Background: To achieve imaging report standardization and improve the quality and efficiency of the intrainterdisciplinary clinical workflow, we proposed an intelligent imaging layout system (IILS) for a clinical decision support system-based ubiquitous healthcare service, which is a lung nodule management system using medical images. Methods: We created a lung IILS based on deep learning for imaging report standardization and workflow optimization for the identification of nodules. Our IILS utilized a deep learning plus adaptive auto layout tool, which trained and tested a neural network with imaging data from all the main CT manufacturers from 11,205 patients. Model performance was evaluated by the receiver operating characteristic curve (ROC) and calculating the corresponding area under the curve (AUC). The clinical application value for our IILS was assessed by a comprehensive comparison of multiple aspects. Findings: Our IILS is clinically applicable due to the consistency with nodules detected by IILS, with its highest consistency of 0•94 and an AUC of 90•6% for malignant pulmonary nodules versus benign nodules with a sensitivity of 76•5% and specificity of 89•1%. Applying this IILS to a dataset of chest CT images, we demonstrate performance comparable to that of human experts in providing a better layout and aiding in diagnosis in 100% valid images and nodule display. The IILS was superior to the traditional manual system in performance, such as reducing the number of clicks from 14•45 ± 0•38 to 2, time consumed from 16•87 ± 0•38 s to 6•92 ± 0•10 s, number of invalid images from 7•06 ± 0•24 to 0, and missing lung nodules from 46•8% to 0%. Interpretation: This IILS might achieve imaging report standardization, and improve the clinical workflow therefore opening a new window for clinical application of artificial intelligence. Fund: The National Natural Science Foundation of China.
The positron range and prompt gamma emission are distinctive with different positron emitters. The performance assessment of an integrated PET/MR scanner with these positron emitters is required for related applications, as the magnetic field interferes with the positron propagation. Such an assessment is to be performed on the United Imaging uPMR 790-integrated PET/MR system. Methods: The performance measurement methods were modified based on NEMA NU 2-2012, involving 18 F, 64 Cu, 68 Ga, 89 Zr, and 124 I as positron emitters. The NEMA IEC phantom was used for evaluations of image qualities. An agarose cap was wrapped around the point source for tissue-simulating spatial resolution measurement. The count rate performance was assessed with selected positron emitters. Images of a 3D-printed Derenzo phantom and representative patients were also acquired.
Results:The image quality measurement showed that all five positron emitters were suitable for the PET/MR system studied. However, due to the magnetic field, the image of the point source showed an elongated comet-tail feature, which could be eliminated by a tissue-simulating cap. This effect is more obvious in 124 I and 68 Ga, due to their long positron ranges. The imaging ability with various positron emitters was further validated with the count rate assessment, the Derenzo phantom, and the clinical images. Conclusions: Different positron emitters could be effectively imaged by the PET/MR system tested. The resolution measurement strategy proposed could be applied to measure PET spatial resolution in the magnetic field.
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