Reduction of image noise in low tube current dynamic CT myocardial perfusion imaging using HYPR processing: A time‐attenuation curve analysis

Abstract

Purpose: This study describes a HighlY constrained backPRojection (HYPR) image processing method for the reduction of image noise in low tube current time-resolved CT myocardial perfusion scans. The effect of this method on myocardial time-attenuation curve noise and fidelity is evaluated in an animal model, using varying levels of tube current. Methods: CT perfusion scans of four healthy pigs (42-59 kg) were acquired at 500, 250, 100, 50, 25, and 10 mA on a 64-slice scanner (4 cm axial coverage, 120 kV, 0.4 s/rotation, 50 s scan duration). For each scan a sequence of ECG-gated images centered on 75% R-R was reconstructed using shortscan filtered back projection (FBP). HYPR processing was applied to the scans acquired at less than 500 mA using parameters designed to maintain the voxel noise level in the 500-mA FBP images. The processing method generates a series of composite images by averaging over a sliding time window and then multiplies the composite images by weighting images to restore temporal fidelity to the image sequence. HYPR voxel noise relative to FBP noise was measured in AHA myocardial segment numbers 1, 5, 6, and 7 at each mA. To quantify the agreement between HYPR and FBP time-attenuation curves (TACs), Bland-Altman analysis was performed on TACs measured in full myocardial segments. The relative degree of TAC fluctuation in smaller subvolumes was quantified by calculating the root mean square deviation of a TAC about the gamma variate curve fit to the TAC data. Results: HYPR image sequences were produced using 2, 7, and 20 beat composite windows for the 250, 100, and 50 mA scans, respectively. At 25 and 10 mA, all available beats were used in the composite (41-60; average 50). A 7-voxel-wide 3D cubic filter kernel was used to form weighting images. The average ratio of HYPR voxel noise to 500-mA FBP voxel noise was 1.06, 1.10, 0.97, 1.11, and 2.15 for HYPR scans at 250, 100, 50, 25, and 10 mA. The average limits-of-agreement between HYPR and FBP TAC values measured 0.02+/−0.91, 0.04+/−1.92, 0.19+/−1.59, 1.13+/−4.22, and 1.07+/−6.37 HU (mean difference +/−1.96 SD). The HYPR image subvolume that yielded a fixed level of TAC fluctuations was smaller, on average, than the FBP subvolume determined at the same mA. Conclusions: HYPR processing is a feasible method for generating low noise myocardial perfusion data from a low-mA time-resolved CT myocardial perfusion scan. The method is applicable to current clinical scanners and uses conventional image reconstructions as input data.

A robust noise reduction technique for time resolved CT

“…One category of noise reduction methods applied to time-resolved CT is based on HighlY constrained back-PRojection (HYPR). [16][17][18][19][20] HYPR utilizes the correlations in anatomical structures between serial images and a low noise composite image to increase the signal-to-noise ratio. However, it requires perfect registration of images across the scan duration.…”

“…For example, HYPR has been reported to cause image resolution loss when tissue misregistration occurs. 16 Although image registration methods can be applied to correct the misregistration prior to noise reduction, the high noise level in time-resolved CT images may make adequate registration challenging. 24,25 To address these limitations, the purpose of this study was to develop a robust image-domain noise reduction technique capable of handling severe motion misregistration in timeresolved CT data, and to evaluate its performance in terms of image noise and functional evaluation.…”

Low‐dose dynamic myocardial perfusion CT imaging using a motion adaptive sparsity prior

“…Investigations have been performed to reduce the radiation dose in DM-PCT, including optimized DM-PCT protocols and advanced image processing techniques. 9,10,11,12,13,14 Among them, without consideration of natural heart motion between consecutive cardiac phases during DM-PCT imaging, the highly constrained backprojection (HYPR) image processing method was studied to reduce image noise in lowdose DM-PCT scans. 9,10 However, in practice, spatial misalignment of the acquired cardiac volumes is an inevitable result of breathing and poor ECG gating, and it leads to artifacts in the reconstructed DM-PCT images.…”

Robust dynamic myocardial perfusion CT deconvolution for accurate residue function estimation via adaptive-weighted tensor total variation regularization: a preclinical study

“…Up to now, various advanced image processing and reconstruction algorithms with different capability for noise suppression in the case of low-mAs data acquisition have been reported (Speidel et al 2013, Ramirez-Giraldo et al 2012, Sawall et al 2012, Lauzier et al 2012, Modgil et al 2014, Lin and Ehsan et al 2014, Tao et al 2014, Bian et al 2015). For instance, Speidel et al (2013) proposed a highly constrained backprojection (HYPR) image processing method to directly reduce the noise in the MPCT image domain.…”

“…For instance, Speidel et al (2013) proposed a highly constrained backprojection (HYPR) image processing method to directly reduce the noise in the MPCT image domain. Modgil et al (2014) developed a dynamic MPCT sinogram data restoration method for successful MPCT image reconstruction using the FBP algorithm.…”