A numerical simulation of diffusion-weighted imaging (DWI) experiments, based on the Bloch-Torrey equation, provides an additional insight into diagnosis using MRI. However, simulation of DWI for largesize models has not been performed because huge computing power is required. We have recently developed a high-speed MRI diffusion-weighted simulator. Here we extend this simulator to the diffusion tensor imaging (DTI). To simulate DTI, as a first step, one needs to calculate DWI along several directions of MPG generated by synthesis of multiple gradients. However, the simulator allows to apply a single gradient at a time to employ the speed-up techniques. To overcome this limitation, the numerical model is rotated by an angle to make MPG always directed toward the same direction. As a result, the synthesized gradients become equivalent to the single gradient and could be treated by the simulator. Finally, the calculated DWI image is rotated back to the original coordinate system. A two-dimensional DWI simulation was performed for a human brain size model and a DTI image was calculated from the obtained images. The results showed that the errors were less than 2%.

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Diagnosis using funduscopy images is a well-established means of early discovery of diabetic retinopathy and other eye disorders. However, issues remain in objectively and accurately ascertaining subtle variations in observations and accurately assessing their correspondence to patient vision. Accordingly, in this study, a diagnostic assistance method was developed. In this method, by using subtraction processing, differential images showing temporal differences between funduscopy images are presented. In addition, degree of temporal change as quantitatively calculated from temporal change magnitudes. Especially, in the case of funduscopy in which the forehead and chin are held motionless and the same imaging device is used, geometric changes between multiple images are limited to linear changes. Therefore, high-precision image registration is easily achieved, yielding differential images containing minimal artifacts due to mis-registration. The present method is capable of computing temporal differential images and degrees of temporal changes for any two images. In this study, the proposed method was applied to a single case (a set of twelve time-series images), in which we set initial examination's image as a reference image. The results verified that pathological changes in each differential image were enhanced. Moreover, the results also clearly indicated a high correlation between the degree of temporal changes and the patient vision at each time instance (0.912 correlation coefficient). In order to generalize this conclusion, it will be necessary to verify the effectiveness of this method by increasing the number of test cases.

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If a kilo-voltage cone-beam computed tomography (CBCT) system mounted on a linear accelerator becomes available for dose calculation, we can confirm the dose distribution of treatment in each day by referring it to the initially planned dose distribution. In this paper, we verified the validity of the calculation method using CBCT images combined with multi-slice CT images. To evaluate the accuracy of calculated dose distribution, γ analysis, distance-to-agreement analysis and dose-volume-histogram analysis were used as the conventional dose calculation methods using CBCT images. The results showed that the dose distribution calculated by our proposed method agreed with the initial treatment plan better compared with the other methods. In addition, our method was so stable that the calculated dose distribution was insensitive to variations in clinical conditions. We demonstrated the feasibility of our proposed method for adaptive radiotherapy.

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Ultrasound is a widely used technology in breast cancer diagnosis. Ultrasound enables the real-time diagnosis, however, the information of general ultrasound scanner is limited (2D) and the tumor detectability is recognized as insufficient compared to that of MRI. Breast MRI imaging has high sensitivity and tumor detectability, but the number of MRI facilities is limited and it is both costly and time-consuming. Thus, we are developing the technology, which constructs a quasi supine MRI image from a prone MRI image using a particle method for fusion with an US image. The biggest challenge to apply US-MRI fusion in breast care area is the deformation of breast tissue; MRI scan is performed with patient in a prone position, whereas the ultrasound diagnosis is performed in a supine position. The present study employed the MPS (Moving Particle Semi-implicit) method to analyze the deformation of breast tissue due to gravity. We manually segmented a prone breast MRI image and generated particles on it for MPS method simulation. Homogeneous and small strain linear elasticity model were used in the simulation. Based on the calculated breast deformation result, we reconstruct a quasi-supine MRI image with backward warping method.

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One of the most important challenges for MRI is reducing scan time, and how to reconstruct high resolution image from limited acquired data is the most important challenge for MRI reconstruction technology. In this article, I demonstrate two fast imaging methods of MRI reconstruction -- parallel imaging and compressed sensing. Both are reconstruction methods which can reduce scan time and keep image quality without improving performance of hardware. Parallel imaging has already applied for various clinical uses and compressed sensing is also expected to contribute to reducing scan time and enhancing image resolution.

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