IQ·SPECT (Siemens Medical Solutions) is a solution for high-sensitivity and short-time acquisition imaging of the heart for a variable angle general purpose gamma camera. It consists of a multi-focal collimator, a cardio-centric orbit and advanced iterative reconstruction, modeling the image formation physics accurately. The multi-focal collimator enables distance-dependent enlargement of the center region while avoiding truncation at the edges. With the specified configuration and a cardio-centric orbit it can obtain a fourfold sensitivity increase for the heart at the center of the scan orbit. Since IQ·SPECT shows characteristic distribution patterns in the myocardium, appropriate acquisition and processing conditions are required, and normal databases are convenient for quantification of both normal and abnormal perfusion images. The use of prone imaging can be a good option when X-ray computed tomography (CT) is not available for attenuation correction. CT-based attenuation correction changes count distribution significantly in the inferior wall and around the apex, hence image interpretation training and additional use of normal databases are recommended. Recent reports regarding its technology, Japanese Society of Nuclear Medicine working group activities, and clinical studies using ²⁰¹Tl and ^99mTc-perfusion tracers in Japan are summarized.

The IQ·SPECT system was introduced by Siemens in 2010 to significantly improve the efficiency of myocardial perfusion imaging (MPI) using conventional, large field-of-view (FOV) SPECT and SPECT·CT systems. With IQ·SPECT, it is possible to perform MPI scans in one-fourth the time or using one-fourth the administered dose as compared to a standard protocol using parallel-hole collimators. This improvement is achieved by means of a proprietary multifocal collimator that rotates around the patient in a cardio-centric orbit resulting in a four-fold magnification of the heart while keeping the entire torso in the FOV. The data are reconstructed using an advanced reconstruction algorithm that incorporates measured values for gantry deflections, collimator-hole angles, and system point response function. This article explores the boundary conditions of IQ·SPECT imaging, as measured using the Data Spectrum ^® cardiac torso phantom with the cardiac insert. Impact on reconstructed image quality was evaluated for variations in positioning of the myocardium relative to the sweet spot, scan-arc limitations, and for low-dose imaging protocols. Reconstructed image quality was assessed visually using the INVIA 4DMSPECT and quantitatively using Siemens internal IQ assessment software. The results indicated that the IQ·SPECT system is capable of tolerating possible mispositioning of the myocardium relative to the sweet spot by the operator, and that no artifacts are introduced by the limited angle coverage. We also found from the study of multiple low dose protocols that the dwell time will need to be adjusted in order to acquire data with sufficient signal-to-noise ratio for good reconstructed image quality.

We investigated the clinical performance of a quantitative multi-modal SPECT/CT reconstruction platform for yielding radioactivity concentrations of bone imaging with 99mTc-methylene diphosphonate (MDP) or 99mTc-dicarboxypropane diphosphonate (DPD). The novel reconstruction incorporates CT-derived tissue information while preserving the delineation of tissue boundaries. We assessed image-based reader concordance and confidence, and determined lesion classification and SUV thresholds from ROC analysis. Seventy-two cancer patients were scanned at three US and two German clinical sites, each contributing two experienced board-certified nuclear medicine physicians as readers. We compared four variants of the reconstructed data resulting from the Flash3D (F3D) and the xSPECT Bone™ (xB) iterative reconstruction methods and presented images to the readers with and without a fused CT, resulting in four combinations. We used an all-or-none approach for inclusion, compiling results only when a reader completed all reads in a subset. After the final read, we conducted a “surrogate truth” reading, presenting all data to each reader. For any remaining discordant lesions, we conducted a consensus read. We next undertook ROC analysis to determine SUV thresholds for differentiating benign and lesional uptake. On a five-point rating scale of image quality, xB was deemed better by almost two points in resolution and one point better in overall acceptance compared to F3D. The absolute agreement of the rendered decision between the nine readers was significantly higher with CT information either inside the reconstruction (xB, xBCT) or simply through image fusion (F3DCT): 0.70 (xBCT), 0.67 (F3DCT), 0.64 (xB), and 0.46 (F3D). The confidence level to characterize the lesion was significantly higher (3.03x w/o CT, 1.32x w/CT) for xB than for F3D. There was high correlation between xB and F3D scores for lesion detection and classification, but lesion detection confidence was 41% higher w/o CT, and 21% higher w/CT for xB compared to F3D. Without CT, xB had 6.6% higher sensitivity, 7.1% higher specificity, and 6.9% greater AUC compared to F3D, and similarly with CT-fusion. The overall SUV-criterion (SUVc) of xB (12) exceeded that for xSPECT Quant™ (xQ; 9), an approach not using the tissue delineation of xB. SUV critical numbers depended on lesion volume and location. For non-joint lesions > 6 ml, the AUC for xQ and xB was 94%, with SUVc > 9.28 (xQ) or > 9.68 (xB); for non-joint lesions ≤ 6 ml, AUCs were 81% (xQ) and 88% (xB), and SUVc > 8.2 (xQ) or > 9.1 (xB). For joint lesions, the AUC was 80% (xQ) and 83% (xB), with SUVc > 8.61 (xQ) or > 13.4 (xB). The incorporation of high-resolution CT-based tissue delineation in SPECT reconstruction (xSPECT Bone) provides better resolution and detects smaller lesions (6 ml), and the CT component facilitates lesion characterization. Our approach increases confidence, concordance, and accuracy for readers with a wide range of experience. The xB method retained high reading accuracy, despite the unfamiliar image presentation, having greatest impact for smaller lesions, and better localization of foci relative to bone anatomy. The quantitative assessment yielded an SUV-threshold for sensitively distinguishing benign and malignant lesions. Ongoing efforts shall establish clinically usable protocols and SUV thresholds for decision-making based on quantitative SPECT.

CdZnTe-based detectors are highly valued because of their high spectral resolution, which is an essential feature for nuclear medical imaging. However, this resolution is compromised when there are substantial defects in the CdZnTe crystals. In this study, we present a learning-based approach to determine the spatially dependent bulk properties and defects in semiconductor detectors. This characterization allows us to mitigate and compensate for the undesired effects caused by crystal impurities. We tested our model with computer-generated noise-free input data, where it showed excellent accuracy, achieving an average RMSE of 0.43% between the predicted and the ground truth crystal properties. In addition, a sensitivity analysis was performed to determine the effect of noisy data on the accuracy of the model.

The data-driven Pixon noise-reduction method is applied to nuclear studies. By using the local information content, it preserves all statistically justifiable image features without generating artifacts. Statistical measures provide the user a feedback to judge if the processing parameters are optimal. The present work focuses on planar nuclear images with known Poisson noise characteristics. Its ultimate goals are to: (a) increase sensitivity for detection of lesions of small size and/or of small activity-to-background ratio, (b) reduce data acquisition time, and (c) reduce patient dose. Data are acquired using Data Spectrum"s cylinder phantom in two configurations: (a) with hot and cold rod inserts at varying total counts and (b) with hot sphere inserts at varying activity-to-background ratios. We show that the method adapts automatically to both hot and cold lesions, concentration ratios, and different noise levels and structure dimensions. In clinical applications, slight adjustment of the parameters may be needed to adapt to the specific clinical protocols and physician preference. Visually, the processed images are comparable to raw images with ~16 times as many counts, and quantitatively the reduced noise equals that obtained with ~50 times as many counts. We also show that the Pixon method allows for identification of spheres at low concentration ratios, where raw planar imaging fails and matched filtering underperforms. Conclusion: The Pixon method significantly improves the image quality of data at either reduced count levels, or low target-to-background ratios. An analysis of clinical studies is now warranted to assess the clinical impact of the method.

Our study explores the feasibility of quantum computing in emission tomography reconstruction, addressing a noisy ill-conditioned inverse problem. In current clinical practice, this is typically solved by iterative methods minimizing a $L^2$ norm. After reviewing quantum computing principles, we propose the use of a commercially available quantum annealer and employ corresponding hybrid solvers, which combine quantum and classical computing to handle more significant problems. We demonstrate how to frame image reconstruction as a combinatorial optimization problem suited for these quantum annealers and hybrid systems. Using a toy problem, we analyze reconstructions of binary and integer-valued images with respect to their image size and compare them to conventional methods. Additionally, we test our method’s performance under noise and data underdetermination. In summary, our method demonstrates competitive performance with traditional algorithms for binary images up to an image size of $32\times 32$ on the toy problem, even under noisy and underdetermined conditions. However, scalability challenges emerge as image size and pixel bit range increase, restricting hybrid quantum computing as a practical tool for emission tomography reconstruction until significant advancements are made to address this issue.