Abstract
Ryo Itoga, Terufumi Kokabu, Yoshinori Hyugaji, Jun Wakasa, Yasushi Yanagibashi, Takahiko Hyakumachi, Yoshihiro Hojo, Ken Nagahama, Masahiro Kanayama, Ryo Fujita, Tomohiro Shimizu, Takashi Ohnishi, Daisuke Ukeba, Hideki Sudo, Katsuhisa Yamada
Abstract
Authors
Institutions
Provenance
crossref
Confidence 100%
ror
Confidence 99%
ror
Confidence 99%
ror
Confidence 99%
pubmed
Confidence 98%
europepmc
Confidence 96%
openalex
Confidence 95%
unpaywall
Confidence 95%
doaj
Confidence 92%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
A review of lumbar spinal stenosis with intermittent neurogenic claudication: disease and diagnosis
10.1093/pm/pnz161 · 2019
Diagnosis of lumbar spinal stenosis: an updated systematic review of the accuracy of diagnostic tests
10.1097/brs.0b013e31828935ac · 2013
The value of dynamic radiographic myelography in addition to magnetic resonance imaging in detection lumbar spinal canal stenosis: a prospective study
10.1016/j.clineuro.2016.01.031 · 2016
Can recumbent magnetic resonance imaging replace myelography or computed tomography myelography for detecting lumbar spinal stenosis?
10.1007/s00590-013-1209-y · 2013
Comparison of magnetic resonance imaging and computed tomography-myelography for quantitative evaluation of lumbar intracanalar cross-section
10.3349/ymj.2011.52.1.137 · 2011
Radiographic assessment of degenerative lumbar spinal stenosis: is MRI superior to CT?
10.1007/s00586-016-4724-9 · 2017
Role of computed tomography and myelography in the diagnosis of central spinal stenosis
10.2106/00004623-198567020-00009 · 1985
Unpaired image-to-image translation using cycle-consistent adversarial networks
2017
Imaging evaluation of a proposed 3D generative model for MRI to CT translation in the lumbar spine
10.1016/j.spinee.2023.06.399 · 2023
Virtual magnetic resonance lumbar spine images generated from computed tomography images using conditional generative adversarial networks
10.1016/j.radi.2021.10.006 · 2022
Spine computed tomography to magnetic resonance image synthesis using generative adversarial networks: a preliminary study
10.3340/jkns.2019.0084 · 2020
Lumbar spine computed tomography to magnetic resonance imaging synthesis using generative adversarial network: visual Turing test
10.3390/diagnostics12020530 · 2022
Generation of synthetic PET/MR fusion images from MR images using a combination of generative adversarial networks and conditional denoising diffusion probabilistic models based on simultaneous 18F-FDG PET/MR image data of pyogenic spondylodiscitis
10.1016/j.spinee.2024.04.007 · 2024
Decompose kV projection using neural network for improved motion tracking in paraspinal SBRT
10.1002/mp.15295 · 2021
CBAM: convolutional block attention module
2018
Spectral normalization for generative adversarial networks
2018
Morphological analysis of Kambin’s triangle using 3D CT/MRI fusion imaging of lumbar nerve root created automatically with artificial intelligence
10.1007/s00586-021-06916-y · 2021
Image quality assessment: from error visibility to structural similarity
10.1109/tip.2003.819861 · 2004
A new grading system of lumbar central canal stenosis on MRI: an easy and reliable method
10.1007/s00256-011-1153-z · 2011
The measurement of observer agreement for categorical data
10.2307/2529310 · 1977
Guidelines of the minimum sample size requirements for Kappa agreement test
2017
Blood cell images segmentation using deep learning semantic segmentation
2018
An active learning approach to train a deep learning algorithm for tumor segmentation from brain MR images
10.1186/s13244-023-01487-6 · 2023
CT myelography: clinical indications and imaging findings
10.1148/rg.2020190135 · 2020
No additional external references are available.