Dynamic CT Myocardial Perfusion Imaging: Detection of Ischemia in a Porcine Model with FFR Verification

SLICR super-voxel algorithm for fast, robust quantification of myocardial blood flow by dynamic computed tomography myocardial perfusion imaging

We created and evaluated a processing method for dynamic computed tomography myocardial perfusion imaging (CT-MPI) of myocardial blood flow (MBF), which combines a modified simple linear iterative clustering algorithm (SLIC) with robust perfusion quantification, hence the name SLICR. SLICR adaptively segments the myocardium into nonuniform super-voxels with similar perfusion time attenuation curves (TACs). Within each super-voxel, an α-trimmed-median TAC was computed to robustly represent the super-voxel and a robust physiological model (RPM) was implemented to semi-analytically estimate MBF. SLICR processing was compared with another voxel-wise MBF preprocessing approach, which included a spatiotemporal bilateral filter (STBF) for noise reduction prior to perfusion quantification. Image data from a digital CT-MPI phantom and a porcine ischemia model were evaluated. SLICR was ∼ 50 -fold faster than voxel-wise RPM and other model-based methods while retaining sufficient resolution to show clinically relevant features, such as a transmural perfusion gradient. SLICR showed markedly improved accuracy and precision, as compared with other methods. At a simulated MBF of 100 mL/min-100 g and a tube current-time product of 100 mAs (50% of nominal), the MBF estimates were 101 ± 12 , 94 ± 56 , and 54 ± 24    mL / min - 100    g for SLICR, the voxel-wise Johnson-Wilson model, and a singular value decomposition-model independent method with STBF, respectively. SLICR estimated MBF precisely and accurately ( 103 ± 23    mL / min - 100    g ) at 25% nominal dose, while other methods resulted in larger errors. With the porcine model, the SLICR results were consistent with the induced ischemia. SLICR simultaneously accelerated and improved the quality of quantitative perfusion processing without compromising clinically relevant distributions of perfusion characteristics.

The role of acquisition and quantification methods in myocardial blood flow estimability for myocardial perfusion imaging CT

In this work, we clarified the role of acquisition parameters and quantification methods in myocardial blood flow (MBF) estimability for myocardial perfusion imaging using CT (MPI-CT). We used a physiologic model with a CT simulator to generate time-attenuation curves across a range of imaging conditions, i.e. tube current-time product, imaging duration, and temporal sampling, and physiologic conditions, i.e. MBF and arterial input function width. We assessed MBF estimability by precision (interquartile range of MBF estimates) and bias (difference between median MBF estimate and reference MBF) for multiple quantification methods. Methods included: six existing model-based deconvolution models, such as the plug-flow tissue uptake model (PTU), Fermi function model, and single-compartment model (SCM); two proposed robust physiologic models (RPM1, RPM2); model-independent singular value decomposition with Tikhonov regularization determined by the L-curve criterion (LSVD); and maximum upslope (MUP). Simulations show that MBF estimability is most affected by changes in imaging duration for model-based methods and by changes in tube current-time product and sampling interval for model-independent methods. Models with three parameters, i.e. RPM1, RPM2, and SCM, gave least biased and most precise MBF estimates. The average relative bias (precision) for RPM1, RPM2, and SCM was  ⩽11% (⩽10%) and the models produced high-quality MBF maps in CT simulated phantom data as well as in a porcine model of coronary artery stenosis. In terms of precision, the methods ranked best-to-worst are: RPM1  >  RPM2  >  Fermi  >  SCM  >  LSVD  >  MUP [Formula: see text] other methods. In terms of bias, the models ranked best-to-worst are: SCM  >  RPM2  >  RPM1  >  PTU  >  LSVD [Formula: see text] other methods. Models with four or more parameters, particularly five-parameter models, had very poor precision (as much as 310% uncertainty) and/or significant bias (as much as 493%) and were sensitive to parameter initialization, thus suggesting the presence of multiple local minima. For improved estimates of MBF from MPI-CT, it is recommended to use reduced models that incorporate prior knowledge of physiology and contrast agent uptake, such as the proposed RPM1 and RPM2 models.

Effect of Beam Hardening on Transmural Myocardial Perfusion Quantification in Myocardial CT Imaging

The detection of subendocardial ischemia exhibiting an abnormal transmural perfusion gradient (TPG) may help identify ischemic conditions due to micro-vascular dysfunction. We evaluated the effect of beam hardening (BH) artifacts on TPG quantification using myocardial CT perfusion (CTP). We used a prototype spectral detector CT scanner (Philips Healthcare) to acquire dynamic myocardial CTP scans in a porcine ischemia model with partial occlusion of the left anterior descending (LAD) coronary artery guided by pressure wire-derived fractional flow reserve (FFR) measurements. Conventional 120 kVp and 70 keV projection-based mono-energetic images were reconstructed from the same projection data and used to compute myocardial blood flow (MBF) using the Johnson-Wilson model. Under moderate LAD occlusion (FFR~0.7), we used three 5 mm short axis slices and divided the myocardium into three LAD segments and three remote segments. For each slice and each segment, we characterized TPG as the mean "endo-to-epi" transmural flow ratio (TFR). BH-induced hypoenhancement on the ischemic anterior wall at 120 kVp resulted in significantly lower mean TFR value as compared to the 70 keV TFR value (0.29±0.01 vs. 0.55±0.01; p<1e-05). No significant difference was measured between 120 kVp and 70 keV mean TFR values on segments moderately affected or unaffected by BH. In the entire ischemic LAD territory, 120 kVp mean endocardial flow was significantly reduced as compared to mean epicardial flow (15.80±10.98 vs. 40.85±23.44 ml/min/100g; p<1e-04). At 70 keV, BH was effectively minimized resulting in mean endocardial MBF of 40.85±15.3407 ml/min/100g vs. 74.09±5.07 ml/min/100g (p=0.0054) in the epicardium. We also found that BH artifact in the conventional 120 kVp images resulted in falsely reduced MBF measurements even under non-ischemic conditions.

Automatic segmentation of microcystic macular edema in OCT

Microcystic macular edema (MME) manifests as small, hyporeflective cystic areas within the retina. For reasons that are still largely unknown, a small proportion of patients with multiple sclerosis (MS) develop MME-predominantly in the inner nuclear layer. These cystoid spaces, denoted pseudocysts, can be imaged using optical coherence tomography (OCT) where they appear as small, discrete, low intensity areas with high contrast to the surrounding tissue. The ability to automatically segment these pseudocysts would enable a more detailed study of MME than has been previously possible. Although larger pseudocysts often appear quite clearly in the OCT images, the multi-frame averaging performed by the Spectralis scanner adds a significant amount of variability to the appearance of smaller pseudocysts. Thus, simple segmentation methods only incorporating intensity information do not perform well. In this work, we propose to use a random forest classifier to classify the MME pixels. An assortment of both intensity and spatial features are used to aid the classification. Using a cross-validation evaluation strategy with manual delineation as ground truth, our method is able to correctly identify 79% of pseudocysts with a precision of 85%. Finally, we constructed a classifier from the output of our algorithm to distinguish clinically identified MME from non-MME subjects yielding an accuracy of 92%.