Dynamic myocardial perfusion in a porcine balloon-induced ischemia model using a prototype spectral detector CT

Abstract

Myocardial CT perfusion (CTP) imaging is an application that should greatly benefit from spectral CT through the significant reduction of beam hardening (BH) artifacts using mono-energetic (monoE) image reconstructions. We used a prototype spectral detector CT (SDCT) scanner (Philips Healthcare) and developed advanced processing tools (registration, segmentation, and deconvolution-based flow estimation) for quantitative myocardial CTP in a porcine ischemia model with different degrees of coronary occlusion using a balloon catheter. The occlusion severity was adjusted with fractional flow reserve (FFR) measurements. The SDCT scanner is a single source, dual-layer detector system, which allows simultaneous acquisitions of low and high energy projections, hence enabling accurate projection-based material decomposition and effective reduction of BH-artifacts. In addition, the SDCT scanner eliminates partial scan artifacts with fast (0.27s), full gantry rotation acquisitions. We acquired CTP data under different hemodynamic conditions and reconstructed conventional 120kVp images and projection-based monoenergetic (monoE) images for energies ranging from 55keV-to-120keV. We computed and compared myocardial blood flow (MBF) between different reconstructions. With balloon completely deflated (FFR=1), we compared the mean attenuation in a myocardial region of interest before iodine arrival and at peak iodine enhancement in the left ventricle (LV), and we found that monoE images at 70keV effectively minimized the difference in attenuation, due to BH, to less than 1 HU compared to 14 HU with conventional 120kVp images. Flow maps under baseline condition (FFR=1) were more uniform throughout the myocardial wall at 70keV, whereas with 120kVp data about 12% reduction in blood flow was noticed on BH-hypoattenuated areas compared to other myocardial regions. We compared MBF maps at different keVs under an ischemic condition (FFR < 0.7), and we found that flow-contrast-to-noise-ratio (CNR f) between LAD ischemic and remote healthy territories attains its maximum (2.87 ± 0.7) at 70keV. As energies diverge from 70keV, we

Comparison of quantitative myocardial perfusion imaging CT to fluorescent microsphere-based flow from high-resolution cryo-images

“…However, we do account for other sources of error which can significantly degrade MPI-CT: the SDCT nearly eliminates beam hardening artifacts through mono-energetic reconstruction, ECG gating and turning the ventilator off during CT scanning reduces motion effects, and full scan acquisition and reconstruction avoids partial scan artifact. We selected 70keV reconstruction in this work based due to its high MBF contrast-to-noise ratio from our previous experience 7, 9 , however noise reduction in mono-energetic reconstruction could enable improved iodine signal enhancement at lower keVs. Overall, the CT data itself is of relatively good quality, so we are confident that errors in MBF estimates stem either from physiologic changes or perfusion processing.…”

“…CT images were acquired using a spectral detector CT system (SDCT, Philips Healthcare) 7, 9 . The SDCT simultaneously acquires high- and low-energy projections using a dual-layer detector design, enabling projection-based material decomposition for mono-energetic image reconstruction even at high gantry rotation speeds 10, 11 .…”

“…Mono-energetic images at 70keV were reconstructed with 2mm slice thickness, 2mm increment, and 120mm field of view. The advantage of mono-energetic images is their negligible beam hardening artifact as compared to conventional poly-energetic reconstruction 7, 9, 11 . Iodinated contrast agent (Omnipaque 350, GE Healthcare) was injected intravenously at 5mL/s for 4 seconds and imaging was initiated at 2 seconds.…”

“…The analysis includes nonrigid registration, cardiac short axis reformatting, myocardium segmentation, and myocardial blood flow (MBF) quantification. MBF was quantified using three approaches: model-independent deconvolution with SVD with a fixed Tikhonov regularization parameter (ThSVD) 12, 13 , SVD with Tikhonov regularization determined in an automated fashion by the L-curve criterion (LSVD) 7, 9, 14 , and parameter estimation using the Johnson-Wilson physiologic model for capillary exchange of contrast agent (JW) 15, 16 . For ThSVD, we selected a Tikhonov parameter of λ=0.09 based on our previous studies 12 .…”

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

“…In this work, to increase the intuitiveness of the evaluation, curve fitting was not adopted. Because of the spatial misalignment among the sequence of DM‐PCT images, rough 3D elastic registration was first performed before TDC extraction with the NiftyReg software to align the 3D cardiac volumes. Hemodynamic parameter maps . The main goal of DM‐PCT imaging is to obtain the functional hemodynamic parameters, which reflect qualitative assessment of blood supply.…”