Preserving Image Quality Research Articles

Retinal Noise Emulation: A Novel Artistic Tool for Cinema That Also Improves Compression Efficiency

In cinema it is standard practice to improve the appearance of images by adding noise that simulates film grain. This is computationally very costly, so it is only done in post-production and not on the set. It is also limiting because the artists are not able to really experiment with the noise nor introduce novel looks. Furthermore, video compression requires a higher bit rate when the source material has film grain or any other type of high frequency texture. In this work, we introduce a method for adding texture to digital cinema that aims to solve these problems. The proposed algorithm is based on modeling retinal noise, with which the images processed by our method have a natural appearance. This “retinal grain” serves a double purpose. One is aesthetic, as it has parameters that allow to vary widely the resulting texture appearance, which make it an artistic tool for cinematographers. Results are validated through psychophysical experiments in which observers, including cinema professionals, prefer our method over film grain synthesis methods from academia and the industry. The other purpose of the retinal noise emulation method is to improve the quality of compressed video by masking compression artifacts, which allows to lower the encoding bit rate while preserving image quality, and to improve image quality while keeping the bit rate fixed. The effectiveness of our approach for improving coding efficiency, with average bit rate savings of 22.5%, has been validated through psychophysical experiments using professional cinema content shot in 4K, color-graded and where the amount of retinal noise was selected by a motion picture specialist based solely on aesthetic preference.

Toward Variable-Rate Generative Compression by Reducing the Channel Redundancy

Compressing large images with a generative model goes beyond typical image encoding standards under a notably low bitrate. In this paper, we step toward practical generative compression systems based on recent advances. Specifically, we show that the channel redundancy of the latent representation produced by an autoencoder network can be effectively compressed via mask compression. The mask compression performs quantization on the channel variance of latent representation instead of original values. Instead of training multiple models, changing the mask leads to a simple and efficient variable rate compression scheme. Then, we estimate the relative bitrate by measuring the L1 norm of the channel variance and hence obtain the rate-distortion formulation. The L1 regularizer assumes a Laplacian prior on the channel variance, through which model we develop corresponding methods to produce approximate images at a target bitrate. This eliminates the need for manually searching hyperparameters for our variable-rate compression. We conduct exhaustive experiments to demonstrate the advanced performance of the proposed method in preserving image quality and semantics.

Assessment of left ventricular ejection fraction with cardiofocal collimators: Comparison between IQ-SPECT, planar equilibrium radionuclide angiography, and cardiac magnetic resonance

BackgroundIQ-SPECT has been shown to significantly reduce acquisition time and administered dose while preserving image quality in myocardial perfusion imaging. Whether IQ-SPECT provides accurate left ventricular ejection fractions (LVEF) with gated blood pool SPECT (GBPS) remains unknown. MethodsSixty patients underwent IQ-SPECT GBPS and planar imaging. Among those patients, 11 underwent both cMRI and GBPS. GBPS LVEF, LVEDV, and LVESV were calculated using 2 validated software; QBS (Cedars-Sinai Medical Center, Los Angeles, USA) and MHI (Montreal Heart Institute, Montreal, Canada). LVEF, LVEDV, and LVESV obtained with the different modalities were compared. ResultsAverage planar LVEF was 48 ± 11% (mean ± SD), average LVEDV was 177 ± 59 mL (range 63 to 342 mL), and average LVESV was 96 ± 46 mL (range 16 to 234 mL). GBPS LVEF and their correlation coefficient with planar LVEF were 40 ± 12% (r = 0.70) and 44 ± 12% (r = 0.83) with QBS and MHI, respectively. Correlation coefficient between cMRI and planar LVEF was 0.65 and were 0.69 and 0.52 between cMRI and GBPS using QBS and MHI, respectively. ConclusionsLVEF calculated with GBPS using IQ-SPECT correlates with planar measurements. Correlation is best using the MHI method and variation is independent of LVEDV.

A convolutional neural network approach to preserve image quality for sparse array data

High-frame-rate ultrasound (HiFRUS) has seen recent interest in resolving highly dynamic spatiotemporal events but transferring the large amount of data generated from the probe remains a hurdle for real-time imaging. One method to lessen the data traffic while preserving the field of view is to reduce the channel count, but this can lead to image quality degradation and the appearance of grating lobe artifacts. In this work, we present a convolutional neural network (CNN) based framework that uses a sparse array (half the channel count) and infers the remaining channels to mimic a fully-populated array. On unfocused transmissions, our results show that on a beamformed image of a multiple point target phantom, grating lobe artifacts are reduced from over 8dB (sparse array) to less than 1 dB (CNN interpolated) when compared to an image beamformed using the full array. Additionally, reconstructions from CNN generated data demonstrated improvement (10 dB) in carotid echolucent flow regions in vivo. Our work demonstrates that, using a deep learning approach to channel-domain radiofrequency data interpolation, the required physical channel count on an array and the corresponding data transfer bandwidth can both be reduced without significant image quality trade-off.

Deep Learning Based Noise Reduction for Brain MR Imaging: Tests on Phantoms and Healthy Volunteers.

Purpose:To test whether our proposed denoising approach with deep learning-based reconstruction (dDLR) can effectively denoise brain MR images.Methods:In an initial experimental study, we obtained brain images from five volunteers and added different artificial noise levels. Denoising was applied to the modified images using a denoising convolutional neural network (DnCNN), a shrinkage convolutional neural network (SCNN), and dDLR. Using these brain MR images, we compared the structural similarity (SSIM) index and peak signal-to-noise ratio (PSNR) between the three denoising methods. Two neuroradiologists assessed the image quality of the three types of images. In the clinical study, we evaluated the denoising effect of dDLR in brain images with different levels of actual noise such as thermal noise. Specifically, we obtained 2D-T2-weighted image, 2D-fluid-attenuated inversion recovery (FLAIR) and 3D-magnetization-prepared rapid acquisition with gradient echo (MPRAGE) from 15 healthy volunteers at two different settings for the number of image acquisitions (NAQ): NAQ2 and NAQ5. We reconstructed dDLR-processed NAQ2 from NAQ2, then compared with SSIM and PSNR. Two neuroradiologists separately assessed the image quality of NAQ5, NAQ2 and dDLR-NAQ2. Statistical analysis was performed in the experimental and clinical study. In the clinical study, the inter-observer agreement was also assessed.Results:In the experimental study, PSNR and SSIM for dDLR were statistically higher than those of DnCNN and SCNN (P < 0.001). The image quality of dDLR was also superior to DnCNN and SCNN. In the clinical study, dDLR-NAQ2 was significantly better than NAQ2 images for SSIM and PSNR in all three sequences (P < 0.05), except for PSNR in FLAIR. For all qualitative items, dDLR-NAQ2 had equivalent or better image quality than NAQ5, and superior quality to that of NAQ2 (P < 0.05), for all criteria except artifact. The inter-observer agreement ranged from substantial to near perfect.Conclusion:dDLR reduces image noise while preserving image quality on brain MR images.

Interview

Xin Tian Doctor Xin Tian from Wuhan University, China, talks to Electronics Letters about the paper ‘Fourier ptychographic reconstruction using mixed Gaussian-Poisson likelihood with total variation regularisation’, page 1041. My main research fields include computational imaging, image super-resolution, image compression, and convex optimisation theory. My PhD research was on image super-resolution, the target of which was to improve the image's optical resolution by using image processing techniques. However, this improvement was limited. In recent years, the optical resolution limit could be broken through by incorporating advanced computational imaging techniques such as Fourier ptychography (FP). This is a promising research field with wide application prospects. I am interested in the possible applications for biomedicine and remote sensing. Imaging is one of the most important observation measurements in the above applications. Therefore, we believe that the breakthrough of image resolution limitation will have significant impacts on these applications. FP is a new computational imaging technique that collects a series of low-resolution images under different angles of illumination from an array of light-emitting diodes. Then, a wide-field and high-resolution image is reconstructed from these images using an iterative phase retrieval algorithm. In this way, a resolution beyond the optical limitation due to the numerical aperture of the objective can be realised. The advantages of this method include improving the depth of focus, working distance, and size of the field of view, which together lower the required numerical aperture. In addition, this method also enables the numerical correction of lens aberrations, which leads to a large space-bandwidth product. These advantages are the reason this has recently become one of the most attractive areas of research in computational imaging and microscopy. In practice, however, most reconstruction methods are sensitive to noise which degrades the quality of the reconstructed high-resolution image. Therefore, how to generate high quality high-resolution image in FP is the issue to be addressed in this work. In our Letter, we introduce total variation (TV) regularisation into the reconstruction of FP. The main advantage of TV regularisation is the ability to preserve image quality while removing noise. In addition, the mixed Gaussian–Poisson likelihood is adopted as a data fidelity term, which keeps the reconstructed image close to the captured data. The augmented Lagrangian method with the Wirtinger derivatives is used to solve the optimisation problem. Both synthetic examples and real captured data are used to test the efficiency of the method. Based on the experimental results, we conclude that the proposed method outperforms other methods, especially in high noise level situations. In FP, noise can have great impact on the reconstructed image quality. It is important to design a proper reconstruction method for FP by incorporating novel and advanced image denoising methods. With the development of sparse coding theory, it has been demonstrated that total variation (TV) based image denoising method is feasible and effective. This inspired me to integrate TV into the reconstruction process of FP. The main challenge is how to construct the proper imaging model with the TV constraints. How to solve the optimisation problem by using the convex optimisation theory is also an important part in this work. In the short term, this work can improve the reconstruction image quality of FP, with the result that the applications of FP will be promoted. As these discoveries are also suitable for other computational imaging techniques, it may bring new motivations or strategies to solve different kinds of computational imaging problems belonging to other research fields in the longer term. In the future, I will study the influence of pupil function for the reconstruction of FP. Meanwhile, how to use the deep learning technique in this field may be also one of my future works. At present, my other work in computational imaging is single photon imaging. This research is also prospective, which can be used to detect objects in extremely low-light conditions. One key issue in it that is similar to FP, is that noise is large in low-light conditions, which degrades the image quality. Therefore, how to generate high-quality single photon images in the noise situation is very important for this work. In recent years, FP has attracted a lot of research interests and it has developed very fast. There are many new computation imaging techniques based on FP, such as three-dimensional FP, colourful FP, and so on. Deep learning is another frontier technology which is a machine learning sub-field. It could generate computer programs or computational systems which can discover data patterns or structures in data itself. This implies that, with the help of deep learning techniques, researchers could design high-performance reconstruction algorithms for FP or other computational imaging techniques in the next ten years.

Iterative CT reconstruction in abdominal low-dose CT used for hybrid SPECT-CT applications: effect on image quality, image noise, detectability, and reader's confidence.

BackgroundIterative computed tomography (CT) image reconstruction shows high potential for the preservation of image quality in diagnostic CT while reducing patients’ exposure; it has become available for low-dose CT (LD-CT) in high-end hybrid imaging systems (e.g. single-photon emission computed tomography [SPECT]-CT).PurposeTo examine the effect of an iterative CT reconstruction algorithm on image quality, image noise, detectability, and the reader’s confidence for LD-CT data by a subjective assessment.Material and MethodsThe LD-CT data were validated for 40 patients examined by an abdominal hybrid SPECT-CT (U = 120 kV, I = 40 mA, pitch = 1.375). LD-CT was reconstructed using either filtered back projection (FBP) or an iterative image reconstruction algorithm (Adaptive Statistical Iterative Reconstruction [ASIR]®) with different parameters (ASIR levels 50% and 100%). The data were validated by two independent blinded readers using a scoring system for image quality, image noise, detectability, and reader confidence, for a predefined set of 16 anatomic substructures.ResultsThe image quality was significantly improved by iterative reconstruction of the LD-CT data compared with FBP (P ≤ 0.0001). While detectability increased in only 2/16 structures (P ≤ 0.03), the reader’s confidence increased significantly due to iterative reconstruction (P ≤ 0.002). Meanwhile, at the ASIR level of 100%, the detectability in bone structure was highly reduced (P = 0.003).ConclusionAn ASIR level of 50% represents a good compromise in abdominal LD-CT image reconstruction. The specific ASIR level improved image quality (reduced image noise) and reader confidence, while preserving detectability of bone structure.

Compressed Sensing 3D-GRASE for faster High-Resolution MRI.

PurposeHigh‐resolution three‐dimensional (3D) structural MRI is useful for delineating complex or small structures of the body. However, it requires long acquisition times and high SAR, limiting its clinical use. The purpose of this work is to accelerate the acquisition of high‐resolution images by combining compressed sensing and parallel imaging (CSPI) on a 3D‐GRASE sequence and to compare it with a (CS)PI 3D‐FSE sequence. Several sampling patterns were investigated to assess their influence on image quality.MethodsThe proposed k‐space sampling patterns are based on two undersampled k‐space grids, variable density (VD) Poisson‐disc, and VD pseudo‐random Gaussian, and five different trajectories described in the literature. Bloch simulations are performed to obtain the transform point spread function and evaluate the coherence of each sampling pattern. Image resolution was assessed by the full‐width at half‐maximum (FWHM). Prospective CSPI 3D‐GRASE phantom and in vivo experiments in knee and brain are carried out to assess image quality, SNR, SAR, and acquisition time compared to PI 3D‐GRASE, PI 3D‐FSE, and CSPI 3D‐FSE acquisitions.ResultsSampling patterns with VD Poisson‐disc obtain the lowest coherence for both PD‐weighted and T2‐weighted acquisitions. VD pseudo‐random Gaussian obtains lower FWHM, but higher sidelobes than VD Poisson‐disc. CSPI 3D‐GRASE reduces acquisition time (43% for PD‐weighted and 40% for T2‐weighted) and SAR (∼45% for PD‐weighted and T2‐weighted) compared to CSPI 3D‐FSE.ConclusionsCSPI 3D‐GRASE reduces acquisition time compared to a CSPI 3DFSE acquisition, preserving image quality. The design of the sampling pattern is crucial for image quality in CSPI 3D‐GRASE image acquisitions.

Advanced CT Techniques for Decreasing Radiation Dose, Reducing Sedation Requirements, and Optimizing Image Quality in Children.

CT is an invaluable diagnostic tool for pediatric patients; however, concerns have arisen about the potential risks of ionizing radiation associated with diagnostic imaging in young patients, particularly for pediatric populations that may require serial CT examinations. Recent attention has also been focused on the immediate and long-term risks of administration of anesthetic medications to infants and young children who require sedation to undergo imaging examinations. These concerns can be mitigated with use of advanced CT techniques that can decrease scan time and radiation dose while preserving image quality. In this article, current state-of-the-art CT acquisition techniques are reviewed as part of a comprehensive strategy to reduce radiation dose, decrease sedation needs, and optimize image quality in infants and young children. Three imaging strategies are discussed, including (a) dual-energy CT (DECT), (b) imaging with a low tube potential, and (c) rapid scanning. Consolidating multiphase imaging protocols into a single phase with virtual nonenhanced imaging on DECT scanners, as well as use of low tube voltage, can reduce the radiation dose while increasing the conspicuity of contrast material-enhanced structures with a reduced volume of iodinated contrast material and a reduced rate of injection. Rapid scanning techniques with either ultrahigh pitch at dual-source CT or with wide-area detector single-source CT facilitate scanning without the need for sedation in many children. ©RSNA, 2019 See discussion on this article by Szczykutowicz .

Feasibility of reduced-dose CT of the head and neck with iterative reconstruction: a phantom and prospective clinical study.

Background There is growing concern over computed tomography (CT)-related radiation exposure, particularly in patients requiring head and neck CT due to the proximity of radiosensitive organs. Purpose We aimed to evaluate the feasibility of reduced-dose CT of the head and neck with iterative reconstruction in a phantom and patient study. Material and Methods In the phantom study, low-contrast resolution test of the American Association of Physicists in Medicine CT performance phantom was scanned to determine the lowest acceptable tube current (mAs). In the patient study, three sets of CT images (standard-dose [200 mAs] and reduced-dose with iterative reconstruction or filtered back projection [FBP]) were prospectively obtained. The image noise, qualitative image quality, and radiation dose were compared. Results In the phantom study, 100 mAs was determined as the lowest acceptable value. In the patient study (n = 29), reduced-dose CT with FBP showed significantly higher image noise and lower qualitative image quality than reduced-dose CT with iterative reconstruction and standard-dose CT ( P &lt; 0.05). There was no significant difference in image noise and qualitative image quality between reduced-dose CT with iterative reconstruction and standard-dose CT ( P &gt; 0.05). The radiation dose of the reduced-dose CT was significantly lower than that of the standard-dose CT (volume CT dose index = 6.9 vs. 14.3, P &lt; 0.001; dose-length product = 223.6 vs. 455.8, P &lt; 0.001). Conclusions Reduced-dose CT with iterative reconstruction is a potential alternative to standard-dose CT of the head and neck, reducing radiation dose by approximately 50% while preserving image quality.

Reversible Data Hiding With Image Contrast Enhancement Based on Two-Dimensional Histogram Modification

Recently, reversible image data hiding with contrast enhancement (CE) has been proposed so that a contrast-changed image can be converted to its original version when needed. Several reversible image CE methods have been proposed by adopting the technique of reversible data hiding (RDH) to embed the recovery information into the contrast-enhanced images. In these methods, the 1D histogram is calculated and then modified to achieve the effects of histogram equalization (HE). Meanwhile, the 2D histogram has been used in image CE and demonstrated remarkable advantages in improving the image quality. In this paper, the method based on the 2D histogram modification is proposed to perform image CE and RDH simultaneously. In particular, a preprocessing strategy is developed to merge the adjacent bins in 2D histogram to prevent the overflow and underflow of the pixel values due to HE. The changes made in preprocessing is minimized by choosing the lowest bins for merging, while the CE effects can be achieved by expanding the highest bins for data embedding. The experimental results on two sets of test images have clearly demonstrated the efficacy of the proposed method for reversible image CE. The evaluation results are given to show its superior performances in CE and image quality preservation by comparing with the state-of-the-art approaches.

Run Length Encoding Based Lossless MRI Image Compression Using LZW and Adaptive Variable Length Coding

Medical image compression is considered one of the most important research fields nowadays in biomedical applications. The majority of medical images must be compressed without loss because each pixel information is of great value. With the widespread use of applications concerning medical imaging in the health-care context and the increased significance in telemedicine technologies, it has become crucial to minimize both the storage and bandwidth requirements needed for archiving and transmission of medical imaging data, rather by employing means of lossless image compression algorithms. Furthermore, providing high resolution and image quality preservation of the processed image data has become of great benefit. The proposed system introduces a lossless image compression technique based on Run Length Encoding (RLE) that encodes the original magnetic resonance imaging (MRI) image into actual values and their numbers of occurrence. The actual image data values are separated from their runs and they are stored in a vector array. Lempel–Ziv–Welch (LZW) is used to provide further compression that is applied to values array only. Finally the Variable Length Coding (VLC) will be applied to code the values and runs arrays for the precise amount of bits adaptively into a binary file. These bit streams are reconstructed using inverse LZW of the values array and inverse RLE to reconstruct the input image. The obtained compression gain is enhanced by 25% after applying LZW to the values array.

Optimization of PET reconstruction algorithm, SUV thresholding algorithm and PET acquisition time in clinical 11C-acetate PET/CT.

Introduction11C-acetate (ACE)-PET/CT is used for staging of high-risk prostate cancer. PET data is reconstructed with iterative algorithms, such as VUEPointHD ViP (VPHD) and VUEPoint HD Sharp IR (SharpIR), the latter with additional resolution recovery. It is expected that the resolution recovery algorithm should render more accurate maximum and mean standardized uptake values (SUVmax and SUVmean) and functional tumor volumes (FTV) than the ordinary OSEM. Performing quantitative analysis, choice of volume-of-interest delineation algorithm (SUV threshold) may influence FTV. Optimizing PET acquisition time is justified if image quality and quantitation do not deteriorate.The aim of this study is to identify the optimal reconstruction algorithm, SUV threshold and acquisition time for ACE-PET/CT.MethodsACE-PET/CT data acquired with a General Electric Discovery 690 PET/CT from 16 consecutive high-risk prostate cancer patients was reconstructed with VPHD and SharpIR. Forty pelvic lymph nodes (LNs) and 14 prostate glands were delineated with 42% and estimated threshold. SUVmax, SUVmean, FTV and total lesion uptake were measured.Default acquisition time was four minutes per bed position. In a subset of lesions, acquisition times of one, two and four minutes were evaluated.Structural tumor volumes (STV) of the LNs were measured with CT for correlation with functional volumetric parameters.To validate SUV quantification under different conditions with SharpIR 42%, recovery coefficients (RCs) of SUVmean and FTV were calculated from a phantom with 18F-fluoro-deoxy-glucose (FDG)-filled volumes 0.1–9.2cm3 and signal-to-background (S/B) ratios 4.3–15.9.ResultsWith SharpIR, SUVmax and SUVmean were higher and FTV lower compared with VPHD, regardless of threshold method, in both prostates and LNs.Total lesion uptake determined with both threshold methods was lower with SharpIR compared with VPHD with both threshold methods, except in subgroup analysis of prostate targets where estimated threshold returned higher values.Longer acquisition times returned higher FTV for both threshold methods, regardless of reconstruction algorithm. The FTV difference was most pronounced with one minute’s acquisition per bed position, which also produced visually the highest noise. SUV parameters were unaffected by varying acquisition times.FTV with SharpIR 42% showed the best correspondence with STV.SharpIR 42% gave higher RCs of SUVmean and FTV with increasing phantom size and S/B-ratio, as expected.ConclusionsDelineation with SharpIR 42% seems to provide the most accurate combined information from SUVmax, SUVmean, FTV and total lesion uptake. Acquisition time may be shortened to two minutes per bed position with preserved image quality.

269. Pediatric front to back X-ray chest exposure: Image quality and dose optimization

Purpose In this study we changed the acquisition parameters in chest X-ray of 5 years old pediatric patients in order to lower radiation dose and preserve image quality. A new acquisition technique (T2) was compared to the previous (T1). The comparison regarded dosimetric and a qualitative assessment on phantom and on patients. Methods For dose evaluation we calculated the ESD on a phantom and on patients and we used PCXMC software to evaluate the effective dose (E). We used the TO.20 Leeds Test and AutoPIA software to perform an analysis on a plexiglass phantom simulating patient with the test in between, exposing the phantom to different parameters in order to choose the best technique to use: T2 and comparing it with T1 [1] . The images were analysed from a quantitative viewpoint determining the Correct Observation Ratio% (COR%) and Normalized Correct Observation Ratio% (NCOR%) [2] in order to establish which had the maximum value. For qualitative analysis of the clinical image, radiologists examined X- ray images of patients using T1 and comparing it with T2. Results We noticed that T2 can entail a lower ESD: −68% and a lower effective dose: −65%. In relation to qualitative analysis, carried out by radiologists, there was no significant preference of one technique over another. T2 proved to be the best also in TO.20 analysis performed on a phantom: COR% is comparable: T2 (36%), T1 (39%) and NCOR% is better: T2 (6.2%), T1 (3.9%). Conclusions By optimising acquisition parameters in chest acquisition of pediatric patients, using filtration 0.1 mmCu + 1mmAl, removing grid and setting lower kV, it is possible to lower radiation dose preserving image quality.

Primary efforts to optimize image quality in chest direct digital radiography of pediatric patient (for the age group 0 to 1 and 1 to 5 years) with survey some effective parameters in image quality and patient dose

Introduction: This study, evaluate pediatric digital radiography. Due to the high rates of this test and the high sensitivity of children to ionizing radiation in comparison with adults, it is attempted to achieve near optimal conditions by reducing the dose and maintaining the image quality. The object of the study is to change the irradiation parameters such as kV and mA and change them to a point where the dose is as high as possible for improved image quality or image quality preservation. Materials and Methods: Irradiation data from 50 children were collected for each of 2 age groups 0-1and 1-5 years old, and the mean values of voltage, current, and tube-to-table distance were obtained. To investigate Improving image quality as the dose reduced, a homogeneous phantom was constructed to produce an equivalent chest distribution with less than 5% accuracy. The Monte Carlo simulation and the equivalent human phantom ORNL were used to make this phantom. Then, with use of a digital radiographic system and dosimetric instruments that calculate the dose, the dose of homogeneous phantom was measured. Subsequently, irradiation parameters such as kV and current were changed to provide voltages higher than reference voltage and lower current flow rates than those referring to a dose lower than the reference dose, the choice of these conditions is due to the decrease in absorption dose. Finally, three parameters MTF, SNR and CNR are measured using the tools designed in this phantom. Results: With the results obtained for three parameters of each of the two age groups, the result was that for the age group 0-1, the best selection for the voltage and current was 60 and 2.5, respectively, in which the dose reduced and image quality increased. For 1 -5 years the voltage 75 with a current range of 3.2 to 5 mAs was close to optimum. Conclusion: This article has determined wide variations in radiation dose of x-ray examinations. Also, to Reduce patient dose and enhance image quality is suggested following solutions: 1- utilization of Automatic Exposure Control (Of course, it's not easy to use for children with small body) 2- Used additional filters such as copper: This filter by removing low-energy beam makes the beam harder, and photoelectric effect reduced, as a result, the patient dose is reduced. The works that was done in this study: Improving the exposure parameter compared to dose reference extracted 50 images for each age group and as well as improved its image quality.

PV-Aware Analog Sizing for Robust Analog Layout Retargeting with Optical Proximity Correction

For analog integrated circuits (ICs) in nanometer technology nodes, process variation (PV) induced by lithography may not only cause serious wafer pattern distortion, but also result in device mismatch, which can readily ruin circuit performance. Although the conventional optical proximity correction (OPC) operations can effectively improve the wafer image fidelity, an analog circuit without robust device sizes is still highly vulnerable to such a mismatch effect. In this article, a PV-aware sizing-inclusive analog layout retargeting framework, which encloses an efficient hybrid OPC scheme for yield enhancement, is proposed. The device sizes are tuned during the layout retargeting process by using a deterministic circuit-sizing algorithm considering PV conditions. Our hybrid OPC method combines global rule-based OPC with local model-based OPC functions to boost the wafer image quality improvement but without degrading the computational efficiency. The experimental results show that our proposed framework can achieve the best wafer image quality and circuit performance preservation compared to any other alternative approaches.

Dual-layer detector CT of chest, abdomen, and pelvis with a one-third iodine dose: image quality, radiation dose, and optimal monoenergetic settings

To compare the image quality and radiation dose of reduced iodine dose dual-layer detector (DL) computed tomography (CT) with those of a conventional 120 kVp protocol for chest-abdomen-pelvis CT (CAP-CT). Forty patients with renal dysfunction (estimated glomerular filtrating ratio <45 ml/min/1.73 m2) underwent reduced iodine dose CAP-CT (120 kVp, 200 mg iodine/kg) on DLCT. Virtual monochromatic images (VMI) at 40-70 keV (5 keV interval) were reconstructed retrospectively. Forty matched patients who underwent conventional CAP-CT (120 kVp, 600 mg iodine/kg, iterative reconstruction) were included as controls. The size-specific dose estimate (SSDE), image noise, CT attenuation, and contrast-to-noise ratio (CNR) were compared between the protocols. Two radiologists rated image contrast, image noise, streak artefact, and diagnostic confidence on a five-point scale. The SSDE of the DLCT group was approximately 20% lower than that of the 120 kVp group (15.4±1.9 versus 19.4±2.3 mGy, p<0.01). DLCT-VMI provided almost constant image noise throughout the range of energies (differences of ≤13%), with the noise being equivalent or lower than 120 kVp in the abdomen. CT attenuation and CNR gradually increased as the energy decreased, with values comparable to 120 kVp being attained at around 45-50 keV. Although streak artefact was accentuated at 40-50 keV (p<0.01), the highest scores for diagnostic confidence were assigned at 40 and 45 keV, both of which were equivalent to 120 kVp (p=1.0). For CAP-CT with a one-third iodine dose, DLCT-VMI at 40-45 keV allows for a 20% reduction in radiation dose, while preserving image quality comparable to that of conventional 120 kVp protocol.

Radiation dose reduction for musculoskeletal computed tomography of the pelvis with preserved image quality.

To analyze the impact of pelvic computed tomography (CT) technique optimization on estimated dose and subjective and objective image quality. An institutional review board (IRB)-approved retrospective records review was performed with waived informed consent. Five CT scanners (various manufacturers/models) were standardized to match the lowest dose profile on campus via subjective assessment of clinical images by experienced musculoskeletal radiologists. The lowest dose profile had previously been established through image assessment by experienced musculoskeletal radiologists after a department-wide radiation dose reduction initiative. A consecutive series of 60 pre- and 59 post-optimization bony pelvis CTs were analyzed by two residents, who obtained signal-to-noise ratio for femoral cortex and marrow, gluteus medius muscle, and subcutaneous and visceral fat in a standardized fashion. Two blinded attending radiologists ranked image quality from poor to excellent. Pre- and post-optimization subjects exhibited no difference in gender, age, or BMI (p > 0.2). Mean CT dose index (CTDIvol) and dose-length product (DLP) decreased by approximately 45%, from 39± 14 to 18± 12mGy (p < 0.0001) and 1,227± 469 to 546± 384mGy-cm (p < 0.0001). Lower body mass index (BMI) was associated with a larger dose reduction and higher BMI with higher DLP regardless of pre- or post-optimization examination. Inter-observer agreement was 0.64-0.92 for SNR measurements. Cortex SNR increased significantly for both observers (p < 0.02). Although qualitative image quality significantly decreased for one observer (p < 0.01), adequate mean quality (3.3 out of 5) was maintained for both observers. Subjective and objective image quality for pelvic CT examination remains adequate, despite a substantially reduced radiation dose.

Reducing the risk of skin injuries in cardiac catheterization procedures: Optimization proposal for obese patients

PurposeDuring interventional cardiology procedures, high doses of X-ray may be delivered to patients. This is especially critical in cases of obese patients and/or high obliquity projections. High dose rates can then be produced in patients’ skin as well as insufficient image quality due to regulatory limitations in the X-ray tube output working in the fluoroscopy mode. In this paper, an optimization action is proposed to reduce patient entrance dose rate and preserve image quality in cases of thick patients. MethodsThe action is based on the evaluation of dose rate to the patient and image quality in a new fluoroscopy protocol with less frame rate (7.5 vs. 15 frames/s) and higher spectral shape filter (0.4 vs. 0.1 mm Cu). The new protocol is tested in an angiography room using a PMMA phantom and a test object. ResultsThe new fluoroscopy protocol (7.5 fr/s and 0.4 mm Cu) reduces entrance surface air kerma in 70%–10% (depending on PMMA thickness), preserving the incident air kerma per frame at the image detector. While at lower PMMA thickness, the MTF measured with bar pattern is better for the default protocol; at PMMA thickness between 32 and 37 cm, the optimized protocol produces better image quality indicators. ConclusionsThis work demonstrates that in the case of high thicknesses of PMMA (32–37 cm), increasing spectral beam filter and reducing frame rate may help improve image quality and maintain entrance surface air kerma so as to fulfil the regulatory requirements.

OA160] A simple method for low contrast detectability, image quality and dose optimization with CT iterative reconstruction algorithms and model observers

Purpose The aim of this work was to evaluate detection of low-contrast objects and image quality in computed tomography (CT) phantom images acquired at different tube loadings (i.e. mAs) and reconstructed with different algorithms, in order to find appropriate settings to reduce the dose to the patient without any image detriment. Methods Images of supraslice low-contrast objects of a CT phantom were acquired using different mAs values. Images were reconstructed using filtered back projection (FBP), hybrid and iterative model-based methods. Image quality parameters were evaluated in terms of modulation transfer function; noise, and uniformity using two software resources. For the definition of low-contrast detectability, studies based on both human (i.e. four alternative forced-choice test) and model observers were performed across the various images. Results Compared to FBP, image quality parameters were improved by using iterative reconstruction (IR) algorithms. In particular, IR model-based methods provided a 60% noise reduction and a 70% dose reduction, preserving image quality and low-contrast detectability for human radiological evaluation. According to the model observer, the diameters of the minimum detectable detail were around 2 mm (up to 100 mAs). Below 100 mAs, the model observer was unable to provide a result. Conclusions IR methods improve CT protocol quality, providing a potential dose reduction while maintaining a good image detectability. Model observer can in principle be useful to assist human performance in CT low contrast detection tasks and in dose optimization.