Dataset Information

Processing-Aware Real-Time Rendering for Optimized Tissue Visualization in Intraoperative 4D OCT

ABSTRACT: Intraoperative Optical Coherence Tomography (iOCT) has advanced in recent years to provide real-time high resolution volumetric imaging for ophthalmic surgery. It enables real-time 3D feedback during precise surgical maneuvers. Intraoperative 4D OCT generally exhibits lower signal-to-noise ratio compared to diagnostic OCT and visualization is complicated by instrument shadows occluding retinal tissue. Additional constraints of processing data rates upwards of 6GB/s create unique challenges for advanced visualization of 4D OCT. Prior approaches for real-time 4D iOCT rendering have been limited to applying simple denoising filters and colorization to improve visualization. We present a novel real-time rendering pipeline that provides enhanced intraoperative visualization and is specifically designed for the high data rates of 4D iOCT. We decompose the volume into a static part consisting of the retinal tissue and a dynamic part including the instrument. Aligning the static parts over time allows temporal compounding of these structures for improved image quality. We employ a translational motion model and use axial projection images to reduce the dimensionality of the alignment. A model-based instrument segmentation on the projections discriminates static from dynamic parts and is used to exclude instruments from the compounding. Our real-time rendering method combines the compounded static information with the latest iOCT data to provide a visualization which compensates instrument shadows and improves instrument visibility. We evaluate the individual parts of our pipeline on pre-recorded OCT volumes and demonstrate the effectiveness of our method on a recorded volume sequence with a moving retinal forceps.

SUBMITTER: Weiss J

PROVIDER: S-EPMC8171113 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Calculation of four-dimensional (4D) dose distributions requires the remapping of dose calculated on each available binned phase of the 4D CT onto a reference phase for summation. Deformable image registration (DIR) is usually used for this task, but unfortunately almost always considers only endpoints rather than the whole motion path. A new algorithm, 4D tissue deformation reconstruction (4D TDR), that uses either CT projection data or all available 4D CT images to reconstruct 4D motion data, was developed. The purpose of this work is to verify the accuracy of the fit of this new algorithm using a realistic tissue phantom. A previously described fresh tissue phantom with implanted electromagnetic tracking (EMT) fiducials was used for this experiment. The phantom was animated using a sinusoidal and a real patient-breathing signal. Four-dimensional computer tomography (4D CT) and EMT tracking were performed. Deformation reconstruction was conducted using the 4D TDR and a modified 4D TDR which takes real tissue hysteresis (4D TDR(Hysteresis)) into account. Deformation estimation results were compared to the EMT and 4D CT coordinate measurements. To eliminate the possibility of the high contrast markers driving the 4D TDR, a comparison was made using the original 4D CT data and data in which the fiducials were electronically masked. For the sinusoidal animation, the average deviation of the 4D TDR compared to the manually determined coordinates from 4D CT data was 1.9 mm, albeit with as large as 4.5 mm deviation. The 4D TDR calculation traces matched 95% of the EMT trace within 2.8 mm. The motion hysteresis generated by real tissue is not properly projected other than at endpoints of motion. Sinusoidal animation resulted in 95% of EMT measured locations to be within less than 1.2 mm of the measured 4D CT motion path, enabling accurate motion characterization of the tissue hysteresis. The 4D TDR(Hysteresis) calculation traces accounted well for the hysteresis and matched 95% of the EMT trace within 1.6 mm. An irregular (in amplitude and frequency) recorded patient trace applied to the same tissue resulted in 95% of the EMT trace points within less than 4.5 mm when compared to both the 4D CT and 4D TDR(Hysteresis) motion paths. The average deviation of 4D TDR(Hysteresis) compared to 4D CT datasets was 0.9 mm under regular sinusoidal and 1.0 mm under irregular patient trace animation. The EMT trace data fit to the 4D TDR(Hysteresis) was within 1.6 mm for sinusoidal and 4.5 mm for patient trace animation. While various algorithms have been validated for end-to-end accuracy, one can only be fully confident in the performance of a predictive algorithm if one looks at data along the full motion path. The 4D TDR, calculating the whole motion path rather than only phase- or endpoints, allows us to fully characterize the accuracy of a predictive algorithm, minimizing assumptions. This algorithm went one step further by allowing for the inclusion of tissue hysteresis effects, a real-world effect that is neglected when endpoint-only validation is performed. Our results show that the 4D TDR(Hysteresis) correctly models the deformation at the endpoints and any intermediate points along the motion path.

Dataset Information

Processing-Aware Real-Time Rendering for Optimized Tissue Visualization in Intraoperative 4D OCT

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets