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

Abstract

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.

Fig. 1

Manual surface swabbing results collected within the Explorer™ Liver navigation system. Digitized surface and feature data are presented for 4 clinical cases following an initial rigid alignment generated by the salient feature registration algorithm of Clements et al. [6]. Data representing the falciform, left inferior ridge, right inferior ridge, and anterior organ surface are presented in red, blue, green, and white respectively.

Fig. 2

The CT segmented preoperative and intraoperative phantom surfaces are presented in red and blue respectively. The differences in surfaces highlights the volumetric deformation undergone in the simulated phantom data.

Fig. 3

Structure of the proposed human-to-phantom data set presented in flowchart form. Human data is aligned, scaled, and projected onto the intraoperative phantom CT surface. Randomly defined sinusoidal waveforms are generated and applied to the projected data to simulate collection noise. Noise patterns are applied independently to the surface and feature data. The right and center columns serve as examples of surface digitization with and without applied noise.

Fig. 4

Schematic of the proposed study. For a given clinical case (n = 13), surface data is aligned, scaled, and projected onto the intraoperative phantom CT surface with a randomly determined noise pattern (A). Our rigid and nonrigid registration methods are applied, while quantifying subsurface TRE (B). The simulated surface is then resampled (C) and registrations are recalculated (D). This process is repeated with 50 different applications of noise per clinical case – creating 50 independent surface acquisitions for each of the (n = 13) clinical organ surface digitization patterns.

Fig. 5

Surface digitizations from two cases are presented. Anterior organ surface, falciform, left inferior ridge, and right inferior ridge data are presented in white, red, blue, and green respectively. (A) Surface data from clinical studies collected with an optically tracked stylus. Surface data are overlaid on the preoperative organ model following rigid registration. (B) Examples of the human-to-phantom data set following translation of the clinical surface data from (A) onto the hepatic deformation phantom – used to simulate clinical collection patterns and sampling. (C) A representation of the spatial data resampling approach applied to the human-to-phantom data in (B). Areas of high density and sparse surface points present as the bright white and gridded white points respectively. For (B) and (C), surface data are overlaid on the intraoperative phantom CT model.

Fig. 6

Deformed surfaces from (A) a clinical/human data case and (B) the phantom case are shown. The color map illustrates the observed deformation in each case as the Euclidean distance between the preoperative and intraoperative organ anterior surfaces (in mm) following rigid registration.

Fig. 7

Histogram of average TRE over the 650 simulated cases using raw and resampled data to drive rigid and nonrigid registration. The asterisk denotes significant reduction in error between the current commercial IGLS rigid registration method (blue) and proposed nonrigid registration with resampled data (gold).

Fig. 8

A scatterplot of average TRE over the 650 simulated cases. The x-axis represents average case TRE using a simulated raw surface data scenario while the y-axis represents that simulated surface data after undergoing resampling. Rigid and nonrigid registration results are presented in blue and gold respectively.