Low-Dose CT Perfusion of the Liver Using Reconstruction of Difference

Abstract

Liver CT perfusion (CTP) is used in the detection, staging, and treatment response analysis of hepatic diseases. Unfortunately, CTP radiation exposures is significant, limiting more widespread use. Traditional CTP data processing reconstructs individual temporal samples, ignoring a large amount of shared anatomical information between temporal samples, suggesting opportunities for improved data processing. We adopt a prior-image-based reconstruction approach called Reconstruction of Difference (RoD) to enable low-exposure CTP acquisition. RoD differs from many algorithms by directly estimating the attenuation changes between the current patient state and a prior CT volume. We propose to use a high-fidelity unenhanced baseline CT image to integrate prior anatomical knowledge into subsequent data reconstructions. Using simulation studies based on a 4D digital anthropomorphic phantom with realistic time-attenuation curves, we compare RoD with conventional filtered-backprojection, penalized-likelihood estimation, and prior image penalized-likelihood estimation. We evaluate each method in comparisons of reconstructions at individual time points, accuracy of estimated time-attenuation curves, and in an analysis of common perfusion metric maps including hepatic arterial perfusion, hepatic portal perfusion, perfusion index, and time-to-peak. Results suggest that RoD enables significant exposure reductions, outperforming standard and more sophisticated model-based reconstruction, making RoD a potentially important tool to enable low-dose liver CTP.

Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks

“…Low-noise prior images can be constructed from previous (non-contrast) scans or averaging the time frames, which will greatly improve the quality of time-frame image reconstruction [25]- [27]. One can also reconstruct the difference between each time frame to "reduce" the number of unknowns [28], [29]. The low-rank penalty can also be applied to the timeframe reconstruction [30].…”

The effects of baseline length in Computed Tomography perfusion of liver

“…Computed Tomography perfusion (CTp) is a functional imaging technique that has shown to be very useful in several clinical applications [1], including oncology [2,3]. Indeed, through the repetition of CT acquisitions of the same tissue portion before, during, and after the administration of a iodinated contrast agent [4] it is possible to extract for each voxel the so-called time concentration curves (TCCs), which allow describing tissue hemodynamics. Several kinetic models can be adopted to describe different aspects of tissue physiology [5].…”

“…In fact, besides affecting the overall signal fitting, in the methods where the baseline is the background signal used to achieve the time attenuation curves [30], possible baseline's inaccuracies can heavily hinder the overall CTp parameters computation [6]. For this reason, some recent guidelines on liver CTp suggest to start acquisition even before contrast agent injection [4]. For instance, five to ten unenhanced scans are recommended in [32] to ensure the acquisition of enough baseline samples so as to obtain reliable perfusion values.…”

Reconstruction-of-difference (RoD) imaging for cone-beam CT neuro-angiography

“…Recently, a new prior-image-based MBIR approach 'reconstruction-of-difference (RoD)' (Pourmorteza et al 2016) was proposed that uses a penalized-likelihood (PL) framework to directly reconstruct the difference between a current data set and a prior image. The RoD approach has demonstrated superior performance in comparison to PL for sparse projection data in applications such as radiotherapy (Zhang et al 2017) and liver perfusion imaging (Seyyedi et al 2018). The RoD approach may be especially well suited for CBCT-A, since the difference image represents the 3D angiogram-i.e.…”