Hassan Mohy-ud-Din

Quantitative myocardial perfusion PET parametric imaging at the voxel-level.

Quantitative myocardial perfusion (MP) PET has the potential to enhance detection of early stages of atherosclerosis or microvascular dysfunction, characterization of flow-limiting effects of coronary artery disease (CAD), and identification of balanced reduction of flow due to multivessel stenosis. We aim to enable quantitative MP-PET at the individual voxel level, which has the potential to allow enhanced visualization and quantification of myocardial blood flow (MBF) and flow reserve (MFR) as computed from uptake parametric images. This framework is especially challenging for the (82)Rb radiotracer. The short half-life enables fast serial imaging and high patient throughput; yet, the acquired dynamic PET images suffer from high noise-levels introducing large variability in uptake parametric images and, therefore, in the estimates of MBF and MFR. Robust estimation requires substantial post-smoothing of noisy data, degrading valuable functional information of physiological and pathological importance. We present a feasible and robust approach to generate parametric images at the voxel-level that substantially reduces noise without significant loss of spatial resolution. The proposed methodology, denoted physiological clustering, makes use of the functional similarity of voxels to penalize deviation of voxel kinetics from physiological partners. The results were validated using extensive simulations (with transmural and non-transmural perfusion defects) and clinical studies. Compared to post-smoothing, physiological clustering depicted enhanced quantitative noise versus bias performance as well as superior recovery of perfusion defects (as quantified by CNR) with minimal increase in bias. Overall, parametric images obtained from the proposed methodology were robust in the presence of high-noise levels as manifested in the voxel time-activity-curves.

Generalized inter-frame and intra-frame motion correction in PET imaging - a simulation study

High resolution PET imaging is severely hampered by patient motion. Frame-acquired PET images suffer from inter-frame and intra-frame motion artifacts degrading image quality. The method of Multiple Acquisition Frames (MAF) [1] corrects inter-frame motion artifacts by removing average motion from all the independently reconstructed frames. The drawback is that a high motion threshold neglects considerable intra-frame motion and a low motion results in acquisition of low-statistic frames, thereby, degrading image quality. Increasing the number of frames proportionally increases reconstruction times [2].

Towards continualized task-based resolution modeling in PET imaging

We propose a generalized resolution modeling (RM) framework, including extensive task-based optimization, wherein we continualize the conventionally discrete framework of RM vs. no RM, to include varying degrees of RM. The proposed framework has the advantage of providing a trade-off between the enhanced contrast recovery by RM and the reduced inter-voxel correlations in the absence of RM, and to enable improved task performance. The investigated context was that of oncologic lung FDG PET imaging. Given a realistic blurring kernel of FWHM h (‘true PSF’), we performed iterative EM including RM using a wide range of ‘modeled PSF’ kernels with varying widths h. In our simulations, h = 6mm, while h varied from 0 (no RM) to 12mm, thus considering both underestimation and overestimation of the true PSF. Detection task performance was performed using prewhitened (PWMF) and nonprewhitened matched filter (NPWMF) observers. It was demonstrated that an underestimated resolution blur (h = 4mm) enhanced task performance, while slight over-estimation (h = 7mm) also achieved enhanced performance. The latter is ironically attributed to the presence of ringing artifacts. Nonetheless, in the case of the NPWMF, the increasing intervoxel correlations with increasing values of h degrade detection task performance, and underestimation of the true PSF provides the optimal task performance. The proposed framework also achieves significant improvement of reproducibility, which is critical in quantitative imaging tasks such as treatment response monitoring.

Parametric Myocardial Perfusion PET Imaging using Physiological Clustering

We propose a novel framework of robust kinetic parameter estimation applied to absolute ow quanti cation in dynamic PET imaging. Kinetic parameter estimation is formulated as a nonlinear least squares with spatial constraints problem (NLLS-SC) where the spatial constraints are computed from a physiologically driven clustering of dynamic images, and used to reduce noise contamination. An ideal clustering of dynamic images depends on the underlying physiology of functional regions, and in turn, physiological processes are quanti ed by kinetic parameter estimation. Physiologically driven clustering of dynamic images is performed using a clustering algorithm (e.g. K-means, Spectral Clustering etc) with Kinetic modeling in an iterative handshaking fashion. This gives a map of labels where each functionally homogenous cluster is represented by mean kinetics (cluster centroid). Parametric images are acquired by solving the NLLS-SC problem for each voxel which penalizes spatial variations from its mean kinetics. This substantially reduces noise in the estimation process for each voxel by utilizing kinetic information from physiologically similar voxels (cluster members). Resolution degradation is also substantially minimized as no spatial smoothing between heterogeneous functional regions is performed. The proposed framework is shown to improve the quantitative accuracy of Myocardial Perfusion (MP) PET imaging, and in turn, has the long-term potential to enhance capabilities of MP PET in the detection, staging and management of coronary artery disease.

Enhanced whole-body PET parametric imaging using hybrid regression and thresholding driven by kinetic correlations

Whole body PET/CT, a well established imaging method in nuclear medicine for the clinical evaluation of a wide variety of metastatic cancer malignancies, commonly involves static scanning over multiple beds. Recently, we proposed a clinically feasible transition of whole-body PET/CT imaging to the dynamic domain, by acquiring (i) an initial 6min dynamic scan over the heart, followed by (ii) an optimized sequence of whole-body PET scans, allowing for quantitative whole body parametric imaging. Comparative evaluation of parametric and SUV images indicated enhanced contrast-to-noise ratio (CNR) but also higher noise for the parametric images. The objective of this study is to further improve parametric image CNR to enhance tumor detectability, by limiting noise in the estimates, while enhancing contrast and quantitative accuracy of parametric images. For this purpose, we utilize the weighted correlation coefficient (WR) of the kinetic model (Patlak) fits at each voxel to determine the cluster of voxels, where (i) advanced, as opposed to conventional, statistical parameter estimation, (ii) spatial smoothing or (iii) thresholding is applied. Thus, we facilitate the integration of whole body parametric imaging into the clinic as a competitive alternative to SUV. Through quantitative analysis on selected tumor regions of the resulting images, we show enhanced CNR when ridge regression is applied only to voxels associated with high WR, while ordinary least squares (OLS) and WR driven post-smoothing is performed to the rest. This hybrid regression method yields reduced mean squared error in tumor regions, compared to OLS. In addition, by setting the WR threshold level in the range [0.85 0.9], CNR is further enhanced for tumor regions of high WR. Finally, for the same type of tumors, hybrid regression also achieves higher CNR, compared to SUV, when the last two dynamic frames are omitted, allowing for shorter acquisition times.

Generalized dynamic PET inter-frame and intra-frame motion correction - Phantom and human validation studies

Patient motion can significantly hamper the high-resolution imaging capability of PET scanners. Frame-acquired (dynamic) PET images are degraded by inter-frame and intraframe motion artifacts that can degrade the quantitative and qualitative analysis of acquired PET data. This calls for appropriate motion-correction techniques that can considerably reduce (ideally eliminate) inter-frame and intra-frame motion artifacts in dynamic PET images. We present a novel approach called Generalized Inter-frame and Intra-frame Motion Correction (GIIMC) algorithm [1] that unifies in one framework the inter-frame motion correction capability of Multiple Acquisition Frames and the intra-frame motion correction feature of (MLEM)-type Deconvolution methods. Our method employs a fairly simple but new approach of using time-weighted average of attenuation sinograms to reconstruct individual (dynamic) frames. We also provide a mean-motion threshold for individual frames to construct a framing sequence.

Generalized PSF modeling for optimized quantitation in PET imaging

Point-spread function (PSF) modeling offers the ability to account for resolution degrading phenomena within the PET image generation framework. PSF modeling improves resolution and enhances contrast, but at the same time significantly alters image noise properties and induces edge overshoot effect. Thus, studying the effect of PSF modeling on quantitation task performance can be very important. Frameworks explored in the past involved a dichotomy of PSF versus no-PSF modeling. By contrast, the present work focuses on quantitative performance evaluation of standard uptake value (SUV) PET images, while incorporating a wide spectrum of PSF models, including those that under- and over-estimate the true PSF, for the potential of enhanced quantitation of SUVs. The developed framework first analytically models the true PSF, considering a range of resolution degradation phenomena (including photon non-collinearity, inter-crystal penetration and scattering) as present in data acquisitions with modern commercial PET systems. In the context of oncologic liver FDG PET imaging, we generated 200 noisy datasets per image-set (with clinically realistic noise levels) using an XCAT anthropomorphic phantom with liver tumours of varying sizes. These were subsequently reconstructed using the OS-EM algorithm with varying PSF modelled kernels. We focused on quantitation of both SUVmean and SUVmax, including assessment of contrast recovery coefficients, as well as noise-bias characteristics (including both image roughness and coefficient of-variability), for different tumours/iterations/PSF kernels. It was observed that overestimated PSF yielded more accurate contrast recovery for a range of tumours, and typically improved quantitative performance. For a clinically reasonable number of iterations, edge enhancement due to PSF modeling (especially due to over-estimated PSF) was in fact seen to lower SUVmean bias in small tumours. Overall, the results indicate that exactly matched PSF modeling does not offer optimized PET quantitation, and that PSF overestimation may provide enhanced SUV quantitation. Furthermore, generalized PSF modeling may provide a valuable approach for quantitative tasks such as treatment-response assessment and prognostication.

Enhancement of dynamic myocardial perfusion PET images based on low-rank plus sparse decomposition

BACKGROUND AND OBJECTIVE The absolute quantification of dynamic myocardial perfusion (MP) PET imaging is challenged by the limited spatial resolution of individual frame images due to division of the data into shorter frames. This study aims to develop a method for restoration and enhancement of dynamic PET images. METHODS We propose that the image restoration model should be based on multiple constraints rather than a single constraint, given the fact that the image characteristic is hardly described by a single constraint alone. At the same time, it may be possible, but not optimal, to regularize the image with multiple constraints simultaneously. Fortunately, MP PET images can be decomposed into a superposition of background vs. dynamic components via low-rank plus sparse (L + S) decomposition. Thus, we propose an L + S decomposition based MP PET image restoration model and express it as a convex optimization problem. An iterative soft thresholding algorithm was developed to solve the problem. Using realistic dynamic 82Rb MP PET scan data, we optimized and compared its performance with other restoration methods. RESULTS The proposed method resulted in substantial visual as well as quantitative accuracy improvements in terms of noise versus bias performance, as demonstrated in extensive 82Rb MP PET simulations. In particular, the myocardium defect in the MP PET images had improved visual as well as contrast versus noise tradeoff. The proposed algorithm was also applied on an 8-min clinical cardiac 82Rb MP PET study performed on the GE Discovery PET/CT, and demonstrated improved quantitative accuracy (CNR and SNR) compared to other algorithms. CONCLUSIONS The proposed method is effective for restoration and enhancement of dynamic PET images.

Fully automatic multi-atlas segmentation of CTA for partial volume correction in cardiac SPECT/CT

Anatomical-based partial volume correction (PVC) has been shown to improve image quality and quantitative accuracy in cardiac SPECT/CT. However, this method requires manual segmentation of various organs from contrast-enhanced computed tomography angiography (CTA) data. In order to achieve fully automatic CTA segmentation for clinical translation, we investigated the most common multi-atlas segmentation methods. We also modified the multi-atlas segmentation method by introducing a novel label fusion algorithm for multiple organ segmentation to eliminate overlap and gap voxels. To evaluate our proposed automatic segmentation, eight canine 99mTc-labeled red blood cell SPECT/CT datasets that incorporated PVC were analyzed, using the leave-one-out approach. The Dice similarity coefficient of each organ was computed. Compared to the conventional label fusion method, our proposed label fusion method effectively eliminated gaps and overlaps and improved the CTA segmentation accuracy. The anatomical-based PVC of cardiac SPECT images with automatic multi-atlas segmentation provided consistent image quality and quantitative estimation of intramyocardial blood volume, as compared to those derived using manual segmentation. In conclusion, our proposed automatic multi-atlas segmentation method of CTAs is feasible, practical, and facilitates anatomical-based PVC of cardiac SPECT/CT images.

A blind deconvolution method incorporated with anatomical‐based filtering for partial volume correction: Validations with 123I‐mIBG cardiac SPECT/CT

Purpose: Segmentation of contrast‐enhanced CT and measurement of SPECT point spread function (PSF) are usually required for conventional partial volume correction (PVC). This study was to develop a segmentation‐free method with blind deconvolution (BD) and anatomical‐based filtering for SPECT PVC. Methods: The proposed method was implemented using an iterative BD algorithm to estimate the restored image and the PSF simultaneously. An anatomical‐based filtering was implemented at each iteration to reduce Gibbs artifact and suppress noise amplification in the deconvolution process. The proposed method was validated with 123I‐metaiodobenzylguanidine (123I‐mIBG) SPECT/CT imaging of NCAT phantoms with and without myocardial perfusion defect and a physical cardiac phantom. Fifteen heart‐to‐mediastinum ratios (HMRs) were configured in the NCAT and physical phantoms. Correlations between SPECT‐quantified and true HMRs were calculated from images without PVC as well as from BD restored images. The proposed method was also performed on a human 123I‐mIBG study. Results: Relative bias and standard deviation images of NCAT phantoms showed that the proposed method reduced both bias and noise. Mean relative bias in the simulated normal myocardium was markedly improved (−16.8% ± 0.4% versus −0.8% ± 0.6% for low noise level; −16.7% ± 0.7% versus −2.3% ± 0.9% for high noise level). Mean relative bias in the simulated myocardial defect was also noticeably improved (−12.7% ± 1.2% versus 1.2% ± 1.6% for low noise level; −13.5% ± 2.4% versus −0.9% ± 2.8% for high noise level). The signal to noise ratio (SNR) of the defect was improved from 2.95 ± 0.09 to 4.07 ± 0.16 for low noise level (38% increase of mean), and from 2.56 ± 0.15 to 3.62 ± 0.22 for high noise level (41% increase of mean). For both NCAT and physical phantoms, HMRs calculated from images without PVC were underestimated (correlations between SPECT‐quantified and true HMRs: y = 0.81x + 0.1 for NCAT phantom; y = 0.82x + 0.14 for physical phantom). HMRs from BD restored images were markedly improved (correlations between SPECT‐quantified and true HMRs: y = x + 0.05 for NCAT phantom; y = 0.97x − 0.12 for physical phantom). After applying the proposed PVC method, the estimation error between the SPECT‐quantified and true HMRs was significantly reduced from −0.75 ± 0.57 to 0.04 ± 0.17 for NCAT phantom (P = 8e‐05), and from −0.68 ± 0.67 to −0.26 ± 0.42 for physical phantom (P = 0.005). The human study demonstrated that the HMR increased by 8% with PVC. Conclusions: The proposed segmentation‐free PVC method has the potential of improving SPECT quantification accuracy and reducing noise without the need for premeasuring the image PSF.