It is very important for proton therapy to make and carry out a quality assurance (QA) program. Operators and medical personnel have to avoid any trouble during treatment and provide patients with high quality and reliable treatment. A proton therapy machine consists of an accelerator, gantry and various beam modifying devices. Although QA for proton therapy is complicated and difficult, we tried to realize daily QA for proton therapy. Two main purposes in daily QA are stable operation of the accelerator and reliable control of the irradiation system. In order to determine the daily QA issues, past problems were considered. We made a daily QA program and used it for one year. In this paper, we show the daily QA program and report our results. The daily QA was clearly effective for stable operation of the accelerator and for controlling quality of the irradiation system.

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In order to support a routine QA of the CT number for treatment planning, we developed a phantom and a sample holder for easy handling. At most particle radiotherapy facilities in Japan, the CT number is calibrated by the poly-binary calibration method using liquid samples of 100% ethanol and 40% K₂HPO₄ which are set in a cylindrical water phantom. However it is hard to remove air bubbles from the calibration liquid sample and maintain its stable concentration for a long time. So much time is needed for QA of the CT number. The new sample holder, which we developed, was able to keep a stable concentration of the liquid for morethan 300 days. Consequently, the CT number of each sample, which was set in a water equivalent solid phantom, was the same as the CT number in a water phantom within 7 HU. In addition, we developed software which could measure the CT number of each sample semi-automatically and could calculate the calibration coefficients between the CT number and water equivalent length (WEL). Using this software, we could check the calibration result instantly at the time of CT data acquisition. These tools should be useful to carry out calibration of the CT-WEL routinely in a short time.

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Carbon-beam therapy has been successfully carried out at HIMAC, Japan. This treatment offers two advantages over conventional radiation therapy: better dose concentration due to the Bragg peak and higher RBE. In treatment planning at HIMAC, the dose distribution is calculated based on dose measurements in water. We previously made three types of phantoms by using CT images: a liver-cancer phantom and two lung-cancer phantoms (one with bone and one without it). This study evaluates carbonbeam attenuation in inhomogeneous layered phantoms and compares their results with beam attenuation in a water phantom. The phantoms consist of plates of tissue-equivalent materials for the x-rays; these plates are stacked along the beam direction. The beam attenuation in the lung-cancer phantom (with bone) is about 23%, similar to the result in the water phantom, attenuation in the lung-cancer phantom (without bone) is about 25%, which is higher than the result in the water phantom by 2%. Finally, the beam attenuation in the liver-cancer phantom is about 33%, which is lower than the result in the water phantom by 3%. Our evaluation of the carbon-beam attenuation using inhomogeneous layered phantoms is successful and comparison with the results in a water phantom is possible.

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To improve the accuracy and robustness of 2D/3D registration of digital subtraction angiography images and magnetic resonance angiography (MRA) data, we have developed an automatic method for anatomical labeling of the cerebral arteries in MRA data. The anatomical labeling method is a location-based method which segments an artery tree to branches and classifies the branches into labeled segments, i. e., internal carotid arteries (ICA), basilar artery (BA), middle cerebral arteries (MCA), Al segments of the anterior cerebral artery (ACA(A1)), other segments of the anterior cerebral artery (ACA), posterior communication arteries (PcomA) and posterior cerebral arteries (PCA), according to their location. Arteries were extracted from MRA data for this labeling method by the region-growing technique. Fifteen cases were examined to evaluate the method accuracy. The number of correctly segmented voxels in each artery segment was determined, and the correct labeling percentage was calculated based on the total number of voxels of the artery. Mean percentages were as follows: ACA,82.7%; Right (R-) ACA(A1),47.1%; Left (L-) ACA(A1),46.1%; R-MCA,80.4%; L-MCA,74.1%; R-PcomA,0.0%; L-PcomA,3.3%; R-PCA,60.3%; LPCA,66.9%; R-ICA,90.7%; L-ICA,90.7%; BA,89.9%; and total arteries,84.1%. The ACA, MCA, ICA and BA were consistently identified correctly.

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We have investigated the properties of three polymergels: G gel, A gel, and C gel. These polymer gels changed only the chemical reagent for the gel. The water equivalence was determined by comparing the polymer gels macroscopic photon and electron interaction cross sections over the energy range from 10 KeV to 50 MeV and by Monte Carlo modeling of percentage depth doses. Polymer gels have a chemical reagent for the gel and monomer concentration and therefore their mass density is up to 1.70 % -2.91 % higher than water. This results in differences between the cross section ratios of the polymer gels and water of up to 0.01 % -3.00 % for the attenuation coefficients ratio and relative stopping power throughout the energy range. Monte Carlo modeling was done for the polymer gels to model the electron and photon transport resulting from a 6 MV photon beam. The absolute percentage differences between each gel and water were within 1 % of the relative percentage differences. The results showed that the A gel formulation had the most suitable water equivalence of the polymer gels investigated due to its lower mass density measurement compared with G gel and C gel.

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The purpose of this study was to introduce a new parameter which enhances breast cancer detection using X-ray mammography. We used the database of X-ray mammograms generated by the Japan Society of Radiological Technology. The new parameter called 'quasi-fractal dimension (Q-FD)' was calculated from the relationship between the cutoff values for the maximum image intensity in the lesion set at 21levels from 20% to 100% at equal intervals and the number of pixels with an intensity exceeding the cutoff value. In addition to Q-FD, the image features such as curvature (C) and eccentricity (E) were extracted. The conventional fractal dimension (C-FD)was also calculated using the box-counting method. We used artificial neural networks (ANNs) as a classification method. When using C, E, C-FD and age as inputs in ANNs and taking the number of neurons in the hidden layer as 50, we found the area under the receiver operating characteristic curve (As) was 0.87 ± 0.07 in the task differentiating between benign and malignant masses. When Q-FD was added to inputs in addition to the above parameters, the As value was significantly improved to become 0.93 ± 0.09. These results suggested that Q-FD is effective for discriminating between benign and malignant masses.

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