Reconstruction of 7T-Like Images From 3T MRI

In the recent MRI scanning, ultra-high-field (7T) MR imaging provides higher resolution and better tissue contrast compared to routine 3T MRI, which may help in more accurate and early brain diseases diagnosis. However, currently, 7T MRI scanners are more expensive and less available at clinical and research centers. These motivate us to propose a method for the reconstruction of images close to the quality of 7T MRI, called 7T-like images, from 3T MRI, to improve the quality in terms of resolution and contrast. By doing so, the post-processing tasks, such as tissue segmentation, can be done more accurately and brain tissues details can be seen with higher resolution and contrast. To do this, we have acquired a unique dataset which includes paired 3T and 7T images scanned from same subjects, and then propose a hierarchical reconstruction based on group sparsity in a novel multi-level Canonical Correlation Analysis (CCA) space, to improve the quality of 3T MR image to be 7T-like MRI. First, overlapping patches are extracted from the input 3T MR image. Then, by extracting the most similar patches from all the aligned 3T and 7T images in the training set, the paired 3T and 7T dictionaries are constructed for each patch. It is worth noting that, for the training, we use pairs of 3T and 7T MR images from each training subject. Then, we propose multi-level CCA to map the paired 3T and 7T patch sets to a common space to increase their correlations. In such space, each input 3T MRI patch is sparsely represented by the 3T dictionary and then the obtained sparse coefficients are used together with the corresponding 7T dictionary to reconstruct the 7T-like patch. Also, to have the structural consistency between adjacent patches, the group sparsity is employed. This reconstruction is performed with changing patch sizes in a hierarchical framework. Experiments have been done using 13 subjects with both 3T and 7T MR images. The results show that our method outperforms previous methods and is able to recover better structural details. Also, to place our proposed method in a medical application context, we evaluated the influence of post-processing methods such as brain tissue segmentation on the reconstructed 7T-like MR images. Results show that our 7T-like images lead to higher accuracy in segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and skull, compared to segmentation of 3T MR images

7T-guided super-resolution of 3T MRI

High-resolution MR images can depict rich details of brain anatomical structures and show subtle changes in longitudinal data. 7T MRI scanners can acquire MR images with higher resolution and better tissue contrast than the routine 3T MRI scanners. However, 7T MRI scanners are currently more expensive and less available in clinical and research centers. To this end, we propose a method to generate super-resolution 3T MRI that resembles 7T MRI, which is called as 7T-like MR image in this paper

Resolution enhancement of lung 4D-CT via group-sparsity

4D-CT typically delivers more accurate information about anatomical structures in the lung, over 3D-CT, due to its ability to capture visual information of the lung motion across different respiratory phases. This helps to better determine the dose during radiation therapy for lung cancer. However, a critical concern with 4D-CT that substantially compromises this advantage is the low superior-inferior resolution due to less number of acquired slices, in order to control the CT radiation dose. To address this limitation, the authors propose an approach to reconstruct missing intermediate slices, so as to improve the superior-inferior resolution

Estimating the 4D respiratory lung motion by spatiotemporal registration and super-resolution image reconstruction

One of the main challenges in lung cancer radiation therapy is how to reduce the treatment margin but accommodate the geometric uncertainty of moving tumor. 4D-CT is able to provide the full range of motion information for the lung and tumor. However, accurate estimation of lung motion with respect to the respiratory phase is difficult due to various challenges in image registration, e.g., motion artifacts and large interslice thickness in 4D-CT. Meanwhile, the temporal coherence across respiration phases is usually not guaranteed in the conventional registration methods which consider each phase image in 4D-CT independently. To address these challenges, the authors present a unified approach to estimate the respiratory lung motion with two iterative steps

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1

ROBUST DEEP LEARNING METHODS FOR SOLVING INVERSE PROBLEMS IN MEDICAL IMAGING

The medical imaging field has a long history of incorporating machine learning algorithms to address inverse problems in image acquisition and analysis. With the impressive successes of deep neural networks on natural images, we seek to answer the obvious question: do these successes also transfer to the medical image domain? The answer may seem straightforward on the surface. Tasks like image-to-image transformation, segmentation, detection, etc., have direct applications for medical images. For example, metal artifact reduction for Computed Tomography (CT) and reconstruction from undersampled k-space signal for Magnetic Resonance (MR) imaging can be formulated as an image-to-image transformation; lesion/tumor detection and segmentation are obvious applications for higher level vision tasks. While these tasks may be similar in formulation, many practical constraints and requirements exist in solving these tasks for medical images. Patient data is highly sensitive and usually only accessible from individual institutions. This creates constraints on the available groundtruth, dataset size, and computational resources in these institutions to train performant models. Due to the mission-critical nature in healthcare applications, requirements such as performance robustness and speed are also stringent. As such, the big-data, dense-computation, supervised learning paradigm in mainstream deep learning is often insufficient to address these situations. In this dissertation, we investigate ways to benefit from the powerful representational capacity of deep neural networks while still satisfying the above-mentioned constraints and requirements. The first part of this dissertation focuses on adapting supervised learning to account for variations such as different medical image modality, image quality, architecture designs, tasks, etc. The second part of this dissertation focuses on improving model robustness on unseen data through domain adaptation, which ameliorates performance degradation due to distribution shifts. The last part of this dissertation focuses on self-supervised learning and learning from synthetic data with a focus in tomographic imaging; this is essential in many situations where the desired groundtruth may not be accessible