{ "item_title" : "Machine Learning for Medical Image Reconstruction", "item_author" : [" Florian Knoll", "Andreas Maier", "Daniel Rueckert "], "item_description" : "Deep Learning for Magnetic Resonance Imaging.- Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction- Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging.- Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network.- APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network.- Accelerated MRI Reconstruction with Dual-domain Generative Adversarial Network.- Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator.- Joint Multi-Anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions.- Modeling and Analysis Brain Development via Discriminative Dictionary Learning.- Deep Learning for Computed Tomography.- Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval.- Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior.- Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks.- Deep Learning based Metal Inpainting in the Projection Domain: Initial Results.- Deep Learning for General Image Reconstruction.- Flexible Conditional Image Generation of Missing Data with Learned Mental Maps.- Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation.- Stain Style Transfer using Transitive Adversarial Networks.- Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer.- Deep Learning based approach to quantification of PET tracer uptake in small tumors.- Task-GAN: Improving Generative Adversarial Network for Image Reconstruction.- Gamma Source Location Learning from Synthetic Multi-Pinhole Collimator Data.- Neural Denoising of Ultra-Low Dose Mammography.- Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging.- Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy.- TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis.- PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/3/03/033/842/3030338428_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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