ParaPET: non-invasive deep learning method for direct parametric brain PET reconstruction using histoimages.

BACKGROUND The indirect method for generating parametric images in positron emission tomography (PET) involves the acquisition and reconstruction of dynamic images and temporal modelling of tissue activity given a measured arterial input function. This approach is not robust, as noise in each dynamic image leads to a degradation in parameter estimation. Direct methods incorporate into the image reconstruction step both the kinetic and noise models, leading to improved parametric images. These methods require extensive computational time and large computing resources. Machine learning methods have demonstrated significant potential in overcoming these challenges. But they are limited by the requirement of a paired training dataset. A further challenge within the existing framework is the use of state-of-the-art arterial input function estimation via temporal arterial blood sampling, which is an invasive procedure, or an additional magnetic resonance imaging (MRI) scan for selecting a region where arterial blood signal can be measured from the PET image. We propose a novel machine learning approach for reconstructing high-quality parametric brain images from histoimages produced from time-of-flight PET data without requiring invasive arterial sampling, an MRI scan, or paired training data from standard field-of-view scanners. RESULT The proposed is tested on a simulated phantom and five oncological subjects undergoing an 18F-FDG-PET scan of the brain using Siemens Biograph Vision Quadra. Kinetic parameters set in the brain phantom correlated strongly with the estimated parameters (K1, k2 and k3, Pearson correlation coefficient of 0.91, 0.92 and 0.93) and a mean squared error of less than 0.0004. In addition, our method significantly outperforms (p < 0.05, paired t-test) the conventional nonlinear least squares method in terms of contrast-to-noise ratio. At last, the proposed method was found to be 37% faster than the conventional method. CONCLUSION We proposed a direct non-invasive DL-based reconstruction method and produced high-quality parametric maps of the brain. The use of histoimages holds promising potential for enhancing the estimation of parametric images, an area that has not been extensively explored thus far. The proposed method can be applied to subject-specific dynamic PET data alone

Doctor of Philosophy

dissertationMagnetic Resonance Imaging (MRI) is one of the most important medical imaging technologies in use today. Unlike other imaging tools, such as X-ray imaging or computed tomography (CT), MRI is noninvasive and without ionizing radiation. A major limitation of MRI, however, is its relatively low imaging speed and low spatial-temporal resolution, as in the case of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). These hinder the clinical use of MRI. In this thesis, we aim to develop novel signal processing techniques to improve the imaging quality and reduce the imaging time of MRI. This thesis consists of two parts, corresponding to our work on parallel MRI and dynamic MRI, respectively. In the first part, we address an important problem in parallel MRI that the coil sensitivities functions are not known exactly and the estimation error often leads to artifacts in the reconstructed image. First, we develop a new framework based on multichannel blind deconvolution (MBD) to jointly estimate the image and the sensitivity functions. For fully sampled MRI, the proposed approach yields more uniform image reconstructions than that of the sum-of-squares (SOS) and other existing methods. Second, we extend this framework to undersampled parallel MRI and develop a new algorithm, termed Sparse BLIP, for blind iterative parallel image reconstruction using compressed sensing (CS). Sparse BLIP reconstructs both the sensitivity functions and the image simultaneously from the undersampled data, while enforcing the sparseness constraint in the image and sensitivities. Superior image constructions can be obtained by Sparse BLIP when compared to other state-of-the-art methods. In the second part of the thesis, we study highly accelerated DCE-MRI and provide a comparative study of the temporal constraint reconstruction (TCR) versus model-based reconstruction. We find that, at high reduction factors, the choice of baseline image greatly affects the convergence of TCR and the improved TCR algorithm with the proposed baseline initialization can achieve good performance without much loss of temporal or spatial resolution for a high reduction factor of 30. The model-based approach, on the other hand, performs inferior to TCR with even the best phase initialization

High-resolution diffusion-weighted brain MRI under motion

Magnetic resonance imaging is one of the fastest developing medical imaging techniques. It provides excellent soft tissue contrast and has been a leading tool for neuroradiology and neuroscience research over the last decades. One of the possible MR imaging contrasts is the ability to visualize diffusion processes. The method, referred to as diffusion-weighted imaging, is one of the most common clinical contrasts but is prone to artifacts and is challenging to acquire at high resolutions. This thesis aimed to improve the resolution of diffusion weighted imaging, both in a clinical and in a research context. While diffusion-weighted imaging traditionally has been considered a 2D technique the manuscripts and methods presented here explore 3D diffusion acquisitions with isotropic resolution. Acquiring multiple small 3D volumes, or slabs, which are combined into one full volume has been the method of choice in this work. The first paper presented explores a parallel imaging driven multi-echo EPI readout to enable high resolution with reduced geometric distortions. The work performed on diffusion phase correction lead to an understanding that was used for the subsequent multi-slab papers. The second and third papers introduce the diffusion-weighted 3D multi-slab echo-planar imaging technique and explore its advantages and performance. As the method requires a slightly increased acquisition time the need for prospective motion correction became apparent. The forth paper suggests a new motion navigator using the subcutaneous fat surrounding the skull for rigid body head motion estimation, dubbed FatNav. The spatially sparse representation of the fat signal allowed for high parallel imaging acceleration factors, short acquisition times, and reduced geometric distortions of the navigator. The fifth manuscript presents a combination of the high-resolution 3D multi-slab technique and a modified FatNav module. Unlike our first FatNav implementation, using a single sagittal slab, this modified navigator acquired orthogonal projections of the head using the fat signal alone. The combined use of both presented methods provides a promising start for a fully motion corrected high-resolution diffusion acquisition in a clinical setting