Implementing a Fitness and Nutrition Program for Special Olympics Athletes

Introduction: Only 17-30% of individuals with ID meet the recommendations for daily exercise Populations of individuals with ID have higher BMI, lower cardiovascular fitness and lower muscle strength compared to the general population Individuals with ID also have many dietary challenges necessitating nutritional education and interventions One study following four athletes with ID, showed that pairing athletes with and without (unified sports) resulted in a positive change in social self-concept for athletes with ID Given the above, we: Created a 6-week pilot training and nutrition program for Special Olympics Vermont (SOVT). Paired athletes with ID with college athletes without ID to promote wellness during the athlete’s off season.https://scholarworks.uvm.edu/comphp_gallery/1229/thumbnail.jp

Data-Driven Volumetric Image Generation from Surface Structures using a Patient-Specific Deep Leaning Model

The advent of computed tomography significantly improves patient health regarding diagnosis, prognosis, and treatment planning and verification. However, tomographic imaging escalates concomitant radiation doses to patients, inducing potential secondary cancer. We demonstrate the feasibility of a data-driven approach to synthesize volumetric images using patient surface images, which can be obtained from a zero-dose surface imaging system. This study includes 500 computed tomography (CT) image sets from 50 patients. Compared to the ground truth CT, the synthetic images result in the evaluation metric values of 26.9 Hounsfield units, 39.1dB, and 0.965 regarding the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure. This approach provides a data integration solution that can potentially enable real-time imaging, which is free of radiation-induced risk and could be applied to image-guided medical procedures

One-step Iterative Estimation of Effective Atomic Number and Electron Density for Dual Energy CT

Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. For proton therapy treatment planning, besides material-selective images, maps of effective atomic number (Z) and relative electron density to that of water ($\rho_e$) can also be achieved and further employed to improve stopping power ratio accuracy and reduce range uncertainty. In this work, we propose a one-step iterative estimation method, which employs multi-domain gradient $L_0$-norm minimization, for Z and $\rho_e$ maps reconstruction. The algorithm was implemented on GPU to accelerate the predictive procedure and to support potential real-time adaptive treatment planning. The performance of the proposed method is demonstrated via both phantom and patient studies

Deep learning-based Fast Volumetric Image Generation for Image-guided Proton FLASH Radiotherapy

Proton FLASH therapy leverages ultra-high dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. To prepare for the delivery of high doses to targets, we aim to develop a deep learning-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization before FLSAH beam delivery. The proposed framework comprises four modules, including orthogonal kV x-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and water equivalent thickness evaluation. We investigated volumetric image reconstruction using four kV projection pairs with different source angles. Thirty lung patients were identified from the institutional database, and each patient contains a four-dimensional computed tomography dataset with ten respiratory phases. The retrospective patient study indicated that the proposed framework could reconstruct patient volumetric anatomy, including tumors and organs at risk from orthogonal x-ray projections. Considering all evaluation metrics, the kV projections with source angles of 135 and 225 degrees yielded the optimal volumetric images. The proposed framework has been demonstrated to reconstruct volumetric images with accurate lesion locations from two orthogonal x-ray projections. The embedded WET module can be used to detect potential proton beam-specific patient anatomy variations. The framework can deliver fast volumetric image generation and can potentially guide treatment delivery systems for proton FLASH therapy

Image-Domain Material Decomposition for Dual-energy CT using Unsupervised Learning with Data-fidelity Loss

Background: Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold. Although deep learning-based decomposition methods have been reported, these methods are in the supervised-learning framework requiring paired data for training, which is not readily available in clinical settings. Purpose: This work aims to develop an unsupervised-learning framework with data-measurement consistency for image-domain material decomposition in DECT

Full-dose PET Synthesis from Low-dose PET Using High-efficiency Diffusion Denoising Probabilistic Model

To reduce the risks associated with ionizing radiation, a reduction of radiation exposure in PET imaging is needed. However, this leads to a detrimental effect on image contrast and quantification. High-quality PET images synthesized from low-dose data offer a solution to reduce radiation exposure. We introduce a diffusion-model-based approach for estimating full-dose PET images from low-dose ones: the PET Consistency Model (PET-CM) yielding synthetic quality comparable to state-of-the-art diffusion-based synthesis models, but with greater efficiency. There are two steps: a forward process that adds Gaussian noise to a full dose PET image at multiple timesteps, and a reverse diffusion process that employs a PET Shifted-window Vision Transformer (PET-VIT) network to learn the denoising procedure conditioned on the corresponding low-dose PETs. In PET-CM, the reverse process learns a consistency function for direct denoising of Gaussian noise to a clean full-dose PET. We evaluated the PET-CM in generating full-dose images using only 1/8 and 1/4 of the standard PET dose. Comparing 1/8 dose to full-dose images, PET-CM demonstrated impressive performance with normalized mean absolute error (NMAE) of 1.233+/-0.131%, peak signal-to-noise ratio (PSNR) of 33.915+/-0.933dB, structural similarity index (SSIM) of 0.964+/-0.009, and normalized cross-correlation (NCC) of 0.968+/-0.011, with an average generation time of 62 seconds per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12x faster. In the 1/4 dose to full-dose image experiments, PET-CM is also competitive, achieving an NMAE 1.058+/-0.092%, PSNR of 35.548+/-0.805dB, SSIM of 0.978+/-0.005, and NCC 0.981+/-0.007 The results indicate promising low-dose PET image quality improvements for clinical applications

Efficacy of amodiaquine, sulphadoxine-pyrimethamine and their combination for the treatment of uncomplicated Plasmodium falciparum malaria in children in Cameroon at the time of policy change to artemisinin-based combination therapy

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.Abstract Background The efficacy of amodiaquine (AQ), sulphadoxine-pyrimethamine (SP) and the combination of SP+AQ in the treatment of Cameroonian children with clinical malaria was investigated. The prevalence of molecular markers for resistance to these drugs was studied to set the baseline for surveillance of their evolution with time. Methods Seven hundred and sixty children aged 6-59 months with uncomplicated falciparum malaria were studied in three ecologically different regions of Cameroon - Mutengene (littoral equatorial forest), Yaoundé (forest-savannah mosaic) and Garoua (guinea-savannah). Study children were randomized to receive either AQ, SP or the combination AQ+SP. Clinical outcome was classified according to WHO criteria, as either early treatment failure (ETF), late clinical failure (LCF), late parasitological failure (LPF) or adequate clinical and parasitological response (ACPR). The occurrence of mutations in pfcrt, pfmdr1, dhfr and dhps genes was studied by either RFLP or dot blot techniques and the prevalence of these mutations related to parasitological and therapeutic failures. Results After correction for the occurrence of re-infection by PCR, ACPRs on day 28 for AQ, SP and AQ+SP were 71.2%, 70.1% and 80.9%, in Garoua, 79.2%, 62.5%, and 81.9% in Mutengene, and 80.3%, 67.5% and 76.2% in Yaoundé respectively. High levels of Pfcrt 76T (87.11%) and Pfmdr1 86Y mutations (73.83%) were associated with quinoline resistance in the south compared to the north, 31.67% (76T) and 22.08% (86Y). There was a significant variation (p < 0.001) of the prevalence of the SGK haplotype between Garoua in the north (8.33%), Yaoundé (36.29%) in the savannah-forest mosaic and Mutengene (66.41%) in the South of Cameroon and a weak relation between SGK haplotype and SP failure. The 540E mutation on the dhps gene was extremely rare (0.3%) and occurred only in Mutengene while the pfmdr1 1034K and 1040D mutations were not detected in any of the three sites. Conclusion In this study the prevalence of molecular markers for quinoline and anti-folate resistances showed high levels and differed between the south and north of Cameroon. AQ, SP and AQ+SP treatments were well tolerated but with low levels of efficacy that suggested alternative treatments were needed in Cameroon since 2005.Published versio

Lesion segmentation on 18F-fluciclovine PET/CT images using deep learning

Background and purposeA novel radiotracer, 18F-fluciclovine (anti-3-18F-FACBC), has been demonstrated to be associated with significantly improved survival when it is used in PET/CT imaging to guide postprostatectomy salvage radiotherapy for prostate cancer. We aimed to investigate the feasibility of using a deep learning method to automatically detect and segment lesions on 18F-fluciclovine PET/CT images.Materials and methodsWe retrospectively identified 84 patients who are enrolled in Arm B of the Emory Molecular Prostate Imaging for Radiotherapy Enhancement (EMPIRE-1) trial. All 84 patients had prostate adenocarcinoma and underwent prostatectomy and 18F-fluciclovine PET/CT imaging with lesions identified and delineated by physicians. Three different neural networks with increasing levels of complexity (U-net, Cascaded U-net, and a cascaded detection segmentation network) were trained and tested on the 84 patients with a fivefold cross-validation strategy and a hold-out test, using manual contours as the ground truth. We also investigated using both PET and CT or using PET only as input to the neural network. Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), center-of-mass distance (CMD), and volume difference (VD) were used to quantify the quality of segmentation results against ground truth contours provided by physicians.ResultsAll three deep learning methods were able to detect 144/155 lesions and 153/155 lesions successfully when PET+CT and PET only, respectively, served as input. Quantitative results demonstrated that the neural network with the best performance was able to segment lesions with an average DSC of 0.68 ± 0.15 and HD95 of 4 ± 2 mm. The center of mass of the segmented contours deviated from physician contours by approximately 2 mm on average, and the volume difference was less than 1 cc. The novel network proposed by us achieves the best performance compared to current networks. The addition of CT as input to the neural network contributed to more cases of failure (DSC = 0), and among those cases of DSC > 0, it was shown to produce no statistically significant difference with the use of only PET as input for our proposed method.ConclusionQuantitative results demonstrated the feasibility of the deep learning methods in automatically segmenting lesions on 18F-fluciclovine PET/CT images. This indicates the great potential of 18F-fluciclovine PET/CT combined with deep learning for providing a second check in identifying lesions as well as saving time and effort for physicians in contouring