ArticleIEEE transactions on medical imaging2018
Statistical Iterative CBCT Reconstruction Based on Neural Network.
Article in IEEE transactions on medical imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.Medical physics · 2022Pooled it
- Low-dose CBCT image reconstruction: a review.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026Review
- Evaluation of a Vendor-Agnostic Deep Learning Model for Noise Reduction and Image Quality Improvement in Dental CBCT.Diagnostics (Basel, Switzerland) · 2024Article
- Noise-Optimized CBCT Imaging of Temporomandibular Joints-The Impact of AI on Image Quality.Journal of clinical medicine · 2024Article
- Deep Filtered Back Projection for CT Reconstruction.IEEE access : practical innovations, open solutions · 2024Article
- Machine Learned Texture Prior From Full-Dose CT Database via Multi-Modality Feature Selection for Bayesian Reconstruction of Low-Dose CT.IEEE transactions on medical imaging · 2023Article
- Artificial Intelligence in Radiation Therapy.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Sparse Angle CBCT Reconstruction Based on Guided Image Filtering.Frontiers in oncology · 2022Article
- Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS).Medical physics · 2021Article
- Experiences with image quality and radiation dose of cone beam computed tomography (CBCT) and multidetector computed tomography (MDCT) in pediatric extremity trauma.Skeletal radiology · 2020Article
- DirectPET: full-size neural network PET reconstruction from sinogram data.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning.IEEE transactions on medical imaging · 2019Article
- Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions.IEEE transactions on medical imaging · 2019Article
- Potential of a machine-learning model for dose optimization in CT quality assurance.European radiology · 2019Article
- DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.Medical image analysis · 2019Article
- Low-dose Cone-Beam Computed Tomography Reconstruction through a fast Three-Dimensional Compressed Sensing Method Based on the Three-Dimensional Pseudo-polar Fourier Transform.Journal of medical signals and sensorsArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Cone-beam computed tomography (CBCT) plays an important role in radiation therapy. Statistical iterative reconstruction (SIR) algorithms with specially designed penalty terms provide good performance for low-dose CBCT imaging. Among others, the total variation (TV) penalty is the current state-of-the-art in removing noises and preserving edges, but one of its well-known limitations is its staircase effect. Recently, various penalty terms with higher order differential operators were proposed to replace the TV penalty to avoid the staircase effect, at the cost of slightly blurring object edges. We developed a novel SIR algorithm using a neural network for CBCT reconstruction. We used a data-driven method to learn the "potential regularization term" rather than design a penalty term manually. This approach converts the problem of designing a penalty term in the traditional statistical iterative framework to designing and training a suitable neural network for CBCT reconstruction. We proposed using transfer learning to overcome the data deficiency problem and an iterative deblurring approach specially designed for the CBCT iterative reconstruction process during which the noise level and resolution of the reconstructed images may change. Through experiments conducted on two physical phantoms, two simulation digital phantoms, and patient data, we demonstrated the excellent performance of the proposed network-based SIR for CBCT reconstruction, both visually and quantitatively. Our proposed method can overcome the staircase effect, preserve both edges and regions with smooth intensity transition, and provide reconstruction results at high resolution and low noise level.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.