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Purpose: Proton computed tomography (CT) is a promising image modality for improving the stopping power estimates and dose calculations for particle therapy. However, the finite range of about 33 cm of water of most commercial proton therapy systems limits the sites that can be scanned from a full 360◦ rotation. In this paper the authors propose a method to overcome the problem using a dual modality reconstruction (DMR) combining the proton data with a cone-beam x-ray prior. Methods: A Catphan 600 phantom was scanned using a cone beam x-ray CT scanner. A digital replica of the phantom was created in the Monte Carlo code Geant4 and a 360◦ proton CT scan was simulated, storing the entrance and exit position and momentum vector of every proton. Proton CT images were reconstructed using a varying number of angles from the scan. The proton CT images were reconstructed using a constrained nonlinear conjugate gradient algorithm, minimizing total variation and the x-ray CT prior while remaining consistent with the proton projection data. The proton histories were reconstructed along curved cubic-spline paths. Results: The spatial resolution of the cone beam CT prior was retained for the fully sampled case and the 90◦ interval case, with the MTF = 0.5 (modulation transfer function) ranging from 5.22 to 5.65 linepairs/cm. In the 45◦ interval case, theMTF = 0.5 dropped to 3.91 linepairs/cm For the fully sampled DMR, the maximal root mean square (RMS) error was 0.006 in units of relative stopping power. For the limited angle cases the maximal RMS error was 0.18, an almost five-fold improvement over the cone beam CT estimate. Conclusions: Dual modality reconstruction yields the high spatial resolution of cone beam x-ray CT while maintaining the improved stopping power estimation of proton CT. In the case of limited angles, the use of prior image proton CT greatly improves the resolution and stopping power estimate, but does not fully achieve the quality of a 360◦ proton CT scan. © 2014 American Association of Physicists in Medicine. [http://dx.doi.org/10.1118/1.4864239]

Although non-linear reconstruction has shown potential for shortening acquisition times, its clinical usage is often hampered by the lengthy reconstruction time. To speedup non-linear reconstruction and enable its clinical usage, we have extended previously published Gadgetron framework to support cloud computing. With this extension (named GT-Plus), any number of Gadgetron processes can run cooperatively across multiple computers. GT-Plus framework was deployed on Amazon EC2 cloud and NIH’s Biowulf system. We demonstrate that with the GT-Plus cloud, a multi-slice free-breathing myocardial cine imaging with the whole ventricular coverage can be completed within 1min, including data acquisition and reconstruction.

Applying non-linear reconstruction to high resolution 3D MRI is challenging because of the lengthy computing time needed for those iterative algorithms. To achieve practical processing duration to enable clinical usage of non-linear reconstruction, we have extended previously published Gadgetron framework to support distributed computing in a cloud environment. This extension is named GT-Plus. A cloud version of 3D l1-SPIRiT was implemented on the GT-Plus framework. We demonstrate that a 3mins reconstruction could be achieved for 1mm3 isotropic resolution neuro scans with significantly improved image quality using two dimensional acceleration factors of R=3×2 and 3×3

This work presents a new open source framework for medical image reconstruction called the “Gadgetron.” The framework implements a flexible system for creating streaming data processing pipelines where data pass through a series of modules or “Gadgets” from raw data to reconstructed images. The data processing pipeline is configured dynamically at run-time based on an extensible markup language configuration description. The framework promotes reuse and sharing of reconstruction modules and new Gadgets can be added to the Gadgetron framework through a plugin-like architecture without recompiling the basic framework infrastructure. Gadgets are typically implemented in C/C++, but the framework includes wrapper Gadgets that allow the user to implement new modules in the Python scripting language for rapid prototyping. In addition to the streaming framework infrastructure, the Gadgetron comes with a set of dedicated toolboxes in shared libraries for medical image reconstruction. This includes generic toolboxes for data-parallel (e.g., GPU-based) execution of compute-intensive components. The basic framework architecture is independent of medical imaging modality, but this article focuses on its application to Cartesian and non-Cartesian parallel magnetic resonance imaging. Magn Reson Med, 2012.

Many image processing problems benefit from a complete solution to connected components labeling. This paper introduces a new data parallel labeling method based on calculation of label propagation sizes from the connectivity between pixels extracted in a pre-processing step and re-usal of established label propagation routes. The method achieves real-time performance for 2D images and it also generalizes to Voronoi diagrams and signed distance fields. Many image processing problems benefit from a complete solution to connected components labeling. This paper introduces a new data parallel labeling method based on calculation of label propagation sizes from the connectivity between pixels extracted in a pre-processing step and re-usal of established label propagation routes. The method achieves real-time performance for 2D images and it also generalizes to Voronoi diagrams and signed distance fields.

Pas de résumé

Careful preoperative planning is of outmost importance -- in particular when considering complex corrective surgery on congenitally malformed hearts. As an aid to such decisionsmaking we describe a system for virtual reconstruction of patient-specific morphology from 3D-capable imaging modalities such as MRI and CT. We introduce and illustrate the concept of virtual cardiotomy as a new tool to preoperatively evaluate the feasibility of different surgical strategies by investigating the anatomical spatial relations through any number of virtual incisions. Ve review the technical and clinical implemtation of the various components of the system, namely 3D imaging, segmentation and reconstruction, visualization, and simulation of tissue elasticity. Finally we summarize the main findings from a resent evaluation study on 42 infants and children.

Four-dimensional (4D) flow imaging has been used to study flow patterns and pathophysiology, usually focused on specific thoracic vessels and cardiac chambers. Whole-heart 4D flow at high measurement accuracy covering the entire thoracic cardiovascular system would be desirable to simplify and improve hemodynamic assessment. This has been a challenge because compensation of respiratory motion is difficult to achieve, but it is paramount to limit artifacts and improve accuracy. In this work we propose a self-gating technique for respiratory motion-compensation integrated into a whole-heart 4D flow acquisition that overcomes these challenges. Flow components are measured in all three directions for each pixel over the complete cardiac cycle, and 1D volume projections are obtained at certain time intervals for respiratory gating in real time during the acquisition. The technique was tested in 15 volunteers, in which stroke volumes (SVs) in the great arteries showed excellent agreement with standard 2D phase-contrast (PC) scans. In contrast, nongated 4D flow with two averages had substantial disagreement with 2D flow. Applied to patients with congenital cardiac left-to-right shunting, the precision of flux data was highly beneficial. The methodology presented here has the potential to allow a complete study of flow pathophysiology of the thoracic cardiovascular system from a single free-breathing scan.

Many existing image registration methods have difficulties in accurately describing significant rotation and bending of entities (e.g. organs) between two datasets. A common problem in this case is to ensure that the resulting registration is physically plausible, i.e. that the registration describes the actual bending/rotation occurring rather than just introducing expansion in some areas and shrinkage in others. In this work we developed a general framework for deformable image registration of two 3D datasets that alleviates this problem. To ensure that only physically feasible and plausible solutions to the registration problem are found, a soft tissue deformable model is used to constrain the search space for the desired correspondence map while minimizing a similarity metric between the source and reference datasets. Results from a deformable phantom experiment were used to verify and evaluate the framework.

This project demonstrates that modern commodity graphics cards (GPUs) can be used to perform fast Cartesian SENSE and k-t SENSE reconstruction. Specifically, the SENSE inversion is accelerated by up to two orders of magnitude and is no longer the time-limiting step. The achieved reconstruction times are now well below the acquisition times thus enabling real-time, interactive SENSE imaging, even with a large number of receive coils. The fast GPU reconstruction is also beneficial for datasets that are not acquired in real-time. We demonstrate it can be used for interactive adjustment of regularization parameters for k-t SENSE in the same way that one would adjust window and level settings. This enables a new way of performing imaging reconstruction, where the user chooses the setting of tuneable reconstruction parameters, in real-time, depending on the context in which the images are interpreted.

Udgivelsesdato: 2008

Udgivelsesdato: July 07 Since the advent of programmable graphics processors (GPUs) their computational powers have been utilized for general purpose computation. Initially by “exploiting” graphics APIs and recently through dedicated parallel computation frameworks such as the Compute Unified Device Architecture (CUDA) from Nvidia. This paper investigates multiple implementations of volumetric Mass-Spring-Damper systems in CUDA. The obtained performance is compared to previous implementations utilizing the GPU through the OpenGL graphics API. We find that both performance and optimization strategies differ widely between the OpenGL and CUDA implementations. Specifically, the previous recommendation of using implicitly connected particles is replaced by a recommendation that supports unstructured meshes and run-time topological changes with an insignificant performance reduction.

In recent years, three-dimensional imaging has provided new opportunities for visualizing congenital cardiac malformations. We present the initial clinical experience using a recently implemented system, which employs some of new interactive, real-time, techniques. We show how three-dimensional rendering based on magnetic resonance imaging can provide detailed spatial information on both intrinsic and extrinsic cardiac relations, and hence how a virtual examination can potentially provide new means to a better understanding of complex congenital cardiac malformations.