Calibration‐free beam hardening correction for myocardial perfusion imaging using CT

Abstract

Purpose Computed tomography myocardial perfusion imaging (CT‐MPI) and coronary CTA have the potential to make CT an ideal noninvasive imaging gatekeeper exam for invasive coronary angiography. However, beam hardening (BH) artifacts prevent accurate blood flow calculation in CT‐MPI. BH correction methods require either energy‐sensitive CT, not widely available, or typically, a calibration‐based method in conventional CT. We propose a calibration‐free, automatic BH correction (ABHC) method suitable for CT‐MPI and evaluate its ability to reduce BH artifacts in single “static‐perfusion” images and to create accurate myocardial blood flow (MBF) in dynamic CT‐MPI. Methods In the algorithm, we used input CT DICOM images and iteratively optimized parameters in a polynomial BH correction until a BH‐sensitive cost function was minimized on output images. An input image was segmented into a soft tissue image and a highly attenuating material (HAM) image containing bones and regions of high iodine concentrations, using mean HU and temporal enhancement properties. We forward projected HAM, corrected projection values according to a polynomial correction, and reconstructed a correction image to obtain the current iteration's BH corrected image. The cost function was sensitive to BH streak artifacts and cupping. We evaluated the algorithm on simulated CT and physical phantom images, and on preclinical porcine with optional coronary obstruction and clinical CT‐MPI data. Assessments included measures of BH artifact in single images as well as MBF estimates. We obtained CT images on a prototype spectral detector CT (SDCT, Philips Healthcare) scanner that provided both conventional and virtual keV images, allowing us to quantitatively compare corrected CT images to virtual keV images. To stress test the method, we evaluated results on images from a different scanner (iCT, Philips Healthcare) and different kVp values. Results In a CT‐simulated digital phantom consisting of water with iodine cylinder insets, BH streak artifacts between simulated iodine inserts were reduced from 13 ± 2 to 0 ± 1 HU. In a similar physical phantom having higher iodine concentrations, BH streak artifacts were reduced from 48 ± 6 to 1 ± 5 HU and cupping was reduced by 86%, from 248 to 23 HU. In preclinical CT‐MPI images without coronary obstruction, BH artifact was reduced from 24 ± 6 HU to less than 5 ± 4 HU at peak enhancement. Standard deviation across different regions of interest (ROI) along the myocardium was reduced from 13.26 to 6.86 HU for ABHC, comparing favorably to measurements in the corresponding virtual keV image. Corrections greatly reduced variations in preclinical MBF maps as obtained in normal animals without obstruction (FFR = 1). Coefficients of variations were 22% (conventional CT), 9% (ABHC), and 5% (virtual keV). Moreover, variations in flow tended to be localized after ABHC, giving result which would not be confused with a flow deficit in a coronary vessel...

Comparison of automated beam hardening correction (ABHC) algorithms for myocardial perfusion imaging using computed tomography

“…21 MBF was computed from dynamic perfusion images after registration, segmentation, and noise reduction, as described previously. 2,21,37 Images were registered using a nonrigid registration algorithm with a normalized mutual information similarity metric. 45 The myocardium was segmented using a semi-automated approach implemented in a research version of the Medis QMass software (www.medis.nl/).…”

“…Details of ABHC-1 can be found in our previous work. 37 The corrected image I C is obtained by subtracting the error image I E from the original image I. The error image I E is found using the equation below, where R and R À1 are the Radon transform and the inverse Radon transform respectively:…”

“…We estimate the "true" HU value of the left ventricle, LV true , within a 4-pixel rim at the edge of the ventricle as described previously. 37 F LV is the sum of the squared difference between every pixel in the left ventricle to LV true :…”

“…Some of these methods are iterative, including iterative beam hardening correction, 35 dynamic iterative beam hardening correction, 36 and our automatic beam hardening correction (ABHC-1) method. 37 There have been multiple polynomial correction models proposed. They include those used by So et al, 23 empirical BHC (EBHC) proposed by Kyriakou et al 38 and an extension of EBHC proposed by Nett and Hsieh, 39 giving two, three, and seven parameters, respectively.…”

Calibration‐free beam hardening reduction in x‐ray CBCT using the epipolar consistency condition and physical constraints

“…A drawback of this function is the assumption of a piecewise continuous object. A work by Levi et al 6 uses the same cost function on applications specific areas of the image to reduce beam hardening.…”