Ms. Risa Shigemasa Profile

Risa Shigemasa

at GE Healthcare

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Publications (2)

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Proceedings Article | 3 April 2023 Poster + Paper

A deep learning method for pinwheel artifact reduction in CT

Utkarsh Agrawal, Rajesh Langoju, Yasuhiro Imai, Risa Shigemasa, Bipul Das

Proceedings Volume 12464, 124641W (2023) https://doi.org/10.1117/12.2653607

KEYWORDS: Education and training, Super resolution, Aliasing, Scanners, Image restoration, Image processing, Deep learning, Image quality, Head, Computed tomography

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Pinwheel artifact is caused in multidetector Computed Tomography (CT) helical scans due to under-sampling of z-planes during thin slice reconstruction. There are a few hardware-based solutions like flying focal spot (FFS), which aims at generating more samples during the acquisition, thus enabling aliasing free thin slice images. However, these methods are expensive both in manufacturing capability, as well as impact on hardware life. Deep learning (DL) based methods have shown significant improvement in pin-wheel artifact reduction. Most DL-based methods use images generated from FFS or similar hardware-based enhancement to train the deep learning network, thus restricting usability of these methods on systems without these hardware enhancements. This work proposes a novel DL method to generate pin-wheel free thin-slice images from helical scans for systems not equipped with these hardware capabilities. Artifact-free thin slice images, which are used as targets for artifact reduction network are generated through DL-based super-resolution along z-direction from thick slice images reconstructed from the same scan. The framework is trained with ~16000 coronal/sagittal slices from GE-Revolution system. Clinical image review and statistical analysis of the inferencing results have shown significant artifact reduction and improved diagnostic image quality while reducing noise. A Likert score study shows significant enhancement of proposed method over other image processing-based solutions available.

Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Enhancing Z-resolution in CT volumes with deep residual learning

Utkarsh Agrawal, Aditya Hegde, Rajesh Langoju, Prasad Sudhakar, Bhushan Patil, Sundar R. K., Yasuhiro Imai, Risa Shigemasa, Omi Yasuo, Bipul Das

Proceedings Volume 11596, 1159629 (2021) https://doi.org/10.1117/12.2581273

KEYWORDS: Tissues, Computed tomography, Image resolution, Visibility, Sensors, Resolution enhancement technologies, Lawrencium, Image enhancement, Deconvolution, Collimation

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CT systems with large detector size suffer from lower z-resolution leading to pixelated images and inability to detect small structures thus adversely impacting the diagnosis and screening. Overlap reconstruction can partially reduce the stair-step artifacts but does not improve the effect of wider slice sensitivity profile (SSP) and thus continues to have reduced visibility of smaller structures. In this work, we propose a supervised deep learning method for z-resolution enhancement such that (a) the effective SSP of resulting image is reduced, (b) quantitative values of tissue (CT numbers) and tissue-contrast are preserved; (c) very limited noise enhancement and (d) improved tissue interface in bone/soft tissue. The proposed method devises a super resolution (SURE) network which is trained to map the low resolution (LR) slices to the corresponding high resolution (HR) slices. A 2D network is trained with sagittal and coronal slices with the LR-HR pair sets. The training is performed using ground truth HR slices obtained from high end systems, and the corresponding LR slices are synthesized by either using retro reconstruction with higher slice thickness and spacing or through averaging of slices in z-direction from HR images. The network is trained with both these types of images with helical acquisition volumes from a range of scanners. Qualitative and quantitative analysis is done on the predicted HR images and compared with the original HR images. FWHM for SSP of the predicted HR images reduced from ~0.98 to ~0.73, when the target was 0.64, thus improving the real z-resolution. HU distribution of different tissue types also showed stability in terms of mean value. Noise measured through standard deviation was slightly higher than the LR image but lower than that of original HR images. PSNR also showed consistent improvement on all the cases across 3 different systems.

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