Purpose: In treatment planning for radiation therapy, the use of computed tomography (CT) images including metal artifacts causes a reduction in the dose calculation accuracy. In clinical practice, the artifacts are manually contoured and assigned an appropriate fixed CT number. To validate the procedure, images taken before and after metal insertion into a patient are required, which may be impractical. We propose a simple method for computationally generating metal artifacts in clinical images. Methods: In the proposed method, a clinical image free of metal artifacts is used. To simulate metal inside a patient, CT numbers of a region in the image are replaced with a fixed extremely high value. A sinogram is created by the forward projection of the image. Data values of the sinogram in the metal region are converted into smaller values. From the sinogram, an image including artifacts is reconstructed with the filtered back projection. Results: The simulated artifacts consisted of dark and bright bands and were observed to be similar to the actual metal artifacts. CT numbers in multiple small regions of interest in the image obtained by the proposed method showed a good agreement with those in the actual image. Conclusion: The proposed method was demonstrated to generate the metal artifacts additionally on the clinical images. The method would be potentially applicable to a validation study for the clinical procedure of manually contouring and assigning CT numbers to metal artifacts.

Purpose: Field of view (FOV) and matrix size determine the pixel size of positron emission tomography (PET) images; however, the effect of any variation in these parameters on the quantitative accuracy is unclear. The FOV and matrix size of PET images are adjusted as per each clinical objective. Therefore, this study aimed to evaluate the quantitative accuracy of PET images under different FOV and matrix sizes. Method: A National Electrical Manufacturers Association (NEMA) body phantom set was filled with ¹⁸F-FDG solution, and imaging data were acquired for 30 min. Images were reconstructed using ordered subset expectation maximization (OSEM) and Bayesian penalized likelihood (BPL), both of which were combined with point spread function (PSF) and time of flight (TOF). In each reconstruction method, the image parameters were set to the following: FOV, 20–70 cm; matrix size, 128×128 to 384×384; and pixel size, 1–3 mm. The images were evaluated by physical assessment of the recovery coefficient (RC) and maximum standardized uptake value ratio (SUV_{max ratio}). Result: The RC of OSEM images was not affected by changes in FOV, whereas the RC of BPL images decreased in small spheres, when FOV was 20 and 30 cm. The SUV_{max ratio} of the OSEM images was not affected by the difference in pixel size. However, the SUV_{max ratio} of BPL images degraded in the 1-mm pixel size; this influence was observed only when the FOV was changed. Conclusion: BPL images reconstructed using a small FOV might degrade the quantitative accuracy of small spheres.

Purpose: We focused on deep learning for a reduction of motion artifacts in MRI. It is difficult to collect a large number of images with and without motion artifacts from clinical images. The purpose of this study was to create motion artifact images in MRI by simulation. Methods: We created motion artifact images by computer simulation. First, 20 different types of vertical pixel-shifted images were created with different shifts, and the amount of pixel shift was set from –10 to 10 pixels. The same method was used to create pixel-shifted images for horizontal shift, diagonal shift, and rotational shift, and a total of 80 types of pixel-shifted images were prepared. These images were Fourier transformed to create 80 types of k-space data. Then, phase encodings in these k-space data were randomly sampled and Fourier transformed to create artifact images. The reproducibility of the simulation images was verified using the deep learning network model of U-net. In this study, the evaluation indices used were the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). Results: The average SSIM and PSNR for the simulation images were 0.95 and 31.5, respectively; those for the clinical images were 0.96 and 31.1, respectively. Conclusion: Our simulation method enables us to create a large number of artifact images in a short time, equivalent to clinical artifact images.

Recently, diffusion-weighted imaging (DWI) has become essential for diagnosing acute cerebral infractions and detecting lesions via magnetic resonance imaging (MRI). Investigations using phantoms have been performed to evaluate the optimizing parameters before clinical practice. However, there have been no studies on extracting appropriate phantom materials. It is known that the apparent diffusion coefficient (ADC) changes with temperature. To extract optimal materials from polyethylene glycol, sucrose, and dextrin in previous studies, evaluations were performed using ADC with temperature change and signal-to-noise ratio (SNR) . Results of comparison with difference between true and measured values depend on the Stokes-Einstein formula for ADC change with temperature change; the highest value was obtained for polyethylene glycol. In the SNR measurement, when the temperature increased, the rate of change of ADC decreased. Polyethylene glycol showed the highest value. According to these results, it can be concluded that polyethylene glycol can be extracted when nearest to true value and when there is a high SNR, thus making polyethylene glycol the most suitable material for diffusion-weighted image phantoms.

Mammography equipment attached to the digital breast tomosynthesis (DBT) system is widespread in Japan. However, there are no guidelines for quality control methods for DBT in Japan. Therefore, it is necessary to rapidly establish a performance evaluation procedure and a quality control procedure for DBT. In this study, we conducted basic experiments using DBTs of five companies (Canon Medical, Fujifilm Medical, GE Healthcare, Hologic, Siemens) already sold in Japan and examined feasible common items. We aimed to establish a quality control method for DBT in Japan. The measurement was performed based on the European Reference Organisation for Quality Assured Breast Screening and Diagnostic Services (EUREF) breast tomosynthesis quality control protocol, version 1.03. In this study, we tried to measure 18 items in DBT. We examined whether the 18 items could be measured using each device; it is not an evaluation of device performance based on the measured values. There were some management items that were difficult to implement due to the specifications of DBT, such as devices that required pressure on DBT operation, problems due to the shape of bucky, and devices that did not have stationary mode. There were also problems with measurement data; for example, devices could not retrieve projection data and reconstruction data. This study clarified points to be considered for establishing common quality control items. In the future, we will carefully refer to the recently published IEC 61223-3-6, consider international harmonization, and establish DBT guidelines customized for the Japanese market.