Appeal No. 0244: Joyce Lewanski v. Chief, Division of Oil and Gas,

Chief\u27s Order 87-2

Improving the Timing Resolution of Positron Emission Tomography Detectors Using Boosted Learning -- A Residual Physics Approach

Artificial intelligence (AI) is entering medical imaging, mainly enhancing image reconstruction. Nevertheless, improvements throughout the entire processing, from signal detection to computation, potentially offer significant benefits. This work presents a novel and versatile approach to detector optimization using machine learning (ML) and residual physics. We apply the concept to positron emission tomography (PET), intending to improve the coincidence time resolution (CTR). PET visualizes metabolic processes in the body by detecting photons with scintillation detectors. Improved CTR performance offers the advantage of reducing radioactive dose exposure for patients. Modern PET detectors with sophisticated concepts and read-out topologies represent complex physical and electronic systems requiring dedicated calibration techniques. Traditional methods primarily depend on analytical formulations successfully describing the main detector characteristics. However, when accounting for higher-order effects, additional complexities arise matching theoretical models to experimental reality. Our work addresses this challenge by combining traditional calibration with AI and residual physics, presenting a highly promising approach. We present a residual physics-based strategy using gradient tree boosting and physics-guided data generation. The explainable AI framework SHapley Additive exPlanations (SHAP) was used to identify known physical effects with learned patterns. In addition, the models were tested against basic physical laws. We were able to improve the CTR significantly (more than 20%) for clinically relevant detectors of 19 mm height, reaching CTRs of 185 ps (450-550 keV)

Perceived weight discrimination and risk of incident dementia

Body mass index (BMI) and obesity have a complex relation with risk of dementia that evolves over the lifespan. Research in other domains indicates that the social experience of body weight, not just BMI, is associated with worse health outcomes. The present research uses data from the Health and Retirement Study (N = 12,053) to test whether weight discrimination is associated with increased risk of dementia over an up to 10-year follow-up independent of BMI and other relevant clinical and behavioral risk factors. Participants who reported weight discrimination had a 40% increased risk of incident dementia (Hazard Ratio = 1.40; 95% Confidence Interval = 1.12–1.74), controlling for age, sex, race, ethnicity, and education. The association between weight discrimination and incident dementia held controlling for BMI, diabetes, hypertension, depressive symptoms, smoking, physical activity, and genetic risk status. The present research indicates that the stigma associated with weight is associated with dementia risk independent from obesity. This research highlights that the detrimental effect of obesity on cognitive health in older adulthood may occur through the adverse social experience of body weight in addition to the biological consequences of excess weight

Perceived weight discrimination, changes in health, and daily stressors

Objective  To examine whether perceived weight discrimination is associated with change in health markers over time and whether it is associated with daily stressors, physical symptoms, and affect.  Methods  Participants were selected from the Midlife in the United States (MIDUS) study if they had data on perceived weight discrimination and health markers at MIDUS II (2004–2006), health markers at MIDUS III (2013–2014), and a body mass index ≥25 kg/m2(N = 1,841). A subset of these participants (N = 1,153) reported on their experiences daily for 8 days as part of the second National Study of Daily Experiences.  Results  Perceived weight discrimination was associated with declines in mental and physical health over time (medianβ =0.06). Participants who reported weight discrimination experienced more daily stressors (β =0.13), physical symptoms (β =0.13), and negative affect (β =0.13) and less positive affect (β =−0.12) over the 8 days of the second National Study of Daily Experiences. Weight discrimination was most strongly associated with interpersonal stressors (medianβ =0.14), feelings of anger (β =0.16) and frustration (β =0.14), lower attention (β =−0.14) and activity (β =−0.16), and more nonspecific physical symptoms (e.g., fatigue;β =0.10).  Conclusions  This research replicates the association between perceived weight discrimination and worse health over time and extends this literature to show that people who experience weight discrimination have more daily stressors, physical symptoms, and negative emotions

Personality nuances and risk of dementia:Evidence from two longitudinal studies

Personality traits are broad constructs composed of nuances, operationalized by personality items, that can provide a more granular understanding of personality associations with health outcomes. This study examined the associations between personality nuances and incident dementia and evaluated whether nuances associations replicate across two samples. Health and Retirement Study (HRS, N = 11,400) participants were assessed in 2006/2008, and the English Longitudinal Study of Ageing (ELSA, N = 7453) participants were assessed in 2010/2011 on personality and covariates. Dementia incidence was tracked for 14 years in the HRS and 8 years in ELSA. In both HRS and ELSA, higher neuroticism domain and nuances (particularly nervous and worry) were related to a higher risk of incident dementia, whereas higher conscientiousness domain and nuances (particularly responsibility and organization) were associated with a lower risk of dementia. To a lesser extent, higher extraversion (active), openness (broad-minded, curious, and imaginative), and agreeableness (helpful, warm, caring, and sympathetic) nuances were associated with a lower risk of dementia, with replicable effects across the two samples. A poly-nuance score, aggregating the effects of personality items, was associated with an increased risk of incident dementia in the HRS and ELSA, with effect sizes slightly stronger than those of the personality domains. Clinical, behavioral, psychological, and genetic covariates partially accounted for these associations. The present study provides novel and replicable evidence for specific personality characteristics associated with the risk of incident dementia

Material differentiation in forensic radiology with single-source dual-energy computed tomography

The goal of this study was to investigate the use of dual-energy computed tomography (CT) in differentiating frequently encountered foreign material on CT images using a standard single-source CT scanner. We scanned 20 different, forensically relevant materials at two X-Ray energy levels (80 and 130kVp) on CT. CT values were measured in each object at both energy levels. Intraclass correlation coefficient (ICC) was used to determine intra-reader reliability. Analysis of variance (ANOVA) was performed to assess significance levels between X-Ray attenuation at 80 and 130kVp. T test was used to investigate significance levels between mean HU values of individual object pairings at single energy levels of 80 and 130kVp, respectively. ANOVA revealed that the difference in attenuation between beam energies of 80kVp compared to 130kVp was statistically significant (p<0.005) for all materials except brass and lead. ICC was excellent at 80kVp (0.999, p<0.001) and at 130kVp (0.998, p<0.001). T test showed that using single energy levels of 80 and 130kVp respectively 181/190 objects pairs could be differentiated from one another based on HU measurements. Using the combined information from both energy levels, 189/190 object pairs could be differentiated. Scanning with different energy levels is a simple way to apply dual-energy technique on a regular single-energy CT and improves the ability to differentiate foreign bodies with CT, based on their attenuation value

A Finely Segmented Semi-Monolithic Detector tailored for High Resolution PET

Preclinical research and organ-dedicated applications require high-resolution positron emission tomography (PET) detectors to visualize small structures and understand biological processes at a finer level of detail. Current commercial systems often employ finely pixelated or monolithic scintillators, each with its limitations. We present a semi-monolithic detector, tailored for high-resolution PET applications, and merging concepts of monolithic and pixelated crystals. The detector features slabs measuring (24 x 10 x 1) sq. mm, coupled to a 12 x 12 readout channel photosensor with 4 mm pitch. The slabs are grouped in two arrays of 44 slabs each to achieve a higher optical photon density. We employ a fan beam collimator for fast calibration to train machine-learning-based positioning models for all three dimensions, including slab identification and depth-of-interaction (DOI), utilizing gradient tree boosting (GTB). Energy calculation was based on a position-dependent energy calibration. Using an analytical timing calibration, time skews were corrected for coincidence timing resolution (CTR) estimation. Leveraging machine-learning-based calibration in all three dimensions, we achieved high detector spatial resolution: down to 1.18 mm full width at half maximum (FWHM) detector spatial resolution and 0.75 mm mean absolute error (MAE) in the planar-monolithic direction along the slabs, and 2.14 mm FWHM and 1.03 mm MAE for depth-of-interaction (DOI) at an energy window of (435-585) keV. Correct slab interaction identification exceeded 80%, alongside an energy resolution of 13.8% and a CTR of 450 ps FWHM. Therewith, the introduced finely segmented, high-resolution slab detector demonstrates an appealing performance suitable for high-resolution PET applications. The current benchtop-based detector calibration routine allows these detectors to be used in PET systems.Comment: 14 pages, 11 figures, IEEE NSS MIC RTSD 202