• Journal of the Korean Society of Radiology
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• v.8 no.7
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• pp.429-433
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• 2014

In this study, the morphology and the x-ray quantum efficient of mercury oxide (HgO) and lead oxide (PbO) sensors derived by particle sedimentation method were discussed. In the pursuit of this purpose, we investigated the electrical characteristics and the x-ray quantum efficiency of various thicknesses of HgO and PbO films in mammographic x-ray energy. We have therefore developed a particle-in-binder sedimentation method of fabricating large area polycrystalline films onto transparent glass substrates coated with indium tin oxide. We are currently optimizing the growth method to improve the quantum efficiency with the ultimate goal of obtaining as quantum efficiency close to that of single crystal performance. Our future efforts will concentrate on optimization of large area film growth techniques specifically for deposition on a-Si:H flat panel readout arrays.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.344-359
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• 2022

Purpose To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer using low-dose mammography imaging. Materials and Methods A total of 6 breast radiologists were included in this prospective study. All radiologists independently evaluated low-dose images for lesion detection and rated them for diagnostic quality using a qualitative scale. After application of the denoising network, the same radiologists evaluated lesion detectability and image quality. For clinical application, a consensus on lesion type and localization on preoperative mammographic examinations of breast cancer patients was reached after discussion. Thereafter, coded low-dose, reconstructed full-dose, and full-dose images were presented and assessed in a random order. Results Lesions on 40% reconstructed full-dose images were better perceived when compared with low-dose images of mastectomy specimens as a reference. In clinical application, as compared to 40% reconstructed images, higher values were given on full-dose images for resolution (p < 0.001); diagnostic quality for calcifications (p < 0.001); and for masses, asymmetry, or architectural distortion (p = 0.037). The 40% reconstructed images showed comparable values to 100% full-dose images for overall quality (p = 0.547), lesion visibility (p = 0.120), and contrast (p = 0.083), without significant differences. Conclusion Effective denoising and image reconstruction processing techniques can enable breast cancer diagnosis with substantial radiation dose reduction.