Acquisition time reduction in pediatric 99mTc‐DMSA planar imaging using deep learning

Aims/Purpose: Our goal is to enhance the visualization of the choroid in clinical standard spectral‐domain OCT (SDOCT) scans using a deep‐learning (DL) approach that uses matching swept‐source OCT (SSOCT). We aim to improve the measurement of the sub‐foveal choroidal thickness (SFCT), total choroidal area (TCA), and choroidal vascularity index (CVI) in SDOCT images.Methods: A total of 105 350 SDOCT and SSOCT B‐scan images (Zeiss Cirrus 5000 and Zeiss PlexElite) were used to develop the DL model. The data set included B‐scans from healthy (238 eye pairs from 155 patients) and glaucoma (192 eye pairs from 122 patients) subjects. The DL model learned deep anatomical features from SSOCT scans and generated enhanced SDOCT images. The peak signal‐to‐noise ratio (PSNR) and structural similarity index metric (SSIM) were used to assess the quality of generated images. In addition, we compared the anatomical parameters derived from the generated and ground‐truth SSOCT images, including SVCT, TCA and CVI values.Results: We tested our method using 20 580 B‐scan image pairs from 84 eyes. The DL‐generated images effectively preserved the anatomical structure of the retina layers and improved the visualization of the choroid. The enhanced SDOCT images had higher image quality than the original SDOCT images, as demonstrated by the higher PSNR (31 dB vs. 18 dB) and SSIM values (3.4 times higher SSIM than the original SDOCT images). SVCT, TCA, and CVI measurements from the DL‐generated SDOCT images showed strong correlations with corresponding measurements using SSOCT with high Pearson's coefficients (0.93, 0.99, and 0.93), high intra‐class correlation indices (0.96, 0.99 and 0.96), and high coefficient of determination r2 values (0.85, 0.98 and 0.86).Conclusions: Our DL model enhances the visibility of the choroid in images, surpassing the capabilities of standard clinical SDOCT images. As a result, accurate measurement of SVCT, TCA and CVI, which were previously limited due to imaging depth constraints of SDOCT, becomes possible. This technology opens up new possibilities for utilizing affordable SDOCT devices to study the choroid in healthy and pathological conditions.

This research paper explores the rising interest in utilising deep learning methods, specifically Convolution Neural Network (CNN), to improve the reconstruction of Compressively Sampled Magnetic Resonance Imaging (CS MRI) images from under-sampled data. By training deep learning architectures on extensive data sets of paired under sampled and fully sampled MR images, these models aim to capture intricate patterns and structures, ultimately enhancing the accuracy of MR image reconstructions. A novel deep learning model is proposed dubbed as “Deep Iterative Shrinkage Thresholding Algorithm-Compressed Sensing Network” (DISTA-CSNet), specifically designed for efficient recovery of CS MRI. Our model showcases impressive results with only 20 epochs and can be effortlessly trained on diverse datasets. To ensure robustness across different datasets, the dropouts are incorporated into the model, evident from testing results. The trained DISTA-CSNet exhibits remarkable performance in recovering CS MRI from various data sets, surpassing several advanced deep learning techniques with changing compression ratios consistently. The proposed model demonstrates significant enhancements in both Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, affirming the efficacy of our proposed model. The capability of our DISTA-CSNet in accurately reconstructing CS MRI images from 5-fold undersampled data shows promise in improving medical imaging applications and advancing the field of compressed sensing MRI.

Image Denoising is a consistent problem from long period of time and still a challenging task for researchers. There evolved many techniques for image denoising which involves filtering techniques in spatial domain, Transform techniques in transform domain (Sekhar et al. in IRECOS 10(10):1012–1017, 2015 [1]), and more recently evolutionary computing tools (ECT) and genetic algorithms proved more effective in denoising of images. There are many ECT available which can be applied for denoising problem (Sekhar et al. in JGIM 25(4) 2017, [2]). In this paper we made an attempt to Denoise both color and grayscale images by applying a new ECT which emerged out with more efficient results. Peak Signal to noise ratio (PSNR), Structural Similarity Index Metric (SSIM), Mean Structural Similarity Index Metric (MSSIM), etc., are considered in this paper as Image quality Assessment metrics. Comparison of proposed method is also compared with state-of-the-art techniques.

Noise Removal in medical images is a requisite pre-processing step for medical image analysis. In this paper, we present three deep learning architectures: Convolutional Auto-encoders, U-Net and a Double U-Net architecture utilizing convolution layers for medical Image De-noising. We use PSNR(Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Metric) to compare the results of the networks.In our experiments, the variant of the U-Net architecture implemented performed better than the two architectures in terms of its SSIM.

Moderating the injected activity and/or reducing acquisition time to minimize potential radiation hazards and increase patient comfort are important trends in PET. This work aims to assess the performance of regular full-dose partial volume corrected (FD-PVC) image synthesis from fast/low-dose (LD) brain PET images using deep learning techniques for 18F-FDG, 18F-Flortaucipir, and 18F-Flutemetamol radiotracers. Clinical brain PET/CT studies of 100 patients for each radiotracer were employed. The 5% of the events were randomly selected from the FD list-mode PET data to simulate a realistic LD acquisition. A modified cycle-consistent generative adversarial network (CycleGAN) model was implemented to predict FD-PVC PET images. Quantitative analysis using established metrics, including the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), root mean square error (RMSE), and standardized uptake value (SUV) bias, were performed. The R2 values were 0.95, 0.90, and 0.93 for 18F-FDG, 18F-Flortaucipir, and 18F-Flutemetamol, respectively. PSNR and SSIM values of 33.62±2.35 and 31.81±3.7, 34.44±4.11 and 0.96±0.02, 0.96±0.03, and 0.95±0.04 were obtained for 18F-FDG, 18F-Flortaucipir, and 18F-Flutemetamol, respectively. The CycleGAN is able to generate PVC images similar to references from under sampled LD images.

Focusing on enhancing the security and privacy of electronic medical images (EMI) which have varying properties and larger in number before they are stored or transmitted by hospitals, labs, and researchers or while they reside in third party storage. EMI should be anywhere, anytime accessible for referencing and medical diagnosis. Therefore, should be accessible in real-time through real-time applications (RTA). This research work uses a large data set having 5856 X-ray images of a total size of 1.6 GB in JPEG format each of these X-ray images vary in properties such as size and dimensions. Two methods have been implemented for enhancing security and privacy Edge-Based Steganography (EBS) and Block-Based Steganography (BBS) in the region of no interest (RONI) of the normalized cover image. Both methods are compared for enhancement concerning steganography properties such as payload capacity, imperceptibility, security, reduction in computational time, and lesser computational complexity which increases their applicability for RTA. RTA makes EMI accessible on heterogeneous devices, therefore, increasing its scalability and usage. This paper performs comparative evaluation based on standardized tests such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Metrics (SSIM), Coefficient of Variation (CV), and Pearson Correlation (R), etc. Results show that both methods achieved a 100% embedding rate, SSIM value close to 1, and low average MSE value of 0.005 for EBS secret images and 0.003 for BBS secret images. Results indicate both methods are capable of hiding large data sets within minutes therefore making them both suitable for RTA while the EBS method takes 2.3 minutes which is less than BBS’s 3.6 minutes encoding time for 5856 images. Therefore, the EBS method was found more suitable than the BBS method based on computational time.

Style transfer is an optimizing technique that aims to blend style of input image to content image. Deep neural networks have previously surpassed humans in tasks such as object identification and detection. Deep neural networks, on the contrary, had been lagging behind in generating higher quality creative products until lately. This article introduces deep-learning techniques, which are vital in accomplishing human characteristics and open up a new world of prospects. The system employs a pre-trained CNN so that the styles of the provided image is transferred to the content image to generate high quality stylized image. The designed systems effectiveness is evaluated based on Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metrics (SSIM), it is noticed that the designed method effectively maintains the structural and textural information of the cover image.

Image enhancement is better achieved when fractional-order masks are used rather than integer-order ones, this is due to the flexibility of fractional-order parameters control. This paper proposes a combination of fractional-order masks to be used in parallel as double filters system structure to improve image enhancement rather than using a single-stage filter. Various performance metrics are used in this work to evaluate the proposed system, such as Information Entropy (IE), Average Gradient (AG), Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR). Based on visual as well as numerical results, it is found that the combination of two double masks is superior to the single fractional-order system in terms of enhancing texture and edges.

An effectively perceptual preprocessing filter method will reduce the sequence’s perceptually insignificant details, which can help to alleviate the video coding’s pressure and benefit the coding results. In this paper, a perceptual rate distortion optimization based preprocessing filter algorithm is presented to achieve better video compression performance. The proposed algorithm employs an adaptively temporal filter with the rate SSIM (Structure Similarity Index Metric) based optimization constraint to produces optimal filter parameters. The evaluation results confirm that the proposed video filter algorithm has achieved reliable rate-visual quality improvements with -4.35% and -2.96% SSIM based BD-Rate performance gain on JM18.3 and HM16.9, respectively. Additionally, the subjective quality comparison results show that our algorithm has reduced significant bits with negligible visual difference for both the experiments on JM18.3 and HM16.9.

The measurement and evaluation of the QoE (Quality of Experience) have become one of the main focuses in the telecommunications to provide services with the expected quality for their users. However, factors like the network parameters and codification can affect the quality of video, limiting the correlation between the objective and subjective metrics. The above increases the complexity to evaluate the real quality of video perceived by users. In this paper, a model based on artificial neural networks such as BPNNs (Backpropagation Neural Networks) and the RNNs (Random Neural Networks) is applied to evaluate the subjective quality metrics MOS (Mean Opinion Score) and the PSNR (Peak Signal Noise Ratio), SSIM (Structural Similarity Index Metric), VQM (Video Quality Metric), and QIBF (Quality Index Based Frame). The proposed model allows establishing the QoS (Quality of Service) based in the strategyDiffserv. The metrics were analyzed through Pearson’s and Spearman’s correlation coefficients, RMSE (Root Mean Square Error), and outliers rate. Correlation values greater than 90% were obtained for all the evaluated metrics.

A small dataset commonly affects generalization, robustness, and overall performance of deep neural networks (DNNs) in medical imaging research. Since gathering large clinical databases is always difficult, we proposed an analytical method for producing a large realistic/diverse dataset. Clinical brain PET/CT/MR images including full-dose (FD), low-dose (LD) corresponding to only 5 % of events acquired in the FD scan, non-attenuated correction (NAC) and CT-based measured attenuation correction (MAC) PET images, CT images and T1 and T2 MR sequences of 35 patients were included. All images were registered to the Montreal Neurological Institute (MNI) template. Laplacian blending was used to make a natural presentation using information in the frequency domain of images from two separate patients, as well as the blending mask. This classical technique from the computer vision and image processing communities is still widely used and unlike modern DNNs, does not require the availability of training data. A modified ResNet DNN was implemented to evaluate four image-to-image translation tasks, including LD to FD, LD+MR to FD, NAC to MAC, and MRI to CT, with and without using the synthesized images. Quantitative analysis using established metrics, including the peak signal-to-noise ratio (PSNR), structural similarity index metric (SSIM), and joint histogram analysis was performed for quantitative evaluation. The quantitative comparison between the registered small dataset containing 35 patients and the large dataset containing 350 synthesized plus 35 real dataset demonstrated improvement of the RMSE and SSIM by 29% and 8% for LD to FD, 40% and 7% for LD+MRI to FD, 16% and 8% for NAC to MAC, and 24% and 11% for MRI to CT mapping task, respectively. The qualitative/quantitative analysis demonstrated that the proposed model improved the performance of all four DNN models through producing images of higher quality and lower quantitative bias and variance compared to reference images.

A fusion-domain color image watermarking based on Haar transform and image correction

The aim was to improve single-photon emission computed tomography (SPECT) quality for sparsely acquired 111In projections by adding deep learning generated synthetic intermediate projections (SIPs). Method: The recently constructed deep convolutional network for generating synthetic intermediate projections (CUSIP) was used for improving 20 sparsely acquired 111In-octreotide SPECTs. Reconstruction was performed with 120 (120P) or 30 (30P) projections, or 120 projections with 90 SIPs generated from 30 projections (30–120SIP). The SPECT reconstructions were performed with attenuation, scatter and collimator response corrections. Postfiltered 30P reconstructed SPECT was also analyzed. Image quality were quantitatively evaluated with root-mean-square error, peak signal-to-noise ratio and structural similarity index metrics. Result: The 30–120SIP reconstructed SPECT had statistically significant improved image quality parameters compared to 30P reconstructed SPECT with and without post filtering. The images visual appearance was similar to slightly filtered 120P SPECTs. Thereby, substantial acquisition time reduction with SIPs seems possible without image quality degradation.

Melanoma and non-melanoma skin cancers have shown a rapidly increasing incidence rate, pointing to skin cancer as a major problem for public health. When analyzing these lesions in dermoscopic images, the hairs and their shadows on the skin may occlude relevant information about the lesion at the time of diagnosis, reducing the ability of automated classification and diagnosis systems. In this work, we present a new approach for the task of hair removal on dermoscopic images based on deep learning techniques. Our proposed model relies on an encoder-decoder architecture, with convolutional neural networks, for the detection and posterior restoration of hair's pixels from the images. Moreover, we introduce a new combined loss function in the network's training phase that combines the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> distance, the total variation loss, and a loss function based on the structural similarity index metric. Currently, there are no datasets that contain the same images with and without hair, which is necessary to quantitatively evaluate our model. Thus, we simulate the presence of hair in hairless images extracted from publicly known datasets. We compare our results with six state-of-the-art algorithms based on traditional computer vision techniques by means of similarity measures that compare the reference hairless image and the one with simulated hair. Finally, the Wilcoxon signed-rank test is used to compare the methods. The results, both qualitatively and quantitatively, demonstrate the effectiveness of our model and how our loss function improves the restoration ability of the proposed model.

Deep Learning (DL) techniques have enabled up to 6-fold acceleration in musculoskeletal magnetic resonance imaging (MRI) while preserving diagnostic image quality. Further, improvements in acceleration and generalization require novel approaches. We propose a DL framework that integrates Simultaneous Multislice (SMS) imaging with Parallel Imaging (PI) to enhance current DL-based reconstruction. To advance musculoskeletal Magnetic Resonance Imaging (MRI), by developing a DL reconstruction framework that combines SMS and PI, enabling acceleration of up to 8-fold and beyond, while maintaining image quality suitable for clinicalinterpretation. End-to-End (E2E) DL framework for reconstructing Turbo Spin Echo (TSE) MRI data acquired with SMS and PI acceleration. The method unrolls a proximal gradient algorithm with Nesterov momentum and integrates a novel DL network for joint regularization across simultaneously acquired slices. Slice separation and k-space-to-image reconstruction are unified by embedding the full SMS forward model into the DL architecture. Data Consistency (DC) is modulated to enhance denoising, and a super-resolution module improves image sharpness. The robust DL model was trained on over 200000 slices from 1.5T to 3T scans with diverse acquisitionsettings. The proposed E2E DL model outperforms prior methods at 8-fold and 12-fold acceleration, as measured by PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) metrics. Evaluation on prospectively acquired clinical scans by two radiologists confirms, that image quality and abnormality detection are comparable to standard acquisitions at loweracceleration. We extend state-of-the-art DL reconstruction frameworks by integrating slice separation directly into the model for SMS acquisitions. Our E2E DL approach achieves clinical-grade image quality at 8-fold acceleration across 20 subjects, reducing acquisition time by 27%. Preliminary results suggest potential for further acceleration up to 12-fold, demonstrating significant advancement beyond existing DLtechniques.