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For high-sensitivity brain imaging, we have developed a two-head single-photon emission computed tomography (SPECT) system using a CdTe semiconductor detector and 4-pixel matched collimator (4-PMC). The term, '4-PMC' indicates that the collimator hole size is matched to a 2 × 2 array of detector pixels. By contrast, a 1-pixel matched collimator (1-PMC) is defined as a collimator whose hole size is matched to one detector pixel. The performance of the higher-sensitivity 4-PMC was experimentally compared with that of the 1-PMC. The sensitivities of the 1-PMC and 4-PMC were 70 cps/MBq/head and 220 cps/MBq/head, respectively. The SPECT system using the 4-PMC provides superior image resolution in cold and hot rods phantom with the same activity and scan time to that of the 1-PMC. In addition, with half the usual scan time the 4-PMC provides comparable image quality to that of the 1-PMC. Furthermore, (99m)Tc-ECD brain perfusion images of healthy volunteers obtained using the 4-PMC demonstrated acceptable image quality for clinical diagnosis. In conclusion, our CdTe SPECT system equipped with the higher-sensitivity 4-PMC can provide better spatial resolution than the 1-PMC either in half the imaging time with the same administered activity, or alternatively, in the same imaging time with half the activity.

An autoradiography method revealed intratumoral inhomogeneity in various solid tumors. It is becoming increasingly important to estimate intratumoral inhomogeneity. However, with low spatial resolution and high scatter noise, it is difficult to detect intratumoral inhomogeneity in clinical settings. We developed a new PET system with CdTe semiconductor detectors to provide images with high spatial resolution and low scatter noise. Both phantom images and patients' images were analyzed to evaluate intratumoral inhomogeneity. Methods: This study was performed with a cold spot phantom that had 6-mm-diameter cold sphenoid defects, a dual-cylinder phantom with an adjusted concentration of 1:2, and an ''H''-shaped hot phantom. These were surrounded with water. Phantom images and 18 F-FDG PET images of patients with nasopharyngeal cancer were compared with conventional bismuth germanate PET images. Profile curves for the phantoms were measured as peak-to-valley ratios to define contrast. Intratumoral inhomogeneity and tumor edge sharpness were evaluated on the images of the patients. Results: The contrast obtained with the semiconductor PET scanner (1.53) was 28% higher than that obtained with the conventional scanner (1.20) for the 6-mm-diameter cold sphenoid phantom. The contrast obtained with the semiconductor PET scanner (1.43) was 27% higher than that obtained with the conventional scanner (1.13) for the dual-cylinder phantom. Similarly, the 2-mm cold region between 1-mm hot rods was identified only by the new PET scanner and not by the conventional scanner. The new PET scanner identified intratumoral inhomogeneity in more detail than the conventional scanner in 6 of 10 patients. The tumor edge was sharper on the images obtained with the new PET scanner than on those obtained with the conventional scanner. Conclusion: These phantom and clinical studies suggested that this new PET scanner has the potential for better identification of intratumoral inhomogeneity, probably because of its high spatial resolution and low scatter noise. PET with 18 F-FDG has been widely used in oncology studies. A high-resolution PET camera permits precise evaluation of tumor localization and treatment effects. Recently, CdTe semiconductors were used for the direct conversion of g-rays without scintillator material (1,2). High energy resolution and flexibility in both the sizing and the fine arrangement of detectors are expected to improve image quality. These characteristics of semiconductor detectors may also lead to improved PET images because the high energy resolution offers a reduction in scatter noise, like that seen with g-camera and SPECT applications (1,3).A depth-of-interaction (DOI) detection system has already been used in some PET scanners, particularly in PET scanners dedicated to use for the human brain. This is because DOI information is very useful for reducing the parallax errors at the periphery of the field of view (FOV) (4,5). With both semiconductor detectors and a DOI system, high-quality PET images with low scat...

We propose a wide aperture parallel-hole collimator that we call a 4-pixel matched collimator (4-PMC) for high-sensitivity SPECT imaging. The hole size of the 4-PMC is matched to four detector pixels; that is, there are four (2 × 2) pixels per collimator hole. By contrast, a 1-pixel matched collimator (1-PMC) is defined as a collimator whose hole size is matched to one detector pixel. We evaluated four types of collimator (high-resolution collimator versions and high-sensitivity collimator versions of both 4-PMC and 1-PMC) by simulation. SPECT images of a cylindrical phantom with cold spots in the noise-free condition demonstrated that the 4-PMC provided a higher-contrast image than the 1-PMC for the same collimator version. In addition, SPECT images at the noise level corresponding to a human cerebral blood flow study suggested that the high-sensitivity version of the 4-PMC provided the highest contrast image among the four collimator types. In conclusion, the high-sensitivity SPECT system using the 4-PMC can improve the trade-off between spatial resolution and sensitivity and will consequently provide improved image contrast for clinical studies of the human brain compared with the SPECT system using the 1-PMC.

Objectives Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI. Methods Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results. Results The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah. Conclusion A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.