Abstract: When a user inputs samples per group, a sample tree and a peak matrix are generated. Peak lists per group are shown in the sample tree, and m/z values and signal strength values from the peak lists are coordinates in the peak matrix. A multivariate analysis is applied to the generated peak matrix. The sample tree, peak matrix, score plot, and loading plot are displayed. When the user clicks a plotted point on the loading plot, a row indicating a corresponding peak on the peak matrix is discriminated. When the user deletes a checkmark corresponding to the discriminated row, the multivariate analysis is applied to the peak matrix from which the peak has been excluded. The score plot and other data are updated. When separation between groups is known from the score plot as failure, the excluded peak may be visually determined as a marker contributing to the group separation.

Abstract: [Problem to be Solved] To select a marker peak which characterizes a difference between groups, even when the number of samples belonging to each group is small. [Solution] A peak matrix is created based on the peaks detected from mass spectra of a plurality of samples belonging to a plurality of groups (S1-S3). Each row of the peak matrix represents a peak-intensity distribution for a large number of samples at one mass-to-charge-ratio value. If there is no difference between the groups at a certain mass-to-charge-ratio value, the peak-intensity distribution at that mass-to-charge-ratio value should be a lognormal distribution (or normal distribution). Accordingly, a hypothesis test for the conformity of the peak-intensity distribution to the lognormal distribution is performed for each mass-to-charge-ratio value (S5). A mass-to-charge-ratio value at which a significant difference has been found is selected as a candidate of the marker peak (S6).

Abstract: When a user inputs samples per group (S2), a sample tree and a peak matrix are generated. Peak lists per group are shown in the sample tree, and m/z values and signal strength values from the peak lists are coordinates in the peak matrix. Then, a multivariate analysis is applied to the generated peak matrix (S3-S4). The sample tree, peak matrix, score plot, and loading plot are displayed on an analysis main screen. When the user clicks a desired plotted point on the loading plot, a row indicative of a corresponding peak on the peak matrix is discriminated (S5-S7). When the user deletes a checkmark corresponding to the discriminated row, the multivariate analysis is applied to the peak matrix from which the peak has been excluded, and the score plot and other data are updated (S9-S10). When the separation between groups is known from the score plot as failure, the excluded peak may be visually determined as a marker contributing to the group separation.

Abstract: Problem to be Solved To select a marker peak which characterizes a difference between groups, even when the number of samples belonging to each group is small. Solution A peak matrix is created based on the peaks detected from mass spectra of a plurality of samples belonging to a plurality of groups (S1-S3). Each row of the peak matrix represents a peak-intensity distribution for a large number of samples at one mass-to-charge-ratio value. If there is no difference between the groups at a certain mass-to-charge-ratio value, the peak-intensity distribution at that mass-to-charge-ratio value should be a lognormal distribution (or normal distribution). Accordingly, a hypothesis test for the conformity of the peak-intensity distribution to the lognormal distribution is performed for each mass-to-charge-ratio value (S5). A mass-to-charge-ratio value at which a significant difference has been found is selected as a candidate of the marker peak (S6).

Abstract: In an image processing, a high-absorber area is set and weighting is carried out according to a length of a path through which X-rays pass in the high-absorber area, so that estimated image taking data at a portion where X-rays pass through the high-absorber area in considering a weight is obtained. Measured projection data at the portion where X-rays pass through the high-absorber area is replaced by data according to overwriting estimated projection data obtained by forward projecting the estimated image to correct and reconstitute the measured projection data. Thus, there can be obtained a corrected fault image having a reduced artifact and a high contrast, and considering the weight in the high-absorber area. Therefore, the artifact formed on the fault image due to absorption or dispersion of X-rays by an X-ray high-absorber, such as metal, can be reduced.

Abstract: In an image processing, a high-absorber area is set and weighting is carried out according to a length of a path through which X-rays pass in the high-absorber area, so that estimated image taking data at a portion where X-rays pass through the high-absorber area in considering a weight is obtained. Measured projection data at the portion where X-rays pass through the high-absorber area is replaced by data according to overwriting estimated projection data obtained by forward projecting the estimated image to correct and reconstitute the measured projection data. Thus, there can be obtained a corrected fault image having a reduced artifact and a high contrast, and considering the weight in the high-absorber area. Therefore, the artifact formed on the fault image due to absorption or dispersion of X-rays by an X-ray high-absorber, such as metal, can be reduced.