volumes between the acquired image volumes. However, these methods are limited to either 2D images and/or are unable to support large variations in the motion between the image volume sequences. In this paper, we present a spatiotemporal volumetric interpolation network (SVIN) designed for 4D dynamic medical images. SVIN introduces dual networks: first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures. We also introduce an adaptive multi-scale architecture to capture the volumetric large anatomy motions. Experimental results demonstrated that our SVIN outperformed state-of-the-art temporal medical interpolation methods and natural video interpolation methods that have been extended to support volumetric images. Our ablation study further exemplified that our motion network was able to better represent the large functional motion compared with the state-of-the-art unsupervised medical registration methods.Comment: 10 pages, 8 figures, Conference on Computer Vision and Pattern Recognition (CVPR) 202

Motion estimation and correction for simultaneous PET/MR using SIRF and CIL

SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'

Motion estimation and correction for simultaneous PET/MR using SIRF and CIL

SIRF is a powerful PET/MR image reconstruction research tool for processing data and developing new algorithms. In this research, new developments to SIRF are presented, with focus on motion estimation and correction. SIRF's recent inclusion of the adjoint of the resampling operator allows gradient propagation through resampling, enabling the MCIR technique. Another enhancement enabled registering and resampling of complex images, suitable for MRI. Furthermore, SIRF's integration with the optimization library CIL enables the use of novel algorithms. Finally, SPM is now supported, in addition to NiftyReg, for registration. Results of MR and PET MCIR reconstructions are presented, using FISTA and PDHG, respectively. These demonstrate the advantages of incorporating motion correction and variational and structural priors. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'

4D Cardiac Segmentation and Respiratory Motion Modeling of the Human Heart

Projecte final de carrera realitzat en col.laboració amb el centre Northeastern UniversityAtrial Fibrillation (AF) is a growing problem in modern societies with an enormous impact in both short term quality of life and long term survival. A recently developed promising approach to cure AF uses radiofrequency (RF) ablation to carry out Pulmonary Vein Antrum Isolation (PVAI) to the heart. However, the lack of proper 3D surgery training, planning, and guidance makes surgery a very difficult task to the surgeons and therefore the risk for the patient increases. Some work has been done developing automatic methods to segment and track the beating movement of the heart, but the purpose of this work is to add the respiratory motion to the existent models. The reconstructed heart surface will serve as a virtual computer model for the 3D surgery training, planning and guidance

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity