Results 131 to 140 of about 412,052 (160)

Super-resolution deep learning reconstruction for improved quality of myocardial CT late enhancement. [PDF]

Jpn J Radiol
Takafuji M, Kitagawa K, Mizutani S, Hamaguchi A, Kisou R, Sasaki K, Funaki Y, Iio K, Ichikawa K, Izumi D, Okabe S, Nagata M, Sakuma H.   +12 more
europepmc   +1 more source

Quantitative Evaluation of Low-Dose CT Image Quality Using Deep Learning Reconstruction: A Comparative Study of Philips Precise Image and GE TrueFidelity. [PDF]

J Imaging
Shim J, Lee Y, Kim K.
europepmc   +1 more source

Deep learning reconstruction for detection of liver lesions at standard-dose and reduced-dose abdominal CT. [PDF]

Eur Radiol
Njølstad TH, Jensen K, Andersen HK, Berstad AE, Hagen G, Johansen CK, Øye K, Glittum J, Dybwad A, Thingstad E, Guren MG, Dormagen JB, Schulz A.   +12 more
europepmc   +1 more source

Deep learning reconstruction of free-breathing, diffusion-weighted imaging of the liver: A comparison with conventional free-breathing acquisition. [PDF]

PLoS One
Yoon J, Lee Y, Yoon S, Sung J, Benkert T, Lee J, Park SH.   +6 more
europepmc   +1 more source

Super-resolution deep learning reconstruction to evaluate lumbar spinal stenosis status on magnetic resonance myelography. [PDF]

Jpn J Radiol
Yasaka K, Asari Y, Morita Y, Kurokawa M, Tajima T, Akai H, Yoshioka N, Akahane M, Ohtomo K, Abe O, Kiryu S.   +10 more
europepmc   +1 more source

Improved image quality and diagnostic performance of coronary computed tomography angiography-derived fractional flow reserve with super-resolution deep learning reconstruction. [PDF]

Quant Imaging Med Surg
Zou LM, Xu C, Xu M, Xu KT, Wang M, Wang Y, Wang YN.   +6 more
europepmc   +1 more source

Transcatheter aortic valve implantation (TAVI) planning CT on 8-cm detector scanners: Proper dose control by combined use of two deep-learning reconstruction algorithms. [PDF]

J Appl Clin Med Phys
Shao Q, Zhou Y, Li J, Zhang G, Li K, Ding Z, Zhou R, Zhang J, Zheng X, Du Y.   +9 more
europepmc   +1 more source

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Deep Learning Reconstruction for FRONSAC

ISMRM Annual Meeting, 2023
This work is the first to apply deep learning to the reconstruction of images encoded with nonlinear gradients. We apply a model-based deep learning network (MoDL) to simulated FRONSAC images and compare these to a PSF-based matrix inversion as well as cg-SENSE.
Zhehong Zhang, Gigi Galiana
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PET image reconstruction with deep progressive learning

Physics in Medicine & Biology, 2021
Abstract Convolutional neural networks (CNNs) have recently achieved state-of-the-art results for positron emission tomography (PET) imaging problems. However direct learning from input image to target image is challenging if the gap is large between two images.
Yang Lv, Chen Xi
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Deep learning in magnetic resonance image reconstruction

Journal of Medical Imaging and Radiation Oncology, 2021
SummaryMagnetic resonance (MR) imaging visualises soft tissue contrast in exquisite detail without harmful ionising radiation. In this work, we provide a state‐of‐the‐art review on the use of deep learning in MR image reconstruction from different image acquisition types involving compressed sensing techniques, parallel image acquisition and multi ...
Shekhar S Chandra, Marlon Bran Lorenzana, Xinwen Liu, Siyu Liu, Steffen Bollmann, Stuart Crozier   +5 more
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