Background Subtraction Angiography with Deep Learning Using Multi-frame Spatiotemporal Angiographic Input.
Catheter Digital Subtraction Angiography (DSA) is markedly degraded by all voluntary, respiratory, or cardiac motion artifact that occurs during the exam acquisition. Prior efforts directed toward improving DSA images with machine learning have focused on extracting vessels from individual, isolated...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 1; pp. 134 - 145 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2024
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| 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=175966513&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966513 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966513 175966513 175966513 10.1007/s10278-023-00921-x 175966513 ppf: 134 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Background Subtraction Angiography with Deep Learning Using Multi-frame Spatiotemporal Angiographic Input. aug: au: Cantrell, Donald R. Cho, Leon Zhou, Chaochao Faruqui, Syed H. A. Potts, Matthew B. Jahromi, Babak S. Abdalla, Ramez Shaibani, Ali Ansari, Sameer A. affil: Department of Radiology, Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, 60611, Chicago, IL, USA sug: subj: Angiography, Digital Subtraction Methods Deep Learning Brain Radiography Human Male Female Adult Middle Age Aged Algorithms Motion Evaluation Software Descriptive Statistics Learning Methods Machine Learning Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Catheter Digital Subtraction Angiography (DSA) is markedly degraded by all voluntary, respiratory, or cardiac motion artifact that occurs during the exam acquisition. Prior efforts directed toward improving DSA images with machine learning have focused on extracting vessels from individual, isolated 2D angiographic frames. In this work, we introduce improved 2D + t deep learning models that leverage the rich temporal information in angiographic timeseries. A total of 516 cerebral angiograms were collected with 8784 individual series. We utilized feature-based computer vision algorithms to separate the database into "motionless" and "motion-degraded" subsets. Motion measured from the "motion degraded" category was then used to create a realistic, but synthetic, motion-augmented dataset suitable for training 2D U-Net, 3D U-Net, SegResNet, and UNETR models. Quantitative results on a hold-out test set demonstrate that the 3D U-Net outperforms competing 2D U-Net architectures, with substantially reduced motion artifacts when compared to DSA. In comparison to single-frame 2D U-Net, the 3D U-Net utilizing 16 input frames achieves a reduced RMSE (35.77 ± 15.02 vs 23.14 ± 9.56, p < 0.0001; mean ± std dev) and an improved Multi-Scale SSIM (0.86 ± 0.08 vs 0.93 ± 0.05, p < 0.0001). The 3D U-Net also performs favorably in comparison to alternative convolutional and transformer-based architectures (U-Net RMSE 23.20 ± 7.55 vs SegResNet 23.99 ± 7.81, p < 0.0001, and UNETR 25.42 ± 7.79, p < 0.0001, mean ± std dev). These results demonstrate that multi-frame temporal information can boost performance of motion-resistant Background Subtraction Deep Learning algorithms, and we have presented a neuroangiography domain-specific synthetic affine motion augmentation pipeline that can be utilized to generate suitable datasets for supervised training of 3D (2d + t) architectures. pubtype: Academic Journal doctype: diagnostic images equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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