• Investigative Magnetic Resonance Imaging
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• v.21 no.4
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• pp.233-241
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• 2017

Purpose: To report on the current practices in breast magnetic resonance imaging (MRI) in Korea. Materials and Methods: We invited the 68 members of the Korean Society of Breast Imaging who were working in hospitals with available breast MRI to participate in a survey on how they performed and interpreted breast MRI. We asked one member from each hospital to respond to the survey. A total of 22 surveys from 22 hospitals were analyzed. Results: Out of 22 hospitals, 13 (59.1%) performed at least 300 breast MRI examinations per year, and 5 out of 22 (22.7%) performed > 1200 per year. Out of 31 machines, 14 (45.2%) machines were 1.5-T scanners and 17 (54.8%) were 3.0-T scanners. All hospitals did contrast-enhanced breast MRI. Full-time breast radiologists supervised the performance and interpreted breast MRI in 19 of 22 (86.4%) of hospitals. All hospitals used BI-RADS for MRI interpretation. For computer-aided detection (CAD), 13 (59.1%) hospitals sometimes or always use it and 9 (40.9%) hospitals did not use CAD. Two (9.1%) and twelve (54.5%) hospitals never and rarely interpreted breast MRI without correlating the mammography or ultrasound, respectively. The majority of respondents rarely (13/21, 61.9%) or never (5/21, 23.8%) interpreted breast MRI performed at an outside facility. Of the hospitals performing contrast-enhanced examinations, 15 of 22 (68.2%) did not perform MRI-guided interventional procedures. Conclusion: Breast MRI is extensively performed in Korea. The indication and practical patterns are diverse. The information from this survey would provide the basis for the development of Korean breast MRI practice guidelines.

• The Korean Journal of Nuclear Medicine
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• v.38 no.3
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• pp.233-240
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• 2004

Purpose: We developed a computer-aided classifier using artificial neural network (ANN) to discriminate the cerebral metabolic pattern of medial and lateral temporal lobe epilepsy (TLE). Materials and Methods: We studied brain F-18-FDG PET images of 113 epilepsy patients sugically and pathologically proven as medial TLE (left 41, right 42) or lateral TLE (left 14, right 16). PET images were spatially transformed onto a standard template and normalized to the mean counts of cortical regions. Asymmetry indices for predefined 17 mirrored regions to hemispheric midline and those for medial and lateral temporal lobes were used as input features for ANN. ANN classifier was composed of 3 independent multi-layered perceptrons (1 for left/right lateralization and 2 for medial/lateral discrimination) and trained to interpret metabolic patterns and produce one of 4 diagnoses (L/R medial TLE or L/R lateral TLE). Randomly selected 8 images from each group were used to train the ANN classifier and remaining 51 images were used as test sets. The accuracy of the diagnosis with ANN was estimated by averaging the agreement rates of independent 50 trials and compared to that of nuclear medicine experts. Results: The accuracy in lateralization was 89% by the human experts and 90% by the ANN classifier Overall accuracy in localization of epileptogenic zones by the ANN classifier was 69%, which was comparable to that by the human experts (72%). Conclusion: We conclude that ANN classifier performed as well as human experts and could be potentially useful supporting tool for the differential diagnosis of TLE.