Mizuho Nishio

Takaaki Matsunaga, Atsushi Kono, Hidetoshi Matsuo et al.

Rationale and Objectives: Pericardial fat (PF), the thoracic visceral fat surrounding the heart, promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. For evaluating PF, this study aimed to generate pericardial fat count images (PFCIs) from chest radiographs (CXRs) using a dedicated deep-learning model. Materials and Methods: The data of 269 consecutive patients who underwent coronary computed tomography (CT) were reviewed. Patients with metal implants, pleural effusion, history of thoracic surgery, or that of malignancy were excluded. Thus, the data of 191 patients were used. PFCIs were generated from the projection of three-dimensional CT images, where fat accumulation was represented by a high pixel value. Three different deep-learning models, including CycleGAN, were combined in the proposed method to generate PFCIs from CXRs. A single CycleGAN-based model was used to generate PFCIs from CXRs for comparison with the proposed method. To evaluate the image quality of the generated PFCIs, structural similarity index measure (SSIM), mean squared error (MSE), and mean absolute error (MAE) of (i) the PFCI generated using the proposed method and (ii) the PFCI generated using the single model were compared. Results: The mean SSIM, MSE, and MAE were as follows: 0.856, 0.0128, and 0.0357, respectively, for the proposed model; and 0.762, 0.0198, and 0.0504, respectively, for the single CycleGAN-based model. Conclusion: PFCIs generated from CXRs with the proposed model showed better performance than those with the single model. PFCI evaluation without CT may be possible with the proposed method.

Hidetoshi Matsuo, Mizuho Nishio, Munenobu Nogami et al.

The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner facilitates the simultaneous acquisition of metabolic information via PET and morphological information with high soft-tissue contrast using MRI. Although PET/MRI facilitates the capture of high-accuracy fusion images, its major drawback can be attributed to the difficulty encountered when performing attenuation correction, which is necessary for quantitative PET evaluation. The combined PET/MRI scanning requires the generation of attenuation-correction maps from MRI owing to no direct relationship between the gamma-ray attenuation information and MRIs. While MRI-based bone-tissue segmentation can be readily performed for the head and pelvis regions, the realization of accurate bone segmentation via chest CT generation remains a challenging task. This can be attributed to the respiratory and cardiac motions occurring in the chest as well as its anatomically complicated structure and relatively thin bone cortex. This paper presents a means to minimise the anatomical structural changes without human annotation by adding structural constraints using a modality-independent neighbourhood descriptor (MIND) to a generative adversarial network (GAN) that can transform unpaired images. The results obtained in this study revealed the proposed U-GAT-IT + MIND approach to outperform all other competing approaches. The findings of this study hint towards possibility of synthesising clinically acceptable CT images from chest MRI without human annotation, thereby minimising the changes in the anatomical structure.

Mizuho Nishio, Sho Koyasu, Shunjiro Noguchi et al.

Background and Purpose: We aimed to develop and evaluate an automatic acute ischemic stroke-related (AIS) detection system involving a two-stage deep learning model. Methods: We included 238 cases from two different institutions. AIS-related findings were annotated on each of the 238 sets of head CT images by referring to head magnetic resonance imaging (MRI) images in which an MRI examination was performed within 24 h following the CT scan. These 238 annotated cases were divided into a training set including 189 cases and test set including 49 cases. Subsequently, a two-stage deep learning detection model was constructed from the training set using the You Only Look Once v3 model and Visual Geometry Group 16 classification model. Then, the two-stage model performed the AIS detection process in the test set. To assess the detection model's results, a board-certified radiologist also evaluated the test set head CT images with and without the aid of the detection model. The sensitivity of AIS detection and number of false positives were calculated for the evaluation of the test set detection results. The sensitivity of the radiologist with and without the software detection results was compared using the McNemar test. A p-value of less than 0.05 was considered statistically significant. Results: For the two-stage model and radiologist without and with the use of the software results, the sensitivity was 37.3%, 33.3%, and 41.3%, respectively, and the number of false positives per one case was 1.265, 0.327, and 0.388, respectively. On using the two-stage detection model's results, the board-certified radiologist's detection sensitivity significantly improved (p-value = 0.0313). Conclusions: Our detection system involving the two-stage deep learning model significantly improved the radiologist's sensitivity in AIS detection.