Improve Image Quality Research Articles

Multitasking optimization for the imaging problem in electrical capacitance tomography

Electrical capacitance tomography excels in measuring multiphase flows, but it faces challenges in reconstructing high-accuracy images. To tap into the potential of this measurement technique, we model the image reconstruction problem as a bilevel multitasking optimization problem, aiming to enhance imaging quality via effective knowledge transfer and integration across various tasks. This new imaging model consists of a target optimization problem and an auxiliary optimization problem, facilitating knowledge transfer and fusion between tasks. We design a new algorithm to solve the target optimization problem and integrate it with an autoencoder used for the cross-task knowledge transfer, forming a new multitasking optimizer to solve this proposed bilevel multitasking optimization imaging model. The extreme learning machine is developed for the prediction of learnable prior images by introducing a novel bilevel optimization framework that enables concurrent parameter selection and model training. A novel nested algorithm, designed to efficiently address optimization problems structured hierarchically, is developed to solve the training model. Evaluation results show that the new imaging algorithm outperforms popular imaging methods in terms of reconstruction accuracy and robustness, while also demonstrating stable performance. This proposed imaging algorithm achieves cross-task knowledge transfer and fusion, integrates measurement physics and machine learning, mines and integrates effective image priors, adaptively learns model parameters, alleviates the ill-posed property of the image reconstruction problem, increase the automation of the model, and improves imaging quality, robustness and performance stability, which provides a comprehensive solution to mitigate challenges in imaging.

Methods for Reducing Ring Artifacts in Tomographic Images Using Wavelet Decomposition and Averaging Techniques

Computed tomography (CT) is one of the fundamental imaging modalities used in medicine, allowing for the acquisition of accurate cross-sectional images of internal body tissues. However, during the acquisition and reconstruction process, various artifacts can arise, and one of them is ring artifacts. These artifacts result from the inherent limitations of CT scanner components and the properties of the scanned material, such as detector defects, non-uniform distribution of radiation from the source, or the presence of metallic elements within the scanning region. The purpose of this study was to identify and reduce ring artifacts in tomographic images using image decomposition and average filtering methods. In this study, tests were conducted on the effectiveness of identifying ring artifacts using wavelet decomposition methods for images. The test was performed on a Shepp–Logan phantom with implemented artifacts of different intensity levels. The analysis was performed using different wavelet families, and linear approximation methods were used to filter the image in the identified areas. Additional filtering was performed using moving average methods and empirical mode decomposition (EMD) techniques. Image comparison methods, i.e., RMSE, SSIM and MS-SSIM, were used to evaluate performance. The results of this study showed a significant improvement in the quality of tomographic phantom images. The authors obtained more than 50% improvement in image quality with reference to the image without any filtration. The different wavelet families had different efficiencies with relation to the identification of the induction regions of ring artifacts. The Haar wavelet and Coiflet 1 showed the best performance in identifying artifact induction regions, with comparative RMSE values for these wavelets of 0.1477 for Haar and 0.1469 for Coiflet 1. The applied additional moving average filtering and EMD permitted us to improve image quality, which is confirmed by the results of the image comparison. The obtained results allow us to assess how the used methods affect the reduction in ring artifacts in phantom images with induced artifacts.

Optimizing SEM parameters for segmentation with AI – Part 2: Designing and training a regression model

Selecting the best microscope parameters for optimal image quality currently relies on microscopists; there exist no procedures or guidelines for tuning parameters to ensure the desired image quality is achieved. More importantly, for quantitative analysis purposes, adequate image quality for segmentation should be prioritized. This paper is the second of two parts, describing a regression model, mixed input, multiple output with Keras TensorFlow, trained to predict the beam energy and probe current, two important parameters for image quality. Specifically, parameters are predicted to optimize the image quality for segmentation, using a generated training set, as described in Part 1 of this paper. Model performance is then tested on models trained with multiple different training sets, and with different proportions of simulated and acquired data. First, to examine the impact of the training set on the prediction accuracy and then, to evaluate the importance of including real data during training. The model successfully predicted the beam energy and probe current to set on the microscope to improve image quality for segmentation. Models trained with both simulated and acquired data performed the best, as evaluated by their efficacy at improving the image quality for feature segmentation.

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques

Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for deep learning (DL)-based image quality improvement. Conventional VWI (Conv-VWI, voxel size 0.51 × 0.51 × 0.45 mm3) and denoised super-resolution DL-VWI (0.28 × 0.28 × 0.45 mm3) of 117 patients were analyzed in this retrospective study. Quality of the images were compared qualitatively and quantitatively. Diagnostic performance for identifying potentially culprit atherosclerotic plaques, using lesion enhancement and presence of intraplaque hemorrhage (IPH), was evaluated. DL-VWI significantly outperformed Conv-VWI in all image quality ratings (all P < .001). DL-VWI demonstrated higher SNR and contrast-to-noise ratio (CNR) than Conv-VWI, both in normal walls (basilar artery; SNR 4.83 ± 1.23 vs. 3.02 ± 0.59, P < .001) and lesions (contrast-enhanced images; SNR 22.12 ± 11.68 vs. 8.33 ± 3.26, P < .001). In the assessment of 86 lesions, DL-VWI showed higher confidence of detection (4.56 ± 0.55 vs. 2.62 ± 0.77, P < .001), more concordant IPH characterization (Cohen’s Kappa 0.85 vs. 0.59) and greater enhancement. For culprit plaque identification, IPH exhibited higher sensitivity in DL-VWI compared to Conv-VWI (70.6% vs. 23.5%) and excellent specificity (94.3% vs. 94.3%). Deep learning application of intracranial vessel wall images successfully improved the quality and resolution of the images. This aided in detecting vessel wall lesions and intraplaque hemorrhage, and in identifying potentially culprit atherosclerotic plaques.

Enhanced 3D Magnetic Resonance Cholangiopancreatography with Fast Gradient and Spin Echo Sequence: Technical Advancements and Clinical Implications

Objective: The application of fast gradient and spin echo (GRASE) sequence in 3D magnetic resonance cholangiopancreatography (MRCP) was investigated. Methods: Sixty patients who underwent 3D MRCP examination using the Philips Ingenia 3.0T superconducting MR imaging system and a 32-channel phased array direct digital RF receiving coil in the hospital from June 2021 to June 2023 were selected. The acquisition times of the two sequences were compared, and the scanned images of the two sequences were subjectively and objectively evaluated, with the scanning sequence including turbo spin echo (TSE) sequence and GRASE sequence. Results: The display scores, image artifact scores, and overall image quality scores of the GRASE sequence on the common bile duct, gallbladder duct, main pancreatic duct, left and right hepatic ducts were higher than those of the TSE sequence, and the differences were statistically significant (P&lt;0.05). The acquisition time of GRASE sequence was 16 seconds, which was about 96% shorter than that of TSE sequence (399.06±84.53) seconds. The contrast to noise ratio (CNR) of the GRASE sequence was higher than that of the TSE sequence, and the difference was statistically significant (P &lt; 0.05); The signal to noise ratio (SNR) of the GRASE sequence was slightly higher than that of the TSE sequence, but the difference was not statistically significant (P &gt; 0.05). Conclusion: The use of the GRASE sequence in 3D MRCP examinations can improve image quality, shorten acquisition time, and ensure CNR.

Effect of deep learning reconstruction on the assessment of pancreatic cystic lesions using computed tomography.

This study aimed to compare the image quality and detection performance of pancreatic cystic lesions between computed tomography (CT) images reconstructed by deep learning reconstruction (DLR) and filtered back projection (FBP). This retrospective study included 54 patients (mean age: 67.7 ± 13.1) who underwent contrast-enhanced CT from May 2023 to August 2023. Among eligible patients, 30 and 24 were positive and negative for pancreatic cystic lesions, respectively. DLR and FBP were used to reconstruct portal venous phase images. Objective image quality analyses calculated quantitative image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) using regions of interest on the abdominal aorta, pancreatic lesion, and pancreatic parenchyma. Three blinded radiologists performed subjective image quality assessment and lesion detection tests. Lesion depiction, normal structure illustration, subjective image noise, and overall image quality were utilized as subjective image quality indicators. DLR significantly reduced quantitative image noise compared with FBP (p < 0.001). SNR and CNR were significantly improved in DLR compared with FBP (p < 0.001). Three radiologists rated significantly higher scores for DLR in all subjective image quality indicators (p ≤ 0.029). Performance of DLR and FBP were comparable in lesion detection, with no statistically significant differences in the area under the receiver operating characteristic curve, sensitivity, specificity and accuracy. DLR reduced image noise and improved image quality with a clearer depiction of pancreatic structures. These improvements may have a positive effect on evaluating pancreatic cystic lesions, which can contribute to appropriate management of these lesions.

AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI.

Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however this optimization is a challenging task. To address this challenge, we introduce a novel deep learning framework, AutoSamp, based on variational information maximization that enables joint optimization of sampling pattern and reconstruction of MRI scans. We represent the encoder as a non-uniform Fast Fourier Transform that allows continuous optimization of k-space sample locations on a non-Cartesian plane, and the decoder as a deep reconstruction network. Experiments on public 3D acquired MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling for both compressed sensing and deep learning reconstructions. We demonstrate that our data-driven sampling optimization method achieves 4.4dB, 2.0dB, 0.75dB, 0.7dB PSNR improvements over reconstruction with Poisson Disc masks for acceleration factors of R = 5, 10, 15, 25, respectively. Prospectively accelerated acquisitions with 3D FSE sequences using our optimized sampling patterns exhibit improved image quality and sharpness. Furthermore, we analyze the characteristics of the learned sampling patterns with respect to changes in acceleration factor, measurement noise, underlying anatomy, and coil sensitivities. We show that all these factors contribute to the optimization result by affecting the sampling density, k-space coverage and point spread functions of the learned sampling patterns.

Aberration correction in 3D transthoracic echocardiography

Three-dimensional cardiac imaging has been available in the clinic for more than two decades. Continuous improvement in image quality has occurred in this period due to the development of probe technology and beamforming techniques. The purpose of this article is to quantitatively and qualitatively analyze the effect of a commercially available aberration correction algorithm on clinically acquired 3D transthoracic echocardiography (3D TTE) images. Clinical triplane and 3D volume cineloops of at least one cardiac cycle of pre-beamformed channel data were captured from 50 patients using a GE HealthCare Vivid E95 system with the 4Vc-D matrix array probe. This resulted in a total of 3200 vol and 3136 triplane frames. The data were post-processed with and without aberration correction. Quantitatively, assessed by an image quality parameter based on coherence, all recordings were improved by aberration correction compared to those without aberration correction. Triplane data obtained a larger improvement in image quality than volume data. Qualitatively, as demonstrated by case examples, aberration-corrected images appear sharper, have a brighter tissue signal compared to the cavity, and provide better delineation of cardiac structures. 3D rendering of valves can also be significantly improved. In general, aberration correction provides a systematic improvement in clinical cardiac triplane and 3D volume images.

An Image-Domain Deep-Learning Denoising Technique for Accelerated Parallel Brain MRI: Prospective Clinical Evaluation

Abstract Background Parallel imaging can accelerate MRI acquisitions, but excessive accelerations can introduce amplified noise and aliasing artifacts. Purpose To evaluate a vendor-agnostic AI-based approach to remove image degradation artifacts in highly-accelerated MRI scans, improving image quality and reducing scan time. Materials and Methods Training was performed by retrospectively degrading standard accelerated images. Evaluation was performed with both retrospective and prospectively-collected highly-accelerated images. Retrospective data were taken from ∼2000 MRI studies obtained between August 2016 and October 2022, and prospective data were collected in &amp;gt; 200 subjects between June and November 2022, using scanners from multiple vendors and locations. Scan time data were collected from prospective studies and used to compute time savings per sequence and per protocol for each vendor. Images were evaluated qualitatively by five board-certified radiologists and quantitatively by assessing noise, contrast and spatial resolution. Paired Wilcoxon signed-rank tests were used to compare model outputs to model inputs and low-acceleration images. Results Images from 101 adults from 5 sites and 6 scanner models from different vendors were enrolled. 89% of imaged subjects had noteworthy imaging features or pathology. Model outputs were rated superior to model inputs (p &amp;lt; 0.001) and most were either non-inferior (p inf &amp;gt; 0.05) or superior (p sup &amp;lt; 0.05) to baseline images in qualitative metrics of image quality and feature visibility. Quantitative evaluation of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) improved for model outputs compared to inputs (p &amp;lt; 0.001) or baseline images (p &amp;lt; 0.005). Apparent resolution measured using the full width at half maximum or minimum (FWHM) was either enhanced (p sup &amp;lt; 0.05) or preserved (non-superior p sup &amp;gt; 0.05 and non-inferior p inf &amp;gt; 0.05). The scan time was reduced by an average of 29% (19%-41% per sequence). Conclusion This vendor-agnostic AI-based method achieved robust scan time savings without loss of image quality, potentially allowing for reduced cost and improved patient experience.

High Spatial-Resolution and Acquisition-Efficiency Cardiac MR T1 Mapping Based on Radial bSSFP and a Low-Rank Tensor Constraint.

Cardiac T1 mapping is valuable for evaluating myocardial fibrosis, yet its resolution and acquisition efficiency are limited, potentially obscuring visualization of small pathologies. To develop a technique for high-resolution cardiac T1 mapping with a less-than-100-millisecond acquisition window based on radial MOdified Look-Locker Inversion recovery (MOLLI) and a calibrationless space-contrast-coil locally low-rank tensor (SCC-LLRT) constrained reconstruction. Prospective. Sixteen healthy subjects (age 25 ± 3 years, 44% females) and 12 patients with suspected cardiomyopathy (age 57 ± 15 years, 42% females), NiCl2-agar phantom. 3-T, standard MOLLI, radial MOLLI, inversion-recovery spin-echo, late gadolinium enhancement. SCC-LLRT was compared to a conventional locally low-rank (LLR) method through simulations using Normalized Root-Mean-Square Error (NRMSE) and Structural Similarity Index Measure (SSIM). Radial MOLLI was compared to standard MOLLI across phantom, healthy subjects, and patients. Three independent readers subjectively evaluated the quality of T1 maps using a 5-point scale (5 = best). Paired t-test, Wilcoxon signed-rank test, intraclass correlation coefficient analysis, linear regression, Bland-Altman analysis. P < 0.05 was considered statistically significant. In simulations, SCC-LLRT demonstrated a significant improvement in NRMSE and SSIM compared to LLR. In phantom, both radial MOLLI and standard MOLLI provided consistent T1 estimates across different heart rates. In healthy subjects, radial MOLLI exhibited a significantly lower mean T1 (1115 ± 39 msec vs. 1155 ± 36 msec), similar T1 SD (74 ± 14 msec vs. 67 ± 23 msec, P = 0.20), and similar T1 reproducibility (28 ± 18 msec vs. 22 ± 15 msec, P = 0.34) compared to standard MOLLI. In patients, the proposed method significantly improved the sharpness of myocardial boundaries (4.50 ± 0.65 vs. 3.25 ± 0.43), the conspicuity of papillary muscles and fine structures (4.33 ± 0.74 vs. 3.33 ± 0.47), and artifacts (4.75 ± 0.43 vs. 3.83 ± 0.55). The reconstruction time for a single slice was 5.2 hours. The proposed method enables high-resolution cardiac T1 mapping with a short acquisition window and improved image quality. 1 TECHNICAL EFFICACY: Stage 1.

Canadian Medical Imaging Inventory 2022–2023: The Medical Imaging Team

A workforce comprising medical radiation technologists (MRTs), radiologists, nuclear medicine specialists, medical imaging physicists, and other medical staff is essential for the delivery of medical imaging services. There is wide variation in the numbers of staff in each profession in Canada, ranging from 45 positions located in 6 provinces for medical imaging physicists to 25,000 positions located across all provinces and territories for MRTs. Data from 2022–2023 suggest that the number of full-time staff has not grown at the same pace as the volume of exams. Sufficient staffing is needed to ensure the sustainable operation and delivery of medical imaging services that is supported by investment in the workforce through equitable education and training opportunities. According to a Canadian Medical Imaging Inventory (CMII) report on wait time strategies, staffing shortages may extend wait times and may lead to disruptions in service delivery. Managing the growing demand for imaging services and clearing the backlog of exams deferred during the pandemic has exacerbated existing staffing shortages and contributed to increased workloads and decreased staff well-being. The adoption of supportive tools and technologies — such as clinical decision support tools, automated order entry, and AI-driven solutions — may assist the workforce by creating efficiencies, improving image quality, and increasing access to medical imaging.

Innovative Noise Extraction and Denoising in Low-Dose CT Using a Supervised Deep Learning Framework

Low-dose computed tomography (LDCT) imaging is a critical tool in medical diagnostics due to its reduced radiation exposure. However, this reduction often results in increased noise levels, compromising image quality and diagnostic accuracy. Despite advancements in denoising techniques, a robust method that effectively balances noise reduction and detail preservation remains a significant need. Current denoising algorithms frequently fail to maintain the necessary balance between suppressing noise and preserving crucial diagnostic details. Addressing this gap, our study focuses on developing a deep learning-based denoising algorithm that enhances LDCT image quality without losing essential diagnostic information. Here we present a novel supervised learning-based LDCT denoising algorithm that employs innovative noise extraction and denoising techniques. Our method significantly enhances LDCT image quality by incorporating multiple attention mechanisms within a U-Net-like architecture. Our approach includes a noise extraction network designed to capture diverse noise patterns precisely. This network is integrated into a comprehensive denoising system consisting of a generator network, a discriminator network, and a feature extraction AutoEncoder network. The generator network removes noise and produces high-quality CT images, while the discriminator network differentiates real images from denoised ones, improving the realism of the outputs. The AutoEncoder network ensures the preservation of image details and diagnostic integrity. Our method improves the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) by 7.777 and 0.128 compared to LDCT, by 0.483 and 0.064 compared to residual encoder–decoder convolutional neural network (RED-CNN), by 4.101 and 0.017 compared to Wasserstein generative adversarial network–visual geometry group (WGAN-VGG), and by 3.895 and 0.011 compared to Wasserstein generative adversarial network–autoencoder (WGAN-AE). This demonstrates that our method has a significant advantage in enhancing the signal-to-noise ratio of images. Extensive experiments on multiple standard datasets demonstrate our method’s superior performance in noise suppression and image quality enhancement compared to existing techniques. Our findings significantly impact medical imaging, particularly improving LDCT scan diagnostic accuracy. The enhanced image clarity and detail preservation offered by our method open new avenues for clinical applications and research. This improvement in LDCT image quality promises substantial contributions to clinical diagnostics, disease detection, and treatment planning, ensuring high-quality diagnostic outcomes while minimizing patient radiation exposure.

Synthesizing High b-Value Diffusion-Weighted Imaging of Gastric Cancer Using an Improved Vision Transformer CycleGAN.

Diffusion-weighted imaging (DWI), a pivotal component of multiparametric magnetic resonance imaging (mpMRI), plays a pivotal role in the detection, diagnosis, and evaluation of gastric cancer. Despite its potential, DWI is often marred by substantial anatomical distortions and sensitivity artifacts, which can hinder its practical utility. Presently, enhancing DWI's image quality necessitates reliance on cutting-edge hardware and extended scanning durations. The development of a rapid technique that optimally balances shortened acquisition time with improved image quality would have substantial clinical relevance. This study aims to construct and evaluate the unsupervised learning framework called attention dual contrast vision transformer cyclegan (ADCVCGAN) for enhancing image quality and reducing scanning time in gastric DWI. The ADCVCGAN framework, proposed in this study, employs high b-value DWI (b = 1200 s/mm2) as a reference for generating synthetic b-value DWI (s-DWI) from acquired lower b-value DWI (a-DWI, b = 800 s/mm2). Specifically, ADCVCGAN incorporates an attention mechanism CBAM module into the CycleGAN generator to enhance feature extraction from the input a-DWI in both the channel and spatial dimensions. Subsequently, a vision transformer module, based on the U-net framework, is introduced to refine detailed features, aiming to produce s-DWI with image quality comparable to that of b-DWI. Finally, images from the source domain are added as negative samples to the discriminator, encouraging the discriminator to steer the generator towards synthesizing images distant from the source domain in the latent space, with the goal of generating more realistic s-DWI. The image quality of the s-DWI is quantitatively assessed using metrics such as the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), mean squared error (MSE), weighted peak signal-to-noise ratio (WPSNR), and weighted mean squared error (WMSE). Subjective evaluations of different DWI images were conducted using the Wilcoxon signed-rank test. The reproducibility and consistency of b-ADC and s-ADC, calculated from b-DWI and s-DWI, respectively, were assessed using the intraclass correlation coefficient (ICC). A statistical significance level of p < 0.05 was considered. The s-DWI generated by the unsupervised learning framework ADCVCGAN scored significantly higher than a-DWI in quantitative metrics such as PSNR, SSIM, FSIM, MSE, WPSNR, and WMSE, with statistical significance (p < 0.001). This performance is comparable to the optimal level achieved by the latest synthetic algorithms. Subjective scores for lesion visibility, image anatomical details, image distortion, and overall image quality were significantly higher for s-DWI and b-DWI compared to a-DWI (p < 0.001). At the same time, there was no significant difference between the scores of s-DWI and b-DWI (p > 0.05). The consistency of b-ADC and s-ADC readings was comparable among different readers (ICC: b-ADC 0.87-0.90; s-ADC 0.88-0.89, respectively). The repeatability of b-ADC and s-ADC readings by the same reader was also comparable (Reader1 ICC: b-ADC 0.85-0.86, s-ADC 0.85-0.93; Reader2 ICC: b-ADC 0.86-0.87, s-ADC 0.89-0.92, respectively). ADCVCGAN shows excellent promise in generating gastric cancer DWI images. It effectively reduces scanning time, improves image quality, and ensures the authenticity of s-DWI images and their s-ADC values, thus providing a basis for assisting clinical decision making.

Stationary prospective cardiac gated computed tomography—dynamic study in phantoms and in vivo

Objective. This study explores the feasibility of a stationary gantry cardiac gated computed tomography (CT) with carbon nanotube (CNT) linear x-ray source arrays. Approach. We developed a stationary gantry CT system utilizing multipixel CNT x-ray sources. Given the advantages of straightforward x-ray pulse control with these sources, we investigated the potential for gated prospective imaging. We implemented prospective respiratory and cardiac gating control and evaluated the system through dynamic phantom imaging studies followed by imaging of a porcine model. Main Results. The findings revealed minimal anatomical motion artifacts in the heart and lungs, confirming successful physiologic gated acquisition in stationary gantry cardiac CT. This indicates the potential of this imaging approach for reducing artifacts and improving image quality. Significance. This study demonstrates the feasibility of prospective physiological gating with CNT x-ray sources in a stationary gantry setup for cardiac imaging. This approach could potentially alleviate the need for beta blocker administration during cardiac CT scans, thereby increasing the flexibility of the imaging system and enabling the imaging of a wider variety of patient cardiac conditions.

Revolutionizing Cardiac Imaging: A Scoping Review of Artificial Intelligence in Echocardiography, CTA, and Cardiac MRI.

Cardiac imaging is crucial for diagnosing heart disorders. Methods like X-rays, ultrasounds, CT scans, and MRIs provide detailed anatomical and functional heart images. AI can enhance these imaging techniques with its advanced learning capabilities. In this scoping review, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Guidelines, we searched PubMed, Scopus, Web of Science, and Google Scholar using related keywords on 16 April 2024. From 3679 articles, we first screened titles and abstracts based on the initial inclusion criteria and then screened the full texts. The authors made the final selections collaboratively. The PRISMA chart shows that 3516 articles were initially selected for evaluation after removing duplicates. Upon reviewing titles, abstracts, and quality, 24 articles were deemed eligible for the review. The findings indicate that AI enhances image quality, speeds up imaging processes, and reduces radiation exposure with sensitivity and specificity comparable to or exceeding those of qualified radiologists or cardiologists. Further research is needed to assess AI's applicability in various types of cardiac imaging, especially in rural hospitals where access to medical doctors is limited. AI improves image quality, reduces human errors and radiation exposure, and can predict cardiac events with acceptable sensitivity and specificity.

Video and image quality improvement using an enhanced optimized dehazing technique

Video and image quality improvement using an enhanced optimized dehazing technique

Robust unrolled network for lensless imaging with enhanced resistance to model mismatch and noise

Lensless imaging has gained popularity in various applications due to its user-friendly nature, cost-effectiveness, and compact design. However, achieving high-quality image reconstruction within this framework remains a significant challenge. Lensless imaging measurements are associated with distinct point spread functions (PSFs), resulting in many PSFs introducing artifacts into the underlying physical model. This discrepancy between the actual and prior models poses challenges for standard reconstruction methods to effectively address high-quality image reconstruction by solving a regularization-based inverse problem. To overcome these issues, we propose MN-FISTA-Net, an unrolled neural network that unfolds the fast iterative shrinkage/thresholding algorithm for solving mixed norm regularization with a deep denoiser prior. Our method enhances mask-based lensless imaging performance by efficiently addressing noise and model mismatch, as evidenced by significant improvements in image quality compared to existing approaches.

Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation

Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and generalization. In this study, we propose a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), that outperforms CNN based networks for image denoising. We train a general CNNT based backbone model from pairwise high-low Signal-to-Noise Ratio (SNR) image volumes, gathered from a single type of fluorescence microscope, an instant Structured Illumination Microscope. Fast adaptation to new microscopes is achieved by fine-tuning the backbone on only 5–10 image volume pairs per new experiment. Results show that the CNNT backbone and fine-tuning scheme significantly reduces training time and improves image quality, outperforming models trained using only CNNs such as 3D-RCAN and Noise2Fast. We show three examples of efficacy of this approach in wide-field, two-photon, and confocal fluorescence microscopy.

Thin and lightweight camera based on Pancake lens and deep learning.

Computational imaging using a Pancake lens can help reduce the size of optical systems by folded optics. However, Pancake cameras frequently exhibit inferior image quality due to stray light, low light transmission, and spatially varying aberrations. In this Letter, we propose a thin and lightweight camera comprising a polarization-based catadioptric Pancake lens and a Fourier Position encoding Network (FPNet). The camera achieves high-quality imaging at an f-number of 0.4 and an expansive 88° field of view. The FPNet encodes the positional order of the point spread functions, mitigating global optical image degradation and improving image quality by 10.13 dB in PSNR. The Pancake camera and FPNet have potential applications in mobile photography and virtual/augmented reality.

Image Quality Restoration on Historical Artifacts Using Histogram Equalization and Contrast Stretching Methods

This research was carried out to improve the image quality of historical artifacts that experience quality degradation such as noise, unclear or dark image contrast, and poor sharpness. Contrast Stretching and Histogram Equalization are image quality improvement methods to expand and even out image grayscale. This research aims to build a system for improving image quality on historical artifacts using contrast stretching and histogram equalization methods. The sample used in this research is RGB image data with 10 test images in .jpg format, while the process uses the Matlab programming language. The results of improving image quality using the contrast stretching and histogram equalization methods from sample 1 to image sample 10 showed the best results in test image_citra_1 with contrast stretching results based on PSNR = 32.61 dB and MSE = 35.86 Db and PSNR = 19.13 Db and MSE = 794.76 in histogram equalization calculations.