Resolving the Ambiguity of Complete-to-Partial Point Cloud Registration for Image-Guided Liver Surgery with Patches-to-Partial Matching.

In image-guided liver surgery, the initial rigid alignment between preoperative and intraoperative data, often represented as point clouds, is crucial for providing sub-surface information from preoperative CT/MRI images to the surgeon during the procedure. Currently, this alignment is typically performed using semi-automatic methods, which, while effective to some extent, are prone to errors that demand manual correction. Alternatively, correspondence-based point cloud registration methods further offer a promising fully automatic solution.

Precision in Liver Surgery: A Comparative Analysis of Volumetry Techniques.

Background: Future liver remnant volume (FLRV) is a critical determinant of safety for hepatectomy. This study assesses concordance between imaging-based measured FLRV (mFLRV), and body surface area (BSA)-based standardized FLRV (sFLRV), and their association with post-hepatectomy complications.

Materials And Methods: All major hepatectomy between 1999 and 2021 were assessed for agreement between mFLRV and sFLRV using concordance correlation coefficient (CCC).

Deformable Mapping of Rectal Cancer Whole-Mount Histology with Restaging MRI at Voxel Scale: A Feasibility Study.

Purpose To develop a radiology-pathology coregistration method for 1:1 automated spatial mapping between preoperative rectal MRI and ex vivo rectal whole-mount histology (WMH). Materials and Methods This retrospective study included consecutive patients with rectal adenocarcinoma who underwent total neoadjuvant therapy followed by total mesorectal excision with preoperative rectal MRI and WMH from January 2019 to January 2022. A gastrointestinal pathologist and a radiologist established three corresponding levels for each patient at rectal MRI and WMH, subsequently delineating external and internal rectal wall contours and the tumor bed at each level and defining eight point-based landmarks.

Comparing regularized Kelvinlet functions and the finite element method for registration of medical images to sparse organ data.

Image-guided surgery collocates patient-specific data with the physical environment to facilitate surgical decision making. Unfortunately, these guidance systems commonly become compromised by intraoperative soft-tissue deformations. Nonrigid image-to-physical registration methods have been proposed to compensate for deformations, but clinical utility requires compatibility of these techniques with data sparsity and temporal constraints in the operating room.

Simulation of Image-Guided Microwave Ablation Therapy Using a Digital Twin Computational Model.

Emerging computational tools such as healthcare digital twin modeling are enabling the creation of patient-specific surgical planning, including microwave ablation to treat primary and secondary liver cancers. Healthcare digital twins (DTs) are anatomically one-to-one biophysical models constructed from structural, functional, and biomarker-based imaging data to simulate patient-specific therapies and guide clinical decision-making. In microwave ablation (MWA), tissue-specific factors including tissue perfusion, hepatic steatosis, and fibrosis affect therapeutic extent, but current thermal dosing guidelines do not account for these parameters.

Comparison study of intraoperative surface acquisition methods on registration accuracy for soft-tissue surgical navigation.

Purpose: To study the difference between rigid registration and nonrigid registration using two forms of digitization (contact and noncontact) in human liver surgery.

Approach: A Conoprobe device attachment and sterilization process was developed to enable prospective noncontact intraoperative acquisition of organ surface data in the operating room (OR). The noncontact Conoprobe digitization method was compared against stylus-based acquisition in the context of image-to-physical registration for image-guided surgical navigation.

A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model.

Unlabelled: Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer.

The Image-to-Physical Liver Registration Sparse Data Challenge: comparison of state-of-the-art using a common dataset.

Purpose: Computational methods for image-to-physical registration during surgical guidance frequently rely on sparse point clouds obtained over a limited region of the organ surface. However, soft tissue deformations complicate the ability to accurately infer anatomical alignments from sparse descriptors of the organ surface. The Image-to-Physical Liver Registration Sparse Data Challenge introduced at SPIE Medical Imaging 2019 seeks to characterize the performance of sparse data registration methods on a common dataset to benchmark and identify effective tactics and limitations that will continue to inform the evolution of image-to-physical registration algorithms.

Registration-based biomarkers for neoadjuvant treatment response of pancreatic cancer via longitudinal image registration.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) frequently presents as hypo- or iso-dense masses with poor contrast delineation from surrounding parenchyma, which decreases reproducibility of manual dimensional measurements obtained during conventional radiographic assessment of treatment response. Longitudinal registration between pre- and post-treatment images may produce imaging biomarkers that more reliably quantify treatment response across serial imaging.

Approach: Thirty patients who prospectively underwent a neoadjuvant chemotherapy regimen as part of a clinical trial were retrospectively analyzed in this study.

Soft Tissue Monitoring of the Surgical Field: Detection and Tracking of Breast Surface Deformations.

Objective: Deformable object tracking is common in the computer vision field, with applications typically focusing on nonrigid shape detection and usually not requiring specific three-dimensional point localization. In surgical guidance however, accurate navigation is intrinsically linked to precise correspondence of tissue structure. This work presents a contactless, automated fiducial acquisition method using stereo video of the operating field to provide reliable three-dimensional fiducial localization for an image guidance framework in breast conserving surgery.

Incorporating heterogeneity and anisotropy for surgical applications in breast deformation modeling.

Background: Simulating soft-tissue breast deformations is of interest for many applications including image fusion, longitudinal registration, and image-guided surgery. For the surgical use case, positional changes cause breast deformations that compromise the use of preoperative imaging to inform tumor excision. Even when acquiring imaging in the supine position, which better reflects surgical presentation, deformations still occur due to arm motion and orientation changes.

Supine magnetic resonance image registration for breast surgery: insights on material mechanics.

Purpose: Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, is being explored since it more closely represents the surgical presentation compared to conventional diagnostic imaging positions. Despite this preoperative imaging position, deformation is still present between the supine imaging and surgical state.

Computational Imaging to Compensate for Soft-Tissue Deformations in Image-Guided Breast Conserving Surgery.

Objective: During breast conserving surgery (BCS), magnetic resonance (MR) images aligned to accurately display intraoperative lesion locations can offer improved understanding of tumor extent and position relative to breast anatomy. Unfortunately, even under consistent supine conditions, soft tissue deformation compromises image-to-physical alignment and results in positional errors.

Methods: A finite element inverse modeling technique has been developed to nonrigidly register preoperative supine MR imaging data to the surgical scene for improved localization accuracy during surgery.

Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation Therapy.

Computational tools are beginning to enable patient-specific surgical planning to localize and prescribe thermal dosing for liver cancer ablation therapy. Tissue-specific factors (e.g.

Impact of deformation on a supine-positioned image-guided breast surgery approach.

Purpose: To reduce reoperation rates for image-guided breast-conserving surgery, the enhanced sensitivity of magnetic resonance (MR) supine imaging may be leveraged. However, accurate tissue correspondence between images and their physical counterpart in the surgical presentation is challenging due to breast deformations (e.g.

Strain Energy Decay Predicts Elastic Registration Accuracy From Intraoperative Data Constraints.

Image-guided intervention for soft tissue organs depends on the accuracy of deformable registration methods to achieve effective results. While registration techniques based on elastic theory are prevalent, no methods yet exist that can prospectively estimate registration uncertainty to regulate sources and mitigate consequences of localization error in deforming organs. This paper introduces registration uncertainty metrics based on dispersion of strain energy from boundary constraints to predict the proportion of target registration error (TRE) remaining after nonrigid elastic registration.

Intraoperative Correction of Liver Deformation Using Sparse Surface and Vascular Features via Linearized Iterative Boundary Reconstruction.

During image guided liver surgery, soft tissue deformation can cause considerable error when attempting to achieve accurate localization of the surgical anatomy through image-to-physical registration. In this paper, a linearized iterative boundary reconstruction technique is proposed to account for these deformations. The approach leverages a superposed formulation of boundary conditions to rapidly and accurately estimate the deformation applied to a preoperative model of the organ given sparse intraoperative data of surface and subsurface features.

Toward Image Data-Driven Predictive Modeling for Guiding Thermal Ablative Therapy.

Objective: Accurate prospective modeling of microwave ablation (MWA) procedures can provide powerful planning and navigational information to physicians. However, patient-specific tissue properties are generally unavailable and can vary based on factors such as relative perfusion and state of disease. Therefore, a need exists for modeling frameworks that account for variations in tissue properties.

Multiphysics modeling toward enhanced guidance in hepatic microwave ablation: a preliminary framework.

We compare a surface-driven, model-based deformation correction method to a clinically relevant rigid registration approach within the application of image-guided microwave ablation for the purpose of demonstrating improved localization and antenna placement in a deformable hepatic phantom. Furthermore, we present preliminary computational modeling of microwave ablation integrated within the navigational environment to lay the groundwork for a more comprehensive procedural planning and guidance framework. To achieve this, we employ a simple, retrospective model of microwave ablation after registration, which allows a preliminary evaluation of the combined therapeutic and navigational framework.

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

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.

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.