Reduced Image Quality Research Articles

Reduction of Speckle Noise in SAR Images With Hybrid Wavelet Filter

Abstract: Images produced by synthetic aperture radar (SAR) are crucial for observing and visualizing situations. However, speckle noise makes it difficult to assess SAR images since it reduces image quality and leads to incorrect interpretation. Multiplicative noise features can be found in speckle noise. For the past few years, experts have concentrated on despeckling or speckle reduction. However, the majority of the current efforts showed a loss of edge information. Since wavelet transform and bivariate shrinkage functions have many advantages, this study is devoted to designing a method for speckle removal. After performing a logarithmic transformation to turn multiplicative noise into additive noise, the suggested approach next applies a Lee filter. Then, a wavelet transform was used to breakdown the filtered image. Prior to applying the median filter, the bivariate shrinkage function was used to estimate each coefficient. The simulation results demonstrate that the suggested approach outperforms previous work and several traditional methods

Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study.

To assess the impact of a new artificial intelligence deep-learning reconstruction (Precise Image; AI-DLR) algorithm on image quality against a hybrid iterative reconstruction (IR) algorithm in abdominal CT for different clinical indications. Acquisitions on phantoms were performed at 5 dose levels (CTDIvol: 13/11/9/6/1.8 mGy). Raw data were reconstructed using level 4 of iDose4 (i4) and 3 levels of AI-DLR (Smoother/Smooth/Standard). Noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed: d' modelled detection of a liver metastasis (LM) and hepatocellular carcinoma at portal (HCCp) and arterial (HCCa) phases. Image quality was subjectively assessed on an anthropomorphic phantom by 2 radiologists. From Standard to Smoother levels, noise magnitude and average NPS spatial frequency decreased and the detectability (d') of all simulated lesions increased. For both inserts, TTF values were similar for all three AI-DLR levels from 13 to 6 mGy but decreased from Standard to Smoother levels at 1.8 mGy. Compared to the i4 used in clinical practice, d' values were higher using theSmoother and Smooth levels and close for the Standard level. For all dose levels, except at 1.8 mGy, radiologists considered images satisfactory for clinical use for the 3 levels of AI-DLR, but rated images too smooth using the Smoother level. Use ofthe Smooth and Smoother levels of AI-DLR reducesthe image noise and improves the detectability of lesions and spatial resolution for standard and low-dose levels. Using the Smooth level is apparently the best compromise between the lowest dose level and adequate image quality. • Evaluation of the impact of a new artificial intelligence deep-learning reconstruction (AI-DLR) on image quality and dose compared to a hybrid iterative reconstruction (IR) algorithm. • The Smooth and Smoother levels of AI-DLR reduced theimage noise and improved thedetectability of lesions and spatial resolution for standard and low-dose levels. • The Smooth level seems the best compromise between the lowest dose level and adequate image quality.

Bone segmentation in contrast enhanced whole-body computed tomography

Segmentation of bone regions allows for enhanced diagnostics, disease characterisation and treatment monitoring in CT imaging. In contrast enhanced whole-body scans accurate automatic segmentation is particularly difficult as low dose whole body protocols reduce image quality and make contrast enhanced regions more difficult to separate when relying on differences in pixel intensities. This paper outlines a U-net architecture with novel preprocessing techniques, based on the windowing of training data and the modification of sigmoid activation threshold selection to successfully segment bone-bone marrow regions from low dose contrast enhanced whole-body CT scans. The proposed method achieved mean Dice coefficients of 0.979 ± 0.02, 0.965 ± 0.03, and 0.934 ± 0.06 on two internal datasets and one external test dataset respectively. We have demonstrated that appropriate preprocessing is important for differentiating between bone and contrast dye, and that excellent results can be achieved with limited data.

Deep learning versus iterative reconstruction on image quality and dose reduction in abdominal CT: a live animal study

Objective. While simulated low-dose CT images and phantom studies cannot fully approximate subjective and objective effects of deep learning (DL) denoising on image quality, live animal models may afford this assessment. This study is to investigate the potential of DL in CT dose reduction on image quality compared to iterative reconstruction (IR). Approach. The upper abdomen of a live 4 year old sheep was scanned on a CT scanner at different exposure levels. Images were reconstructed using FBP and ADMIRE with 5 strengths. A modularized DL network with 5 modules was used for image reconstruction via progressive denoising. Radiomic features were extracted from a region over the liver. Concordance correlation coefficient (CCC) was applied to quantify agreement between any two sets of radiomic features. Coefficient of variation was calculated to measure variation in a radiomic feature series. Structural similarity index (SSIM) was used to measure the similarity between any two images. Diagnostic quality, low-contrast detectability, and image texture were qualitatively evaluated by two radiologists. Pearson correlation coefficient was computed across all dose-reconstruction/denoising combinations. Results. A total of 66 image sets, with 405 radiomic features extracted from each, are analyzed. IR and DL can improve diagnostic quality and low-contrast detectability and similarly modulate image texture features. In terms of SSIM, DL has higher potential in preserving image structure. There is strong correlation between SSIM and radiologists’ evaluations for diagnostic quality (0.559) and low-contrast detectability (0.635) but moderate correlation for texture (0.313). There is moderate correlation between CCC of radiomic features and radiologists’ evaluation for diagnostic quality (0.397), low-contrast detectability (0.417), and texture (0.326), implying that improvement of image features may not relate to improvement of diagnostic quality. Conclusion. DL shows potential to further reduce radiation dose while preserving structural similarity, while IR is favored by radiologists and more predictably alters radiomic features.

Coronary computed tomography and cardiac devices : Diagnostic results or nothing but artifacts?

Coronary computed tomography (CT) angiography has become amajor cornerstone in the diagnostic workup of cardiologic patients, particularly for evaluation of the coronary arteries and preprocedural planning of interventions for structural heart disease. Despite the possible problems that intensive electromagnetic radiation (including X‑rays) might cause when directly impacting on implanted cardiac devices, cardiac CT is asafe diagnostic test and should not be withheld from patients with devices if properly indicated. Sufficient image quality is paramount for the evaluation; hence, special attention should be paid to alow heart rate (< 60 bpm) and sufficient compliance with breathing instructions. Furthermore, pacemaker or implantable cardioverter-defibrillator (ICD) leads may cause metal artifacts, especially around the lead tip. Their dense material causes beam hardening and streak artifacts which may result in reduced image quality and limited diagnostic assessability. The prevalence of such artifacts depends not only on lead material but also on lead positioning relative to the gantry plane. Metal artifacts are more frequent in patients with unipolar leads and shock coils, which can impair the assessment of coronary arteries, mainly of the right coronary artery (RCA). Artifacts caused by left ventricular (LV) leads of cardiac resynchronization therapy (CRT) systems tend to affect assessment of the left circumflex artery (LCX). By using dual energy CT and postprocessing algorithms, the impact of artifacts can be reduced and diagnostic image quality can be achieved in most cases. Unfortunately, the actual occurrence of such artifacts or the degree of impairment of image quality cannot be reliably predicted.

Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.

BackgroundRecently, computed tomography (CT) manufacturers have developed deep‐learning‐based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolution's dependence on contrast and dose levels.PurposeTo assess the impact of an artificial intelligence deep‐learning reconstruction (AI‐DLR) algorithm on image quality and dose reduction compared with a hybrid IR algorithm in chest CT for different clinical indications.MethodsAcquisitions on the CT American College of Radiology (ACR) 464 and CT Torso CTU‐41 phantoms were performed at five dose levels (CTDIvol: 9.5/7.5/6/2.5/0.4 mGy) used for chest CT conditions. Raw data were reconstructed using filtered backprojection, two levels of IR (iDose4 levels 4 (i4) and 7 (i7)), and five levels of AI‐DLR (Precise Image; Smoother, Smooth, Standard, Sharp, Sharper). Noise power spectrum (NPS), task‐based transfer function, and detectability index (d′) were computed: d′‐modeled detection of a soft tissue mediastinal nodule (low‐contrast soft tissue chest nodule within the mediastinum [LCN]), ground‐glass opacity (GGO), or high‐contrast pulmonary (HCP) lesion. The subjective image quality of chest anthropomorphic phantom images was independently evaluated by two radiologists. They assessed image noise, image smoothing, contrast between vessels and fat in the mediastinum for mediastinal images, visual border detection between bronchus and lung parenchyma for parenchymal images, and overall image quality using a commonly used four‐ or five‐point scale.ResultsFrom Standard to Smoother levels, on average, the noise magnitude decreased (for all dose levels: −66.3% ± 0.5% for mediastinal images and −63.1% ± 0.1% for parenchymal images), the average NPS spatial frequency decreased (for all dose levels: −35.3% ± 2.2% for mediastinal images and −13.3% ± 2.2% for parenchymal images), and the detectability (d′) of the three lesions increased. The opposite pattern was found from Standard to Sharper levels. From Smoother to Sharper levels, the spatial resolution increased for the low‐contrast polyethylene insert and the opposite for the high‐contrast air insert. Compared to the i4 used in clinical practice, d′ values were higher using Smoother (mean for all dose levels: 338.7% ± 29.4%), Smooth (103.4% ± 11.2%), and Standard (34.1% ± 6.6%) levels for the LCN on mediastinal images and Smoother (169.5% ± 53.2% for GGO and 136.9% ± 1.6% for HCP) and Smooth (36.4% ± 22.1% and 24.1% ± 0.9%, respectively) levels for parenchymal images. Radiologists considered the images satisfactory for clinical use at these levels, but adaptation to the dose level of the protocol is required.ConclusionWith AI‐DLR, the smoothest levels reduced the noise and improved the detectability of chest lesions but increased the image smoothing. The opposite was found with the sharpest levels. The choice of level depends on the dose level and type of image: mediastinal or parenchymal.

Automated analysis of low-field brain MRI in cerebral malaria.

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images. These methods, however, may not translate to low-resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low-field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due toCM.

Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning

Parallel imaging is the most clinically used acceleration technique for magnetic resonance imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based parallel imaging reconstruction, sub-sampled k-space data are interpolated using linear convolutions. At high acceleration rates these methods have inherent noise amplification and reduced image quality. On the other hand, non-linear deep learning methods provide improved image quality at high acceleration, but the availability of training databases for different scans, as well as their interpretability hinder their adaptation. In this work, we present an extension of Robust Artificial-neural-networks for k-space Interpolation (RAKI), called residual-RAKI (rRAKI), which achieves scan-specific machine learning reconstruction using a hybrid linear and non-linear methodology. In rRAKI, non-linear CNNs are trained jointly with a linear convolution implemented via a skip connection. In effect, the linear part provides a baseline reconstruction, while the non-linear CNN that runs in parallel provides further reduction of artifacts and noise arising from the linear part. The explicit split between the linear and non-linear aspects of the reconstruction also help improve interpretability compared to purely non-linear methods. Experiments were conducted on the publicly available fastMRI datasets, as well as high-resolution anatomical imaging, comparing GRAPPA and its variants, compressed sensing, RAKI, Scan Specific Artifact Reduction in K-space (SPARK) and the proposed rRAKI. Additionally, highly-accelerated simultaneous multi-slice (SMS) functional MRI reconstructions were also performed, where the proposed rRAKI was compred to Read-out SENSE-GRAPPA and RAKI. Our results show that the proposed rRAKI method substantially improves the image quality compared to conventional parallel imaging, and offers sharper images compared to SPARK and ℓ1-SPIRiT. Furthermore, rRAKI shows improved preservation of time-varying dynamics compared to both parallel imaging and RAKI in highly-accelerated SMS fMRI.

Image Enhancement based on the Histogram Equalization and Multiresolution Discrete Stationary Wavelet Transforms

In recent years, due to the tremendous development that took place on the Internet and its applications in the aspects of human life, the demands of using digital images have also increased dramatically, and that opens up horizons for scientific research in the field of improving the quality of the digital images by removing the noise that caused by to processes that are applied within network transmissions like performing storage, retrieval, and encryption to preserve privacy. All these effects are yielded to reduce image quality and loss of visual information. To surmount this problem, image enhancement methods are used to eliminate the noise while preserving supreme exact details and essential characteristics as much as possible in the digital image. The wavelet image enhancement technique played a critical role in this field by attempts to reduce noise in the image while retaining the vital features of the image due to the capability to separate the image into sub-bands (sub-images) and influence the frequencies of each sub-band separately, where acquiring the original image content is essential to obtaining reliable performance.&#x0D; Different enhancement techniques have been realized by many researchers so far. Each technique has its own privileges and shortcomings. In this work, a proposed procedure is presented and effectuated to the image modified by Additive White Gaussian Noise (AWGN). The proposed enhancement operation was achieved using the combination of Histogram Equalization with a two-dimensional stationary discrete wavelet transform (2D-SWT) as a multi-resolution analysis technique in image processing at three levels of decomposition to obtain revised results of the method of noise removing. To distinguish and eliminate noise from affected pixel points in the wavelet domain the 2D-SWT is used based on the hard and soft threshold systems on both high and low frequencies to decrease noise from the noisy image. Then, the multi-level 2D inverse wavelet transform (2D-IWT) is applied to eliminate noise and complete the synthesis of the image by the proposed image enhancement techniques.&#x0D; In the end, the performance of the proposed methods has been evaluated by using the Peak Signal to Noise Ratio (PSNR). Experimental measurements determine that the results of the proposed techniques enhanced the PSNR by about (16.16%) with respect to the results of the related works, and the structure of the image quality has also been improved in terms of edges retaining and greater noise elimination.&#x0D;

Image quality assessment of three cone beam computed tomography scanners-an analysis of the visibility of anatomical landmarks.

The aim of this study was to evaluate large field of view (FoV) subjective image quality of three cone-beam computed tomography (CBCT) machines to assess the threshold for reliable diagnostic perceptibility when lowering exposure settings. One entire cadaver's head was scanned using three CBCT scanners. The largest available FoV of each device, imaging orthodontic relevant structures, was applied. CBCT datasets with different image quality based on standard to minimum scanning parameters were acquired. Five dentists evaluated the visibility of selected anatomical structures of the upper and lower jaw using a 5-point rating scale. Image quality depends on parameters such as a minimum voxel size of 0.2 to 0.4mm or a minimum of 4mA. A reduction in number of images (scanning protocol) reduces image quality. Visualization of different anatomical structures for orthodontic treatment planning requires distinct scanning protocols to support adequate perception of these structures. This study does not account for the evaluation of paediatric anatomical structures due to the availability of cadaver's head. CBCT scans performed for orthodontic purposes using a large FoV with reduced parameters (400 µm, 2 to 4 mA and low dose protocols) are acceptable for visualization of large anatomical structures. Further lowering these parameters will not be sufficient to view small anatomical structures. Orthodontic indications will have to define specific anatomical structures to choose adequate scanning protocols to reduce dose and ensure reliable diagnostic visibility.

Improving image quality by optimizing beam width and helical pitch in CT.

Expanding computed tomography (CT) detector coverage broadens the beam width, but inaccurate tube current application can reduce image quality at the boundaries between body regions with different attenuation values along the z-axis. This study aims to develop and validate a new CT scanning technique with a fixed pitch to achieve higher imaging quality. A cylindrical water phantom and an anthropomorphic chest phantom with different diameters represent a human body with different attenuation values. By optimizing the beam width and helical pitch, the pitch is fixed during scanning. The mean noise of the images and the standard deviation were calculated, and the coefficient of variation (COV) was compared to evaluate the uniformity of image noise according to the beam width. At the boundaries between regions with different attenuation values, the 10 mm beam width (COV: 0.065) in the water phantom showed a 47.7% COV reduction of image noise compared with the 20 mm beam width (COV: 0.125). In addition, the 20 mm beam width (COV: 0.146) in the chest phantom showed a 29.3% COV reduction of image noise compared with the 40 mm beam width (COV: 0.206). Thus, as the beam was narrowed, the mean noise was similar, but the standard deviation was reduced. The proposed CT scanning technique with a fixed pitch, optimized beam width, and helical pitch demonstrates that image quality can be improved without increasing radiation dose at the boundary between regions with different attenuation values.

Comparison of Sensitivity Encoding (SENSE) and Compressed Sensing-SENSE for Contrast-Enhanced T1-Weighted Imaging in Patients With Crohn Disease Undergoing MR Enterography.

BACKGROUND. Long acquisition times for breath-hold contrast-enhanced (CE) T1-weighted imaging in MR enterography (MRE) protocols result in reduced image quality. OBJECTIVE. The purpose of this study was to compare CE T1-weighted imaging performed using sensitivity encoding (SENSE) and compressed sensing-SENSE (CS-SENSE) in terms of image quality and diagnostic performance for active inflammation in Crohn disease (CD). METHODS. This retrospective study included 41 patients (31 men, 10 women; mean age, 34 ± 12 [SD] years) who underwent MRE for known or suspected CD between June 2020 and September 2020. MRE was performed in one of two scanning rooms depending on scheduling availability. Per institutional protocol, in one room, the enteric phase was acquired using SENSE (acceleration factor, 3) and the portal phase was acquired using CS-SENSE (acceleration factor, 5); this order was reversed in the other room. Two radiologists independently assessed sequences for subjective image quality measures at the patient level and for active inflammation at the bowel-segment level. Mean image quality scores between readers were computed. Diagnostic performance for active inflammation was compared between SENSE and CS-SENSE using generalized estimating equations; a separate experienced radiologist reviewed the full MRE protocol to establish the reference standard. RESULTS. The mean acquisition time of CE T1-weighted imaging was 17.2 ± 1.1 seconds for SENSE versus 11.5 ± 0.8 seconds for CS-SENSE (p < .001). CS-SENSE scored significantly better than SENSE in overall image quality (4.2 ± 0.7 vs 3.7 ± 1.1; p = .02), motion artifacts (4.0 ± 0.8 vs 3.6 ± 1.2; p = .006), and aliasing artifacts (4.8 ± 0.4 vs 4.2 ± 0.6; p < .001). CS-SENSE scored significantly worse than SENSE in synthetic appearance (4.6 ± 0.5 vs 4.8 ± 0.4; p = .003). Contrast, sharpness, and blurring were not different between sequences (p > .05). For reader 1, CS-SENSE, compared with SENSE, showed a sensitivity of 86% versus 81% (p = .09), specificity of 88% versus 83% (p = .08), and accuracy of 87% versus 82% (p = .56). For reader 2, CS-SENSE, compared with SENSE, showed a sensitivity of 92% versus 79% (p = .006), specificity of 90% versus 98% (p = .16), and accuracy of 91% versus 86% (p = .002). CONCLUSION. Use of CS-SENSE for CE T1-weighted imaging in MRE protocols results in reduced scan times with reduced artifact and improved image quality. CLINICAL IMPACT. The benefits of CS-SENSE in MRE protocols may improve the diagnostic performance for active inflammation in CD.

Novel U-net based deep neural networks for transmission tomography.

The fusion of computer tomography and deep learning is an effective way of achieving improved image quality and artifact reduction in reconstructed images. In this paper, we present two novel neural network architectures for tomographic reconstruction with reduced effects of beam hardening and electrical noise. In the case of the proposed novel architectures, the image reconstruction step is located inside the neural networks, which allows the network to be trained by taking the mathematical model of the projections into account. This strong connection enables us to enhance the projection data and the reconstructed image together. We tested the two proposed models against three other methods on two datasets. The datasets contain physically correct simulated data, and they show strong signs of beam hardening and electrical noise. We also performed a numerical evaluation of the neural networks on the reconstructed images according to three error measurements and provided a scoring system of the methods derived from the three measures. The results showed the superiority of the novel architecture called TomoNet2. TomoNet2 improved the quality of the images according to the average Structural Similarity Index from 0.9372 to 0.9977 and 0.9519 to 0.9886 on the two data sets, when compared to the FBP method. This network also yielded the best results for 79.2 and 53.0 percent for the two datasets according to Peak-Signal-to-Noise-Ratio compared to the other improvement techniques. Our experimental results showed that the reconstruction step used in skip connections in deep neural networks improves the quality of the reconstructions. We are confident that our proposed method can be effectively applied to other datasets for tomographic purposes.

Shift-Invariant-Subspace Discretization and Volume Reconstruction for Light Field Microscopy

Light Field Microscopy (LFM) is an imaging technique that captures 3D spatial information with a single 2D image. LFM is attractive because of its relatively simple implementation and fast volume acquisition rate. Capturing volume time series at a camera frame rate can enable the study of the behaviour of many biological systems. For instance, it could provide insights into the communication dynamics of living 3D neural networks. However, conventional 3D reconstruction algorithms for LFM typically suffer from high computational cost, low lateral resolution, and reconstruction artifacts. In this work, we study the origin of these issues and propose novel techniques to improve the performance of the reconstruction process. First, we propose a discretization approach that uses shift-invariant subspaces to generalize the typical discretization framework used in LFM. Then, we study the shift-invariant-subspace assumption as a prior for volume reconstruction under ideal conditions. Furthermore, we present a method to reduce the computational time of the forward model by using singular value decomposition (SVD). Finally, we propose to use iterative approaches that incorporate additional priors to perform artifact-free 3D reconstruction from real light field images. We experimentally show that our approach performs better than Richardson-Lucy-based strategies in computational time, image quality, and artifact reduction.

Effect of a new deep learning image reconstruction algorithm for abdominal computed tomography imaging on image quality and dose reduction compared with two iterative reconstruction algorithms: a phantom study.

New reconstruction algorithms based on deep learning have been developed to correct the image texture changes related to the use of iterative reconstruction algorithms. The purpose of this study was to evaluate the impact of a new deep learning image reconstruction [Advanced intelligent Clear-IQ Engine (AiCE)] algorithm on image-quality and dose reduction compared to a hybrid iterative reconstruction (AIDR 3D) algorithm and a model-based iterative reconstruction (FIRST) algorithm. Acquisitions were carried out using the ACR 464 phantom (and its body ring) at six dose levels (volume computed tomography dose index 15/10/7.5/5/2.5/1 mGy). Raw data were reconstructed using three levels (Mild/Standard/Strong) of AIDR 3D, of FIRST and AiCE. Noise-power-spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index was computed to model the detection of a small calcification (1.5-mm diameter and 500 HU) and a large mass in the liver (25-mm diameter and 120 HU). NPS peaks were lower with AiCE than with AIDR 3D (-41%±6% for all levels) or FIRST (-15%±6% for Strong level and -41%±11% for both other levels). The average NPS spatial frequency was lower with AICE than AIDR 3D (-9%±2% using Mild and -3%±2% using Strong) but higher than FIRST for Standard (6%±3%) and Strong (25%±3%) levels. For acrylic insert, values of TTF at 50 percent were higher with AICE than AIDR 3D and FIRST, except for Mild level (-6%±6% and -13%±3%, respectively). For bone insert, values of TTF at 50 percent were higher with AICE than AIDR 3D but lower than FIRST (-19%±14%). For both simulated lesions, detectability index values were higher with AICE than AIDR 3D and FIRST (except for Strong level and for the small feature; -21%±14%). Using the Standard level, dose could be reduced by -79% for the small calcification and -57% for the large mass using AICE compared to AIDR 3D. The new deep learning image reconstruction algorithm AiCE generates an image-quality with less noise and/or less smudged/smooth images and a higher detectability than the AIDR 3D or FIRST algorithms. The outcomes of our phantom study suggest a good potential of dose reduction using AiCE but it should be confirmed clinically in patients.

Low-Dose Three-Dimensional Rotational Angiography for Evaluating Intracranial Aneurysms: Analysis of Image Quality and Radiation Dose.

ObjectiveThis study aimed to evaluate the image quality and dose reduction of low-dose three-dimensional (3D) rotational angiography (RA) for evaluating intracranial aneurysms.Materials and MethodsWe retrospectively evaluated the clinical data and 3D RA datasets obtained from 146 prospectively registered patients (male:female, 46:100; median age, 58 years; range, 19–81 years). The subjective image quality of 79 examinations obtained from a conventional method and 67 examinations obtained from a low-dose (5-seconds and 0.10-µGy/frame) method was assessed by two neurointerventionists using a 3-point scale for four evaluation criteria. The total image quality score was then obtained as the average of the four scores. The image quality scores were compared between the two methods using a noninferiority statistical testing, with a margin of -0.2 (i.e., score of low-dose group – score of conventional group). For the evaluation of dose reduction, dose-area product (DAP) and air kerma (AK) were analyzed and compared between the two groups.ResultsThe mean total image quality score ± standard deviation of the 3D RA was 2.97 ± 0.17 by reader 1 and 2.95 ± 0.20 by reader 2 for conventional group and 2.92 ± 0.30 and 2.95 ± 0.22, respectively, for low-dose group. The image quality of the 3D RA in the low-dose group was not inferior to that of the conventional group according to the total image quality score as well as individual scores for the four criteria in both readers. The mean DAP and AK per rotation were 5.87 Gy-cm2 and 0.56 Gy, respectively, in the conventional group, and 1.32 Gy-cm2 (p < 0.001) and 0.17 Gy (p < 0.001), respectively, in the low-dose group.ConclusionLow-dose 3D RA was not inferior in image quality and reduced the radiation dose by 70%–77% compared to the conventional 3D RA in evaluating intracranial aneurysms.

Design secure multi-level communication system based on duffing chaotic map and steganography

Cryptography and steganography are among the most important sciences that have been properly used to keep confidential data from potential spies and hackers. They can be used separately or together. Encryption involves the basic principle of instantaneous conversion of valuable information into a specific form that unauthorized persons will not understand to decrypt it. While steganography is the science of embedding confidential data inside a cover, in a way that cannot be recognized or seen by the human eye. This paper presents a high-resolution chaotic approach applied to images that hide information. A more secure and reliable system is designed to properly include confidential data transmitted through transmission channels. This is done by working the use of encryption and steganography together. This work proposed a new method that achieves a very high level of hidden information based on non-uniform systems by generating a random index vector (RIV) for hidden data within least significant bit (LSB) image pixels. This method prevents the reduction of image quality. The simulation results also show that the peak signal to noise ratio (PSNR) is up to 74.87 dB and the mean square error (MSE) values is up to 0.0828, which sufficiently indicates the effectiveness of the proposed algorithm.

Motion-robust MR imaging of the shoulder using compressed SENSE MultiVane

PurposeMotion artifacts caused by breathing or involuntary motion of patients, which may lead to reduced image quality and a loss of diagnostic information, are a major problem in shoulder magnetic resonance imaging (MRI). The MultiVane (MV) technique decreases motion artifacts; however, it tends to prolong the acquisition time. As a parallel imaging technique, SENSitivity Encoding (SENSE) can be combined with the compressed sensing method to produce compressed SENSE (C-SENSE), resulting in a markedly reduced acquisition time. This study aimed to evaluate the use of C-SENSE MV for MRI of the shoulder joint. MethodsThirty-one patients who were scheduled to undergo MRI of the shoulder were included. This prospective study was approved by our institution’s medical ethics committee, and written informed consent was obtained from all 31 patients. Two sets of oblique coronal images derived from the standard protocol were acquired without (standard) or with C-SENSE MV: proton-density weighted imaging (PDWI), PDWI with C-SENSE MV, T2-weighted imaging (T2WI) with fat suppression (fs), and T2WI fs with C-SENSE MV. Two radiologists graded motion artifacts and the detectability of anatomical shoulder structures on a 4-point scale (3, no artifacts/excellent delineation; 0, severe artifacts/difficulty with delineation). The Wilcoxon signed-rank test was used to compare the data for the standard and C-SENSE MV images. ResultsMotion artifacts were significantly reduced on the C-SENSE MV images (p < 0.001). Regarding the detectability of anatomical structures, the ratings for the C-SENSE MV sequences were significantly better (p < 0.001).In conclusion, in shoulder MRI the newly developed C-SENSE MV technique reduces motion artifacts and increases the detectability of anatomical structures compared with standard sequences.

Application of region of interest extraction method based on deep learning in UAV high performance image compression

UAV has been widely used in detecting targets, but the image transmission of UAV still faces the problems of distortion and frame loss. Most image compression methods based on deep learning are lossy compression, and lossy compression reduces image quality in exchange for higher compression ratio. In order to improve the quality of the region of interest (ROI) in the reconstructed image with a certain bit rate, an importance map extraction module is embedded in the encoder, and the importance map is generated by extracting the output features of the last layer of the encoder. Finally, the mask is generated to guide the efficient allocation of bit rate in the process of drop coding. At the same time, a decoder enhancement module is embedded in the decoder output to predict the high frequency components in the reconstructed image and improve the quality of the reconstructed image by enhancing the details in the reconstructed image. The experimental results show that the proposed method is superior to the comparison method when multi-scale structural similarity (MS-SSIM) is used as the evaluation index, and the proposed method achieves better visual perception quality.

Variation in digital breast tomosynthesis image quality at differing heights above the detector.

IntroductionThe aim of this preliminary work was to determine if image quality in digital breast tomosynthesis (DBT) changes when tomosynthesis image slices were obtained at differing heights above the detector and in differing breast thicknesses.MethodsA CIRS Model 020 BR3D breast imaging phantom was used to obtain the DBT images. The images were also acquired at different tube voltages, and each exposure was determined by the automatic exposure control system. Contrast‐to‐noise ratio (CNR) and figure‐of‐merit (FOM) values were obtained and compared.ResultsAt a phantom thickness of 5 cm or greater, there was a significant reduction (P ≤ 0.05) of image CNR values obtained from the images near the top of the phantom to those obtained near the bottom of the phantom. When the phantom thickness was 4 cm, there was no significant difference in CNR values between DBT images acquired at any height in the phantom. FOM values generally showed no difference when images were obtained at differing heights above the detector.ConclusionImage quality, as measured by the CNR, was reduced when tomosynthesis slice image heights were at the top of the phantom and when the thickness of the phantom was more than 4 cm. From this preliminary work, clinicians need to be aware that DBT images obtained near the top of the breast, when breast thickness is greater than 4 cm, may have reduced image quality. Further work is needed to fully assess any DBT image quality changes when images are obtained near the top of the breast.