Myocardial defect detection using PET-CT: phantom studies

The impact of the air pollution on health in New York City

New York City is attempting to find a solution to an issue that many states and cities face: how to minimize air pollution so that it has fewer negative impacts on human health. Despite having the highest population in the United States (US), New York City typically has reasonably clean air. As the City and State of New York have worked to reduce emissions from local and regional sources, the air quality in New York City has improved during the past few decades. Despite these advancements, air pollution still poses a severe hazard to the health of everyone living in New York's environment. Various diseases including respiratory, circulatory, neurological, gastrointestinal, and urinary illnesses, which can be fatal, are intimately associated with air pollution. This review article will concentrate on how air pollution affects respiratory diseases such as asthma in children. In addition to analyzing the severe effects of air pollution on the vulnerable population, this review article will highlight the health repercussions of air pollution on New York City and its residents. furthermore, we argue for potential ideas and discoveries while also putting up a policy option to lower air pollution.

Improved myocardial perfusion PET imaging using artificial neural networks

Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of patients with coronary artery disease. In this work, we proposed a patch-based artificial neural network (ANN) fusion approach that integrates information from the ML and the post-smoothed ML reconstruction to improve MP PET imaging. The proposed method was applied to images reconstructed from different noise levels to enhance quantification and task-based MP defect detection. Using the XCAT phantom, we simulated three MP PET imaging cases, one with normal perfusion and the other two with non-transmural and transmural regionally reduced perfusion of the left ventricular (LV) myocardium. The proposed ANN fusion technique was quantitatively evaluated in terms of the noise versus bias and noise versus contrast tradeoff, and compared with the post-smoothed ML reconstruction. Using the channelized Hotelling observer, we evaluated the detectability of the non-transmural and transmural defects through the receiver operating characteristic analysis. The quantitative results demonstrated that the ANN enhancement method reduced bias and improved contrast while reaching comparable noise to what the post-smoothed ML reconstruction achieved. Moreover, the ANN fusion technique significantly improved the defect detectability of both the non-transmural and transmural defects. In addition to the simulation study, we further evaluated the proposed method using patient data. Compared with the post-smoothed ML reconstruction, the ANN fusion improved the tradeoff between noise and the mean value on the LV myocardium, indicating its potential clinical application in MP PET imaging.

Human and model observer performance for lesion detection in breast cone beam CT images with the FDK reconstruction

We investigate the detectability of breast cone beam computed tomography images using human and model observers and the variations of exponent, β, of the inverse power-law spectrum for various reconstruction filters and interpolation methods in the Feldkamp-Davis-Kress (FDK) reconstruction. Using computer simulation, a breast volume with a 50% volume glandular fraction and a 2mm diameter lesion are generated and projection data are acquired. In the FDK reconstruction, projection data are apodized using one of three reconstruction filters; Hanning, Shepp-Logan, or Ram-Lak, and back-projection is performed with and without Fourier interpolation. We conduct signal-known-exactly and background-known-statistically detection tasks. Detectability is evaluated by human observers and their performance is compared with anthropomorphic model observers (a non-prewhitening observer with eye filter (NPWE) and a channelized Hotelling observer with either Gabor channels or dense difference-of-Gaussian channels). Our results show that the NPWE observer with a peak frequency of 7cyc/degree attains the best correlation with human observers for the various reconstruction filters and interpolation methods. We also discover that breast images with smaller β do not yield higher detectability in the presence of quantum noise.

Comparative analysis of iterative reconstruction algorithms with resolution recovery and time of flight modeling for 18F-FDG cardiac PET: A multi-center phantom study

BACKGROUND

The purpose of this study was to evaluate the image quality in cardiac 18F-FDG PET using the time of flight (TOF) and/or point spread function (PSF) modeling in the iterative reconstruction (IR).

METHODS

Three scanners and an anthropomorphic cardiac phantom with an insert simulating a transmural defect (TD) were used. Two sets of scans (with/without TD) were acquired, and four reconstruction schemes were considered: (1) IR; (2) IR + PSF, (3) IR + TOF, and (4) IR + TOF + PSF. LV wall thickness (FWHM), contrast between LV wall and inner chamber (C IC), and TD contrast in LV wall (C TD) were evaluated.

RESULTS

Tests of the reconstruction protocols showed a decrease in FWHM from IR (13 mm) to IR + PSF (11 mm); an increase in the C IC from IR (65%) to IR + PSF (71%) and from IR + TOF (72%) to IR + TOF + PSF (77%); and an increase in the C TD from IR + PSF (72%) to IR + TOF (75%) and to IR + TOF + PSF (77%). Tests of the scanner/software combinations showed a decrease in FWHM from Gemini_TF (13 mm) to Biograph_mCT (12 mm) and to Discovery_690 (11 mm); an increase in the C IC from Gemini_TF (65%) to Biograph_mCT (73%) and to Discovery_690 (75%); and an increase in the C TD from Gemini_TF/Biograph_mCT (72%) to Discovery_690 (77%).

CONCLUSION

The introduction of TOF and PSF increases image quality in cardiac 18F-FDG PET. The scanner/software combinations exhibit different performances, which should be taken into consideration when making cross comparisons.

Importance of Defect Detectability in Positron Emission Tomography Imaging of Abdominal Lesions

OBJECTIVES

This study was designed to assess defect detectability in positron emission tomography (PET) imaging of abdominal lesions.

METHODS

A National Electrical Manufactures Association International Electrotechnical Commission phantom was used. The simulated abdominal lesion was scanned for 10 min using dynamic list-mode acquisition method. Images, acquired with scan duration of 1-10 min, were reconstructed using VUE point HD and a 4.7 mm full-width at half-maximum (FWHM) Gaussian filter. Iteration-subset combinations of 2-16 and 2-32 were used. Visual and physical analyses were performed using the acquired images. To sequentially evaluate defect detectability in clinical settings, we examined two middle-aged male subjects. One had a liver cyst (approximately 10 mm in diameter) and the other suffered from pancreatic cancer with an inner defect region (approximately 9 mm in diameter).

RESULTS

In the phantom study, at least 6 and 3 min acquisition durations were required to visualize 10 and 13 mm defect spheres, respectively. On the other hand, spheres with diameters ≥17 mm could be detected even if the acquisition duration was only 1 min. The visual scores were significantly correlated with background (BG) variability. In clinical settings, the liver cyst could be slightly visualized with an acquisition duration of 6 min, although image quality was suboptimal. For pancreatic cancer, the acquisition duration of 3 min was insufficient to clearly describe the defect region.

CONCLUSION

The improvement of BG variability is the most important factor for enhancing lesion detection. Our clinical scan duration (3 min/bed) may not be suitable for the detection of small lesions or accurate tumor delineation since an acquisition duration of at least 6 min is required to visualize 10 mm lesions, regardless of reconstruction parameters. Improvements in defect detectability are important for radiation treatment planning and accurate PET-based diagnosis.