A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery

Abstract

The resection of small, low-dense or deep lung nodules during video-assisted thoracoscopic surgery (VATS) is surgically challenging. Nodule localization methods in clinical practice typically rely on the preoperative placement of markers, which may lead to clinical complications. We propose a markerless lung nodule localization framework for VATS based on a hybrid method combining intraoperative cone-beam CT (CBCT) imaging, free-form deformation image registration, and a poroelastic lung model with allowance for air evacuation. The difficult problem of estimating intraoperative lung deformations is decomposed into two more tractable sub-problems: (i) estimating the deformation due the change of patient pose from preoperative CT (supine) to intraoperative CBCT (lateral decubitus); and (ii) estimating the pneumothorax deformation, i.e. a collapse of the lung within the thoracic cage. We were able to demonstrate the feasibility of our localization framework with a retrospective validation study on 5 VATS clinical cases. Average initial errors in the range of 22 to 38 mm were reduced to the range of 4 to 14 mm, corresponding to an error correction in the range of 63 to 85%. To our knowledge, this is the first markerless lung deformation compensation method dedicated to VATS and validated on actual clinical data.

Keywords: Biomechanical modeling; Image registration; Lung deformation; Video-assisted thoracoscopic surgery.

Fig. 1.

Left: preoperative CT image with the patient in supine position. Right: intraoperative CBCT images of the inflated (CBCT_inf) and deflated (CBCT_def) lung with the patient in lateral decubitus position. Middle: superposition of the preoperative CT image rigidly registered to the intraoperative CBCT_def image. The FOV of the CBCT_def image (outlined in yellow) only provides a partial view of the lung. The nodule is encircled in the preoperative CT image and is visible in all other images.

Fig. 2.

Overview of the proposed nodule localization framework. The process is split into two stages, Phase 1 and Phase 2, that respectively estimate the change of pose deformation then the pneumothorax deformation.

Fig. 3.

Schematic diagram of the Phase 1 process to estimate the change of pose deformation. The top block illustrates the image-based registration of the preoperative CT and intraoperative CBCT_inf images. After rigidly registering the spine, an elastic registration based on anatomical segmentations of the lung is carried out. The bottom block concerns the estimation of the complete lung geometry after the change of pose deformation. The previously computed deformation field is transferred as imposed displacements boundary conditions on a FEM model. This model extrapolates the deformation to the whole extent of the lung, including regions that are not within the FOV of the CBCT_inf image.

Fig. 4.

Schematic diagram of the Phase 2 stage to estimate the pneumothorax deformation. Intraoperative images are processed to segment the surface of the deflated lung, and to compute a deformation field approximating the hilum deformation between CBCT_inf and CBCT_def. An inverse problem based on FE simulations estimated the pneumothorax deformation. Tissue parameters were optimized until the simulated model best fits the intraoperative data. Finally, the intraoperative nodule position is obtained by warping the undeformed position with the simulated pneumothorax deformation.

Fig. 5.

Schematic representation of the pneumothorax phenomenon. Left, at end of expiration the lung is in equilibrium and there is no airflow. Right, the rupture in the chest wall causes an increase of the intrapleural pressure and a decrease of the transmural pressure. The chest wall no longer pulls the surface of the lung outwards. The lung collapses due to alveoli inward recoil and gravity. The flow of air is indicated with black arrows.

Fig. 6.

Spatial distribution of anatomical landmarks within the lung FE mesh reconstructed from the preoperative CT image.

Fig. 7.

TRE distributions for rigid and elastic registration between the preoperative CT and intraoperative CBCT_inf (Phase 1, change of pose).

Fig. 8.

Qualitative results of rigid and elastic registration between the preoperative CT (green) and intraoperative CBCT_inf (magenta) images. Coronal slices are shown for two representative cases. The target CBCT_inf image in gray-scale is shown in the far right column.

Fig. 9.

TRE distributions for our complete deformation compensation framework, alongside the errors expected without deformation compensation. These latter distributions correspond to rigidly registering the preoperative CT with the CBCT_inf and CBCT_def images, respectively.

Fig. 10.

Qualitative results of our deformation compensation framework for two clinical cases. Left: final deformed lung FE mesh superposed over the extracted deflated lung surface (in green). Middle: Registered landmark errors, deformed FE lung mesh and thoracic cage contact surface. Right: Initial nodule position (wireframe, black surface), ground truth nodule position (wireframe, green surface) and predicted nodule position (solid, purple surface). Results for all cases are available in the online supplementary materials.

Fig. 11.

Qualitative results of our deformation compensation framework for two representative cases. The CT and CBCT_inf images are rigidly registered to the CBCT_def image. Coronal slices of exactly the same region of interest are shown for all images. The color contours illustrate the position of the FE mesh at the beginning of Phase 1 (cyan) and Phase 2 (orange), as well as at the end of Phase 2 (purple). Results for all cases are available in the online supplementary materials.

Fig. 12.

TRE distributions for three variants of the proposed lung deformation compensation method.

Fig. 13.

TRE distributions for our deformation compensation framework with and without including the upward diaphragm movement.