Numerous medical imaging applications necessitate removing the subject’s bed from X-ray computed tomography (CT) images. Here we propose and explore a novel automatic approach for bed removal in small animal CT scans...
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ISBN:
(数字)9798350388152
ISBN:
(纸本)9798350388169
Numerous medical imaging applications necessitate removing the subject’s bed from X-ray computed tomography (CT) images. Here we propose and explore a novel automatic approach for bed removal in small animal CT scans. The solution uses a 2D neural network for semantic segmentation of the rats. To the best of our knowledge, this is the first time a CNN approach has been used for bed segmentation/removal. The entire dataset used for this study included whole-body CT scans of thirteen rats taken in two different sites by the same scanner model but with different beds designs. After finding the most suitable network architecture, loss function, and training duration (epochs), we performed a 5-fold cross-validation on the remaining dataset acquired at the first site. The results demonstrated high compatibility between the network output and the ground truth labels, with an intersection over union (IoU) value of 0.943 and a Dice similarity coefficient (DSC) value of 0.969. However, when training on datasets acquired on the first site and testing on the dataset taken at the second site, the suggested solution didn’t work as well, most likely due to the differences between the datasets.
Positron emission tomography (PET) is an imaging method for cancer, heart disease, and neurological disorders. PET images are reconstructed from emitted positron data using complex mathematical algorithms, among which...
Positron emission tomography (PET) is an imaging method for cancer, heart disease, and neurological disorders. PET images are reconstructed from emitted positron data using complex mathematical algorithms, among which ordered subset algorithms, including ordered subset expectation maximization (OSEM) and block sequential regularized expectation maximization (BSREM), are commonly employed. OSEM provides faster image reconstruction but with higher noise, while BSREM offers improved image quality, and reduced noise, albeit at the cost of increased computational complexity. To improve the accuracy and quality of OSEM-reconstructed brain PET images while maintaining time efficiency, we utilized a generative adversarial network (GAN) to enhance OSEM images to a level of quality and accuracy comparable to that of images generated by the BSREM algorithm. We collected 18 FDG PET scans from Alzheimer’s disease patients, which were preprocessed into a list-mode format and reconstructed using OSEM with 2 iterations and 2 subsets, and BSREM with 25 iterations. We trained a cGAN model, consisting of a U-net generator and a discriminator that received OSEM-reconstructed PET images as input, BSREM images as the target, and generated higher quality and accuracy PET images as output. Results were evaluated using PSNR, SSIM, and NRMSE. Using the cGAN model, we improved the SNR and contrast of the OSEM-reconstructed PET images with a 17% enhancement in the PSNR and a 60% decrease in the NRMSE, although the SSIM was not significantly improved. Our findings show that the GAN model can convert low-SNR, low-contrast OSEM images to high-quality, high-accuracy images similar to those generated by the BSREM algorithm. This approach offers potential advantages in achieving the image quality and accuracy of BSREM with the shorter reconstruction time of OSEM.
We report a split ring photonic crystal that demonstrates an order of magnitude larger peak energy density compared to traditional photonic crystals. The split ring offers highly focused optical energy in an accessibl...
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ISBN:
(数字)9781957171050
ISBN:
(纸本)9781665466660
We report a split ring photonic crystal that demonstrates an order of magnitude larger peak energy density compared to traditional photonic crystals. The split ring offers highly focused optical energy in an accessible subwavelength gap.
Non-invasive vibration measurements from the knee offer a convenient and affordable alternative to benchtop or biomechanics lab joint health monitoring systems. Recently, joint acoustic emissions (JAEs) measured from ...
Non-invasive vibration measurements from the knee offer a convenient and affordable alternative to benchtop or biomechanics lab joint health monitoring systems. Recently, joint acoustic emissions (JAEs) measured from the knee were shown to be an indicator of knee health. However, the origin of JAEs is still not fully understood, which limits its acceptance and use by clinical experts. In this proof-of-concept study, rather than relying on the movements of the knee and corresponding frictional rubbing of internal surfaces to produce vibrations, we propose using an active vibration sensing approach with a known vibration source interrogating the knee. We aim to elucidate the linkage between knee vibration characteristics and structural changes in the joint following injuries. We measured tibial vibration responses of two participants using a laser vibrometer system to quantify the frequency band where the most repeatable tibial vibration measurement can be taken. Subsequently, a custom-designed wearable system measured mid-activity tibial vibration characteristics from four participants (five healthy knees and three knees with prior acute injury) during unloaded knee flexion-extensions. An active sensing knee health score was defined as the ratio of the changes in low- to high-frequency response during flexion-extension. Since changes in the boundary of tibia would alter low-frequency response more than high frequency response, we found that increased knee laxity with acute injuries resulted in an increased active sensing knee health score. Our findings demonstrate the potential of active vibration sensing as an interpretable, computationally inexpensive alternative to JAEs for wearable knee health assessment.
Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chro...
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ISBN:
(数字)9798350379839
ISBN:
(纸本)9798350379846
Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chronic diseases in adulthood. Therefore, early identification and prevention efforts for stunting are crucial. Classifying toddlers into categories of at-risk for stunting or not is essential to provide timely and appropriate interventions. This study employs data mining techniques using the decision tree algorithm to expedite the stunting detection process and improve the accuracy of nutritional status classification in children. The results indicate that the constructed decision tree model can classify children's nutritional status with an accuracy of 83.26%. The decision tree achieves high accuracy in classifying stunting in toddlers due to its ability to handle complex data and identify significant patterns within the data.
We for the first time study characteristic fluctuation of gate-all-around silicon nanosheet MOSFETs induced by random dopants fluctuation (RDF), interface trap fluctuation (ITF), and work function fluctuation (WKF), a...
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In this paper we consider the modeling of measurement error for fund returns data. In particular, given access to a time-series of discretely observed log-returns and the associated maximum over the observation period...
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Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic ...
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This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal ima...
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Photodynamic therapy(PDT)is attracting attention as a next-generation cancer treatment that can selectively destroy malignant tissues,exhibit fewer side effects,and lack pain during *** PDT systems have recently been ...
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Photodynamic therapy(PDT)is attracting attention as a next-generation cancer treatment that can selectively destroy malignant tissues,exhibit fewer side effects,and lack pain during *** PDT systems have recently been developed to resolve the issues of bulky and expensive conventional PDT systems and to implement continuous and repetitive *** implantable PDT systems,however,are not able to perform multiple functions simultaneously,such as modulating light intensity,measuring,and transmitting tumor-related data,resulting in the complexity of cancer ***,we introduce a flexible and fully implantable wireless optoelectronic system capable of continuous and effective cancer treatment by fusing PDT and hyperthermia and enabling tumor size monitoring in *** system exploits micro inorganic light-emitting diodes(μ-LED)that emit light with a wavelength of 624 nm,designed not to affect surrounding normal tissues by utilizing a fully programmable light intensity ofμ-LED and precisely monitoring the tumor size by Si phototransistor during a long-term implantation(2–3 weeks).The superiority of simultaneous cancer treatment and tumor size monitoring capabilities of our system operated by wireless power and data transmissions with a cell phone was confirmed through in vitro experiments,ray-tracing simulation results,and a tumor xenograft mouse model in *** all-in-one single system for cancer treatment offers opportunities to not only enable effective treatment of tumors located deep in the tissue but also enable precise and continuous monitoring of tumor size in real-time.
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