Parallel Computation of Nonrigid Image Registration

Automatic intensity-based nonrigid image registration brings significant impact in medical applications such as multimodality fusion of images, serial comparison for monitoring disease progression or regression, and minimally invasive image-guided interventions. However, due to memory and compute intensive nature of the operations, intensity-based image registration has remained too slow to be practical for clinical adoption, with its use limited primarily to as a pre-operative too. Efficient registration methods can lead to new possibilities for development of improved and interactive intraoperative tools and capabilities. In this thesis, we propose an efficient parallel implementation for intensity-based three-dimensional nonrigid image registration on a commodity graphics processing unit. Optimization techniques are developed to accelerate the compute-intensive mutual information computation. The study is performed on the hierarchical volume subdivision-based algorithm, which is inherently faster than other nonrigid registration algorithms and structurally well-suited for data-parallel computation platforms. The proposed implementation achieves more than 50-fold runtime improvement over a standard implementation on a CPU. The execution time of nonrigid image registration is reduced from hours to minutes while retaining the same level of registration accuracy

Quicksilver: Fast Predictive Image Registration - a Deep Learning Approach

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni- / multi- modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.Comment: Add new discussion

High-performance image registration algorithms for multi-core processors

Deformable registration consists of aligning two or more 3D images into a common coordinate frame. Fusing multiple images in this fashion quantifies changes in organ shape, size, and position as described by the image set, thus providing physicians with a more complete understanding of patient anatomy and function. In the field of image-guided surgery, for example, neurosurgeons can track localized deformations within the brain during surgical procedures, thereby reducing the amount of unresected tumor.Though deformable registration has the potential to improve the geometric precision for a variety of medical procedures, most modern algorithms are time consuming and, therefore, go unused for routine clinical procedures. This thesis develops highly data-parallel registration algorithms suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Specific contributions include the following:Parallel versions of both unimodal and multi-modal B-spline registration algorithms where the deformation is described in terms of uniform cubic B-spline coefficients. The unimodal case involves aligning images obtained using the same imaging technique whereas multi-modal registration aligns images obtained via differing imaging techniques by employing the concept of statistical mutual information.Multi-core versions of an analytical regularization method that imposes smoothness constraints on the deformation derived by both unimodal and multi-modal registration.The proposed method operates entirely on the B-spline coefficients which parameterize the deformation and, therefore, exhibits superior performance, in terms of execution-time overhead, over numerical methods that use central differencing.The above contributions have been implemented as part of the high-performance medical image registration software package Plastimatch, which can be downloaded under an open source license from www.plastimatch.org. Plastimatch significantly reduces the execution time incurred by B-spline based registration algorithms: compared to highly optimized sequential implementations on the CPU, we achieve a speedup of approximately 21 times for GPU-based multi-modal deformable registration while maintaining near-identical registration quality and a speedup of approximately 600 times for multi-core CPU-based regularization. It is hoped that these improvements in processing speed will allow deformable registration to be routinely used in time-sensitive procedures such as image-guided surgery and image-guided radiotherapy which require low latency from imaging to analysis.Ph.D., Computer Engineering -- Drexel University, 201

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

GPU Accelerated Registration of Hyperspectral Images Using KAZE Features

This is a post-peer-review, pre-copyedit version of an article published in The Journal of Supercomputing. The final authenticated version is available online at: https://doi.org/10.1007/s11227-020-03214-0Image registration is a common task in remote sensing, consisting in aligning different images of the same scene. It is a computationally expensive process, especially if high precision is required, the resolution is high, or consist of a large number of bands, as is the case of the hyperspectral images. HSIKAZEisaregistration method specially adapted for hyperspectral images that is based on feature detection and takes profit of the spatial and the spectral information available in those images. In this paper, an implementation of the HSI–KAZE registration algorithm on GPUs using CUDA is proposed. It detects keypoints based on non–linear diffusion filtering and is suitable for on–board processing of high resolution hyperspectral images. The algorithm includes a band selection method based on the entropy, construction of a scale-space through of non-linear filtering, keypoint detection with position refinement, and keypoint descriptors with spatial and spectral parts. Several techniques have been applied to obtain optimum performance on the GPUThis work was supported in part by the Consellería de Educación, Universidade e Formación Profesional [Grant Nos. GRC2014/008, ED431C 2018/19 and ED431G/08] and Ministerio de Economía y Empresa, Government of Spain [grant number TIN2016-76373-P] and by Junta de Castilla y Leon - ERDF (PROPHET Project) [Grant No. VA082P17]. All are cofunded by the European Regional Development Fund (ERDF). The work of Álvaro Ordóñez was also supported by Ministerio de Ciencia, Innovación y Universidades, Government of Spain, under a FPU Grant [Grant Nos. FPU16/03537 and EST18/00602]S

A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration

Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for \textit{unsupervised} affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.Comment: Accepted: Medical Image Analysis - Elsevie

NiftyNet: a deep-learning platform for medical imaging

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this application requires substantial implementation effort. Thus, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. NiftyNet provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on TensorFlow and supports TensorBoard visualization of 2D and 3D images and computational graphs by default. We present 3 illustrative medical image analysis applications built using NiftyNet: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. NiftyNet enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications.Comment: Wenqi Li and Eli Gibson contributed equally to this work. M. Jorge Cardoso and Tom Vercauteren contributed equally to this work. 26 pages, 6 figures; Update includes additional applications, updated author list and formatting for journal submissio