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%.
Laparoscopic liver resection is increasingly being performed with results comparable to open cases while incurring less
trauma and reducing recovery time. The tradeoff is increased difficulty due to limited visibility and restricted freedom of
movement. Image-guided surgical navigation systems have the potential to help localize anatomical features to improve
procedural safety and achieve better surgical resection outcome. Previous research has demonstrated that intraoperative
surface data can be used to drive a finite element tissue mechanics organ model such that high resolution preoperative
scans are registered and visualized in the context of the current surgical pose. In this paper we present an investigation of
using sparse data as imposed by laparoscopic limitations to drive a registration model. Non-contact laparoscopicallyacquired
surface swabbing and mock-ultrasound subsurface data were used within the context of a nonrigid registration
methodology to align mock deformed intraoperative surface data to the corresponding preoperative liver model as
derived from pre-operative image segmentations. The mock testing setup to validate the potential of this approach used a
tissue-mimicking liver phantom with a realistic abdomen-port patient configuration. Experimental results demonstrates a
range of target registration errors (TRE) on the order of 5mm were achieving using only surface swab data, while use of
only subsurface data yielded errors on the order of 6mm. Registrations using a combination of both datasets achieved
TRE on the order of 2.5mm and represent a sizeable improvement over either dataset alone.
In the context of open abdominal image-guided liver surgery, the efficacy of an image-guidance system relies on its ability to (1) accurately depict tool locations with respect to the anatomy, and (2) maintain the work flow of the surgical team. Laser-range scanned (LRS) partial surface measurements can be taken intraoperatively with relatively little impact on the surgical work flow, as opposed to other intraoperative imaging modalities. Previous research has demonstrated that this kind of partial surface data may be (1) used to drive a rigid registration of the preoperative CT image volume to intraoperative patient space, and (2) extrapolated and combined with a tissue-mechanics-based organ model to drive a non-rigid registration, thus compensating for organ deformations. In this paper we present a novel approach for intraoperative nonrigid liver registration which iteratively reconstructs a displacement field on the posterior side of the organ in order to minimize the error between the deformed model and the intraopreative surface data. Experimental results with a phantom liver undergoing large deformations demonstrate that this method achieves target registration errors (TRE) with a mean of 4.0 mm in the prediction of a set of 58 locations inside the phantom, which represents a 50% improvement over rigid registration alone, and a 44% improvement over the prior non-iterative single-solve method of extrapolating boundary conditions via a surface Laplacian.
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