Performance comparison between total variation (TV)-based compressed sensing and statistical iterative reconstruction algorithms

By Jie Tang, Brian Nett, Guang-Hong Chen. Physics in Medicine and Biology, 2009

313 citations recorded by the source corpus.

Of all available reconstruction methods, statistical iterative reconstruction algorithms appear particularly promising since they enable accurate physical noise modeling. The newly developed compressive sampling/compressed sensing (CS) algorithm has shown the potential to accurately reconstruct images from highly undersampled data. The CS algorithm can be implemented in the statistical reconstruction framework as well. In this study, we compared the performance of two standard statistical reconstruction algorithms (penalized weighted least squares and q-GGMRF) to the CS algorithm. In assessing the image quality using these iterative reconstructions, it is critical to utilize realistic background anatomy as the reconstruction results are object dependent. A cadaver head was scanned on a Varian Trilogy system at different dose levels. Several figures of merit including the relative root mean square error and a quality factor which accounts for the noise performance and the spatial resolution were introduced to objectively evaluate reconstruction performance. A comparison is presented between the three algorithms for a constant undersampling factor comparing different algorithms at several dose levels. To facilitate this comparison, the original CS method was formulated in the framework of the statistical image reconstruction algorithms. Important conclusions of the measurements from our studies are that (1) for realistic neuro-anatomy, over 100 projections are required to avoid streak artifacts in the reconstructed images even with CS reconstruction, (2) regardless of the algorithm employed, it is beneficial to distribute the total dose to more views as long as each view remains quantum noise limited and (3) the total variation-based CS method is not appropriate for very low dose levels because while it can mitigate streaking artifacts, the images exhibit patchy behavior, which is potentially harmful for medical diagnosis.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b2a95934e6dbc52325fa0d3fade00ed14e68f35d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence (2018, background): “Over the last decade, several investigators have presented IR solutions [50 – 55], which have the potential to be clinically introduced in the future.”
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…”
PyHST2: an hybrid distributed code for high speed tomographic reconstruction with iterative reconstruction and a priori knowledge capabilities (2013, unknown): “For materials with a few phases of constant absorption, a classical penalization function f in tomographic reconstruction is the total variation of the image [9, 10, 11, 12, 13, 14], that is a 1 norm of the image gradient.”
Tensor-Based Formulation and Nuclear Norm Regularization for Multienergy Computed Tomography (2013, methodology): “One of the most widely used edge preserving regularization technique is total variation (TV) [41] which has been applied to CT as well [42], [43].”