AutoCorNN: An Unsupervised Physics-Aware Deep Learning Model for Geometric Distortion Correction of Brain MRI Images Towards MR-Only Stereotactic Radiosurgery.
Geometric distortions in brain MRI images arising from susceptibility artifacts at air-tissue interfaces pose a significant challenge for high-precision radiation therapy modalities like stereotactic radiosurgery, necessitating sub-millimeter accuracy. To achieve this goal, we developed AutoCorNN, a...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 587 - 602 |
|---|---|
| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
|
| 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=184471468&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471468 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471468 184471468 189894358 184471468 10.1007/s10278-024-01171-1 184471468 ppf: 587 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: AutoCorNN: An Unsupervised Physics-Aware Deep Learning Model for Geometric Distortion Correction of Brain MRI Images Towards MR-Only Stereotactic Radiosurgery. aug: au: Hosseini, Mahboube Sadat Aghamiri, Seyed Mahmoud Reza Fatemi Ardekani, Ali BagheriMofidi, Seyed Mehdi Safari, Mojtaba affil: https://ror.org/0091vmj44 Department of Medical Radiation Engineering, Shahid Beheshti University, 1983969411, Tehran, Iran sug: subj: Brain Surgery Methods Radiosurgery Methods Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Image Enhancement Deep Learning Evaluation Mathematics Human Radiotherapy Validity Brain Pathology Convolutional Neural Networks Neuroma, Acoustic Pathology Brain Mapping Dose-Response Relationship, Radiation Neuroma, Acoustic Radiotherapy Dosimetry Evaluation Treatment Outcomes ab: Geometric distortions in brain MRI images arising from susceptibility artifacts at air-tissue interfaces pose a significant challenge for high-precision radiation therapy modalities like stereotactic radiosurgery, necessitating sub-millimeter accuracy. To achieve this goal, we developed AutoCorNN, an unsupervised physics-aware deep-learning model for correcting geometric distortions. Two publicly available datasets, the MPI-Leipzig Mind-Brain-Body with 318 subjects, and the Vestibular Schwannoma-SEG dataset, encompassing 242 patients were utilized. AutoCorNN integrates two 2D convolutional encoder-decoder neural networks with the forward physical model of MRI signal generation to predict undistorted MR and field map images from distorted MR input. The network is trained in an unsupervised manner by minimizing the mean absolute error between the measured and estimated k-space data, without requiring ground truth images during training or deployment. The model was evaluated on vestibular schwannoma cases. AutoCorNN achieved a peak signal-to-noise ratio (PSNR) of 41.35 ± 0.02 dB, a root mean square error (RMSE) of 0.02 ± 0.003, and a structural similarity index (SSIM) of 0.99 ± 0.02 outperforming uncorrected and B0-mapping correction methods. Geometric distortions of about 1.6 mm were observed at the air-tissue interfaces at the air canal and nasal cavity borders. Geometrically, distortion correction increased the target volume from 3.12 ± 0.52 cc to 3.84 ± 0.54 cc. Dosimetrically, AutoCorNN improved target coverage (0.96 ± 0.01 to 0.97 ± 0.02), conformity index (0.92 ± 0.03 to 0.94 ± 0.03), and reduced dose gradients outside the target. AutoCorNN achieves accurate geometric distortion correction comparable to conventional iterative methods while offering substantial computational acceleration, enabling precise target delineation and conformal dose delivery for improved radiation therapy outcomes. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|