Efficient convolution-based pairwise elastic image registration on three multimodal similarity metrics

Producción CientíficaThis paper proposes a complete convolutional formulation for 2D multimodal pairwise image registration problems based on free-form deformations. We have reformulated in terms of discrete 1D convolutions the evaluation of spatial transformations, the regularization term, and their gradients for three different multimodal registration metrics, namely, normalized cross correlation, mutual information, and normalized mutual information. A sufficient condition on the metric gradient is provided for further extension to other metrics. The proposed approach has been tested, as a proof of concept, on contrast-enhanced first-pass perfusion cardiac magnetic resonance images. Execution times have been compared with the corresponding execution times of the classical tensor product formulation, both on CPU and GPU. The speed-up achieved by using convolutions instead of tensor products depends on the image size and the number of control points considered, the larger those magnitudes, the greater the execution time reduction. Furthermore, the speed-up will be more significant when gradient operations constitute the major bottleneck in the optimization process.Ministerio de Economía, Industria y Competitividad (grants TEC2017-82408-R and PID2020-115339RB-I00)ESAOTE Ltd (grant 18IQBM

Computación paralela heterogénea en registro de imágenes y aplicaciones de álgebra lineal

This doctoral thesis focuses on GPU acceleration of medical image registration and sparse general matrix-matrix multiplication (SpGEMM). The comprehensive work presented here aims to enable new possibilities in Image Guided Surgery (IGS). IGS provides the surgeon with advanced navigation tools during surgery. Image registration, which is a part of IGS, is computationally demanding, therefore GPU acceleration is greatly desirable. spGEMM, which is an essential part in many scientific and data analytics applications, e.g., graph applications, is also a useful tool in biomechanical modeling and sparse vessel network registration. We present this work in two parts. The first part of this thesis describes the optimization of the most demanding part of non-rigid Free Form Deformation registration, i.e., B-spline interpolation. Our novel optimization technique minimizes the data movement between processing cores and memory and maximizes the utilization of the very fast register file. In addition, our approach re-formulates B-spline interpolation to fully utilize Fused Multiply Accumulation instructions for additional benefits in performance and accuracy. Our optimized B-spline interpolation provides significant speedup to image registration. The second part describes the optimization of spGEMM. Hardware manufacturers, with the aim of increasing the performance of deep-learning, created specialized dense matrix multiplication units, called Tensor Core Units (TCUs). However, until now, no work takes advantage of TCUs for sparse matrix multiplication. With this work we provide the first TCU implementation of spGEMM and prove its benefits over conventional GPU spGEMM.Esta tesis doctoral se centra en la aceleración por GPU del registro de imágenes médicas y la multiplicación de matrices dispersas (SpGEMM). El exhaustivo trabajo presentado aquí tiene como objetivo permitir nuevas posibilidades en la cirugía guiada por imagen (IGS). IGS proporciona al cirujano herramientas de navegación avanzadas durante la cirugía. El registro de imágenes, parte de IGS computacionalmente exigente, por lo tanto, la aceleración en GPU es muy deseable. spGEMM, la cual es una parte esencial en muchas aplicaciones científicas y de análisis de datos, por ejemplo, aplicaciones de gráficos, también es una herramienta útil en el modelado biomecánico y el registro de redes de vasos dispersos. Presentamos este trabajo en dos partes. La primera parte de esta tesis describe la optimización de la parte más exigente del registro de deformación de forma libre no rígida, es decir, la interpolación B-spline. Nuestra novedosa técnica de optimización minimiza el movimiento de datos entre los núcleos de procesamiento y la memoria y maximiza la utilización del archivo de registro rápido. Además, nuestro enfoque reformula la interpolación B-spline para utilizar completamente las instrucciones de multiplicación-acumulación fusionada (FMAC) para obtener beneficios adicionales en rendimiento y precisión. Nuestra interpolación B-spline optimizada proporciona una aceleración significativa en el registro de imágenes. La segunda parte describe la optimización de spGEMM. Los fabricantes de hardware, con el objetivo de aumentar el rendimiento del aprendizaje profundo, crearon unidades especializadas de multiplicación de matrices densas, llamadas Tensor Core Units (TCU). Sin embargo, hasta ahora, no se ha encontrado ningún trabajo aprovecha las TCU para la multiplicación de matrices dispersas. Con este trabajo, proporcionamos la primera implementación TCU de spGEMM y demostramos sus beneficios sobre la spGEMM convencional operada sobre dispositivos GPU

Parallel mutual information estimation for inferring gene regulatory networks on GPUs

<p>Abstract</p> <p>Background</p> <p>Mutual information is a measure of similarity between two variables. It has been widely used in various application domains including computational biology, machine learning, statistics, image processing, and financial computing. Previously used simple histogram based mutual information estimators lack the precision in quality compared to kernel based methods. The recently introduced B-spline function based mutual information estimation method is competitive to the kernel based methods in terms of quality but at a lower computational complexity.</p> <p>Results</p> <p>We present a new approach to accelerate the B-spline function based mutual information estimation algorithm with commodity graphics hardware. To derive an efficient mapping onto this type of architecture, we have used the Compute Unified Device Architecture (CUDA) programming model to design and implement a new parallel algorithm. Our implementation, called CUDA-MI, can achieve speedups of up to 82 using double precision on a single GPU compared to a multi-threaded implementation on a quad-core CPU for large microarray datasets. We have used the results obtained by CUDA-MI to infer gene regulatory networks (GRNs) from microarray data. The comparisons to existing methods including ARACNE and TINGe show that CUDA-MI produces GRNs of higher quality in less time.</p> <p>Conclusions</p> <p>CUDA-MI is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant speedup over sequential multi-threaded implementation by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.</p

Graphics Processing Unit–Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations

Rationale and Objectives: Accuracy and speed are essential for the intraprocedural nonrigid MR-to-CT image registration in the assessment of tumor margins during CT-guided liver tumor ablations. While both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique based on volume subdivision with hardware acceleration using a graphical processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. Materials and Methods: Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD)), and total processing time including contouring of ROIs and computation were compared using a paired Student’s t-test. Results: Accuracy of the GPU-accelerated registrations and B-spline registrations, respectively were 88.3 ± 3.7% vs 89.3 ± 4.9% (p = 0.41) for DSC and 13.1 ± 5.2 mm vs 11.4 ± 6.3 mm (p = 0.15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 s vs 557 ± 116 s (p < 0.000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (p = 0.71). Conclusion: The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly less processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into routine clinical practice

Graphics Processing Unit–Accelerated Nonrigid Registration of MR Images to CT Images During CT-Guided Percutaneous Liver Tumor Ablations

Rationale and Objectives: Accuracy and speed are essential for the intraprocedural nonrigid MR-to-CT image registration in the assessment of tumor margins during CT-guided liver tumor ablations. While both accuracy and speed can be improved by limiting the registration to a region of interest (ROI), manual contouring of the ROI prolongs the registration process substantially. To achieve accurate and fast registration without the use of an ROI, we combined a nonrigid registration technique based on volume subdivision with hardware acceleration using a graphical processing unit (GPU). We compared the registration accuracy and processing time of GPU-accelerated volume subdivision-based nonrigid registration technique to the conventional nonrigid B-spline registration technique. Materials and Methods: Fourteen image data sets of preprocedural MR and intraprocedural CT images for percutaneous CT-guided liver tumor ablations were obtained. Each set of images was registered using the GPU-accelerated volume subdivision technique and the B-spline technique. Manual contouring of ROI was used only for the B-spline technique. Registration accuracies (Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD)), and total processing time including contouring of ROIs and computation were compared using a paired Student’s t-test. Results: Accuracy of the GPU-accelerated registrations and B-spline registrations, respectively were 88.3 ± 3.7% vs 89.3 ± 4.9% (p = 0.41) for DSC and 13.1 ± 5.2 mm vs 11.4 ± 6.3 mm (p = 0.15) for HD. Total processing time of the GPU-accelerated registration and B-spline registration techniques was 88 ± 14 s vs 557 ± 116 s (p < 0.000000002), respectively; there was no significant difference in computation time despite the difference in the complexity of the algorithms (p = 0.71). Conclusion: The GPU-accelerated volume subdivision technique was as accurate as the B-spline technique and required significantly less processing time. The GPU-accelerated volume subdivision technique may enable the implementation of nonrigid registration into routine clinical practice

Fast registration of contrast-enhanced magnetic resonance images of the breast

Master'sMASTER OF ENGINEERIN

FFD:Fast Feature Detector

Scale-invariance, good localization and robustness to noise and distortions are the main properties that a local feature detector should possess. Most existing local feature detectors find excessive unstable feature points that increase the number of keypoints to be matched and the computational time of the matching step. In this paper, we show that robust and accurate keypoints exist in the specific scale-space domain. To this end, we first formulate the superimposition problem into a mathematical model and then derive a closed-form solution for multiscale analysis. The model is formulated via difference-of-Gaussian (DoG) kernels in the continuous scale-space domain, and it is proved that setting the scale-space pyramid's blurring ratio and smoothness to 2 and 0.627, respectively, facilitates the detection of reliable keypoints. For the applicability of the proposed model to discrete images, we discretize it using the undecimated wavelet transform and the cubic spline function. Theoretically, the complexity of our method is less than 5\% of that of the popular baseline Scale Invariant Feature Transform (SIFT). Extensive experimental results show the superiority of the proposed feature detector over the existing representative hand-crafted and learning-based techniques in accuracy and computational time. The code and supplementary materials can be found at~{\url{https://github.com/mogvision/FFD}}