Carlos R. Castro-Pareja

Direct aperture deformation: An interfraction image guidance strategy

A new scheme, called direct aperture deformation (DAD), for online correction of interfraction geometric uncertainties under volumetric imaging guidance is presented. Using deformable image registration, the three-dimensional geometric transformation matrix can be derived that associates the planning image set and the images acquired on the day of treatment. Rather than replanning or moving the patient, we use the deformation matrix to morph the treatment apertures as a potential online correction method. A proof-of-principle study using an intensity-modulated radiation therapy plan for a prostate cancer patient was conducted. The method, procedure, and algorithm of DAD are described. The dose-volume histograms from the original plan, reoptimized plan, and rigid-body translation plan are compared with the ones from the DAD plan. The study showed the feasibility of the DAD as a general method for both target dislocation and deformation. As compared with using couch translation to move the patient, DAD is capable of correcting both target dislocation and deformations. As compared with reoptimization, online correction using the DAD scheme could be completed within a few minutes rather than tens of minutes and the speed gain would be at a very small cost of plan quality.

Automatic segmentation of phase-correlated CT scans through nonrigid image registration using geometrically regularized free-form deformation.

Conventional radiotherapy is planned using free-breathing computed tomography (CT), ignoring the motion and deformation of the anatomy from respiration. New breath-hold-synchronized, gated, and four-dimensional (4D) CT acquisition strategies are enabling radiotherapy planning utilizing a set of CT scans belonging to different phases of the breathing cycle. Such 4D treatment planning relies on the availability of tumor and organ contours in all phases. The current practice of manual segmentation is impractical for 4D CT, because it is time consuming and tedious. A viable solution is registration-based segmentation, through which contours provided by an expert for a particular phase are propagated to all other phases while accounting for phase-to-phase motion and anatomical deformation. Deformable image registration is central to this task, and a free-form deformation-based nonrigid image registration algorithm will be presented. Compared with the original algorithm, this version uses novel, computationally simpler geometric constraints to preserve the topology of the dense control-point grid used to represent free-form deformation and prevent tissue fold-over. Using mean squared difference as an image similarity criterion, the inhale phase is registered to the exhale phase of lung CT scans of five patients and of characteristically low-contrast abdominal CT scans of four patients. In addition, using expert contours for the inhale phase, the corresponding contours were automatically generated for the exhale phase. The accuracy of the segmentation (and hence deformable image registration) was judged by comparing automatically segmented contours with expert contours traced directly in the exhale phase scan using three metrics: volume overlap index, root mean square distance, and Hausdorff distance. The accuracy of the segmentation (in terms of radial distance mismatch) was approximately 2 mm in the thorax and 3 mm in the abdomen, which compares favorably to the accuracies reported elsewhere. Unlike most prior work, segmentation of the tumor is also presented. The clinical implementation of 4D treatment planning is critically dependent on automatic segmentation, for which is offered one of the most accurate algorithms yet presented.

FPGA-based real-time anisotropic diffusion filtering of 3D ultrasound images

Three-dimensional ultrasonic imaging, especially the emerging real-time version of it, is particularly valuable in medical applications such as echocardiography, obstetrics and surgical navigation. A known problem with ultrasound images is their high level of speckle noise. Anisotropic diffusion filtering has been shown to be effective in enhancing the visual quality of 3D ultrasound images and as preprocessing prior to advanced image processing. However, due to its arithmetic complexity and the sheer size of 3D ultrasound images, it is not possible to perform online, real-time anisotropic diffusion filtering using standard software implementations. We present an FPGA-based architecture that allows performing anisotropic diffusion filtering of 3D images at acquisition rates, thus enabling the use of this filtering technique in real-time applications, such as visualization, registration and volume rendering.

Adaptive reduction of intensity levels in 3D images for mutual information-based registration

Mutual information is currently one of the most widely used image similarity measures for multimodality image registration. An important step in the calculation of the mutual information of two images is the estimation of their joint histogram. Most algorithms use lateral joint histogram sizes that are smaller than the actual number of intensity levels present in the images being registered. Using a reduced joint histogram size is especially useful when registering small portions of the images to obtain local deformations in nonrigid registration algorithms, and when implementing hardware solutions for acceleration of mutual information calculation. The most commonly used method for reducing the size of the joint histogram is to perform a linear rescaling of intensity values. The main problem with this method is that image regions with similar intensity values but corresponding to distinct tissue types tend to merge, thus compromising the accuracy of registration. We present new algorithms for reducing the number of gray levels present in 3D medical images, and compare their performance with previously reported ones. The tested algorithms are classified in three categories: histogram shape preserving algorithms, entropy maximization algorithms and quantization error minimization algorithms. Results show that in CT and MRI registration the best accuracy is achieved using entropy maximization algorithms, while in PET and MRI registration the best accuracy is achieved using histogram shape preservation algorithms.

An FPGA-based 3D image processor with median and convolution filters for real-time applications

Median filtering and convolution operations constitute a significant portion of the preprocessing operations performed on digital images. Software implementations of 3D filters in standard general-purpose microprocessors do not match the speed requirements for real-time performance. Field Programmable Gate Arrays (FPGAs) allow implementing reconfigurable architectures that are sufficiently flexible to implement more than one operation in the existing hardware, yielding higher speed for real-time execution without compromising flexibility. We present an FPGA-based 3D image processor that allows real-time median and convolution filtering of 3D images. It includes a linear systolic array architecture for median filtering, which implements an algorithm based on bit-serial searching and majority voting, and a convolution pipeline, based on the fast embedded multiplier units in the FPGA and an optimized carry-save adders. The application of the above designs to 3D image preprocessing is described. A prototype implementation achieved voxel rates in excess of 220MHz.

A cubic interpolation pipeline for fast computation of 3D deformation fields modeled using B-splines

Fast computation of 3D deformation fields is critical to bringing the application of automated elastic image registration algorithms to routine clinical practice. However, it lies beyond the computational power of current microprocessors; therefore requiring implementations using either massively parallel computers or application-specific hardware accelerators. The use of massively parallel computers in a clinical setting is not practical or cost-effective, therefore making the use of hardware accelerators necessary. We present a hardware pipeline that allows accelerating the computation of 3D deformation fields to speeds up to two orders of magnitude faster than software implementations on current workstations and about 64 times faster than other previously reported architectures. The pipeline implements a version of the free-form deformation calculation algorithm, which is optimized to minimize the number of arithmetic operations required to calculate the transformation of a given set of neighboring voxels, thereby achieving an efficient and compact implementation in hardware which allows its use as part of a larger system.

Elastic registration using 3D ChainMail: application to virtual colonoscopy

We present an elastic registration algorithm based on local deformations modeled using cubic B-splines and controlled using 3D ChainMail. Our algorithm eliminates the appearance of folding artifacts and allows local rigidity and compressibility control independent of the image similarity metric being used. 3D ChainMail propagates large internal deformations between neighboring B-Spline control points, thereby preserving the topology of the transformed image without requiring the addition of penalty terms based on rigidity of the transformation field to the equation used to maximize image similarity. A novel application to virtual colonoscopy is presented where the algorithm is used to significantly improve cross-localization between colon locations in prone and supine CT images.

A scalable beamforming architecture for real-time 3D ultrasonic imaging using nonuniform sampling

Real-time acquisition of 3D volumes is an emerging trend in medical imaging. True real-time 3D ultrasonic imaging is particularly valuable for echocardiography and trauma imaging as well as an intraoperative imaging technique for surgical navigation. Since the frame rate of ultrasonic imaging is fundamentally limited by the speed of sound, many schemes of forming multiple receive beams with a single transmit event have been proposed. With the advent of parallel receive beamforming, several architectures to form multiple (4-8) scan lines at a time have been suggested. Most of these architectures employ uniform sampling and input memory banks to store the samples acquired from all the channels. Some recent developments like crossed electrode array, coded excitation, and synthetic aperture imaging facilitate forming an entire 2D plane with a single transmit event. These techniques are speeding up frame rate to eventually accomplish true real-time 3D ultrasonic imaging. We present an FPGA-based scalable architecture capable of forming a complete scan plane in the time it usually takes to form a single scan line. Our current implementation supports 32 input channels per FPGA and up to 128 dynamically focused beam outputs. The desired focusing delay resolution is achieved using a hybrid scheme, with a combination of nonuniform sampling of the analog channels and linear interpolation for nonsparse delays within a user-specified minimum sampling interval. Overall, our pipelined architecture is capable of processing the input RF data in an online fashion, thereby reducing the input storage requirements and potentially providing better image quality.

A hardware-accelerated approach to computing multiple image similarity measures from joint histogram

Image similarity-based image registration is an iterative process that, depending on the number of degrees of freedom in the underlying transformation, may require hundreds to tens of thousands of image similarity computations to converge on a solution. Computation time often limits the use of such algorithms in real-life applications. We have previously shown that hardware acceleration can significantly reduce the time required to register two images. However, the hardware architectures we presented were limited to mutual information calculation, which is one of several commonly used image similarity measures. In this article we show how our architecture can be adapted for the calculation of other image similarity measures in approximately the same time and using the same hardware resources as those for the mutual information case. As in the case of mutual information calculation, the joint histogram is calculated as a first step. The image similarity measures considered are mutual information, normalized mutual information, normalized cross correlation, mean-square sum of differences and ratio image uniformity. We show how all these image similarities can be calculated from the joint histogram in a small fraction of the time required to calculate the joint histogram itself.

WE‐D‐ValB‐08: 4D Dose Calculation Using 3D Elastic Dose Registration in Lung IMRT

Purpose: To develop an elastic registration algorithm that will register dose distributions computed on each 3D data set of a 4D CTimages set. Methods and Materials: The goal of image registration is to find the best matching point pair of two images. The coordinates between the two points were related by a transformation field. Mean‐squared intensity difference was used for similarity measurement. The optimal transformation was found by maximizing similarity. Eight 3D CTimage set were obtained by phase based sorting. CTimage corresponding to end‐exhale was selected as reference image. The remaining images were registered to the reference image. We performed an IMRT planning on the reference image. The prescribed dose was 66 Gy with 2 Gy/fraction. The plan parameters were superimposed on the images corresponding to the remaining phases. A convolution algorithm was used to calculate the resulting dose distributions. After that each 3D dose was transformed to the reference phase by applying the resultant transformation fields. Equally weighted superposition of these transformed dose was calculated. DVH of the planned and registered doses were compared. Results:Image registration improved the matching of anatomical features. RMS error of the intensity difference between the reference and floating images reduced from 81.2 before registration to 70.6 after registration. When compared the planned and registered doses, 95.4% of the tumor will receive the prescribed dose without motion compensation. However, after registration only 81.8% of the tumor received the prescribed dose. The heart and left lung had a less than 1% mean dose difference between the planned and registered doses. The right lung had a 5.8% mean dose difference. Conclusions: Our algorithm has the potential for automatic registration. It further helped us determine the difference between the planned and true dose distribution.