To realize the high precision radiotherapy, localized radiation field of the moving target is very important, and visualization of a temporal location of the target can help to improve the accuracy of the target localization. However, conditions of the breathing and the patient’s own motion differ from the situation of the treatment planning. Therefore, positions of the tumor are affected by these changes. In this study, we implemented a method to reconstruct target motions obtained with the 4D CBCT using the sorted projection data according to the phase and displacement of the extracorporeal infrared monitor signal, and evaluated the proposed method with a moving phantom. In this method, motion cycles and positions of the marker were sorted to reconstruct the image, and evaluated the image quality affected by changes in the cycle, phase, and positions of the marker. As a result, we realized the visualization of the moving target using the sorted projection data according to the infrared monitor signal. This method was based on the projection binning, in which the signal of the infrared monitor was surrogate of the tumor motion. Thus, further major efforts are needed to ensure the accuracy of the infrared monitor signal.
In magnetic resonance imaging (MRI), when radiofrequency (RF) is irradiated to a subject with metallic implant, it can generate heat by RF irradiation. Recently 3 T MRI scanner has spread widely and imaging for any regions of whole body has been conducted. However specific absorption rate (SAR) of 3 T MRI becomes approximately four times as much as the 1.5 T, which can significantly affect the heat generation of metallic implants. So, we evaluated RF heating of artificial hip joints in different shapes and materials in 1.5 T and 3 T MRI. Three types of artificial hip joints made of stainless alloy, titanium alloy and cobalt chrome alloy were embedded in the human body-equivalent phantom respectively and their temperature change were measured for twenty minutes by 1.5 T and 3 T MRI. The maximum temperature rise was observed at the bottom head in all of three types of artificial hip joints, the rise being 12°C for stainless alloy, 11.9°C for titanium alloy and 6.1°C for cobalt chrome alloy in 1.5 T. The temperature rise depended on SAR and the increase of SAR had a good linear relationship with the temperature rise. It was found from the result that the RF heating of metallic implants can take place in various kinds of material and the increase of SAR has a good linear relationship with the temperature rise. This experience shows that reduction of SAR can decrease temperature of metallic implants.
Purpose: The purpose of this study was to evaluate the effects of target diameter and display-field of view (D-FOV) in modulation transfer function (MTF) by circular edge strategy using the computed tomography (CT) image measurement program “CTmeasure”. Methods: We calculated the MTF (MTFedge) using the circular edge strategy applied to cylindrical phantom (200 mmφ) that inserted with cylinders have 10, 20, 30, and 40 mm diameters. The phantom images were reconstructed using filtered back projection method varied with D-FOV (240, 320, 400, and 500 mm). The study compared both MTFedge and MTFwire at MTF50% and MTF10% for target diameter and D-FOV, respectively. Results: The MTFedge by the different of target diameter indicated in rough compatibility. However, MTFedge of D-FOV diameters (320, 400, and 500 mm) decreased in the high frequency range. Conclusions: The circular edge strategy for MTF depended on the D-FOV, however, it was little dependent on target diameter using the CT image measurement program “CTmeasure”.
Background: A clear coronary CT angiography (CCTA) can be obtained when temporal resolution (TR) is shorter than slow filling (SF) duration. The SF duration was calculated by the following equation: SF=−443+0.742 (RR-PQ). Although, the TR of half and full reconstruction using 320-ADCT (0.275 s/r) are known, the TR of automatic patient motion correction (APMC) reconstruction is not clear. The purpose of this study is to clarify the each minimum value of (RR-PQ) for acquiring a clear CCTA that was made by half, full or APMC reconstruction. Method: CCTA was performed in consecutive 345 (M/F=195/150, Age: 69±10 years) patients except for arrhythmia and the final heart rate (controlled by β-blocker) ≥80 bpm using 320-ADCT (Aquilion ONE, 0.275 s/r). In all subjects, 3 CCTAs were generated by half, full, or APMC reconstruction at the same optimal phase. Image quality (A: excellent, B: acceptable, C: poor) was estimated by the consensus of three trained researchers. We classified (RR-PQ) into 15 groups by each 50 ms interval. Results: The A or B % prediction (y) significantly correlated (y=−240.08+0.401x, r=0.98, p=0.0006 in half, y=−238.26+0.378x, r=0.98, p=0.0001 in APMC, and y=−236.84+0.332x, r=0.97, p<0.0001 in full reconstruction) with (RR-PQ) (x), respectively. Conclusion: The minimum values of (RR-PQ) for 95% prediction of A or B image quality were ≥836 ms in half, ≥881 ms in APMC, and ≥998 ms in full reconstruction.
The measurement methods of contrast to noise ratio (CNR) and signal difference to noise ratio (SDNR) in digital mammography are different among several quality assurance (QA) guidelines, that is, the type of pixel value (PV), phantom shape, location of aluminum plate, and the size of region of interest (ROI) principally differ in data acquisition. We compared CNR (SDNR) obtained from three QA guidelines. They are the European Reference Organisation for Quality Assured Breast Screening and Diagnostic Services (EUREF), the International Electrotechnical Commission (IEC), and the International Atomic Energy Agency (IAEA). In EUREF and IEC, CNR was calculated using linearized pixel value (LPV). In IAEA, because the type of pixel value to use in SDNR was not specified, SDNR was calculated using PV and LPV, and CNR was calculated using LPV. Target/filter combinations are molybdenum/molybdenum (Mo/Mo) and molybdenum/rhodium (Mo/Rh). Applied various tube voltages are 25, 30, and 35 kV, and various phantom thicknesses are 20, 45, and 70 mm of polymethyl methacrylate (PMMA). The PV-SDNR of IAEA showed the largest value among the three methods, following LPV-CNR of IEC, LPV-CNR of EUREF at 20 mm PMMA thickness. In IAEA, SDNR changed by the kind of pixel value (PV or LPV). When CNR is calculated, every researcher should describe the type of guidelines, the kind of pixel value, and formula for calculation.