We propose a computer-aided diagnostic (CAD) system that uses time-intensity curves to distinguish between benign and malignant mammary tumors. Many malignant tumors show a washout pattern in time-intensity curves. Therefore, we designed a program that automatically detects the position with the strongest washout effect using the technique, such as the subtraction technique, which extracts only the washout area in the tumor, and by scanning data in 2×2 pixel region of interest (ROI). Operation of this independently developed program was verified using a phantom system that simulated tumors. In three cases of malignant tumors, the washout pattern detection rate in images with manually set ROI was ≤6%, whereas the detection rate with our novel method was 100%. In one case of a benign tumor, when the same method was used, we checked that there was no washout effect and detected the persistent pattern. Thus, the distinction between benign and malignant tumors using our method was completely consistent with the pathological diagnoses made. Our novel method is therefore effective for differentiating between benign and malignant mammary tumors in dynamic magnetic resonance images.

It is important for high-precision radiation therapy that tracking accuracy in dynamic tumor tracking (DTT) using the gimbal X-ray head. We evaluated the tracking accuracy under various respiratory patterns differ from a correlation model [four-dimensional model (4D-model)] in real-time using a digital camera. A sheet of paper with luminous line was placed on the programmable respiratory motion table (CIRS Inc.) and operated with the laser projector. The luminous line was defined as a target and the laser was defined as a gimbal. Motion table was operated at a period of 4 s and amplitude of ±10 mm to create 4D-modeling. This movement was defined as the basic operation. To investigate the tracking accuracy, target and gimbal positions were recorded using a digital camera under amplitudes (±5–20 mm) and periods (2–8 s) and analyzed by ImageJ software (NIH). The maximum tracking errors under various period and amplitude were 1.7–0.9 mm and 0.4–1.9 mm, respectively. From the creation of 4D-modeling, it was confirmed that when the period has shortened and the amplitude has increased, tracking accuracy was reduced.

The QSPECT dual table autoradiography (DTARG) method can be used for quantitative determination of cerebral blood flow. We verified the influence on quantitative values obtained for cerebral blood flow in the case when usual acquisition was impossible and evaluated those values. Results obtained with an acquisition time of 30 min were considered to be true values, and the correlation and consistency with results of other times were evaluated. Values obtained with a shortened acquisition time showed a high correlation with the true value. As for consistency, there were differences among the various data collection intervals. Nevertheless, regardless of the use of a shortened acquisition time and the data acquisition interval, values obtained with the QSPECT program showed a high correlation with the true value. Based on our findings showing a high correlation, a quantitative evaluation of cerebral blood flow can be performed with the QSPECT DTARG method, even with complications, such as examination interruption, thus, it is considered to be a flexible method.