Jarrod A. Collins

The anatomy of the aging aorta

Age‐associated changes to aortic anatomy and physiology have an effect on hemodynamics and play a large role in the genesis of cardiovascular pathologies including atherosclerosis, congestive heart failure, and aortic aneurysm. Despite their recognized role in hemodynamics, the complete mechanism for aortic aging is still not clear and their full effects on cardiovascular pathologies are unknown. This article serves as a review of the normal anatomy of the human aorta and its known age‐associated changes. Clin. Anat. 27:463–466, 2014.

Evaluation of model-based deformation correction in image-guided liver surgery via tracked intraoperative ultrasound.

Abstract. Soft-tissue deformation represents a significant error source in current surgical navigation systems used for open hepatic procedures. While numerous algorithms have been proposed to rectify the tissue deformation that is encountered during open liver surgery, clinical validation of the proposed methods has been limited to surface-based metrics, and subsurface validation has largely been performed via phantom experiments. The proposed method involves the analysis of two deformation-correction algorithms for open hepatic image-guided surgery systems via subsurface targets digitized with tracked intraoperative ultrasound (iUS). Intraoperative surface digitizations were acquired via a laser range scanner and an optically tracked stylus for the purposes of computing the physical-to-image space registration and for use in retrospective deformation-correction algorithms. Upon completion of surface digitization, the organ was interrogated with a tracked iUS transducer where the iUS images and corresponding tracked locations were recorded. Mean closest-point distances between the feature contours delineated in the iUS images and corresponding three-dimensional anatomical model generated from preoperative tomograms were computed to quantify the extent to which the deformation-correction algorithms improved registration accuracy. The results for six patients, including eight anatomical targets, indicate that deformation correction can facilitate reduction in target error of ∼52%.

Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework

In open image-guided liver surgery (IGLS), a sparse representation of the intraoperative organ surface can be acquired to drive image-to-physical registration. We hypothesize that uncharacterized error induced by variation in the collection patterns of organ surface data limits the accuracy and robustness of an IGLS registration. Clinical validation of such registration methods is challenged due to the difficulty in obtaining data representative of the true state of organ deformation. We propose a novel human-to-phantom validation framework that transforms surface collection patterns from in vivo IGLS procedures (n = 13) onto a well-characterized hepatic deformation phantom for the purpose of validating surface-driven, volumetric nonrigid registration methods. An important feature of the approach is that it centers on combining workflow-realistic data acquisition and surgical deformations that are appropriate in behavior and magnitude. Using the approach, we investigate volumetric target registration error (TRE) with both current rigid IGLS and our improved nonrigid registration methods. Additionally, we introduce a spatial data resampling approach to mitigate the workflow-sensitive sampling problem. Using our human-to-phantom approach, TRE after routine rigid registration was 10.9 ± 0.6 mm with a signed closest point distance associated with residual surface fit in the range of ±10 mm, highly representative of open liver resections. After applying our novel resampling strategy and improved deformation correction method, TRE was reduced by 51%, i.e., a TRE of 5.3 ± 0.5 mm. This paper reported herein realizes a novel tractable approach for the validation of image-to-physical registration methods and demonstrates promising results for our correction method.

Validation of model-based deformation correction in image-guided liver surgery via tracked intraoperative ultrasound: preliminary method and results

Soft tissue deformation represents a significant error source in current surgical navigation systems used for open hepatic procedures. While numerous algorithms have been proposed to rectify the tissue deformation that is encountered during open liver surgery, clinical validation of the proposed methods has been limited to surface based metrics and sub-surface validation has largely been performed via phantom experiments. Tracked intraoperative ultrasound (iUS) provides a means to digitize sub-surface anatomical landmarks during clinical procedures. The proposed method involves the validation of a deformation correction algorithm for open hepatic image-guided surgery systems via sub-surface targets digitized with tracked iUS. Intraoperative surface digitizations were acquired via a laser range scanner and an optically tracked stylus for the purposes of computing the physical-to-image space registration within the guidance system and for use in retrospective deformation correction. Upon completion of surface digitization, the organ was interrogated with a tracked iUS transducer where the iUS images and corresponding tracked locations were recorded. After the procedure, the clinician reviewed the iUS images to delineate contours of anatomical target features for use in the validation procedure. Mean closest point distances between the feature contours delineated in the iUS images and corresponding 3-D anatomical model generated from the preoperative tomograms were computed to quantify the extent to which the deformation correction algorithm improved registration accuracy. The preliminary results for two patients indicate that the deformation correction method resulted in a reduction in target error of approximately 50%.

Technical note: Nonrigid registration for laparoscopic liver surgery using sparse intraoperative data

Soft tissue deformation can be a major source of error for image-guided interventions. Deformations associated with laparoscopic liver surgery can be substantially different from those concomitant with open approaches due to intraoperative practices such as abdominal insufflation and variable degrees of mobilization from the supporting ligaments of the liver. This technical note outlines recent contributions towards nonrigid registration for laparoscopic liver surgery published in the Journal of Medical Imaging special issue on image-guided procedures, robotic interventions, and modeling [10]. In particular, we review (1) a characterization of intraoperative liver deformation from clinically-acquired sparse digitizations of the organ surface through a series of laparoscopic-to-open conversions, and (2) a novel deformation correction strategy that leverages a set of control points placed across anatomical regions of mechanical support provided to the organ. Perturbations of these control points on a finite element model were used to iteratively reconstruct the intraoperative deformed organ shape from sparse measurements of the liver surface. These characterization and correction methods for laparoscopic deformation were applied to a retrospective clinical series of 25 laparoscopic-to-open conversions performed under image guidance and a phantom validation framework.

Deformation correction for image guided liver surgery: An intraoperative fidelity assessment

Background: Although systems of 3‐dimensional image‐guided surgery are a valuable adjunct across numerous procedures, differences in organ shape between that reflected in the preoperative image data and the intraoperative state can compromise the fidelity of such guidance based on the image. In this work, we assessed in real time a novel, 3‐dimensional image‐guided operation platform that incorporates soft tissue deformation. Methods: A series of 125 alignment evaluations were performed across 20 patients. During the operation, the surgeon assessed the liver by swabbing an optically tracked stylus over the liver surface and viewing the image‐guided operation display. Each patient had approximately 6 intraoperative comparative evaluations. For each assessment, 1 of only 2 types of alignments were considered: conventional rigid and novel deformable. The series of alignment types used was randomized and blinded to the surgeon. The surgeon provided a rating, R, from −3 to +3 for each display compared with the previous display, whereby a negative rating indicated degradation in fidelity and a positive rating an improvement. Results: A statistical analysis of the series of rating data by the clinician indicated that the surgeons were able to perceive an improvement (defined as a R > 1) of the model‐based registration over the rigid registration (P = .01) as well as a degradation (defined as R < −1) when the rigid registration was compared with the novel deformable guidance information (P = .03). Conclusion: This study provides evidence of the benefit of deformation correction in providing an accurate location for the liver for use in image‐guided surgery systems.

Using an Android application to assess registration strategies in open hepatic procedures: a planning and simulation tool

Sparse surface digitization with an optically tracked stylus for use in an organ surface-based image-to-physical registration is an established approach for image-guided open liver surgery procedures. However, variability in sparse data collections during open hepatic procedures can produce disparity in registration alignments. In part, this variability arises from inconsistencies with the patterns and fidelity of collected intraoperative data. The liver lacks distinct landmarks and experiences considerable soft tissue deformation. Furthermore, data coverage of the organ is often incomplete or unevenly distributed. While more robust feature-based registration methodologies have been developed for image-guided liver surgery, it is still unclear how variation in sparse intraoperative data affects registration. In this work, we have developed an application to allow surgeons to study the performance of surface digitization patterns on registration. Given the intrinsic nature of soft-tissue, we incorporate realistic organ deformation when assessing fidelity of a rigid registration methodology. We report the construction of our application and preliminary registration results using four participants. Our preliminary results indicate that registration quality improves as users acquire more experience selecting patterns of sparse intraoperative surface data.

Emulation of the laparoscopic environment for image-guided liver surgery via an abdominal phantom system with anatomical ligamenture

In order to rigorously validate techniques for image-guided liver surgery (IGLS), an accurate mock representation of the intraoperative surgical scene with quantifiable localization of subsurface targets would be highly desirable. However, many attempts to reproduce the laparoscopic environment have encountered limited success due to neglect of several crucial design aspects. The laparoscopic setting is complicated by factors such as gas insufflation of the abdomen, changes in patient orientation, incomplete organ mobilization from ligaments, and limited access to organ surface data. The ability to accurately represent the influences of anatomical changes and procedural limitations is critical for appropriate evaluation of IGLS methodologies such as registration and deformation correction. However, these influences have not yet been comprehensively integrated into a platform usable for assessment of methods in laparoscopic IGLS. In this work, a mock laparoscopic liver simulator was created with realistic ligamenture to emulate the complexities of this constrained surgical environment for the realization of laparoscopic IGLS. The mock surgical system reproduces an insufflated abdominal cavity with dissectible ligaments, variable levels of incline matching intraoperative patient positioning, and port locations in accordance with surgical protocol. True positions of targets embedded in a tissue-mimicking phantom are measured from CT images. Using this setup, image-to-physical registration accuracy was evaluated for simulations of laparoscopic right and left lobe mobilization to assess rigid registration performance under more realistic laparoscopic conditions. Preliminary results suggest that non-rigid organ deformations and the region of organ surface data collected affect the ability to attain highly accurate registrations in laparoscopic applications.

On the nature of data collection for soft-tissue image-to-physical organ registration: a noise characterization study

In image-guided liver surgery (IGLS), sparse representations of the anterior organ surface may be collected intraoperatively to drive image-to-physical space registration. Soft tissue deformation represents a significant source of error for IGLS techniques. This work investigates the impact of surface data quality on current surface based IGLS registration methods. In this work, we characterize the robustness of our IGLS registration methods to noise in organ surface digitization. We study this within a novel human-to-phantom data framework that allows a rapid evaluation of clinically realistic data and noise patterns on a fully characterized hepatic deformation phantom. Additionally, we implement a surface data resampling strategy that is designed to decrease the impact of differences in surface acquisition. For this analysis, n=5 cases of clinical intraoperative data consisting of organ surface and salient feature digitizations from open liver resection were collected and analyzed within our human-to-phantom validation framework. As expected, results indicate that increasing levels of noise in surface acquisition cause registration fidelity to deteriorate. With respect to rigid registration using the raw and resampled data at clinically realistic levels of noise (i.e. a magnitude of 1.5 mm), resampling improved TRE by 21%. In terms of nonrigid registration, registrations using resampled data outperformed the raw data result by 14% at clinically realistic levels and were less susceptible to noise across the range of noise investigated. These results demonstrate the types of analyses our novel human-to-phantom validation framework can provide and indicate the considerable benefits of resampling strategies.

Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery

Abstract. Laparoscopic liver surgery is challenging to perform due to a compromised ability of the surgeon to localize subsurface anatomy in the constrained environment. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflation and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The severity of laparoscopic deformation in humans has not been characterized, and current laparoscopic correction methods do not account for the mechanics of how intraoperative deformation is applied to the liver. We first measure the degree of laparoscopic deformation at two insufflation pressures over the course of laparoscopic-to-open conversion in 25 patients. With this clinical data alongside a mock laparoscopic phantom setup, we report a biomechanical correction approach that leverages anatomically load-bearing support surfaces from ligament attachments to iteratively reconstruct and account for intraoperative deformations. Laparoscopic deformations were significantly larger than deformations associated with open surgery, and our correction approach yielded subsurface target error of 6.7±1.3  mm and surface error of 0.8±0.4  mm using only sparse surface data with realistic surgical extent. Laparoscopic surface data extents were examined and found to impact registration accuracy. Finally, we demonstrate viability of the correction method with clinical data.