Purpose To evaluate and compare the theoretically achievable accuracy of two families of two-parameter photon cross-section models: basis vector model (BVM) and modified parametric fit model (mPFM). Method The modified PFM assumes that photoelectric absorption and scattering cross sections can be accurately represented by power functions in effective atomic number and/or energy plus the Klein-Nishina cross section, along with empirical corrections that enforce exact prediction of elemental cross sections. Two PFM variants were investigated: the widely used Torikoshi model (tPFM) and a more complex “VCU” variant (vPFM). For 43 standard soft and bony tissues and phantom materials, all consisting of elements with atomic number less than 20 (except iodine), we evaluated the theoretically achievable accuracy of tPFM and vPFM for predicting linear attenuation, photoelectric absorption, and energy absorption coefficients, and we compared it to a previously investigated separable, linear two-parameter model, BVM. Results For an idealized dual-energy computed tomography (DECT) imaging scenario, the cross-section mapping process demonstrates that BVM more accurately predicts photon cross sections of biological mixtures than either tPFM or vPFM. Maximum linear attenuation coefficient prediction errors were 15% and 5% for tPFM and BVM, respectively. The root-mean-square (RMS) prediction errors of total linear attenuation over the 20 keV to 1000 keV energy range of tPFM and BVM were 0.93% (tPFM) and 0.1% (BVM) for adipose tissue, 0.8% (tPFM) and 0.2% (BVM) for muscle tissue, and 1.6% (tPFM) and 0.2% (BVM) for cortical bone tissue. With exception of the thyroid and Teflon, the RMS error for photoelectric absorption and scattering coefficient was within 4% for the tPFM and 2% for the BVM. Neither model predicts the photon cross sections of thyroid tissue accurately, exhibiting relative errors as large as 20%. For the energy absorption coefficients prediction error, RMS errors for the BVM were less than 1.5%, while for the tPFM, the RMS errors were as large as 16%. Conclusion Compared to modified PFMs, BVM shows superior potential to support dual-energy CT cross-section mapping. In addition, the linear, separable BVM can be more efficiently deployed by iterative model-based DECT image reconstruction algorithms.

Purpose To assess the potential of a joint dual‐energy computerized tomography (CT) reconstruction process (statistical image reconstruction method built on a basis vector model (JSIR‐BVM)) implemented on a 16‐slice commercial CT scanner to measure high spatial resolution stopping‐power ratio (SPR) maps with uncertainties of less than 1%. Methods JSIR‐BVM was used to reconstruct images of effective electron density and mean excitation energy from dual‐energy CT (DECT) sinograms for 10 high‐purity samples of known density and atomic composition inserted into head and body phantoms. The measured DECT data consisted of 90 and 140 kVp axial sinograms serially acquired on a Philips Brilliance Big Bore CT scanner without beam‐hardening corrections. The corresponding SPRs were subsequently measured directly via ion chamber measurements on a MEVION S250 superconducting synchrocyclotron and evaluated theoretically from the known sample compositions and densities. Deviations of JSIR‐BVM SPR values from their theoretically calculated and directly measured ground‐truth values were evaluated for our JSIR‐BVM method and our implementation of the Hünemohr–Saito (H‐S) DECT image‐domain decomposition technique for SPR imaging. A thorough uncertainty analysis was then performed for five different scenarios (comparison of JSIR‐BVM stopping‐power ratio/stopping power (SPR/SP) to International Commission on Radiation Measurements and Units benchmarks; comparison of JSIR‐BVM SPR to measured benchmarks; and uncertainties in JSIR‐BVM SPR/SP maps for patients of unknown composition) per the Joint Committee for Guides in Metrology and the Guide to Expression of Uncertainty in Measurement, including the impact of uncertainties in measured photon spectra, sample composition and density, photon cross section and I‐value models, and random measurement uncertainty. Estimated SPR uncertainty for three main tissue groups in patients of unknown composition and the weighted proportion of each tissue type for three proton treatment sites were then used to derive a composite range uncertainty for our method. Results Mean JSIR‐BVM SPR estimates deviated by less than 1% from their theoretical and directly measured ground‐truth values for most inserts and phantom geometries except for high‐density Delrin and Teflon samples with SPR error relative to proton measurements of 1.1% and −1.0% (head phantom) and 1.1% and −1.1% (body phantom). The overall root‐mean‐square (RMS) deviations over all samples were 0.39% and 0.52% (head phantom) and 0.43% and 0.57% (body phantom) relative to theoretical and directly measured ground‐truth SPRs, respectively. The corresponding RMS (maximum) errors for the image‐domain decomposition method were 2.68% and 2.73% (4.68% and 4.99%) for the head phantom and 0.71% and 0.87% (1.37% and 1.66%) for the body phantom. Compared to H‐S SPR maps, JSIR‐BVM yielded 30% sharper and twofold sharper images for soft tissues and bone‐like surrogates, respectively, while reducing noise by factors of 6 and 3, respectively. The unce...

Objectives:Although previous studies have shown that oral diseases can impact certain systemic conditions, dental care has been historically separated from medical healthcare organizations in middle-income countries. There is a lack of research approaches which test the independent relationship between oral health and multidimensional measures of general health. This study analyses the influence of tooth loss on self-rated health (SRH), hypothesizing that, relatively to certain morbidity conditions, tooth loss is a health condition associated with SRH. This study analyses the influence of tooth loss on self-rated health (SRH), hypothesizing that, relative to certain morbidity conditions, tooth loss is a health condition associated with SRH. Methods: Data were obtained from the Costa Rican Longevity and Healthy AgingStudy 1945-1955 Retirement Cohort, a national representative longitudinal survey including residents born between 1945 and 1955. The association between severe tooth loss and SRH was analysed cross-sectionally using the first wave of the study conducted in 2010. A multivariable logistic regression, adjusted for potential confounders, was performed on 2797 participants. A counterfactual analysis was additionally performed to illustrate the theoretical change on SRH prevalence-if all the participants were not to have had severe tooth loss.Results: Severe tooth loss was associated with poor SRH, after adjustment for smoking, morbidity, biomarkers and performance-based physical measures. The counterfactual analysis showed that severe tooth loss was the fifth most important morbidity condition in determining poor SRH. Declaring a poor SRH would have been decreased by 2.0 percentage points if those participants having severe tooth loss had shared the same risk pattern of those who had not lost the majority of their teeth.Conclusion: Individuals consider their oral health status to a similar extent as other morbidity conditions when evaluating their general health. A stronger focus on oral health, and its impact on general health, could lead to better planning of national resources, thereby improving accessibility to health care and modifying prevailing conceptions of health care in low-and middle-income countries. K E Y W O R D SCosta Rica, edentulism, middle-income country, oral health, self-rated health, tooth loss | 359 BARBOZA-SOLÍS et AL.

Purpose: To investigate via Monte Carlo simulations, the impact of scan subject size, antiscatter grid (ASG), collimator size, and bowtie filter on the distribution of scatter radiation in a typical realistically modeled third generation 16 slice diagnostic computed tomography (CT) scanner. Methods: Full radiation transport was simulated with Geant4 in a realistic CT scanner geometric model, including the imaging phantom, bowtie filter (BTF), collimators and detector assembly, except for the ASGs. An analytical method was employed to quantify the probable transmission through the ASG of each photon intersecting the detector array. Normalized scatter profiles (NSP) and scatter-to-primary-ratio (SPR) profiles were simulated for 90 and 140 kVp beams for different size phantoms and slice thicknesses. The impact of CT scatter on the reconstructed attenuation coefficient factor was also studied as were the modulating effects of phantom-and patient-tissue heterogeneities on scatter profiles. A method to characterize the relative spatial frequency content of sinogram signals was developed to assess the latter. Results: For the 21.4-cm diameter phantom, NSP and SPR increase linearly with collimator opening for both tube potentials, with the 90 kVp scan exhibiting slightly larger NSP and SPR. The BTF modestly modulates scatter under the phantom center, reducing the prominent off-axis lobes by factors of 1.1-1.3. The ASG reduces scatter on the central axis NSP threefold, and reduces scatter at the detectors outside the phantom shadow by factors of 25 to 500. For the phantoms with diameters of 27 and 32 cm, the scatter increases roughly three-and fourfold, respectively, demonstrating that scatter monotonically increases with phantom size, despite deployment of the ASG and BTF. In the absence of a scan subject, the ASG reduces the signal profile arising photons scattered by the BTF. Without ASG, the in-air scatter profile is relatively flat compared to the scatter profile when the ASG is present. For both 90 and 140 kVp photon spectra, the calculated attenuation coefficient decreases linearly with increasing collimation size. For both homogeneous and heterogeneous objects, NSPs are dominated by low spatial frequency content compared to the primary signal. However, the SPR, which quantifies the local magnitude of nonlinear detector response and is dominated by the high frequency content of the primary profile, can contribute strongly to high-spatial frequency streaking artifacts near high-density structures in reconstructed image artifacts. Conclusion: Public-domain Monte Carlo codes, Geant-4 in particular, is a feasible method for characterizing CT detector response to scattered-and off-focal radiation. Our study demonstrates that the ASG substantially reduces the scatter radiation and reshapes scatter-radiation profiles and affects the accuracy with which the detector array can measure narrow-beam attenuation due its inability to distinguish between true uncollided primary and narrow-angle coherently scattered photons. Hence...

Purpose: This work aims at reducing the uncertainty in proton stopping power (SP) estimation by a novel combination of a linear, separable basis vector model (BVM) for stopping power calculation (Med Phys 43:600) and a statistical, model‐based dual‐energy CT (DECT) image reconstruction algorithm (TMI 35:685). The method was applied to experimental data. Methods: BVM assumes the photon attenuation coefficients, electron densities, and mean excitation energies (I‐values) of unknown materials can be approximated by a combination of the corresponding quantities of two reference materials. The DECT projection data for a phantom with 5 different known materials was collected on a Philips Brilliance scanner using two scans at 90 kVp and 140 kVp. The line integral alternating minimization (LIAM) algorithm was used to recover the two BVM coefficient images using the measured source spectra. The proton stopping powers are then estimated from the Bethe‐Bloch equation using electron densities and I‐values derived from the BVM coefficients. The proton stopping powers and proton ranges for the phantom materials estimated via our BVM based DECT method are compared to ICRU reference values and a post‐processing DECT analysis (Yang PMB 55:1343) applied to vendorreconstructed images using the Torikoshi parametric fit model (tPFM). Results: For the phantom materials, the average stopping power estimations for 175 MeV protons derived from our method are within 1% of the ICRU reference values (except for Teflon with a 1.48% error), with an average standard deviation of 0.46% over pixels. The resultant proton ranges agree with the reference values within 2 mm. Conclusion: Our principled DECT iterative reconstruction algorithm, incorporating optimal beam hardening and scatter corrections, in conjunction with a simple linear BVM model, achieves more accurate and robust proton stopping power maps than the post‐processing, nonlinear tPFM based DECT analysis applied to conventional reconstructions of low and high energy scans. Funding Support: NIH R01CA 75371; NCI grant R01 CA 149305.