Towards accurate partial volume correction in 99mTc oncology SPECT: perturbation for case-specific resolution estimation.

BACKGROUND: Currently, there is no consensus on the optimal partial volume correction (PVC) algorithm for oncology imaging. Several existing PVC methods require knowledge of the reconstructed resolution, usually as the point spread function (PSF)-often assumed to be spatially invariant. However, this is not the case for SPECT imaging. This work aimed to assess the accuracy of SPECT quantification when PVC is applied using a case-specific PSF. METHODS: Simulations of SPECT [Formula: see text]Tc imaging were performed for a range of activity distributions, including those replicating typical clinical oncology studies. Gaussian PSFs in reconstructed images were estimated using perturbation with a small point source. Estimates of the PSF were made in situations which could be encountered in a patient study, including; different positions in the field of view, different lesion shapes, sizes and contrasts, noise-free and noisy data. Ground truth images were convolved with the perturbation-estimated PSF, and with a PSF reflecting the resolution at the centre of the field of view. Both were compared with reconstructed images and the root-mean-square error calculated to assess the accuracy of the estimated PSF. PVC was applied using Single Target Correction, incorporating the perturbation-estimated PSF. Corrected regional mean values were assessed for quantitative accuracy. RESULTS: Perturbation-estimated PSF values demonstrated dependence on the position in the Field of View and the number of OSEM iterations. A lower root mean squared error was observed when convolution of the ground truth image was performed with the perturbation-estimated PSF, compared with convolution using a different PSF. Regional mean values following PVC using the perturbation-estimated PSF were more accurate than uncorrected data, or data corrected with PVC using an unsuitable PSF. This was the case for both simple and anthropomorphic phantoms. For the simple phantom, regional mean values were within 0.7% of the ground truth values. Accuracy improved after 5 or more OSEM iterations (10 subsets). For the anthropomorphic phantoms, post-correction regional mean values were within 1.6% of the ground truth values for noise-free uniform lesions. CONCLUSION: Perturbation using a simulated point source could potentially improve quantitative SPECT accuracy via the application of PVC, provided that sufficient reconstruction iterations are used.

Clinical applications of textural analysis in non-small cell lung cancer.

Lung cancer is the leading cause of cancer mortality worldwide. Treatment pathways include regular cross-sectional imaging, generating large data sets which present intriguing possibilities for exploitation beyond standard visual interpretation. This additional data mining has been termed "radiomics" and includes semantic and agnostic approaches. Textural analysis (TA) is an example of the latter, and uses a range of mathematically derived features to describe an image or region of an image. Often TA is used to describe a suspected or known tumour. TA is an attractive tool as large existing image sets can be submitted to diverse techniques for data processing, presentation, interpretation and hypothesis testing with annotated clinical outcomes. There is a growing anthology of published data using different TA techniques to differentiate between benign and malignant lung nodules, differentiate tissue subtypes of lung cancer, prognosticate and predict outcome and treatment response, as well as predict treatment side effects and potentially aid radiotherapy planning. The aim of this systematic review is to summarize the current published data and understand the potential future role of TA in managing lung cancer.

Performance of a high-sensitivity dedicated cardiac SPECT scanner for striatal uptake quantification in the brain based on analysis of projection data.

PURPOSE: The authors have previously reported the advantages of high-sensitivity single-photon emission computed tomography (SPECT) systems for imaging structures located deep inside the brain. DaTscan (Isoflupane I-123) is a dopamine transporter (DaT) imaging agent that has shown potential for early detection of Parkinson disease (PD), as well as for monitoring progression of the disease. Realizing the full potential of DaTscan requires efficient estimation of striatal uptake from SPECT images. They have evaluated two SPECT systems, a conventional dual-head gamma camera with low-energy high-resolution collimators (conventional) and a dedicated high-sensitivity multidetector cardiac imaging system (dedicated) for imaging tasks related to PD. METHODS: Cramer-Rao bounds (CRB) on precision of estimates of striatal and background activity concentrations were calculated from high-count, separate acquisitions of the compartments (right striata, left striata, background) of a striatal phantom. CRB on striatal and background activity concentration were calculated from essentially noise-free projection datasets, synthesized by scaling and summing the compartment projection datasets, for a range of total detected counts. They also calculated variances of estimates of specific-to-nonspecific binding ratios (BR) and asymmetry indices from these values using propagation of error analysis, as well as the precision of measuring changes in BR on the order of the average annual decline in early PD. RESULTS: Under typical clinical conditions, the conventional camera detected 2 M counts while the dedicated camera detected 12 M counts. Assuming a normal BR of 5, the standard deviation of BR estimates was 0.042 and 0.021 for the conventional and dedicated system, respectively. For an 8% decrease to BR = 4.6, the signal-to-noise ratio were 6.8 (conventional) and 13.3 (dedicated); for a 5% decrease, they were 4.2 (conventional) and 8.3 (dedicated). CONCLUSIONS: This implies that PD can be detected earlier with the dedicated system than with the conventional system; therefore, earlier identification of PD progression should be possible with the high-sensitivity dedicated SPECT camera.

Evaluation of a method for projection-based tissue-activity estimation within small volumes of interest.

A new method of compensating for tissue-fraction and count-spillover effects, which require tissue segmentation only within a small volume surrounding the primary lesion of interest, was evaluated for SPECT imaging. Tissue-activity concentration estimates are obtained by fitting the measured projection data to a statistical model of the segmented tissue projections. Multiple realizations of two simulated human-torso phantoms, each containing 20 spherical 'tumours', 1.6 cm in diameter, with tumour-to-background ratios of 8:1 and 4:1, were simulated. Estimates of tumour- and background-activity concentration values for homogeneous as well as inhomogeneous tissue activities were compared to the standard uptake value (SUV) metrics on the basis of accuracy and precision. For perfectly registered, high-contrast, superficial lesions in a homogeneous background without scatter, the method yielded accurate (<0.4% bias) and precise (<6.1%) recovery of the simulated activity values, significantly outperforming the SUV metrics. Tissue inhomogeneities, greater tumour depth and lower contrast ratios degraded precision (up to 11.7%), but the estimates remained almost unbiased. The method was comparable in accuracy but more precise than a well-established matrix inversion approach, even when errors in tumour size and position were introduced to simulate moderate inaccuracies in segmentation and image registration. Photon scatter in the object did not significantly affect the accuracy or precision of the estimates.

Improved regional activity quantitation in nuclear medicine using a new approach to correct for tissue partial volume and spillover effects.

We have developed a new method of compensating for effects of partial volume and spillover in dual-modality imaging. The approach requires segmentation of just a few tissue types within a small volume-of-interest (VOI) surrounding a lesion; the algorithm estimates simultaneously, from projection data, the activity concentration within each segmented tissue inside the VOI. Measured emission projections were fitted to the sum of resolution-blurred projections of each such tissue, scaled by its unknown activity concentration, plus a global background contribution obtained by reprojection through the reconstructed image volume outside the VOI. The method was evaluated using multiple-pinhole µSPECT data simulated for the MOBY mouse phantom containing two spherical lung tumors and one liver tumor, as well as using multiple-bead phantom data acquired on µSPECT and µCT scanners. Each VOI in the simulation study was 4.8 mm (12 voxels) cubed and, depending on location, contained up to four tissues (tumor, liver, heart, lung) with different values of relative (99m)Tc concentration. All tumor activity estimates achieved bias after ∼ 15 ordered-subsets expectation maximization (OSEM) iterations (×10 subsets) , with better than 8% precision ( ≤ 25% greater than the Cramer-Rao lower bound). The projection-based fitting approach also outperformed three standardized uptake value (SUV)-like metrics, one of which was corrected for count spillover. In the bead phantom experiment, the mean ± standard deviation of the bias of VOI estimates of bead concentration were 0.9±9.5%, comparable to those of a perturbation geometric transfer matrix (pGTM) approach (-5.4±8.6%); however, VOI estimates were more stable with increasing iteration number than pGTM estimates, even in the presence of substantial axial misalignment between µCT and µSPECT image volumes.

A novel method for incorporating respiratory-matched attenuation correction in the motion correction of cardiac PET-CT studies.

Mismatches between PET and CT datasets due to respiratory effects can lead to artefactual perfusion defects. To overcome this, we have proposed a method of aligning a single CT with each frame of a gated PET study in a semi-automatic manner, incorporating a statistical shape model of the diaphragm and a rigid registration of the heart. This ensures that the structures that could influence the appearance of the reconstructed cardiac activity are correctly matched between emission and transmission datasets. When tested on two patient studies, it was found in both cases that attenuation correction using the proposed technique resulted in PET images that were closer to the gold standard of attenuation correction with a gated CT, compared with scenarios where only heart matching was considered (and not the diaphragm) or where no transformation was performed (i.e. where a single CT frame was used to attenuation-correct all PET frames). These preliminary results suggest that diaphragm matching between PET and CT improves the quantitative accuracy of reconstructed PET images and that the proposed method of using a statistical shape model to describe the diaphragm shape and motion, in combination with a rigid registration to determine respiratory-induced heart motion, is a feasible method of achieving this.

Joint optimization of collimator and reconstruction parameters in SPECT imaging for lesion quantification.

Obtaining the best possible task performance using reconstructed SPECT images requires optimization of both the collimator and reconstruction parameters. The goal of this study is to determine how to perform this optimization, namely whether the collimator parameters can be optimized solely from projection data, or whether reconstruction parameters should also be considered. In order to answer this question, and to determine the optimal collimation, a digital phantom representing a human torso with 16 mm diameter hot lesions (activity ratio 8:1) was generated and used to simulate clinical SPECT studies with parallel-hole collimation. Two approaches to optimizing the SPECT system were then compared in a lesion quantification task: sequential optimization, where collimation was optimized on projection data using the Cramer­Rao bound, and joint optimization, which simultaneously optimized collimator and reconstruction parameters. For every condition, quantification performance in reconstructed images was evaluated using the root-mean-squared-error of 400 estimates of lesion activity. Compared to the joint-optimization approach, the sequential-optimization approach favoured a poorer resolution collimator, which, under some conditions, resulted in sub-optimal estimation performance. This implies that inclusion of the reconstruction parameters in the optimization procedure is important in obtaining the best possible task performance; in this study, this was achieved with a collimator resolution similar to that of a general-purpose (LEGP) collimator. This collimator was found to outperform the more commonly used high-resolution (LEHR) collimator, in agreement with other task-based studies, using both quantification and detection tasks.

Sources of attenuation-correction artefacts in cardiac PET/CT and SPECT/CT.

PURPOSE: Respiratory motion during myocardial perfusion imaging can cause artefacts in both positron emission tomography (PET) and single-photon emission computed tomography (SPECT) images when mismatches between emission and transmission datasets arise. In this study, artefacts from different breathing motions were quantified in both modalities to assess key factors in attenuation-correction accuracy. METHODS: Activity maps were generated using the NURBS-based cardiac-torso phantom for different respiratory cycles, which were projected, attenuation-corrected and reconstructed to form PET and SPECT images. Attenuation-correction was performed with maps at mismatched respiratory phases to observe the effect on the left-ventricular myocardium. Myocardial non-uniformity was assessed in terms of the standard deviation in scores obtained from the 17-segment model and changes in uniformity were compared for each mismatch and modality. RESULTS: Certain types of mismatch led to artefacts and corresponding increases in the myocardial non-uniformity. For each mismatch in PET, the increases in non-uniformity relative to an artefact-free image were as follows: (a) cardiac translation mismatch, 84% +/- 11%; (b) liver mismatch, 59% +/- 10%, (c) lung mismatch from diaphragm contraction, 28% +/- 8%; and (d) lung mismatch from chest-wall motion, 6% +/- 7%. The corresponding factors for SPECT were (a) 61% +/- 8%, (b) 34% +/- 8%, (c) -2% +/- 7)% and (d) -4% +/- 6%. CONCLUSIONS: Attenuation-correction artefacts were seen in PET and SPECT images, with PET being more severely affected. The most severe artefacts were produced from mismatches in cardiac and liver position, whereas lung mismatches were less critical. Both cardiac and liver positions must, therefore, be correctly matched during attenuation correction.