ArticleIEEE transactions on medical imaging2018
Detector Blur and Correlated Noise Modeling for Digital Breast Tomosynthesis Reconstruction.
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 15 papers.
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Who cites it
15 citing papers in PubMed.
- Improving depth-resolution, in-plane contrast, and reducing non-uniformity artifacts for wide-angle DBT.ArXiv · 2026Article
- Image Quality Enhancement for Digital Breast Tomosynthesis: High-Density Object Artifact Reduction.Journal of imaging informatics in medicine · 2024Article
- Pre-Reconstruction Processing with the Cycle-Consist Generative Adversarial Network Combined with Attention Gate to Improve Image Quality in Digital Breast Tomosynthesis.Diagnostics (Basel, Switzerland) · 2024Article
- X-ray source motion blur modeling and deblurring with generative diffusion for digital breast tomosynthesis.Physics in medicine and biology · 2024Article
- Model-based deep CNN-regularized reconstruction for digital breast tomosynthesis with a task-based CNN image assessment approach.Physics in medicine and biology · 2023Article
- On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Evaluation of a Generative Adversarial Network to Improve Image Quality and Reduce Radiation-Dose during Digital Breast Tomosynthesis.Diagnostics (Basel, Switzerland) · 2022Article
- Deep Convolutional Neural Network With Adversarial Training for Denoising Digital Breast Tomosynthesis Images.IEEE transactions on medical imaging · 2021Article
- Effect of source blur on digital breast tomosynthesis reconstruction.Medical physics · 2019Article
- Blind deconvolution in model-based iterative reconstruction for CT using a normalized sparsity measure.Physics in medicine and biology · 2019Article
- Imaging of fiber-like structures in digital breast tomosynthesis.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Predicting image properties in penalized-likelihood reconstructions of flat-panel CBCT.Medical physics · 2019Article
- Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image.Physics in medicine and biology · 2018Article
- Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction.Medical physics · 2018Article
- Penalized-Likelihood Reconstruction With High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging.IEEE transactions on medical imaging · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper describes a new image reconstruction method for digital breast tomosynthesis (DBT). The new method incorporates detector blur into the forward model. The detector blur in DBT causes correlation in the measurement noise. By making a few approximations that are reasonable for breast imaging, we formulated a regularized quadratic optimization problem with a data-fit term that incorporates models for detector blur and correlated noise (DBCN). We derived a computationally efficient separable quadratic surrogate (SQS) algorithm to solve the optimization problem that has a non-diagonal noise covariance matrix. We evaluated the SQS-DBCN method by reconstructing DBT scans of breast phantoms and human subjects. The contrast-to-noise ratio and sharpness of microcalcifications were analyzed and compared with those by the simultaneous algebraic reconstruction technique. The quality of soft tissue lesions and parenchymal patterns was examined. The results demonstrate the potential to improve the image quality of reconstructed DBT images by incorporating the system physics model. This paper is a first step toward model-based iterative reconstruction for DBT.
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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.