Objective To assess the classification performance between Parkinson's disease (PD) and normal control (NC) when semi-quantitative indicators and shape features obtained on dopamine transporter (DAT) single photon emission computed tomography (SPECT) are combined as a feature of machine learning (ML). Methods A total of 100 cases of both PD and normal control (NC) from the Parkinson's Progression Markers Initiative database were evaluated. A summed image was generated and regions of interests were set to the left and right striata. Area, equivalent diameter, major axis length, minor axis length, perimeter and circularity were calculated as shape features. Striatum binding ratios (SBR putamen and SBR caudate) were used as comparison features. The classification performance of the PD and NC groups according to receiver operating characteristic analysis of the shape features was compared in terms of SBRs. Furthermore, we compared the classification performance of ML when shape features or SBRs were used alone and in combination. Results The shape features (except minor axis length) and SBRs indicated significant differences between the NC and PD groups (p < 0.05). The top five areas under the curves (AUC) were as follows: circularity (0.972), SBR putamen (0.972), major axis length (0.945), SBR caudate (0.928) and perimeter (0.896). When classification was done using ML, AUC was as follows: circularity and SBRs (0.995), circularity alone (0.990), and SBRs (0.973). The classification

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Background We hypothesised that the radiomics signature, which includes texture information of dopamine transporter single-photon emission computed tomography (DAT-SPECT) images for Parkinson’s disease (PD), may assist semi-quantitative indices. Herein, we constructed a radiomics signature using DAT-SPECT-derived radiomics features that effectively discriminated PD from healthy individuals and evaluated its classification performance. Results We analysed 413 cases of both normal control (NC, n = 101) and PD (n = 312) groups from the Parkinson’s Progression Markers Initiative database. Data were divided into the training and two test datasets with different SPECT manufacturers. DAT-SPECT images were spatially normalised to the Montreal Neurologic Institute space. We calculated 930 radiomics features, including intensity- and texture-based features in the caudate, putamen, and pallidum volumes of interest. The striatum uptake ratios (SURs) of the caudate, putamen, and pallidum were also calculated as conventional semi-quantification indices. The least absolute shrinkage and selection operator was used for feature selection and construction of the radiomics signature. The four classification models were constructed using a radiomics signature and/or semi-quantitative indicator. Furthermore, we compared the classification performance of the semi-quantitative indicator alone and the combination with the radiomics signature for the classification models. The receiver operating characteristics (ROC) analysis was used to evaluate the classification performance. The classification performance of SURputamen was higher than that of other semi-quantitative indicators. The radiomics signature resulted in a slightly increased area under the ROC curve (AUC) compared to SURputamen in each test dataset. When combined with SURputamen and radiomics signature, all classification models showed slightly higher AUCs than that of SURputamen alone. Conclusion We constructed a DAT-SPECT image-derived radiomics signature. Performance analysis showed that the current radiomics signature would be helpful for the diagnosis of PD and has the potential to provide robust diagnostic performance.

The early WR and AUTAC showed high performance for distinguishing LBRD from PS, and the combination diagnosis with early H/M ratio and early WR contribute to improve the diagnostic performance. Thus, these parameters would be useful for reducing the examination time of myocardial (123)I-MIBG scintigraphy to diagnose LBRD.

The quality control of liquid-crystal display (LCD) monitors has become one of the important topics for maintaining reliable soft-copy readings in the interpretation of diagnostic images. In this paper, the effects of correction in the luminance measurement of an LCD monitor by use of a telescopic-type luminance meter were investigated. The luminance of the LCD monitor in different ambient-lighting conditions was measured and compared to the results obtained with no ambient lighting (0 lux). The reproducibility of luminance measurements and luminance ratios without a baffled tube was lower than those measured with the baffled tube due to the effect of ambient light. These tendencies were obvious at a relatively low luminance. The correction method by subtraction of the reflected ambient light on the surface of the LCD monitor and the stray light of the telescopic-type luminance meter from the measured luminance was examined. We found that the correction was able to bring the luminance close to that measured with the baffled tube.