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Simultaneous Tl-201/Tc-99m dual isotope myocardial perfusion SPECT is seriously hampered by down-scatter from Tc-99m into the Tl-201 energy window. This paper presents and optimises the ordered-subsets-expectation-maximisation-(OS-EM-) based reconstruction algorithm, which corrects the down-scatter using an efficient Monte Carlo (MC) simulator. The algorithm starts by first reconstructing the Tc-99m image with attenuation, collimator response, and MC-based scatter correction. The reconstructed Tc-99m image is then used as an input for an efficient MC-based down-scatter simulation of Tc-99m photons into the Tl-201 window. This down-scatter estimate is finally used in the Tl-201 reconstruction to correct the crosstalk between the two isotopes. The mathematical 4D NCAT phantom and physical cardiac phantoms were used to optimise the number of OS-EM iterations where the scatter estimate is updated and the number of MC simulated photons. The results showed that two scatter update iterations and 105 simulated photons are enough for the Tc-99m and Tl-201 reconstructions, whereas 106 simulated photons are needed to generate good quality down-scatter estimates.With these parameters, the entire Tl-201/Tc-99m dual isotope reconstruction can be accomplished in less than 3 minutes. [ABSTRACT FROM AUTHOR]

Poor resolution of single photon emission computed tomography (SPECT) has degraded its use in clinical practice. Collimator correction has been shown to improve the reconstructed resolution, but the correction can generate ringing artefacts, which lower image quality. This paper investigates whether Bayesian reconstruction methods could reduce these artefacts. We have applied and tested three Bayesian reconstruction methods: smoothing prior, median root prior, and anatomical prior. To demonstrate the efficacy of these methods, we compared their physical and visual performance both in phantom and patient studies. All the three Bayesian reconstruction methods reduced the collimator correction artefacts. Images reconstructed using the smoothing prior and the median root prior had slightly lower contrast than the standard reconstruction with collimator correction, whereas the anatomical prior produced images with good resolution and contrast. [ABSTRACT FROM AUTHOR]

Aims: The present study investigated cerebral glucose metabolism and structural atrophy in controls and subjects with mild cognitive impairment (MCI). Methods: The study included 13 controls, 7 MCI subjects considered as prodromal Alzheimer’s disease (MCI of the Alzheimer type, aMCI) and 7 MCI subjects having cognitive decline due to other causes, established by clinical evaluation (MCI of the non-Alzheimer type, naMCI). Glucose metabolism in the frontal, parietal and posterior cingulate cortices, the hippocampus and parahippocampal gyrus was evaluated using Statistical Parametric Mapping 2 (SPM2). Structural analysis of the whole-brain grey matter was performed with voxel-based morphometry in SPM2. Results: Significant hypometabolism was found in the medial temporal lobe in aMCI subjects compared to the controls and naMCI subjects. In addition, both the aMCI and naMCI patients had hypometabolism of the posterior cingulum relative to controls. The naMCI subjects showed atrophy of frontal and occipital areas compared to controls and aMCI patients, whereas the aMCI subjects did not show atrophy compared to the other groups. Conclusion: aMCI subjects have reduced glucose uptake levels, particularly in areas susceptible to pathological changes in Alzheimer’s disease, and the changes are more pronounced in aMCI than naMCI subjects. Our results also suggest that functional changes may be more prominent than structural changes in MCI. Copyright © 2008 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Objective . The quality of myocardial perfusion SPECT (MPS) images is often hampered by low count statistics. Poor image quality might hinder reporting the studies and in the worst case lead to erroneous diagnosis. Deep learning (DL)-based methods can be used to improve the quality of the low count studies. DL can be applied in several different methods, which might affect the outcome. The aim of this study was to investigate the differences between post reconstruction- and reconstruction-based denoising methods. Approach . A UNET-type network was trained using ordered subsets expectation maximization (OSEM) reconstructed MPS studies acquired with half, quarter and eighth of full-activity. The trained network was applied as a post reconstruction denoiser (OSEM+DL) and it was incorporated into a regularized reconstruction algorithm as a deep learning penalty (DLP). OSEM+DL and DLP were compared against each other and against OSEM images without DL denoising in terms of noise level, myocardium-ventricle contrast and defect detection performance with signal-to-noise ratio of a non-prewhitening matched filter (NPWMF-SNR) applied to artificial perfusion defects inserted into defect-free clinical MPS scans. Comparisons were made using half-, quarter- and eighth-activity data. Main results . OSEM+DL provided lower noise level at all activities than other methods. DLP's noise level was also always lower than matching activity OSEM's. In addition, OSEM+DL and DLP outperformed OSEM in defect detection performance, but contrary to noise level ranking DLP had higher NPWMF-SNR overall than OSEM+DL. The myocardium-ventricle contrast was highest with DLP and lowest with OSEM+DL. Both OSEM+DL and DLP offered better image quality than OSEM, but visually perfusion defects were deeper in OSEM images at low activities. Significance . Both post reconstruction- and reconstruction-based DL denoising methods have great potential for MPS. The preference between these methods is a trade-off between smoother images and better defect detection performance., (© 2023 IOP Publishing Ltd.)