岡本 尚之

Micro-computed tomography (micro-CT) enables the non-destructive acquisition of three-dimensional (3D) morphological structures at the micrometer scale. Although it is expected to be used in pathology and histology to analyze the 3D microstructure of tissues, micro-CT imaging of tissue specimens requires a long scan time. A high-speed imaging method, sparse-view CT, can reduce the total scan time and radiation dose; however, it causes severe streak artifacts on tomographic images reconstructed with analytical algorithms due to insufficient sampling. In this paper, we propose an artifact reduction method for 3D volume projection data from sparse-view micro-CT. Specifically, we developed a patch-based lightweight fully convolutional network to estimate full-view 3D volume projection data from sparse-view 3D volume projection data. We evaluated the effectiveness of the proposed method using physically acquired datasets. The qualitative and quantitative results showed that the proposed method achieved high estimation accuracy and suppressed streak artifacts in the reconstructed images. In addition, we confirmed that the proposed method requires both short training and prediction times. Our study demonstrates that the proposed method has great potential for artifact reduction for 3D volume projection data under sparse-view conditions.

Sparse-view computed tomography (CT), an imaging technique that reduces the number of projections, can reduce the total scan duration and radiation dose. However, sparse data sampling causes streak artifacts on images reconstructed with analytical algorithms. In this paper, we propose an artifact reduction method for sparse-view CT using deep learning. We developed a light-weight fully convolutional network to estimate a fully sampled sinogram from a sparse-view sinogram by enlargement in the vertical direction. Furthermore, we introduced the band patch, a rectangular region cropped in the vertical direction, as an input image for the network based on the sinogram’s characteristics. Comparison experiments using a swine rib dataset of micro-CT scans and a chest dataset of clinical CT scans were conducted to compare the proposed method, improved U-net from a previous study, and the U-net with band patches. The experimental results showed that the proposed method achieved the best performance and the U-net with band patches had the second-best result in terms of accuracy and prediction time. In addition, the reconstructed images of the proposed method suppressed streak artifacts while preserving the object’s structural information. We confirmed that the proposed method and band patch are useful for artifact reduction for sparse-view CT.

Laparoscopic surgery allows reduction in surgical incision size and leads to faster recovery compared with open surgery. When bleeding takes place, hemostasis treatment is planned according to the state and location of the bleeding. However, it is difficult to find the bleeding source due to low visibility caused by the narrow field of view of the laparoscope. In this paper, we propose the concept of a hemostasis support system that automatically identifies blood regions and indicates them to the surgeon. We mainly describe a blood region identification method that is one of technical challenges to realize the support system. The proposed method is based on a machine learning technique called the support vector machine, working in real time. Within this method, all the pixels in the image are classified as either blood or non-blood pixels based on color features (e.g., a combination of RGB and HSV values). The suitable combination of feature values used for the classification is determined by a simple feature selection method. Three feature values were determined to identify the blood region. We then validated the proposed method with ten sequences of laparoscopic images by cross-validation. The average accuracy exceeded 95% with a processing time of about 12.6 ms/frame. The proposed method was able to accurately identify blood regions and was suitable for real-time applications.