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Penalized PET Reconstruction Using Deep Learning Prior and Local Linear Fitting
Kim, Kyungsang,Wu, Dufan,Gong, Kuang,Dutta, Joyita,Kim, Jong Hoon,Son, Young Don,Kim, Hang Keun,El Fakhri, Georges,Li, Quanzheng IEEE 2018 IEEE transactions on medical imaging Vol.37 No.6
<P>Motivated by the great potential of deep learning in medical imaging, we propose an iterative positron emission tomography reconstruction framework using a deep learning-based prior. We utilized the denoising convolutional neural network (DnCNN) method and trained the network using full-dose images as the ground truth and low dose images reconstructed from downsampled data by Poisson thinning as input. Since most published deep networks are trained at a predetermined noise level, the noise level disparity of training and testing data is a major problem for their applicability as a generalized prior. In particular, the noise level significantly changes in each iteration, which can potentially degrade the overall performance of iterative reconstruction. Due to insufficient existing studies, we conducted simulations and evaluated the degradation of performance at various noise conditions. Our findings indicated that DnCNN produces additional bias induced by the disparity of noise levels. To address this issue, we propose a local linear fitting function incorporated with the DnCNN prior to improve the image quality by preventing unwanted bias. We demonstrate that the resultant method is robust against noise level disparities despite the network being trained at a predetermined noise level. By means of bias and standard deviation studies via both simulations and clinical experiments, we show that the proposed method outperforms conventional methods based on total variation and non-local means penalties. We thereby confirm that the proposed method improves the reconstruction result both quantitatively and qualitatively.</P>
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John T. Reynolds,John M. Watkins,Tarek A. Dufan,Shrikant S. Kubsad 대한암학회 2012 Cancer Research and Treatment Vol.44 No.2
Purpose Chemotherapy has demonstrated ability to generate tumor antigens secondary to induction of apoptosis, against which human leukocyte antigen-compatible, irradiated, related donor mononuclear cells may be administered with immune stimulation to activate antigen presenting and cytotoxic T cells, while minimizing risk of graft-versus-host disease (GVHD). The present study endeavours to describe feasibility and efficacy of this treatment, specifically in the community setting. Materials and Methods Eligible patients had rapidly progressive, chemorefractory metastatic solid tumors. Treatment consisted of intravenous etoposide and cyclosporine for three days followed by granulocytemacrophage colony-stimulating factor for 5 days. The following week, 5×107 haploidentical or more closely matched irradiated donor mononuclear cells were given weekly for 10 weeks along with interleukin-2. Results Three patients were enrolled, and the regimen was well-tolerated, with no GVHD observed. All patients had clinical response, despite advanced and heavily pretreated disease. Conclusion The above-outlined protocol demonstrates favorable tolerability and efficacy, and appears to be feasible in the community setting. While the optimal chemotherapy, immunostimulation, and irradiation regimens may be further optimized, future investigation appears warranted, and may include community oncology programs.
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