High-angular resolution diffusion imaging generation using 3d u-net.
Purpose: To investigate the effects on tractography of artificial intelligence-based prediction of motion-probing gradients (MPGs) in diffusion-weighted imaging (DWI). Methods: The 251 participants in this study were patients with brain tumors or epileptic seizures who underwent MRI to depict tracto...
| Publicado en: | Neuroradiology Vol. 66; no. 3; pp. 371 - 388 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175359642&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175359642 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Mar2024 vid: 66 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175359642 174859064 175359642 175359642 10.1007/s00234-024-03282-6 175359642 ppf: 371 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: High-angular resolution diffusion imaging generation using 3d u-net. aug: au: Suzuki, Yuichi Ueyama, Tsuyoshi Sakata, Kentarou Kasahara, Akihiro Iwanaga, Hideyuki Yasaka, Koichiro Abe, Osamu affil: Radiology Center, The University of Tokyo Hospital, Tokyo, Japan sug: subj: Magnetic Resonance Imaging Methods Imaging, Three-Dimensional Artificial Intelligence Utilization Brain Pathology Brain Neoplasms Pathology Epilepsy Pathology Prediction Models Human Adult Middle Age Aged Descriptive Statistics Neural Networks (Computer) Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years ab: Purpose: To investigate the effects on tractography of artificial intelligence-based prediction of motion-probing gradients (MPGs) in diffusion-weighted imaging (DWI). Methods: The 251 participants in this study were patients with brain tumors or epileptic seizures who underwent MRI to depict tractography. DWI was performed with 64 MPG directions and b = 0 s/mm2 images. The dataset was divided into a training set of 191 (mean age 45.7 [± 19.1] years), a validation set of 30 (mean age 41.6 [± 19.1] years), and a test set of 30 (mean age 49.6 [± 18.3] years) patients. Supervised training of a convolutional neural network was performed using b = 0 images and the first 32 axes of MPG images as the input data and the second 32 axes as the reference data. The trained model was applied to the test data, and tractography was performed using (a) input data only; (b) input plus prediction data; and (c) b = 0 images and the 64 MPG data (as a reference). Results: In Q-ball imaging tractography, the average dice similarity coefficient (DSC) of the input plus prediction data was 0.715 (± 0.064), which was significantly higher than that of the input data alone (0.697 [± 0.070]) (p < 0.05). In generalized q-sampling imaging tractography, the average DSC of the input plus prediction data was 0.769 (± 0.091), which was also significantly higher than that of the input data alone (0.738 [± 0.118]) (p < 0.01). Conclusion: Diffusion tractography is improved by adding predicted MPG images generated by an artificial intelligence model. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|