Omkar Dandekar

Live augmented reality: a new visualization method for laparoscopic surgery using continuous volumetric computed tomography

BackgroundCurrent laparoscopic images are rich in surface detail but lack information on deeper structures. This report presents a novel method for highlighting these structures during laparoscopic surgery using continuous multislice computed tomography (CT). This has resulted in a more accurate augmented reality (AR) approach, termed “live AR,” which merges three-dimensional (3D) anatomy from live low-dose intraoperative CT with live images from the laparoscope.MethodsA series of procedures with swine was conducted in a CT room with a fully equipped laparoscopic surgical suite. A 64-slice CT scanner was used to image the surgical field approximately once per second. The procedures began with a contrast-enhanced, diagnostic-quality CT scan (initial CT) of the liver followed by continuous intraoperative CT and laparoscopic imaging with an optically tracked laparoscope. Intraoperative anatomic changes included user-applied deformations and those from breathing. Through deformable image registration, an intermediate image processing step, the initial CT was warped to align spatially with the low-dose intraoperative CT scans. The registered initial CT then was rendered and merged with laparoscopic images to create live AR.ResultsSuperior compensation for soft tissue deformations using the described method led to more accurate spatial registration between laparoscopic and rendered CT images with live AR than with conventional AR. Moreover, substitution of low-dose CT with registered initial CT helped with continuous visualization of the vasculature and offered the potential of at least an eightfold reduction in intraoperative X-ray dose.ConclusionsThe authors proposed and developed live AR, a new surgical visualization approach that merges rich surface detail from a laparoscope with instantaneous 3D anatomy from continuous CT scanning of the surgical field. Through innovative use of deformable image registration, they also demonstrated the feasibility of continuous visualization of the vasculature and considerable X-ray dose reduction. This study provides motivation for further investigation and development of live AR.

FPGA-Accelerated Deformable Image Registration for Improved Target-Delineation During CT-Guided Interventions

Minimally invasive image-guided interventions (IGIs) are time and cost efficient, minimize unintended damage to healthy tissue, and lead to faster patient recovery. With the advent of multislice computed tomography (CT), many IGIs are now being performed under volumetric CT guidance. Registering pre-and intraprocedural images for improved intraprocedural target delineation is a fundamental need in the IGI workflow. Earlier approaches to meet this need primarily employed rigid body approximation, which may not be valid because of nonrigid tissue misalignment between these images. Intensity-based automatic deformable registration is a promising option to correct for this misalignment; however, the long execution times of these algorithms have prevented their use in clinical workflow. This article presents a field-programmable gate array-based architecture for accelerated implementation of mutual information (Ml)-based deformable registration. The reported implementation reduces the execution time of MI-based deformable registration from hours to a few minutes. This work also demonstrates successful registration of abdominal intraprocedural noncontrast CT (iCT) images with preprocedural contrast-enhanced CT (preCT) and positron emission tomography (PET) images using the reported solution. The registration accuracy for this application was evaluated using 5 iCT-preCT and 5 iCT-PET image pairs. The registration accuracy of the hardware implementation is comparable with that achieved using a software implementation and is on the order of a few millimeters. This registration accuracy, coupled with the execution speed and compact implementation of the reported solution, makes it suitable for integration in the IGI-workflow.

Image registration accuracy with low-dose CT: how low can we go?

Image-guided interventions are known to lead to improved outcomes and significantly faster patient recovery as compared with conventional open, invasive procedures. Common intraoperative imaging techniques such as endoscopy and fluoroscopy are two-dimensional (2D), and provide a 2D representation of the 3D anatomy. Use of recently emerged multislice computed tomography (CT) can facilitate 3D visualization of anatomy during an intervention. The speed and dimensionality of these CT scanners make their use in image-guided interventions technically feasible. For clinical acceptance, however, the net radiation dose to the patient and the interventionist must be minimized. This article suggests a strategy to reduce radiation dose and describes an evaluation scheme to identify the optimal dose which does not sacrifice the specificity of the image-guided procedure. Our work indicates at least a tenfold reduction in radiation dose

Effect of Ultrasound Probe on Dose Delivery During Real-time Ultrasound-Guided Tumor Tracking

Ultrasound is a noninvasive and less costly modality for real-time imaging of soft tissues. It has the capability of tracking soft tissue at levels of submillimeter precision even in the presence of radiation beams. The effect of a transducer on radiation dose is not fully known. The best imaging location for an ultrasound transducer happens to coincide with the path of an anterior-posterior beam in intensity modulated radiation therapy (IMRT). This study indicates a significant change in dose when this juxtaposition occurs. If the anterior-posterior beam is avoided in IMRT planning, however, the effect of the transducer on radiotherapy is found to be negligible

Towards a Heterogeneous Medical Image Registration Acceleration Platform

For the past decade, improving the performance and accuracy of medical image registration has been a driving force of innovation in medical imaging. Accurate image registration enhances diagnoses of patients, accounts for changes in morphology of structures over time, and even combines images from different modalities. The ultimate goal of medical image registration research is to create a robust, real time, elastic registration solution that may be used on many modalities. With such a computationally intensive and multifaceted problem, researchers have exploited parallelism at different levels to improve the performance of this application, but there has yet to be a solution fast enough and effective enough to gain widespread clinical use. To achieve real time elastic registration, an implementation must simultaneously exploit multiple types of parallelism in the application by targeting a heterogeneous platform whose computational components (e.g. multiprocessors, graphics processors, field programmable gate arrays) match these types of parallelism. Our initial experiments indicate that an 8 node heterogeneous cluster can realize over 100times speedup compared to a high performance uniprocessor system. By creating a platform based on modern hardware, we believe that a heterogeneous compute platform customized for image registration can provide robust, scalable, cost effective sub-minute medical image registration capabilities.

Multiobjective Optimization of FPGA-Based Medical Image Registration

With a multitude of technological innovations, one emerging trend in image processing, and medical image processing, in particular, is custom hardware implementation of computationally intensive algorithms in the quest to achieve real-time performance. For reasons of area-efficiency and performance, these implementations often employ limited-precision datapaths. Identifying effective wordlengths for these datapaths while accounting for tradeoffs between design complexity and accuracy is a critical and time consuming aspect of this design process. Having access to optimized tradeoff curves can equip designers to adapt their designs to different performance requirements and target specific devices while reducing design time. This paper presents a multiobjective optimization strategy developed in the context of field-programmable gate array-based implementation of medical image registration. Within this framework, we compare several search methods and demonstrate the applicability of an evolutionary algorithm-based search for efficiently identifying superior multiobjective tradeoff curves. This strategy can easily be adapted to a wide range of signal processing applications, including areas of image and video processing beyond the medical domain.

Towards systematic exploration of tradeoffs for medical image registration on heterogeneous platforms

For the past decade, improving the performance and accuracy of medical image registration has been a driving force of innovation in medical imaging. The ultimate goal of accurate, robust, real-time image registration will enhance diagnoses of patients and enable new image-guided intervention techniques. With such a computationally intensive and multifaceted problem, improvements have been found in high performance platforms such as graphics processors (GPUs) and general purpose clusters, but there has yet to be a solution fast enough and effective enough to gain widespread clinical use. In this study, we examine the differences in accuracy and speed of implementations of the same image registration algorithm on a general purpose uniprocessor, a GPU, and a cluster of GPUs. We utilize a novel domain specific framework that allows us to simultaneously exploit parallelism on a heterogeneous platform. Using a set of representative images, we examine implementations with speedups of up to two orders of magnitude and accuracy varying from sub-millimeter to 2.6 millimeters of average error.

Development of continuous CT-guided minimally invasive surgery

Minimally invasive laparoscopic surgeries are known to lead to improved outcomes, less scarring, and significantly faster patient recovery as compared with conventional open invasive surgeries. Laparoscopes, used to visualize internal anatomy and guide laparoscopic surgeries, however, remain limited in visualization capability. Not only do they provide a relatively flat representation of the three-dimensional (3D) anatomy, they show only the exposed surfaces. A surgeon is thus unable to see inside a structure, which limits the precision of current-generation minimally invasive surgeries and is often a source of complications. To see inside a structure before dissecting it has been a long-standing need in minimally invasive laparoscopic surgeries, a need that laparoscopy is fundamentally limited in meeting. In this work we propose to use continuous computed tomography (CT) of the surgical field as a supplementary imaging tool to guide laparoscopic surgeries. The recent emergence of 64-slice CT and its continuing evolution make it an ideal candidate for four-dimensional (3D space + time) intraoperative imaging. We also propose a novel, elastic image registration-based technique to keep the net radiation dose within acceptable levels. We have successfully created 3D renderings from multislice CT corresponding to anatomy visible within the field of view of the laparoscope in a swine. These renderings show the underlying vasculature along with their latest intraoperative orientation. With additional developments, our research has the potential to help improve the precision of laparoscopic surgeries further, reduce complications, and expand the scope of minimally invasive surgeries.

Utilizing Hierarchical Multiprocessing for Medical Image Registration

This work discusses an approach to utilize hierarchical multiprocessing in the context of medical image registration. By first organizing application parallelism into a domain-specific taxonomy, an algorithm is structured to target a set of multicore platforms.The approach on a cluster of graphics processing units (GPUs) requiring the use of two parallel programming environments to achieve fast execution times is demonstrated.There is negligible loss in accuracy for rigid registration when employing GPU acceleration, but it does adversely effect our nonrigid registration implementation due to our usage of a gradient descent approach.

PET guidance for liver radiofrequency ablation: an evaluation

Radiofrequency ablation (RFA) is emerging as the primary mode of treatment of unresectable malignant liver tumors. With current intraoperative imaging modalities, quick, precise, and complete localization of lesions remains a challenge for liver RFA. Fusion of intraoperative CT and preoperative PET images, which relies on PET and CT registration, can produce a new image with complementary metabolic and anatomic data and thus greatly improve the targeting accuracy. Unlike neurological images, alignment of abdominal images by combined PET/CT scanner is prone to errors as a result of large nonrigid misalignment in abdominal images. Our use of a normalized mutual information-based 3D nonrigid registration technique has proven powerful for whole-body PET and CT registration. We demonstrate here that this technique is capable of acceptable abdominal PET and CT registration as well. In five clinical cases, both qualitative and quantitative validation showed that the registration is robust and accurate. Quantitative accuracy was evaluated by comparison between the result from the algorithm and clinical experts. The accuracy of registration is much less than the allowable margin in liver RFA. Study findings show the techniques potential to enable the augmentation of intraoperative CT with preoperative PET to reduce procedure time, avoid repeating procedures, provide clinicians with complementary functional/anatomic maps, avoid omitting dispersed small lesions, and improve the accuracy of tumor targeting in liver RFA.