• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• Journal of radiological science and technology
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• v.36 no.4
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• pp.313-318
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• 2013

Positron emission tomography/computed tomography (PET/CT) imaging with fluorodeoxyglucose (FDG) have been used as a powerful fusion modality in nuclear medicine not only for detecting cancer but also for staging and therapy monitoring. Nevertheless, there are various causes of FDG uptake in normal and/or benign tissues. The purpose of present study was to investigate whether additional delayed imaging can improve the diagnosis to differentiate the rates of FDG uptake at axillary lymph nodes (ALN) between malignant and benign in breast cancer patients. 180 PET/CT images were obtained for 27 patients with ALN uptake. The patients who had radiotherapy and chemotherapy were excluded from the study. $^{18}F$-FDG PET/CT scan at 50 min (early phase) and 90 min (delayed phase) after $^{18}F$-FDG injection were included in this retrospective study. The staging of cancers was confirmed by final clinical according to radiologic follow-up and pathologic findings. The standardized uptake value (SUV) of ALN was measured at the Syngo Acquisition Workplace by Siemens. The 27 patients included 18 malignant and 9 ALN benign groups and the 18 malignant groups were classified into the 3 groups according to number of metastatic ALN in each patient. ALNs were categorized less than or equal 3 as N1, between 4 to 9 as N2 and more than 10 as N3 group. Results are expressed as the mean${\pm}$standard deviation (S.D.) and statistically analyzed by SPSS. As a result, Retention index (RI-SUV max) in metastasis was significantly higher than that in non-metastasis about 5 fold increased. On the other hand, RI-SUV max in N group tended to decrease gradually from N1 to N3. However, we could not prove significance statistically in malignant group with ANOVA. As a consequence, RI-SUV max was good indicator for differentiating ALN positive group from node negative group in breast cancer patients. These results show that dual-time-point scan appears to be useful in distinguishing malignant from benign.

• The Korean Journal of Nuclear Medicine
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• v.36 no.6
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• pp.344-356
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• 2002

Purpose: RBC blood pool SPECT has been used to diagnose focal liver lesion such as hemangioma owing to its high specificity. However, low spatial resolution is a major limitation of this modality. Recently, ordered subset expectation maximization (OSEM) has been introduced to obtain tomographic images for clinical application. We compared this new modified iterative reconstruction method, OSEM with conventional filtered back projection (FBP) in imaging of liver hemangioma. Materials and Methods: Sixty four projection data were acquired using dual head gamma camera in 28 lesions of 24 patients with cavernous hemangioma of liver and these raw data were transferred to LINUX based personal computer. After the replacement of header file as interfile, OSEM was performed under various conditions of subsets (1,2,4,8,16, and 32) and iteration numbers (1,2,4,8, and 16) to obtain the best setting for liver imaging. The best condition for imaging in our investigation was considered to be 4 iterations and 16 subsets. After then, all the images were processed by both FBP and OSEM. Three experts reviewed these images without any information. Results: According to blind review of 28 lesions, OSEM images revealed at least same or better image quality than those of FBP in nearly all cases. Although there showed no significant difference in detection of large lesions more than 3 cm, 5 lesions with 1.5 to 3 cm in diameter were detected by OSEM only. However, both techniques failed to depict 4 cases of small lesions less than 1.5 cm. Conclusion: OSEM revealed better contrast and define in depiction of liver hemangioma as well as higher sensitivity in detection of small lesions. Furthermore this reconstruction method dose not require high performance computer system or long reconstruction time, therefore OSEM is supposed to be good method that can be applied to RBC blood pool SPECT for the diagnosis of liver hemangioma.