Prior image constrained compressed sensing (PICCS)

By Guang-Hong Chen, Jie Tang, Shuai Leng. SPIE BiOS, 2008

1,161 citations recorded by the source corpus.

Subfields: Computational Mechanics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0dbfddf7920ac8e4c837399256a35a8fec15f0e8

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In Computational Mechanics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? (2009, background): “Further work in CT considers practical issues such as including prior scan information [67] and subject motion [68].”
CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) (2018, background): “5) Joint Sparsifying Transform Loss: The total variation (TV) has demonstrated the state-of-the-art performance in promoting image sparsity and reducing noise in piecewise-constant images [59], [60].”
A limited-angle CT reconstruction method based on anisotropic TV minimization (2013, methodology): “Exemplary algorithms are POCS with TV minimization (POCS + TV) (Sidky et al 2006, 2009, Bian et al 2010), PICCS (Chen et al 2008, 2009, Tang et al 2009), simultaneous algebraic reconstruction technique (SART) with TV (SART + TV) (Yu and Wang 2009), soft-thresholding algorithms (Yu and Wang 2010, Xu ”
Compressed Sensing using Generative Models (2017, methodology): “Compressed sensing has been studied extensively for medical applications including computed tomography (CT) (Chen et al., 2008), rapid MRI (Lustig et al.”
Quantitative X-ray tomography (2014, methodology): “Chen et al.166 have shown that when reconstructing a dynamic series it is possible to use a variant where the target image is subtracted at each time frame from the prior image (obtained by FBP using many projections) using a so-called prior image constrained compressed sensing method (PICCS) showin”
RODEO: Robust DE-aliasing autoencOder for real-time medical image reconstruction (2017, methodology): “This was originally developed with convex sparsity promoting l1-norm [38, 39] but was later shown to yield even better results with non-convex lp-norm (NCPICCS) [40].”
Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization (2008, methodology): “The image TV has been used as a penalty term in iterative, image reconstruction algorithms (Vogel and Oman 1996, Panin et al 1999, Persson et al 2001, Song et al 2007, Chen et al 2008).”
The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence (2018, positive): “Through the fact that there is shared anatomical information in between the scans, one can utilize this fact in an IR algorithm to significantly improve diagnostic image quality while reducing radiation exposure [58 – 61].”
Prior Image-Constrained Reconstruction using Style-Based Generative Models (2021, positive): “Solving the inverse problem can then be cast as obtaining the solution to versions of the following optimization problem (Chen et al., 2008; Mota et al., 2017; Weizman et al., 2016): It is important to note that the assumption of sparse differences between the prior image and the ground truth may…”
Tradeoffs Between Convergence Speed and Reconstruction Accuracy in Inverse Problems (2016, methodology): “This approach has been applied to improve the quali ty of MRI [14], [21], [29] and CT [10] scans, and also in the general context of sparse recovery [15], [22], [28].”
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models (2021, background): “Such inverse problems naturally appear in many applications including medical imaging, single pixel reconstruction and other domains (Lustig et al., 2007; 2008; Chen et al., 2008; Duarte et al., 2008; Qaisar et al., 2013; Hegde et al., 2009).”