Multi-Device Parallel MRI Reconstruction: Efficient Partitioning for Undersampled 5D Cardiac CINE.

Cardiac CINE, a form of dynamic cardiac MRI, is indispensable in the diagnosis and treatment of heart conditions, offering detailed visualization essential for the early detection of cardiac diseases. As the demand for higher-resolution images increases, so does the volume of data requiring processing, presenting significant computational challenges that can impede the efficiency of diagnostic imaging. Our research presents an approach that takes advantage of the computational power of multiple Graphics Processing Units (GPUs) to address these challenges. GPUs are devices capable of performing large volumes of computations in a short period, and have significantly improved the cardiac MRI reconstruction process, allowing images to be produced faster. The innovation of our work resides in utilizing a multi-device system capable of processing the substantial data volumes demanded by high-resolution, five-dimensional cardiac MRI. This system surpasses the memory capacity limitations of single GPUs by partitioning large datasets into smaller, manageable segments for parallel processing, thereby preserving image integrity and accelerating reconstruction times. Utilizing OpenCL technology, our system offers adaptability and cross-platform functionality, ensuring wider applicability. The proposed multi-device approach offers an advancement in medical imaging, accelerating the reconstruction process and facilitating faster and more effective cardiac health assessment.

Computing thickness of irregularly-shaped thin walls using a locally semi-implicit scheme with extrapolation to solve the Laplace equation: Application to the right ventricle.

Cardiac Magnetic Resonance (CMR) Imaging is currently considered the gold standard imaging modality in cardiology. However, it is accompanied by a tradeoff between spatial resolution and acquisition time. Providing accurate measures of thin walls relative to the image resolution may prove challenging. One such anatomical structure is the cardiac right ventricle. Methods for measuring thickness of wall-like anatomical structures often rely on the Laplace equation to provide point-to-point correspondences between both boundaries. This work presents limex, a novel method to solve the Laplace equation using ghost nodes and providing extrapolated values, which is tested on three different datasets: a mathematical phantom, a set of biventricular segmentations from CMR images of ten pigs and the database used at the RV Segmentation Challenge held at MICCAI'12. Thickness measurements using the proposed methodology are more accurate than state-of-the-art methods, especially with the coarsest image resolutions, yielding mean L1 norms of the error between 43.28% and 86.52% lower than the second-best methods on the different test datasets. It is also computationally affordable. Limex has outperformed other state-of-the-art methods in classifying RV myocardial segments by their thickness.

A clinically viable vendor-independent and device-agnostic solution for accelerated cardiac MRI reconstruction.

BACKGROUND AND OBJECTIVE: Recent research has reported methods that reconstruct cardiac MR images acquired with acceleration factors as high as 15 in Cartesian coordinates. However, the computational cost of these techniques is quite high, taking about 40 min of CPU time in a typical current machine. This delay between acquisition and final result can completely rule out the use of MRI in clinical environments in favor of other techniques, such as CT. In spite of this, reconstruction methods reported elsewhere can be parallelized to a high degree, a fact that makes them suitable for GPU-type computing devices. This paper contributes a vendor-independent, device-agnostic implementation of such a method to reconstruct 2D motion-compensated, compressed-sensing MRI sequences in clinically viable times. METHODS: By leveraging our OpenCLIPER framework, the proposed system works in any computing device (CPU, GPU, DSP, FPGA, etc.), as long as an OpenCL implementation is available, and development is significantly simplified versus a pure OpenCL implementation. In OpenCLIPER, the problem is partitioned in independent black boxes which may be connected as needed, while device initialization and maintenance is handled automatically. Parallel implementations of both a groupwise FFD-based registration method, as well as a multicoil extension of the NESTA algorithm have been carried out as processes of OpenCLIPER. Our platform also includes significant development and debugging aids. HIP code and precompiled libraries can be integrated seamlessly as well since OpenCLIPER makes data objects shareable between OpenCL and HIP. This also opens an opportunity to include CUDA source code (via HIP) in prospective developments. RESULTS: The proposed solution can reconstruct a whole 12-14 slice CINE volume acquired in 19-32 coils and 20 phases, with an acceleration factor of ranging 4-8, in a few seconds, with results comparable to another popular platform (BART). If motion compensation is included, reconstruction time is in the order of one minute. CONCLUSIONS: We have obtained clinically-viable times in GPUs from different vendors, with delays in some platforms that do not have correspondence with its price in the market. We also contribute a parallel groupwise registration subsystem for motion estimation/compensation and a parallel multicoil NESTA subsystem for l1-l2-norm problem solving.

Fast 4D elastic group-wise image registration. Convolutional interpolation revisited.

BACKGROUND AND OBJECTIVE: This paper proposes a new and highly efficient implementation of 3D+t groupwise registration based on the free-form deformation paradigm. METHODS: Deformation is posed as a cascade of 1D convolutions, achieving great reduction in execution time for evaluation of transformations and gradients. RESULTS: The proposed method has been applied to 4D cardiac MRI and 4D thoracic CT monomodal datasets. Results show an average runtime reduction above 90%, both in CPU and GPU executions, compared with the classical tensor product formulation. CONCLUSIONS: Our implementation, although fully developed for the metric sum of squared differences, can be extended to other metrics and its adaptation to multiresolution strategies is straightforward. Therefore, it can be extremely useful to speed up image registration procedures in different applications where high dimensional data are involved.

Reconstruction techniques for cardiac cine MRI.

The present survey describes the state-of-the-art techniques for dynamic cardiac magnetic resonance image reconstruction. Additionally, clinical relevance, main challenges, and future trends of this image modality are outlined. Thus, this paper aims to provide a general vision about cine MRI as the standard procedure in functional evaluation of the heart, focusing on technical methodologies.

OpenCLIPER: An OpenCL-Based C++ Framework for Overhead-Reduced Medical Image Processing and Reconstruction on Heterogeneous Devices.

Medical image processing is often limited by the computational cost of the involved algorithms. Whereas dedicated computing devices (GPUs in particular) exist and do provide significant efficiency boosts, they have an extra cost of use in terms of housekeeping tasks (device selection and initialization, data streaming, synchronization with the CPU, and others), which may hinder developers from using them. This paper describes an OpenCL-based framework that is capable of handling dedicated computing devices seamlessly and that allows the developer to concentrate on image processing tasks. The framework handles automatically device discovery and initialization, data transfers to and from the device and the file system and kernel loading and compiling. Data structures need to be defined only once independently of the computing device; code is unique, consequently, for every device, including the host CPU. Pinned memory/buffer mapping is used to achieve maximum performance in data transfers. Code fragments included in the paper show how the computing device is almost immediately and effortlessly available to the users algorithms, so they can focus on productive work. Code required for device selection and initialization, data loading and streaming and kernel compilation is minimal and systematic. Algorithms can be thought of as mathematical operators (called processes), with input, output and parameters, and they may be chained one after another easily and efficiently. Also for efficiency, processes can have their initialization work split from their core workload, so process chains and loops do not incur in performance penalties. Algorithm code is independent of the device type targeted.

Jacobian weighted temporal total variation for motion compensated compressed sensing reconstruction of dynamic MRI.

PURPOSE: To eliminate the need of spatial intraframe regularization in a recently reported dynamic MRI compressed-sensing-based reconstruction method with motion compensation and to increase its performance. THEORY AND METHODS: We propose a new regularization metric based on the introduction of a spatial weighting measure given by the Jacobian of the estimated deformations. It shows convenient discretization properties and, as a byproduct, it also provides a theoretical support to a result reported by others based on an intuitive design. The method has been applied to the reconstruction of both short and long axis views of the heart of four healthy volunteers. Quantitative image quality metrics as well as straightforward visual assessment are reported. RESULTS: Short and long axis reconstructions of cardiac cine MRI sequences have shown superior results than previously reported methods both in terms of quantitative metrics and of visual assessment. Fine details are better preserved due to the lack of additional intraframe regularization, with no significant image artifacts even for an acceleration factor of 12. CONCLUSIONS: The proposed Jacobian Weighted temporal Total Variation results in better reconstructions of highly undersampled cardiac cine MRI than previously proposed methods and sets a theoretical ground for forward and backward predictors used elsewhere. Magn Reson Med 77:1208-1215, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Nonrigid groupwise registration for motion estimation and compensation in compressed sensing reconstruction of breath-hold cardiac cine MRI.

PURPOSE: Compressed sensing methods with motion estimation and compensation techniques have been proposed for the reconstruction of accelerated dynamic MRI. However, artifacts that naturally arise in compressed sensing reconstruction procedures hinder the estimation of motion from reconstructed images, especially at high acceleration factors. This work introduces a robust groupwise nonrigid motion estimation technique applied to the compressed sensing reconstruction of dynamic cardiac cine MRI sequences. THEORY AND METHODS: A spatio-temporal regularized, groupwise, nonrigid registration method based on a B-splines deformation model and a least squares metric is used to estimate and to compensate the movement of the heart in breath-hold cine acquisitions and to obtain a quasistatic sequence with highly sparse representation in temporally transformed domains. RESULTS: Short axis in vivo datasets are used for validation, both original multicoil as well as DICOM data. Fully sampled data were retrospectively undersampled with various acceleration factors and reconstructions were compared with the two well-known methods k-t FOCUSS and MASTeR. The proposed method achieves higher signal to error ratio and structure similarity index for medium to high acceleration factors. CONCLUSIONS: Reconstruction methods based on groupwise registration show higher quality reconstructions for cardiac cine images than the pairwise counterparts tested.

A multichannel model-based methodology for extubation readiness decision of patients on weaning trials.

Discontinuation of mechanical ventilation is a challenging task that involves a number of subtle clinical issues. The gradual removal of the respiratory support (referred to as weaning) should be performed as soon as autonomous respiration can be sustained. However, the prediction rate of successful extubation is still below 25% based on previous studies. Construction of an automatic system that provides information on extubation readiness is thus desirable. Recent works have demonstrated that the breathing pattern variability is a useful extubation readiness indicator, with improving performance when multiple respiratory signals are jointly processed. However, the existing methods for predictor extraction present several drawbacks when length-limited time series are to be processed in heterogeneous groups of patients. In this paper, we propose a model-based methodology for automatic readiness prediction. It is intended to deal with multichannel, nonstationary, short records of the breathing pattern. Results on experimental data yield an 87.27% of successful readiness prediction, which is in line with the best figures reported in the literature. A comparative analysis shows that our methodology overcomes the shortcomings of so far proposed methods when applied to length-limited records on heterogeneous groups of patients.

Group-Slicer: a collaborative extension of 3D-Slicer.

In this paper, we describe a first step towards a collaborative extension of the well-known 3D-Slicer; this platform is nowadays used as a standalone tool for both surgical planning and medical intervention. We show how this tool can be easily modified to make it collaborative so that it may constitute an integrated environment for expertise exchange as well as a useful tool for academic purposes.