ArticleMedical physics2017
A new CT reconstruction technique using adaptive deformation recovery and intensity correction (ADRIC).
Article in Medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed.
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- 3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR).Physics in medicine and biology · 2024Article
- In silico simulation of hepatic arteries: An open-source algorithm for efficient synthetic data generation.Medical physics · 2023Article
- Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR).Physics in medicine and biology · 2023Article
- TransMorph: Transformer for unsupervised medical image registration.Medical image analysis · 2022Article
- Generating anthropomorphic phantoms using fully unsupervised deformable image registration with convolutional neural networks.Medical physics · 2020Article
- Virtual clinical trials in medical imaging: a review.Journal of medical imaging (Bellingham, Wash.) · 2020Review
- Advanced 4-dimensional cone-beam computed tomography reconstruction by combining motion estimation, motion-compensated reconstruction, biomechanical modeling and deep learning.Visual computing for industry, biomedicine, and art · 2019Article
- Performance of sparse-view CT reconstruction with multi-directional gradient operators.PloS one · 2019Article
- Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation.Computers in biology and medicine · 2018Article
- Application of the 4-D XCAT Phantoms in Biomedical Imaging and Beyond.IEEE transactions on medical imaging · 2018Review
- A biomechanical modeling-guided simultaneous motion estimation and image reconstruction technique (SMEIR-Bio) for 4D-CBCT reconstruction.Physics in medicine and biology · 2018Article
- Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction.Inverse problems · 2018Article
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Abstract
purposeSequential same-patient CT images may involve deformation-induced and non-deformation-induced voxel intensity changes. An adaptive deformation recovery and intensity correction (ADRIC) technique was developed to improve the CT reconstruction accuracy, and to separate deformation from non-deformation-induced voxel intensity changes between sequential CT images. MATERIALS AND
methodsADRIC views the new CT volume as a deformation of a prior high-quality CT volume, but with additional non-deformation-induced voxel intensity changes. ADRIC first applies the 2D-3D deformation technique to recover the deformation field between the prior CT volume and the new, to-be-reconstructed CT volume. Using the deformation-recovered new CT volume, ADRIC further corrects the non-deformation-induced voxel intensity changes with an updated algebraic reconstruction technique ("ART-dTV"). The resulting intensity-corrected new CT volume is subsequently fed back into the 2D-3D deformation process to further correct the residual deformation errors, which forms an iterative loop. By ADRIC, the deformation field and the non-deformation voxel intensity corrections are optimized separately and alternately to reconstruct the final CT. CT myocardial perfusion imaging scenarios were employed to evaluate the efficacy of ADRIC, using both simulated data of the extended-cardiac-torso (XCAT) digital phantom and experimentally acquired porcine data. The reconstruction accuracy of the ADRIC technique was compared to the technique using ART-dTV alone, and to the technique using 2D-3D deformation alone. The relative error metric and the universal quality index metric are calculated between the images for quantitative analysis. The relative error is defined as the square root of the sum of squared voxel intensity differences between the reconstructed volume and the "ground-truth" volume, normalized by the square root of the sum of squared "ground-truth" voxel intensities. In addition to the XCAT and porcine studies, a physical lung phantom measurement study was also conducted. Water-filled balloons with various shapes/volumes and concentrations of iodinated contrasts were put inside the phantom to simulate both deformations and non-deformation-induced intensity changes for ADRIC reconstruction. The ADRIC-solved deformations and intensity changes from limited-view projections were compared to those of the "gold-standard" volumes reconstructed from fully sampled projections.
resultsFor the XCAT simulation study, the relative errors of the reconstructed CT volume by the 2D-3D deformation technique, the ART-dTV technique, and the ADRIC technique were 14.64%, 19.21%, and 11.90% respectively, by using 20 projections for reconstruction. Using 60 projections for reconstruction reduced the relative errors to 12.33%, 11.04%, and 7.92% for the three techniques, respectively. For the porcine study, the corresponding results were 13.61%, 8.78%, and 6.80% by using 20 projections; and 12.14%, 6.91%, and 5.29% by using 60 projections. The ADRIC technique also demonstrated robustness to varying projection exposure levels. For the physical phantom study, the average DICE coefficient between the initial prior balloon volume and the new "gold-standard" balloon volumes was 0.460. ADRIC reconstruction by 21 projections increased the average DICE coefficient to 0.954.
conclusionThe ADRIC technique outperformed both the 2D-3D deformation technique and the ART-dTV technique in reconstruction accuracy. The alternately solved deformation field and non-deformation voxel intensity corrections can benefit multiple clinical applications, including tumor tracking, radiotherapy dose accumulation, and treatment outcome analysis.
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