Iterative three-dimensional expectation maximization restoration of single photon emission computed tomography images: Application in striatal imaging

Relation of Brain Perfusion Patterns to Sudden Unexpected Death Risk Stratification: A Study in Drug Resistant Focal Epilepsy

To explore the role of the interictal and ictal SPECT to identity functional neuroimaging biomarkers for SUDEP risk stratification in patients with drug-resistant focal epilepsy (DRFE). Twenty-nine interictal-ictal Single photon emission computed tomography (SPECT) scans were obtained from nine DRFE patients. A methodology for the relative quantification of cerebral blood flow of 74 cortical and sub-cortical structures was employed. The optimal number of clusters (K) was estimated using a modified v-fold cross-validation for the use of K means algorithm. The two regions of interest (ROIs) that represent the hypoperfused and hyperperfused areas were identified. To select the structures related to the SUDEP-7 inventory score, a data mining method that computes an automatic feature selection was used. During the interictal and ictal state, the hyperperfused ROIs in the largest part of patients were the bilateral rectus gyrus, putamen as well as globus pallidus ipsilateral to the seizure onset zone. The hypoperfused ROIs included the red nucleus, substantia nigra, medulla, and entorhinal area. The findings indicated that the nearly invariability in the perfusion pattern during the interictal to ictal transition observed in the ipsi-lateral putamen F = 12.60, p = 0.03, entorhinal area F = 25.80, p = 0.01, and temporal middle gyrus F = 12.60, p = 0.03 is a potential biomarker of SUDEP risk. The results presented in this paper allowed identifying hypo- and hyperperfused brain regions during the ictal and interictal state potentially related to SUDEP risk stratification.

Estimation of radioactivity in single-photon emission computed tomography for sentinel lymph node biopsy in a torso phantom study

OBJECTIVE

The number of lymph nodes to be removed is determined from residual counts. Advance estimation of residual radioactivity in lymphatic nodes before a biopsy is useful for reducing surgical operation time. The purpose of this study was to estimate the total radioactivity of a small hotspot in single-photon emission computed tomography (SPECT) of a torso phantom.

METHODS

A cross-calibration study was performed to convert counts in SPECT images to radioactivity. A simulation study was performed to estimate the size of the volume of interest (VOI) covering a hotspot corrupted with full-width at half-maximum between 8 and 16 mm. The estimation of total radioactivity was validated in a torso phantom study using small sources.

RESULTS

True radioactivity was approximately equal to integrated values of hotspots using the VOI with a diameter of 40 mm in our simulation study. The difference was less than 18% in cases of more than 9.4 kBq.

CONCLUSION

The total radioactivity in small sources simulating a typical sentinel node was estimated from SPECT images using a VOI of 40 mm in a torso phantom study. Because the difference from actual values was less than 10% on average when radioactivities were more than 9.4 kBq, the total radioactivity of a lymph node can be estimated in a clinical examination.

Region-Based Partial Volume Correction Techniques for PET Imaging: Sinogram Implementation and Robustness

Background/Purpose. Limited spatial resolution of positron emission tomography (PET) requires partial volume correction (PVC). Region-based PVC methods are based on geometric transfer matrix implemented either in image-space (GTM) or sinogram-space (GTMo), both with similar performance. Although GTMo is slower, it more closely simulates the 3D PET image acquisition, accounts for local variations of point spread function, and can be implemented for iterative reconstructions. A recent image-based symmetric GTM (sGTM) has shown improvement in noise characteristics and robustness to misregistration over GTM. This study implements the sGTM method in sinogram space (sGTMo), validates it, and evaluates its performance. Methods. Two 3D sphere and brain digital phantoms and a physical sphere phantom were used. All four region-based PVC methods (GTM, GTMo, sGTM, and sGTMo) were implemented and their performance was evaluated. Results. All four PVC methods had similar accuracies. Both noise propagation and robustness of the sGTMo method were similar to those of sGTM method while they were better than those of GTMo method especially for smaller objects. Conclusion. The sGTMo was implemented and validated. The performance of the sGTMo in terms of noise characteristics and robustness to misregistration is similar to that of the sGTM method and improved compared to the GTMo method.

Radius dependence of FP-CIT quantification: a Monte Carlo-based simulation study

OBJECTIVE

Dopamine transporter imaging with SPECT is a valuable tool for both clinical routine and research studies. Semi-quantitative analysis plays a key role in interpreting the scans, but is dependent on numerous factors, rotational radius being one of them. This study systematically evaluates the potential influence of radius of rotation on apparent tracer binding and describes methods for correction.

METHODS

Monte Carlo simulation scans of a digital brain phantom with various disease states and various radii of rotation ranging from 13 to 30 cm were analyzed using 4 different methods of semi-quantification. Different volumes of interest as well as a method with partial volume correction were applied.

RESULTS

For conventional 3D semi-quantification methods the decrease of measured striatal binding per cm additional radius rotation lied in the range between 2.5 and 3.1 %, whereas effects were negligible when applying recovery-corrected quantification. Effects were independent of disease state.

CONCLUSION

Partial volume effects with increasing radius of rotation can lead to considerable decrease of measured binding ratios, particularly when applying dopamine transporter imaging in a research setting. Standardization of acquisition radius can avoid the effect; correction seems feasible, but the correction factors depend on the quantification approach applied.

A method for partial volume correction of PET-imaged tumor heterogeneity using expectation maximization with a spatially varying point spread function

Tumor heterogeneities observed in positron emission tomography (PET) imaging are frequently compromised by partial volume effects which may affect treatment prognosis, assessment or future implementations such as biologically optimized treatment planning (dose painting). This paper presents a method for partial volume correction of PET-imaged heterogeneous tumors. A point source was scanned on a GE Discovery LS at positions of increasing radii from the scanner's center to obtain the spatially varying point spread function (PSF). PSF images were fit in three dimensions to Gaussian distributions using least squares optimization. Continuous expressions were devised for each Gaussian width as a function of radial distance, allowing for generation of the system PSF at any position in space. A spatially varying partial volume correction (SV-PVC) technique was developed using expectation maximization (EM) and a stopping criterion based on the method's correction matrix generated for each iteration. The SV-PVC was validated using a standard tumor phantom and a tumor heterogeneity phantom and was applied to a heterogeneous patient tumor. SV-PVC results were compared to results obtained from spatially invariant partial volume correction (SINV-PVC), which used directionally uniform three-dimensional kernels. SV-PVC of the standard tumor phantom increased the maximum observed sphere activity by 55 and 40% for 10 and 13 mm diameter spheres, respectively. Tumor heterogeneity phantom results demonstrated that as net changes in the EM correction matrix decreased below 35%, further iterations improved overall quantitative accuracy by less than 1%. SV-PVC of clinically observed tumors frequently exhibited changes of +/-30% in regions of heterogeneity. The SV-PVC method implemented spatially varying kernel widths and automatically determined the number of iterations for optimal restoration, parameters which are arbitrarily chosen in SINV-PVC. Comparing SV-PVC to SINV-PVC demonstrated that similar results could be reached using both methods, but large differences result for the arbitrary selection of SINV-PVC parameters. The presented SV-PVC method was performed without user intervention, requiring only a tumor mask as input. Research involving PET-imaged tumor heterogeneity should include correcting for partial volume effects to improve the quantitative accuracy of results.

Phantom experiments to improve parathyroid lesion detection

This investigation tested the hypothesis that visual analysis of iteratively reconstructed tomograms by ordered subset expectation maximization (OSEM) provides the highest accuracy for localizing parathyroid lesions using 99mTc-sestamibi SPECT data. From an Institutional Review Board approved retrospective review of 531 patients evaluated for parathyroid localization, image characteristics were determined for 85 99mTc-sestamibi SPECT studies originally read as equivocal (EQ). Seventy-two plexiglas phantoms using cylindrical simulated lesions were acquired for a clinically realistic range of counts (mean simulated lesion counts of 75 +/- 50 counts/pixel) and target-to-background (T:B) ratios (range = 2.0 to 8.0) to determine an optimal filter for OSEM. Two experienced nuclear physicians graded simulated lesions, blinded to whether chambers contained radioactivity or plain water, and two observers used the same scale to read all phantom and clinical SPECT studies, blinded to pathology findings and clinical information. For phantom data and all clinical data, T : B analyses were not statistically different for OSEM versus FB, but visual readings were significantly more accurate than T : B (88 +/- 6% versus 68 +/- 6%, p = 0.001) for OSEM processing, and OSEM was significantly more accurate than FB for visual readings (88 +/- 6% versus 58 +/- 6%, p < 0.0001). These data suggest that visual analysis of iteratively reconstructed MIBI tomograms should be incorporated into imaging protocols performed to localize parathyroid lesions.