arXiv subjects

Taiyo Ishikawa

Publications and source records attributed to Taiyo Ishikawa.

2 recordsLinked to original sources

arXiv2025

Imaging simulation of a dual-panel PET geometry with ultrafast TOF detectors

In positron emission tomography (PET), time-of-flight (TOF) information localizes source positions along lines of response. Cherenkov-radiator-integrated microchannel-plate photomultiplier tubes have achieved 30 ps TOF resolution, demonstrating cross-sectional imaging without reconstruction. Such ultrafast TOF detectors would free PET from conventional ring geometries. Therefore, this study aimed at investigating imaging characteristics of a dual-panel PET with ultrafast TOF detectors using Geant4 simulation. Two detector panels ($137 \times 137~\text{mm}^2$), which consisted of 5.0 mm-thick bismuth germanate pixelized crystals with a 5.75 mm pitch, were placed face-to-face at a 300 mm distance. Imaging characteristics with various TOF resolutions from 30 to 90 ps were evaluated. Because degraded efficiency may cancel TOF gain in image quality, detection efficiency was also parameterized by reducing coincidence counts. Data acquisitions for a numerical multi-rod and uniform phantom (21 MBq) and a modified NEMA NU2 image quality phantom were simulated for 600 s. Results of the maximum likelihood expectation maximization (MLEM) reconstruction were compared with those of a backprojection (i.e., no reconstruction). The dual-panel PET required a 40 ps TOF resolution to have a similar spatial resolution to that of a non-TOF ring PET (300 mm in diameter) for the same detection efficiency. TOF showed benefit in the reconstruction of image quality phantom with 40% efficiency, and the image noise with 20% efficiency at 30 ps TOF was similar to the complete efficiency at 40 ps TOF. MLEM provided better imaging performance than backprojection, even at 30 ps TOF. The feasibility of the proposed dual-panel PET was shown.

physics.med-ph↗

arXiv2023

SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery

This paper introduces the ``SurgT: Surgical Tracking" challenge which was organised in conjunction with MICCAI 2022. There were two purposes for the creation of this challenge: (1) the establishment of the first standardised benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, have been provided. Participants were assigned the task of developing algorithms to track the movement of soft tissues, represented by bounding boxes, in stereo endoscopic videos. At the end of the challenge, the developed methods were assessed on a previously hidden test subset. This assessment uses benchmarking metrics that were purposely developed for this challenge, to verify the efficacy of unsupervised deep learning algorithms in tracking soft-tissue. The metric used for ranking the methods was the Expected Average Overlap (EAO) score, which measures the average overlap between a tracker's and the ground truth bounding boxes. Coming first in the challenge was the deep learning submission by ICVS-2Ai with a superior EAO score of 0.617. This method employs ARFlow to estimate unsupervised dense optical flow from cropped images, using photometric and regularization losses. Second, Jmees with an EAO of 0.583, uses deep learning for surgical tool segmentation on top of a non-deep learning baseline method: CSRT. CSRT by itself scores a similar EAO of 0.563. The results from this challenge show that currently, non-deep learning methods are still competitive. The dataset and benchmarking tool created for this challenge have been made publicly available at https://surgt.grand-challenge.org/.

cs.CV↗