Volume-based 18 F-FDG PET analysis of cardiac sarcoidosis using the descending aorta as a reference tissue.

OBJECTIVE: 18 F-FDG PET can be used to calculate the threshold value of myocardial volume based on the mean standardised uptake value (SUV mean ) of the aorta to detect highly integrated regions of cardiac sarcoidosis. The present study investigated the myocardial volume when the position and number of volumes of interest (VOIs) were changed in the aorta. METHODS: The present study examined PET/computed tomography images of 47 consecutive cardiac sarcoidosis cases. VOIs were set at three locations in the myocardium and aorta (descending thoracic aorta, superior hepatic margin and near the pre-branch of the common iliac artery). The volume was calculated for each threshold using 1.1-1.5 times the SUV mean (median of three cross-sections) of the aorta as the threshold to detect high myocardial 18 F-FDG accumulation. The detected volume, correlation coefficient with the visually manually measured volume and the relative error were also calculated. RESULTS: The optimum threshold value for detecting high 18 F-FDG accumulation was 1.4 times that of the single cross-section of the aorta and showed the smallest relative errors of 33.84% and 25.14% and correlation coefficients of 0.974 and 0.987 for single and three cross-sections, respectively. CONCLUSION: The SUV mean of the descending aorta may be detected in good agreement with the visual high accumulation by multiplying the same threshold constant for both single and multiple cross-sections.

Automated classification of increased uptake regions in bone single-photon emission computed tomography/computed tomography images using three-dimensional deep convolutional neural network.

OBJECTIVE: This study proposes an automated classification of benign and malignant in highly integrated regions in bone single-photon emission computed tomography/computed tomography (SPECT/CT) using a three-dimensional deep convolutional neural network (3D-DCNN). METHODS: We examined 100 regions of 35 patients with bone SPECT/CT classified as benign and malignant by other examinations and follow-ups. First, SPECT and CT images were extracted at the same coordinates in a cube, with a long side two times the diameter of a high concentration in SPECT images. Next, we inputted the extracted image to DCNN and obtained the probability of benignity and malignancy. Integrating the output from DCNN of each SPECT and CT image provided the overall result. To validate the efficacy of the proposed method, the malignancy of all images was assessed using the leave-one-out cross-validation method; besides, the overall classification accuracy was evaluated. Furthermore, we compared the analysis results of SPECT/CT, SPECT alone, CT alone, and whole-body planar scintigraphy in the highly integrated region of the same site. RESULTS: The extracted volume of interest was 50 benign and malignant regions, respectively. The overall classification accuracy of SPECT alone and CT alone was 73% and 68%, respectively, while that of the whole-body planar analysis at the same site was 74%. When SPECT/CT images were used, the overall classification accuracy was the highest (80%), while the classification accuracy of malignant and benign was 82 and 78%, respectively. CONCLUSIONS: This study suggests that DCNN could be used for the direct classification of benign and malignant regions without extracting the features of SPECT/CT accumulation patterns.

Prediction of pulmonary function after major lung resection using lung perfusion scintigraphy with single-photon emission computed tomography/computed tomography.

OBJECTIVE: Precise prediction of postoperative pulmonary function is extremely important for accurately evaluating the risk of perioperative morbidity and mortality after major surgery for lung cancer. This study aimed to compare the accuracy of a single-photon emission computed tomography/computed tomography (SPECT/CT) method that we recently developed for predicting postoperative pulmonary function versus the accuracy of both the conventional simplified calculating (SC) method and the method using planar images of lung perfusion scintigraphy. METHODS: The relationship between the postoperative observed % values of the forced expiratory volume in 1 second (FEV1) or diffusing capacity for carbon monoxide (DLCO or DLCO') and the % predicted postoperative (%ppo) values of FEV1, DLCO, or DLCO' calculated by the three methods were analyzed in 30 consecutive patients with lung cancer undergoing lobectomy. RESULTS: The relationship between the postoperative observed % values and %ppo values calculated by the three methods exhibited a strong correlation (Pearson r>0.8, two-tailed p<0.0001). The limits of agreement between the postoperative % values and %ppo values did not differ among the three methods. The absolute values of the differences between the postoperative % values and %ppo values for FEV1 and DLCO' were comparable among the three methods, whereas those for DLCO of SPECT/CT were significantly higher than those of the planar method. Conversely, in patients with preoperative %DLCO' of <80% predicted, the absolute values of the differences between the postoperative %DLCO' and %ppoDLCO' of SPECT/CT tended to be smaller than those of the SC and planar methods. CONCLUSION: The accuracy of SPECT/CT for predicting postoperative pulmonary function is comparable with that of conventional methods in most cases, other than in some patients with diffusion impairment.