7 research outputs found
Trainable Joint Bilateral Filters for Enhanced Prediction Stability in Low-dose CT
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced
patient dose in routine CT acquisitions while maintaining high image quality.
Recently, deep learning~(DL)-based methods were introduced, outperforming
conventional denoising algorithms on this task due to their high model
capacity. However, for the transition of DL-based denoising to clinical
practice, these data-driven approaches must generalize robustly beyond the seen
training data. We, therefore, propose a hybrid denoising approach consisting of
a set of trainable joint bilateral filters (JBFs) combined with a convolutional
DL-based denoising network to predict the guidance image. Our proposed
denoising pipeline combines the high model capacity enabled by DL-based feature
extraction with the reliability of the conventional JBF. The pipeline's ability
to generalize is demonstrated by training on abdomen CT scans without metal
implants and testing on abdomen scans with metal implants as well as on head CT
data. When embedding two well-established DL-based denoisers (RED-CNN/QAE) in
our pipeline, the denoising performance is improved by / (RMSE)
and / (PSNR) in regions containing metal and by /
(RMSE) and / (PSNR) on head CT data, compared to the respective
vanilla model. Concluding, the proposed trainable JBFs limit the error bound of
deep neural networks to facilitate the applicability of DL-based denoisers in
low-dose CT pipelines
On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting
Computed tomography (CT) is routinely used for three-dimensional non-invasive
imaging. Numerous data-driven image denoising algorithms were proposed to
restore image quality in low-dose acquisitions. However, considerably less
research investigates methods already intervening in the raw detector data due
to limited access to suitable projection data or correct reconstruction
algorithms. In this work, we present an end-to-end trainable CT reconstruction
pipeline that contains denoising operators in both the projection and the image
domain and that are optimized simultaneously without requiring ground-truth
high-dose CT data. Our experiments demonstrate that including an additional
projection denoising operator improved the overall denoising performance by
82.4-94.1%/12.5-41.7% (PSNR/SSIM) on abdomen CT and 1.5-2.9%/0.4-0.5%
(PSNR/SSIM) on XRM data relative to the low-dose baseline. We make our entire
helical CT reconstruction framework publicly available that contains a raw
projection rebinning step to render helical projection data suitable for
differentiable fan-beam reconstruction operators and end-to-end learning.Comment: This work has been submitted to the IEEE for possible publication.
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Noise2Contrast: Multi-Contrast Fusion Enables Self-Supervised Tomographic Image Denoising
Self-supervised image denoising techniques emerged as convenient methods that
allow training denoising models without requiring ground-truth noise-free data.
Existing methods usually optimize loss metrics that are calculated from
multiple noisy realizations of similar images, e.g., from neighboring
tomographic slices. However, those approaches fail to utilize the multiple
contrasts that are routinely acquired in medical imaging modalities like MRI or
dual-energy CT. In this work, we propose the new self-supervised training
scheme Noise2Contrast that combines information from multiple measured image
contrasts to train a denoising model. We stack denoising with domain-transfer
operators to utilize the independent noise realizations of different image
contrasts to derive a self-supervised loss. The trained denoising operator
achieves convincing quantitative and qualitative results, outperforming
state-of-the-art self-supervised methods by 4.7-11.0%/4.8-7.3% (PSNR/SSIM) on
brain MRI data and by 43.6-50.5%/57.1-77.1% (PSNR/SSIM) on dual-energy CT X-ray
microscopy data with respect to the noisy baseline. Our experiments on
different real measured data sets indicate that Noise2Contrast training
generalizes to other multi-contrast imaging modalities
Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-RadarāA Feasibility Study
Blood pressure monitoring is of paramount importance in the assessment of a humanās cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitationsāit only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and usedātogether with the calibration parameters of age, gender, height, and weightāas input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approachās predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of 9.2Ā±8.3 mmHg (mean error Ā± standard deviation) and a diastolic error of 7.7Ā±5.7 mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further
Islamic economics: a survey of the literature
A central thesis of this paper is that social science is the study of human experience, and hence is strongly conditioned by history. Modern Western political, economic and social structures have emerged as a consequence of the repudiation of religion associated with the Enlightenment and are based on secular principles. Many of these are inimical to Islamic principles, and cannot be adapted to an Islamic society. Muslim societies achieved freedom from colonial rule in the first half of the twentieth century and have sought to construct institutions in conformity with Islam. The development of Islamic economics is part of this process of transition away from Western colonial institutions. This paper is a survey of the literature on Islamic economics, which focuses on the contrasts between Western economic theories and Islamic approaches to the organization of economic affairs