Adaptive Voxel Matching for Temporal CT Subtraction
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ORIGINAL PAPER
Adaptive Voxel Matching for Temporal CT Subtraction Toru Tanaka 1 & Ryo Ishikawa 1 & Keita Nakagomi 1 & Kazuhiro Miyasa 1 & Kiyohide Satoh 1 & Masahiro Yakami 2,3 & Thai Akasaka 2 & Koji Onoue 2 & Takeshi Kubo 2 & Mizuho Nishio 2,3 & Yutaka Emoto 4 & Kaori Togashi 2 Received: 14 October 2019 / Revised: 19 June 2020 / Accepted: 23 July 2020 # Society for Imaging Informatics in Medicine 2020
Abstract Temporal subtraction (TS) technique calculates a subtraction image between a pair of registered images acquired from the same patient at different times. Previous studies have shown that TS is effective for visualizing pathological changes over time; therefore, TS should be a useful tool for radiologists. However, artifacts caused by partial volume effects degrade the quality of thick-slice subtraction images, even with accurate image registration. Here, we propose a subtraction method for reducing artifacts in thick-slice images and discuss its implementation in high-speed processing. The proposed method is based on voxel matching, which reduces artifacts by considering gaps in discretized positions of two images in subtraction calculations. There are two different features between the proposed method and conventional voxel matching: (1) the size of a searching region to reduce artifacts is determined based on discretized position gaps between images and (2) the searching region is set on both images for symmetrical subtraction. The proposed method is implemented by adopting an accelerated subtraction calculation method that exploit the nature of liner interpolation for calculating the signal value at a point among discretized positions. We quantitatively evaluated the proposed method using synthetic data and qualitatively using clinical data interpreted by radiologists. The evaluation showed that the proposed method was superior to conventional methods. Moreover, the processing speed using the proposed method was almost unchanged from that of the conventional methods. The results indicate that the proposed method can improve the quality of subtraction images acquired from thick-slice images. Keywords Temporal subtraction . CT . Artifact reduction . Bone metastasis
Introduction In the field of diagnostic medical imaging, radiologists commonly interpret and compare two images (previous and current images) acquired from the same patient at different times to detect pathological changes. However, radiologists are frequently burdened by having to interpret three-dimensional
* Toru Tanaka [email protected] 1
Canon Inc., 30-2, Shimomaruko 3-chome, Ohta-ku, Tokyo 146-8501, Japan
2
Graduate School of Medicine, Kyoto University, 54 Kawaharacho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
3
Preemptive Medicine and Lifestyle-related Disease Research Center, Kyoto University Hospital, 53 Kawaharacho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan
4
Kyoto College of Medical Science, 1-3, Imakita, Oyama-Higashimachi, Sonobe-cho, Nantan, Kyoto 622-0041, Japan
(3D) images, such as computed tomography (CT) imag
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