Michael D. Bindschadler

Sinogram smoothing techniques for myocardial blood flow estimation from dose-reduced dynamic computed tomography

Abstract. Dynamic contrast-enhanced computed tomography (CT) could provide an accurate and widely available technique for myocardial blood flow (MBF) estimation to aid in the diagnosis and treatment of coronary artery disease. However, one of its primary limitations is the radiation dose imparted to the patient. We are exploring techniques to reduce the patient dose by either reducing the tube current or by reducing the number of temporal frames in the dynamic CT sequence. Both of these dose reduction techniques result in noisy data. In order to extract the MBF information from the noisy acquisitions, we have explored several data-domain smoothing techniques. In this work, we investigate two specific smoothing techniques: the sinogram restoration technique in both the spatial and temporal domains and the use of the Karhunen–Loeve (KL) transform to provide temporal smoothing in the sinogram domain. The KL transform smoothing technique has been previously applied to dynamic image sequences in positron emission tomography. We apply a quantitative two-compartment blood flow model to estimate MBF from the time-attenuation curves and determine which smoothing method provides the most accurate MBF estimates in a series of simulations of different dose levels, dynamic contrast-enhanced cardiac CT acquisitions. As measured by root mean square percentage error (% RMSE) in MBF estimates, sinogram smoothing generally provides the best MBF estimates except for the cases of the lowest simulated dose levels (tube current=25  mAs, 2 or 3 s temporal spacing), where the KL transform method provides the best MBF estimates. The KL transform technique provides improved MBF estimates compared to conventional processing only at very low doses (<7  mSv). Results suggest that the proposed smoothing techniques could provide high fidelity MBF information and allow for substantial radiation dose savings.

Adaptive temporal smoothing of sinogram data using Karhunen-Loeve (KL) transform for myocardial blood flow estimation from dose-reduced dynamic CT

There is a strong need for an accurate and easily available technique for myocardial blood flow (MBF) estimation to aid in the diagnosis and treatment of coronary artery disease (CAD). Dynamic CT would provide a quick and widely available technique to do so. However, its biggest limitation is the dose imparted to the patient. We are exploring techniques to reduce the patient dose by either reducing the tube current or by reducing the number of temporal frames in the dynamic CT sequence. Both of these dose reduction techniques result in very noisy data. In order to extract the myocardial blood flow information from the noisy sinograms, we have been looking at several data-domain smoothing techniques. In our previous work,1 we explored the sinogram restoration technique in both the spatial and temporal domain. In this work, we explore the use of Karhunen-Loeve (KL) transform to provide temporal smoothing in the sinogram domain. This technique has been applied previously to dynamic image sequences in PET.2, 3 We find that the cluster-based KL transform method yields noticeable improvement in the smoothness of time attenuation curves (TAC). We make use of a quantitative blood flow model to estimate MBF from these TACs and determine which smoothing method provides the most accurate MBF estimates.

Evaluation of static and dynamic perfusion cardiac computed tomography for quantitation and classification tasks.

Abstract. Cardiac computed tomography (CT) acquisitions for perfusion assessment can be performed in a dynamic or static mode. Either method may be used for a variety of clinical tasks, including (1) stratifying patients into categories of ischemia and (2) using a quantitative myocardial blood flow (MBF) estimate to evaluate disease severity. In this simulation study, we compare method performance on these classification and quantification tasks for matched radiation dose levels and for different flow states, patient sizes, and injected contrast levels. Under conditions simulated, the dynamic method has low bias in MBF estimates (0 to 0.1  ml/min/g) compared to linearly interpreted static assessment (0.45 to 0.48  ml/min/g), making it more suitable for quantitative estimation. At matched radiation dose levels, receiver operating characteristic analysis demonstrated that the static method, with its high bias but generally lower variance, had superior performance (p<0.05) in stratifying patients, especially for larger patients and lower contrast doses [area under the curve (AUC)=0.95 to 96 versus 0.86]. We also demonstrate that static assessment with a correctly tuned exponential relationship between the apparent CT number and MBF has superior quantification performance to static assessment with a linear relationship and to dynamic assessment. However, tuning the exponential relationship to the patient and scan characteristics will likely prove challenging. This study demonstrates that the selection and optimization of static or dynamic acquisition modes should depend on the specific clinical task.

Adaptive sampling of CT data for myocardial blood flow estimation from dose-reduced dynamic CT

Quantification of myocardial blood flow (MBF) can aid in the diagnosis and treatment of coronary artery disease (CAD). However, there are no widely accepted clinical methods for estimating MBF. Dynamic CT holds the promise of providing a quick and easy method to measure MBF quantitatively, however the need for repeated scans has raised concerns about the potential for high radiation dose. In our previous work, we explored techniques to reduce the patient dose by either uniformly reducing the tube current or by uniformly reducing the number of temporal frames in the dynamic CT sequence. These dose reduction techniques result in very noisy data, which can give rise to large errors in MBF estimation. In this work, we seek to investigate whether nonuniformly varying the tube current or sampling intervals can yield more accurate MBF estimates. Specifically, we try to minimize the dose and obtain the most accurate MBF estimate through addressing the following questions: when in the time attenuation curve (TAC) should the CT data be collected and at what tube current(s). We hypothesize that increasing the sampling rate and/or tube current during the time frames when the myocardial CT number is most sensitive to the flow rate, while reducing them elsewhere, can achieve better estimation accuracy for the same dose. We perform simulations of contrast agent kinetics and CT acquisitions to evaluate the relative MBF estimation performance of several clinically viable adaptive acquisition methods. We found that adaptive temporal and tube current sequences can be performed that impart an effective dose of about 5 mSv and allow for reductions in MBF estimation RMSE on the order of 11% compared to uniform acquisition sequences with comparable or higher radiation doses.

Evaluation of radiation dose reduction via myocardial frame reduction in dynamic cardiac CT for perfusion quantitation

Dynamic contrast enhanced cardiac CT acquisitions can quantify myocardial blood flow (MBF) in absolute units (ml/min/g), but repeated scans increase X-ray radiation dose to the patient. We propose a novel approach using high temporal sampling of the input function with reduced temporal sampling of the myocardial tissue response. This type of data could be acquired with current bolus tracking acquisitions or with new acquisition sequences offering reduced radiation dose and potentially easier data processing and flow estimation. To evaluate this type of data, we prospectively acquired a full dynamic series [12 -18 frames (mean 14.5±1.4) over 23 to 44 seconds (mean 31.3±5.0 sec)] on 28 patients at rest and stress (N=56 studies) and examined the relative performance of myocardial perfusion estimation when the myocardial data is subsampled down to 8, 4, 2 or 1 frame(s). Unlike previous studies, for all frame rates, we consider a well-sampled input function. As expected, subsampling linearly reduces radiation dose while progressively decreasing estimation accuracy, with the typical absolute error in MBF (as compared to the full-length series) increasing from 0.22 to 0.30 to 0.35 to 1.12 ml/min/g as the number of frames used for estimation decreases from 8 to 4 to 2 to 1, respectively. These results suggest that high temporal sampling of the input function with low temporal sampling of the myocardial response can provide much of the benefit of dynamic CT for MBF quantification with dramatic reductions in the required number of myocardial acquisitions and the associated radiation dose (e.g. 77% dose reduction for 2-frame case).

Variable temporal sampling and tube current modulation for myocardial blood flow estimation from dose-reduced dynamic computed tomography

Abstract. Quantification of myocardial blood flow (MBF) can aid in the diagnosis and treatment of coronary artery disease. However, there are no widely accepted clinical methods for estimating MBF. Dynamic cardiac perfusion computed tomography (CT) holds the promise of providing a quick and easy method to measure MBF quantitatively. However, the need for repeated scans can potentially result in a high patient radiation dose, limiting the clinical acceptance of this approach. In our previous work, we explored techniques to reduce the patient dose by either uniformly reducing the tube current or by uniformly reducing the number of temporal frames in the dynamic CT sequence. These dose reduction techniques result in noisy time-attenuation curves (TACs), which can give rise to significant errors in MBF estimation. We seek to investigate whether nonuniformly varying the tube current and/or sampling intervals can yield more accurate MBF estimates for a given dose. Specifically, we try to minimize the dose and obtain the most accurate MBF estimate by addressing the following questions: when in the TAC should the CT data be collected and at what tube current(s)? We hypothesize that increasing the sampling rate and/or tube current during the time frames when the myocardial CT number is most sensitive to the flow rate, while reducing them elsewhere, can achieve better estimation accuracy for the same dose. We perform simulations of contrast agent kinetics and CT acquisitions to evaluate the relative MBF estimation performance of several clinically viable variable acquisition methods. We find that variable temporal and tube current sequences can be performed that impart an effective dose of 5.5 mSv and allow for reductions in MBF estimation root-mean-square error on the order of 20% compared to uniform acquisition sequences with comparable or higher radiation doses.

Performance comparison between static and dynamic cardiac CT on perfusion quantitation and patient classification tasks

Cardiac CT acquisitions for perfusion assessment can be performed in a dynamic or static mode. In this simulation study, we evaluate the relative classification and quantification performance of these modes for assessing myocardial blood flow (MBF). In the dynamic method, a series of low dose cardiac CT acquisitions yields data on contrast bolus dynamics over time; these data are fit with a model to give a quantitative MBF estimate. In the static method, a single CT acquisition is obtained, and the relative CT numbers in the myocardium are used to infer perfusion states. The static method does not directly yield a quantitative estimate of MBF, but these estimates can be roughly approximated by introducing assumed linear relationships between CT number and MBF, consistent with the ways such images are typically visually interpreted. Data obtained by either method may be used for a variety of clinical tasks, including 1) stratifying patients into differing categories of ischemia and 2) using the quantitative MBF estimate directly to evaluate ischemic disease severity. Through simulations, we evaluate the performance on each of these tasks. The dynamic method has very low bias in MBF estimates, making it particularly suitable for quantitative estimation. At matched radiation dose levels, ROC analysis demonstrated that the static method, with its high bias but generally lower variance, has superior performance in stratifying patients, especially for larger patients.

Vectorial total variation denoising for myocardial blood flow estimation in dynamic CT

Dynamic CT could provide a relatively inexpensive and widely available technique to measure myocardial blood flow (MBF) for evaluating coronary artery disease (CAD). The main limitation to clinical acceptance is the substantial radiation dose. The dose can be reduced by decreasing the photon flux or the number of temporal frames, both resulting in noisy 4D data from which accurate MBF information needs to be extracted. In this work, we investigate two novel image domain denoising techniques based on total variation (TV). These methods are possible generalizations of the Rudin Osher Fatemi denoising model to multiple frames of dynamic CT data. Our first method, scalar total variation (STV), considers the sum of total variation of multiple temporal images as the penalty. Our second method, vectorial total variation (VTV) leverages the fact that images reconstructed from different dynamic frames have a common edge structure. We performed simulation studies of low-dose, dynamic cardiac CT acquisitions (25 mAs, 30 frames) of several flow states (flow = 0.5, 1.0, 2.0, 3.0 ml/g/min) and compared the accuracy of MBF estimates obtained from the proposed STV and VTV methods to a sinogram-domain 4D smoothing technique. Through simulation studies, we found that the proposed VTV algorithm gave us the best MBF estimate for 25 mAs tube current. The VTV method is fast and versatile, offering a pragmatic filtering strategy to improve the quality of dynamic 4D CT images. The STV method did not perform as well as the other two denoising methods.

Simulation evaluation of quantitative myocardial perfusion assessment from cardiac CT

Contrast enhancement on cardiac CT provides valuable information about myocardial perfusion and methods have been proposed to assess perfusion with static and dynamic acquisitions. There is a lack of knowledge and consensus on the appropriate approach to ensure 1) sufficient diagnostic accuracy for clinical decisions and 2) low radiation doses for patient safety. This work developed a thorough dynamic CT simulation and several accepted blood flow estimation techniques to evaluate the performance of perfusion assessment across a range of acquisition and estimation scenarios. Cardiac CT acquisitions were simulated for a range of flow states (Flow = 0.5, 1, 2, 3 ml/g/min, cardiac output = 3,5,8 L/min). CT acquisitions were simulated with a validated CT simulator incorporating polyenergetic data acquisition and realistic x-ray flux levels for dynamic acquisitions with a range of scenarios including 1, 2, 3 sec sampling for 30 sec with 25, 70, 140 mAs. Images were generated using conventional image reconstruction with additional image-based beam hardening correction to account for iodine content. Time attenuation curves were extracted for multiple regions around the myocardium and used to estimate flow. In total, 2,700 independent realizations of dynamic sequences were generated and multiple MBF estimation methods were applied to each of these. Evaluation of quantitative kinetic modeling yielded blood flow estimates with an root mean square error (RMSE) of ~0.6 ml/g/min averaged across multiple scenarios. Semi-quantitative modeling and qualitative static imaging resulted in significantly more error (RMSE = ~1.2 and ~1.2 ml/min/g respectively). For quantitative methods, dose reduction through reduced temporal sampling or reduced tube current had comparable impact on the MBF estimate fidelity. On average, half dose acquisitions increased the RMSE of estimates by only 18% suggesting that substantial dose reductions can be employed in the context of quantitative myocardial blood flow estimation. In conclusion, quantitative model-based dynamic cardiac CT perfusion assessment is capable of accurately estimating MBF across a range of cardiac outputs and tissue perfusion states, outperforms comparable static perfusion estimates, and is relatively robust to noise and temporal subsampling.