dB Peak Signal-to-noise Ratio Research Articles

Acquisition Acceleration of Ultra-low Field MRI with Parallel Imaging and Compressed Sensing in Microtesla Fields.

In recent years, ultra-low field (ULF) magnetic resonance imaging (MRI) has gained widespread attention due to its advantages, such as low cost, light weight, and portability. However, the low signal-to-noise ratio (SNR) leads to a long scan time. Herein, we study the acceleration performance of parallel imaging (PI) and compressed sensing (CS) in different kspace sampling strategies at 0.12 mT. This study employs phantoms to assess the efficiency of acceleration methods at ULF MRI, in which signals are detected by ultra-sensitive superconducting quantum interference devices (SQUIDs). We compare the performance of fast Fourier transform (FFT), generalized auto-calibrating partially parallel acquisitions (GRAPPA), and eigenvector-based SPIRiT (ESPIRiT) in Cartesian sampling, while also evaluating non-uniform FFT (NUFFT), GRAPPA operator gridding, and ESPIRiT in nonCartesian sampling. We design a resolution phantom to investigate the effectiveness of these methods in maintaining image resolution. In Cartesian sampling, GRAPPA and ESPIRiT jointly regularized by total variation and ℓ1-norm (TVJℓ1 -ESPIRiT) methods reconstructed good-quality phantom images with an acceleration factor of R = 2. In contrast, TVJℓ1-ESPIRiT exhibited improved image quality and much less signal loss even for R = 4. In radial sampling, TVJℓ1-ESPIRiT reduced the acquisition time to 1.69 minutes at R = 4, with a respective improvement of 12.26 dB in peak SNR compared to NUFFT. The resolution phantom imaging showed that the reconstructions by PI and CS maintained the original resolution of 2 mm. This study improves the practicality of ULF MRI at microtesla fields by implementing imaging acceleration with PI and CS in different k-space sampling.

A encoder-decoder deblurring network combined with high-frequency a priori

Convolutional neural network based methods have been proposed to address blind deblurring. Aiming at the difficulty of reconstructing high-frequency features of images with existing network models, this paper proposes a two-stage convolution-based encoder-decoder for fusing high-frequency a prior. In the first stage, both blurred and high-frequency images are input, and the image features extracted by the network and the high-frequency features are fused. In the second stage, the fused features are further refined and recovered as potentially clear images, and the reconstruction ability of the model for high-frequency features is enhanced. In addition, this paper proposes a deep feature reorganization module that integrates multi-layer semantic information in the encoder-decoder and targets the encoder semantics to further enhance the feature characterization capability of the model. Comprehensive experimental results show that our method achieves 0.9085 structural similarity index (SSIM) and 30.66db peak signal-to-noise ratio (PSNR) on the GoPro dataset. Meanwhile, our method achieves 0.8514 SSIM and 27.39db PSNR on the Lai dataset.

Two-and-a-half order score-based model for solving 3D ill-posed inverse problems

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are crucial technologies in the field of medical imaging. Score-based models demonstrated effectiveness in addressing different inverse problems encountered in the field of CT and MRI, such as sparse-view CT and fast MRI reconstruction. However, these models face challenges in achieving accurate three dimensional (3D) volumetric reconstruction. The existing score-based models predominantly concentrate on reconstructing two-dimensional (2D) data distributions, resulting in inconsistencies between adjacent slices in the reconstructed 3D volumetric images. To overcome this limitation, we propose a novel two-and-a-half order score-based model (TOSM). During the training phase, our TOSM learns data distributions in 2D space, simplifying the training process compared to working directly on 3D volumes. However, during the reconstruction phase, the TOSM utilizes complementary scores along three directions (sagittal, coronal, and transaxial) to achieve a more precise reconstruction. The development of TOSM is built on robust theoretical principles, ensuring its reliability and efficacy. Through extensive experimentation on large-scale sparse-view CT and fast MRI datasets, our method achieved state-of-the-art (SOTA) results in solving 3D ill-posed inverse problems, averaging a 1.56 dB peak signal-to-noise ratio (PSNR) improvement over existing sparse-view CT reconstruction methods across 29 views and 0.87 dB PSNR improvement over existing fast MRI reconstruction methods with × 2 acceleration. In summary, TOSM significantly addresses the issue of inconsistency in 3D ill-posed problems by modeling the distribution of 3D data rather than 2D distribution which has achieved remarkable results in both CT and MRI reconstruction tasks.

A 22.3-Bit Third-Order Delta-Sigma Modulator for EEG Signal Acquisition Systems

This paper presents a high resolution delta-sigma modulator for continuous acquisition of electroencephalography (EEG) signals. The third-order single-loop architecture with a 1-bit quantizer is adopted to achieve 22.3-bit resolution. The effects of thermal noise on the performance of the delta-sigma modulator are analyzed to reasonably allocate the switched-capacitor sizes for optimal signal to noise ratio (SNR) and minimum chip area. The coefficients in feedback path and input path are optimized to avoid the signal distortion under the full-scale input voltage range with almost no increase in total capacitance sizes. Fabricated in 0.5 µm CMOS technology and powered by a 5 V voltage supply, the proposed delta-sigma modulator can achieve 136 dB peak SNR with 16 Hz input and 137 dB dynamic range in 100 Hz signal bandwidth with an oversampling ratio of 512. The modulator dissipates 700 µA. The core chip area is 1.96 mm2. The modulator occupies 1.41 mm2 and the decimator occupies 0.55 mm2.

Dynamic controllable residual generative adversarial network for low-dose computed tomography imaging.

Computed tomography (CT) imaging technology has become an indispensable auxiliary method in medical diagnosis and treatment. In mitigating the radiation damage caused by X-rays, low-dose computed tomography (LDCT) scanning is becoming more widely applied. However, LDCT scanning reduces the signal-to-noise ratio of the projection, and the resulting images suffer from serious streak artifacts and spot noise. In particular, the intensity of noise and artifacts varies significantly across different body parts under a single low-dose protocol. To improve the quality of different degraded LDCT images in a unified framework, we developed a generative adversarial learning framework with a dynamic controllable residual. First, the generator network consists of the basic subnetwork and the conditional subnetwork. Inspired by the dynamic control strategy, we designed the basic subnetwork to adopt a residual architecture, with the conditional subnetwork providing weights to control the residual intensity. Second, we chose the Visual Geometry Group Network-128 (VGG-128) as the discriminator to improve the noise artifact suppression and feature retention ability of the generator. Additionally, a hybrid loss function was specifically designed, including the mean square error (MSE) loss, structural similarity index metric (SSIM) loss, adversarial loss, and gradient penalty (GP) loss. The results obtained on two datasets show the competitive performance of the proposed framework, with a 3.22 dB peak signal-to-noise ratio (PSNR) margin, 0.03 SSIM margin, and 0.2 contrast-to-noise ratio margin on the Challenge data and a 1.0 dB PSNR margin and 0.01 SSIM margin on the real data. Experimental results demonstrated the competitive performance of the proposed method in terms of noise decrease, structural retention, and visual impression improvement.

Automatic Contrast Generation from Contrastless Computed Tomography.

The use of contrast-enhanced computed tomography (CTCA) for detection of coronary artery disease (CAD) exposes patients to the risks of iodine contrast-agents and excessive radiation, increases scanning time and healthcare costs. Deep learning generative models have the potential to artificially create a pseudo-enhanced image from non-contrast computed tomography (CT) scans.In this work, two specific models of generative adversarial networks (GANs) - the Pix2Pix-GAN and the Cycle-GAN - were tested with paired non-contrasted CT and CTCA scans from a private and public dataset. Furthermore, an exploratory analysis of the trade-off of using 2D and 3D inputs and architectures was performed. Using only the Structural Similarity Index Measure (SSIM) and the Peak Signal-to-Noise Ratio (PSNR), it could be concluded that the Pix2Pix-GAN using 2D data reached better results with 0.492 SSIM and 16.375 dB PSNR. However, visual analysis of the output shows significant blur in the generated images, which is not the case for the Cycle-GAN models. This behavior can be captured by the evaluation of the Fréchet Inception Distance (FID), that represents a fundamental performance metric that is usually not considered by related works in the literature.Clinical relevance- Contrast-enhanced computed tomography is the first line imaging modality to detect CAD resulting in unnecessary exposition to the risk of iodine contrast and radiation in particularly in young patients with no disease. This algorithm has the potential of being translated into clinical practice as a screening method for CAD in asymptomatic subjects or quick rule-out method of CAD in the acute setting or centres with no CTCA service. This strategy can eventually represent a reduction in the need for CTCA reducing its burden and associated costs.

Self-supervised denoising of Nyquist-sampled volumetric images via deep learning.

Deep learning has demonstrated excellent performance enhancing noisy or degraded biomedical images. However, many of these models require access to a noise-free version of the images to provide supervision during training, which limits their utility. Here, we develop an algorithm (noise2Nyquist) that leverages the fact that Nyquist sampling provides guarantees about the maximum difference between adjacent slices in a volumetric image, which allows denoising to be performed without access to clean images. We aim to show that our method is more broadly applicable and more effective than other self-supervised denoising algorithms on real biomedical images, and provides comparable performance to algorithms that need clean images during training. We first provide a theoretical analysis of noise2Nyquist and an upper bound for denoising error based on sampling rate. We go on to demonstrate its effectiveness in denoising in a simulated example as well as real fluorescence confocal microscopy, computed tomography, and optical coherence tomography images. We find that our method has better denoising performance than existing self-supervised methods and is applicable to datasets where clean versions are not available. Our method resulted in peak signal to noise ratio (PSNR) within 1dB and structural similarity (SSIM) index within 0.02 of supervised methods. On medical images, it outperforms existing self-supervised methods by an average of 3dB in PSNR and 0.1 in SSIM. noise2Nyquist can be used to denoise any volumetric dataset sampled at at least the Nyquist rate making it useful for a wide variety of existing datasets.

Learned digital lens enabled single optics achromatic imaging.

High-quality imaging with reduced optical complexity has been extensively investigated owing to its promising future in academic and industrial research. However, the practical performance of most imaging systems has encountered a bottleneck posed by optics rather than electronics. Here, we propose a digital lens (DL) to compensate for the chromatic aberration induced by physical optical elements, while the residual wavelength-independent degradation is tackled through a self-designed neural network. By transforming physical aberration correction to an algorithm-based computational imaging task, the proposed DL enables our framework to reduce optical complexity and achieve achromatic imaging in the analog domain. Real experiments have been conducted with an off-the-shelf single lens and recovered images show up to 14.62 dB higher peak signal-to-noise ratio (PSNR) than the original chromatic input. Furthermore, we run a comprehensive ablation study to highlight the contribution of embedding the proposed DL, which shows a 4.83 dB PSNR improvement compared with the methods without DL. Technically, the proposed method can be an alternative for future applications that require both simple optics and high-fidelity visualization.

SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

With the ever growth of Internet users, video applications, and massive data traffic across the network, there is a higher need for reliable bandwidth-efficient multimedia communication. Versatile Video Coding (VVC/H.266) is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding (HEVC) while providing versatile effective use for Ultra-High Definition (HD) videos. This article analyzes the quality performance of convolutional codes, turbo codes and self-concatenated convolutional (SCC) codes based on performance metrics for reliable future video communication. The advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon limit. Turbo codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional (RSC) encoders in a parallel fashion. Constituent RSC encoders may be operating on the same or different architectures and code rates. The proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading (SP-DSTS) for video transmission in order to provide bandwidth-efficient wireless video communication. Moreover, simulation results show that turbo codes defeat convolutional codes with an averaged Eb/N0 gain of 1.5 dB while convolutional codes outperform compared to SCC codes with an Eb/N0 gain of 3.5 dB at Bit Error Rate (BER) of . The Peak Signal to Noise Ratio (PSNR) results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at Eb/N0 value of 4.5 dB.

Eliminating CT radiation for clinical PET examination using deep learning

Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT significantly increases the risk of ionizing radiation exposure for patients. We propose a deep learning framework to learn the relationship mapping between attenuation corrected (AC) PET and non-attenuation corrected (NAC) PET images to estimate PET attenuation maps and generate pseudo-CT images for medical observation. In this study, 5760, 1608 and 1351 pairs of transverse PET-CT slices were used as the training, validation, and testing sets, respectively, to implement the proposed framework. A pix2pix model was adopted to predict AC PET images from NAC PET images, which allowed the calculation of PET attenuation maps (µ-maps). The same model was then applied to generate realistic CT images from the calculated µ-maps. The quality of predicted AC PET and CT was assessed using normalized root mean square error (NRMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and Pearson correlation coefficient (PCC). Relative to true AC PET, the synthetic AC PET achieved superior quantitative performances with 2.20 ± 1.17% NRMSE, 34.03 ± 4.73 dB PSNR, 97.90 ± 1.22% SSIM and 98.45 ± 1.31% PCC. The synthetic CT and synthetic AC PET images were deemed acceptable by radiologists who rated the images, as they provided sufficient anatomical and functional information, respectively. This work demonstrates that the proposed deep learning framework is a promising method in clinical applications, such as radiotherapy and low-dose imaging.

DNF: diffractive neural field for lensless microscopic imaging.

Lensless imaging has emerged as a robust means for the observation of microscopic scenes, enabling vast applications like whole-slide imaging, wave-front detection and microfluidic on-chip imaging. Such system captures diffractive measurements in a compact optical setup without the use of optical lens, and then typically applies phase retrieval algorithms to recover the complex field of target object. However existing techniques still suffer from unsatisfactory performance with noticeable reconstruction artifacts especially when the imaging parameter is not well calibrated. Here we propose a novel unsupervised Diffractive Neural Field (DNF) method to accurately characterize the imaging physical process to best reconstruct desired complex field of the target object through very limited measurement snapshots by jointly optimizing the imaging parameter and implicit mapping between spatial coordinates and complex field. Both simulations and experiments reveal the superior performance of proposed method, having > 6 dB PSNR (Peak Signal-to-Noise Ratio) gains on synthetic data quantitatively, and clear qualitative improvement on real-world samples. The proposed DNF also promises attractive prospects in practical applications because of its ultra lightweight complexity (e.g., 50× model size reduction) and plug-to-play advantage (e.g., random measurements with a coarse parameter estimation).

Steganography using dual tree complex wavelet transform with LSB indicator technique

Image steganography is undoubtedly significant in the field of secure multimedia communication. The undetectability and high payload capacity are two of the important characteristics of any form of steganography. In this paper, the level of image security is improved by combining the steganography and cryptography techniques in order to produce the secured image. The proposed method depends on using LSBs as an indicator for hiding encrypted bits in dual tree complex wavelet coefficient DT-CWT. The cover image is divided into non overlapping blocks of size (3*3). After that, a Key is produced by extracting the center pixel (pc) from each block to encrypt each character in the secret text. The cover image is converted using DT-CWT, then the produced key is used to determine the starting pixel in each block for hiding and the direction of hiding (clockwise or anticlockwise). The proposed method is applied on many images with different embedding rate, and many metrics are used to evaluate the performance of the proposed method such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), correlation factor (CF) and Structural Similarity Index Measure (SSIM). It achieves in average 52.225 dB of PSNR, 0.3215 of MSE, 0.9952 of SSIM and 0.9997 of CF with embedding rate 1.5.

QoE-Driven UAV-Enabled Pseudo-Analog Wireless Video Broadcast: A Joint Optimization of Power and Trajectory

The explosive demands for high quality mobile video services have caused heavy overload to the existing cellular networks. Although the small cell has been proposed to alleviate such a problem, the network operators may not be interested in deploying numerous base stations (BSs) due to expensive infrastructure construction and maintenance. The unmanned aerial vehicles (UAVs) can provide the low-cost and quick deployment, which can support high-quality line-of-sight communications and have become promising mobile BSs. In this paper, we propose a quality-of-experience (QoE)-driven UAV-enabled pseudo-analog wireless video broadcast scheme, which provides mobile video broadcast services for ground users (GUs). Due to limited energy available in UAV, the aim of the proposed scheme is to maximize the minimum peak signal-to-noise ratio (PSNR) of GUs’ video reconstruction quality by jointly optimizing the transmission power allocation strategy and the UAV trajectory. Firstly, the reconstructed video quality at GUs is defined under the constraints of the UAV's total energy and motion mechanism, and the proposed scheme is formulated as a complex non-convex optimization problem. Then, the optimization problem is simplified to obtain a tractable suboptimal solution with the help of the block coordinate descent model and the successive convex approximation model. Finally, the experimental results are presented to show the effectiveness of the proposed scheme. Specifically, the proposed scheme can achieve over 1.6 dB PSNR gains in terms of GUs’ minimum PSNR, compared with the state-of-the-art schemes, e.g., DVB, SoftCast, and SharpCast.

Biprediction-Based Video Quality Enhancement via Learning.

Convolutional neural networks (CNNs)-based video quality enhancement generally employs optical flow for pixelwise motion estimation and compensation, followed by utilizing motion-compensated frames and jointly exploring the spatiotemporal correlation across frames to facilitate the enhancement. This method, called the optical-flow-based method (OPT), usually achieves high accuracy at the expense of high computational complexity. In this article, we develop a new framework, referred to as biprediction-based multiframe video enhancement (PMVE), to achieve a one-pass enhancement procedure. PMVE designs two networks, that is, the prediction network (Pred-net) and the frame-fusion network (FF-net), to implement the two steps of synthesization and fusion, respectively. Specifically, the Pred-net leverages frame pairs to synthesize the so-called virtual frames (VFs) for those low-quality frames (LFs) through biprediction. Afterward, the slowly fused FF-net takes the VFs as the input to extract the correlation across the VFs and the related LFs, to obtain an enhanced version of those LFs. Such a framework allows PMVE to leverage the cross-correlation between successive frames for enhancement, hence capable of achieving high accuracy performance. Meanwhile, PMVE effectively avoids the explicit operations of motion estimation and compensation, hence greatly reducing the complexity compared to OPT. The experimental results demonstrate that the peak signal-to-noise ratio (PSNR) performance of PMVE is fully on par with that of OPT while its computational complexity is only 1% of OPT. Compared with other state-of-the-art methods in the literature, PMVE is also confirmed to achieve superior performance in both objective quality and visual quality at a reasonable complexity level. For instance, PMVE can surpass its best counterpart method by up to 0.42 dB in PSNR.

Dynamic Spectrum Access for Multimedia Transmission Over Multi-User, Multi-Channel Cognitive Radio Networks

The optimal spectrum access strategy is investigated for multi-user multi-channel scenario in cognitive radio networks. At first, an online learning method based on Dirichlet Process is adopted to predict the channel usage based on ACK/NACK feedbacks, which can avoid frequent information exchange among users. Based on the prediction result, the delay performance can be computed when a user transmits certain percentage of multimedia packets on a specific channel. Second, the packet delivery ratio (PDR) is derived from the prediction result of channel usage to reflect the accessing competition among multiple users. Finally, the quality of service (QoS) of multimedia applications is defined as the joint delay and throughput performances. Moreover, a dynamic spectrum access scheme is proposed to optimize the QoS metrics. The simulation results demonstrate that the QoS and the peak-signal-to-noise ratio (PSNR) of the proposed spectrum access algorithm outperform the three existing spectrum access algorithms, i.e., cognitive cross-layer algorithm, dynamic learning algorithm, and dynamic least interference algorithm. The proposed algorithm achieves more than 21.8%, 5.4%, and 3.9% PDR enhancement and over 3.23 dB, 0.82 dB, and 0.50 dB PSNR gains, compared with those three algorithms, given the transmission power as 10, 20, and 30 units, respectively.

Cross‐layer 1 and 5 user grouping/power allocation/subcarrier allocations for downlink OFDMA/NOMA video communications

SummaryWang et al proposed cross‐layer resource allocation for orthogonal frequency division multiple access (OFDMA) video transmission systems. Unlike Wang et al, we add non‐orthogonal multiple access (NOMA) to the downlink OFDMA video transmission system and propose power allocation for users on each subcarrier (cluster) to minimize sum of video mean square error (MSE) to increase the peak signal‐to‐noise ratio (PSNR), the video quality. For OFDMA/NOMA video communication systems, we propose cross‐layer user clustering to reassign the subcarriers based on sum video distortion minimization and derive the optimal power allocation among NOMA users on the same subcarrier to minimize the sum video distortion. Numerical results show that the proposed scheme outperforms the previous OFDMA cross‐layer scheme by Wang et al by 2.2 to 4.5 dB in PSNR and previous OFDMA NOMA physical layer scheme by Ali et al by 2 to 4.4 dB in PSNR, when SNR = 15 dB, and the number of users is 6 to 12.

Block-Based Noisy/Clean Classification of Images Using the Common Vector Approach

In this paper, a novel method is proposed to determine noisy blocks of an image. Three different threshold values for noisy/clean classification of the blocks of any image are determined by applying the common vector approach to the reference data set consisting of the clean samples of that image. The noise addressed in this paper is Gaussian noise with zero mean. By making a block-based noisy/clean classification of any image, it is possible to expose only the noisy blocks to the denoising process, rather than the entire image to denoising. When the first threshold value (Threshold 1) is considered for all peak signal-to-noise ratio (PSNR) values, more than 94% classification results are obtained for all 8 × 8 block-sized test images except images with 28–31 dB PSNR and more than 98.8% classification results are obtained for all 12 × 12 and 16 × 16 block-sized test images except images with 29-31 dB PSNR. Finally, popular image denoising algorithms are applied to the noisy images for comparison. It is observed that the PSNR values of noisy images are appreciably increased after the process.

Cross Layer Power Control and User Pairing for DL Multi-antenna NOMA

In a previous study, Kim et al. proposed a suboptimal user pairing and optimal power control algorithm for maximizing sum capacity in downlink multi-antenna non-orthogonal multiple access (NOMA) systems. First, Kim et al. calculate the correlation between all users. If the channel correlation is greater than the threshold, the user’s channel gain difference is stored in the channel gain-difference set. Then, the first and second large channel gain-differences of the user pairs are selected from the set. Based on the user pairing of Kim et al. in the physical layer, we propose novel iterative user substitution algorithm where a user is substituted if the peak signal-to-noise ratio (PSNR), the video quality, in the application layer increases after substitution. And we propose an optimal power control that minimizes the sum video distortion to maximize PSNR in the application layer. The numerical results show that the proposed cross layer optimal power control and user substitution outperforms Kim et al. by 1.4 dB in PSNR and only 1 dB away from exhaustive upper bound, when SNR = 15 dB and the number of users is 16.

Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss.

Our goal was to use a generative adversarial network (GAN) with feature matching and task-specific perceptual loss to synthesize standard-dose amyloid Positron emission tomography (PET) images of high quality and including accurate pathological features from ultra-low-dose PET images only. Forty PET datasets from 39 participants were acquired with a simultaneous PET/MRI scanner following injection of 330±30MBq of the amyloid radiotracer 18F-florbetaben. The raw list-mode PET data were reconstructed as the standard-dose ground truth and were randomly undersampled by a factor of 100 to reconstruct 1% low-dose PET scans. A 2D encoder-decoder network was implemented as the generator to synthesize a standard-dose image and a discriminator was used to evaluate them. The two networks contested with each other to achieve high-visual quality PET from the ultra-low-dose PET. Multi-slice inputs were used to reduce noise by providing the network with 2.5D information. Feature matching was applied to reduce hallucinated structures. Task-specific perceptual loss was designed to maintain the correct pathological features. The image quality was evaluated by peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) metrics with and without each of these modules. Two expert radiologists were asked to score image quality on a 5-point scale and identified the amyloid status (positive or negative). With only low-dose PET as input, the proposed method significantly outperformed Chen et al.'s method (Chen et al. Radiology. 2018;290:649-656) (which shows the best performance in this task) with the same input (PET-only model) by 1.87dB in PSNR, 2.04% in SSIM, and 24.75% in RMSE. It also achieved comparable results to Chen et al.'s method which used additional magnetic resonance imaging (MRI) inputs (PET-MR model). Experts' reading results showed that the proposed method could achieve better overall image quality and maintain better pathological features indicating amyloid status than both PET-only and PET-MR models proposed by Chen et al. CONCLUSION: Standard-dose amyloid PET images can be synthesized from ultra-low-dose images using GAN. Applying adversarial learning, feature matching, and task-specific perceptual loss are essential to ensure image quality and the preservation of pathological features.

Enhanced method of using Contourlet transform for medical image compression

With the aim of improving the compression performance using contourlet transform, singular value decomposition (SVD) of intermediate sub-bands has been experimented. In this way, the size of contourlet transform sub-bands can be efficiently reduced to induce compression. This novel lossy compression technique enhances the compression performance of contourlet transform and produces good quality image even at lower bit rates. In addition to SVD, normalisation and prediction of decomposed sub band coefficients also improve the compression performance. The method was tested using medical magnetic resonance imaging (MRI) and computed tomography (CT) imaging modalities. The statistical results confirm the efficiency of the proposed method in terms of compression ratio (CR), peak signal to noise ratio (PSNR) and bits per pixel (BPP). This method produces good compression with approximately 47 dB PSNR at bit rate as low as 0.1 BPP. This is suggested good for medical image communication and storage applications such as picture archiving communication system (PACS), radiology information system (RIS), etc., and also helps in easy search and retrieval process.