Terms Of Image Quality Research Articles

Ultrasound Image Noise Reduction and Enhancement Model based on Yellow Saddle Goatfish Optimization Algorithm

In the modern-day diagnostics, ultrasound play an important role in different applications such as vascular, gynecological, cardiac, and obstetrical for diagnosis the various diseases. The main benefit of the ultrasound is that it is non-invasive method and inexpensive. However, in the real-scenario, ultrasound images contain speckle noise which negatively impact the image quality in terms of edges, texture information, and boundaries. In order to eliminate noise, various filters are deployed by researchers in the literature. The limitations of their method are that a fixed level of noise is removed using conventional filters in which parameter values of the filters are fixed. However, in the real-time situation, the noise is random and adaptive filters are required which eliminate any level of noise. To achieve this goal, this paper proposes an adaptive filtering model for eliminate speckle noise based on yellow saddle goatfish optimization (YSGO) algorithm. The YSGO algorithm is based on the hunting behaviour of the fishes. In the proposed model, bilateral filter and speckle-reducing anisotropic diffusion filtering methods and enhancement power law method are taken under consideration. Further, the parameter values of the filtering method and enhancement methods are determined using the nature-inspired YSGO algorithm. The YSGO algorithm minimize the noise and enhances the image brightness and edge information based on the objective function. In our model, mean square error (MSE) and entropy is taken as the objective function. Further, the proposed model is applied on the standard ultrasound images. The visual analysis of the images is done based on the subjective analysis whereas various performance metrics are measured for measure the image quality in the objective analysis. The results reveals that the proposed model outperforms over the existing models in terms of PSNR.

Adaptive Coarse-to-Fine Single Pixel Imaging With Generative Adversarial Network Based Reconstruction

Single Pixel Imaging (SPI) that only uses one light intensity sensor has been researched extensively as an alternative imaging method. It is proven to work at low light conditions and unconventional wavelength bands where the classical pixel array sensors are limited. However, the major issues of SPI remain as the image quality and computational efficiency. Thus, this paper proposes an adaptive coarse-to-fine (C2F) sampling method to replace the typical uniform sampling method to achieve image reconstruction with better quality. This scalable sampling mechanism is adaptive to the target scene as it will progressively sample according to the image complexity and quality indicator. Subsequently, a deep Generative Adversarial Network (GAN) model is also proposed to improve the time efficiency of the multi-scale image reconstruction. The results show that C2F sampling consistently outperforms uniform sampling in terms of image quality (21% in SSIM, 8% in PSNR and 17% in RMSE). Besides, improvement in the efficiency is also achieved by the proposed GAN reconstruction, whereby the total time taken is only 0.025% of the time taken for the conventional L1 reconstruction method (≈ 4000 times faster). In conclusion, the proposed adaptive C2F SPI using GAN reconstruction method can serve as an optimised solution to improve both the image quality and computational efficiency in SPI.

Deep Learning Imaging for 1-D Aperture Synthesis Radiometers

For one-dimensional aperture synthesis (1-D AS) radiometers, truncated sampling occurs in the frequency domain due to the system baseline limitation. Therefore, there is an obvious Gibbs oscillation in the reconstructed image. To solve this problem, an imaging method based on a one-dimensional convolutional neural network (1-D CNN) is proposed in this paper. Compared with deep learning methods based on two-dimensional convolutions, the 1-D convolution not only reduces the amount of computation but also produces further performance improvements. The input data of the network are the 1-D visibility function samples, and the output data are the 1-D brightness temperature (BT) samples. The network learns the mapping relationship from the training of the 1-D visibility function samples and 1-D BT samples to complete 1-D AS imaging without any prior knowledge. To verify the performance of this imaging method, simulations and experiments based on the airborne C-band one-dimensional microwave interferometric radiometer (ACMIR) system are implemented. The simulation and experimental results demonstrate that the proposed AS-CNN method achieves higher performance than the IFFT method in terms of image quality and Gibbs phenomenon suppression. In the case of an antenna failure and missing baseline, the AS-CNN method proposed in this paper can still obtain a BT image with high imaging quality, which shows that the robustness of the network is better than that of the IFFT method.

Impact of Photon-Counting Detector Computed Tomography on Image Quality and Radiation Dose in Patients With Multiple Myeloma.

Computed tomography (CT) is an established method for the diagnosis, staging, and treatment of multiple myeloma. Here, we investigated the potential of photon-counting detector computed tomography (PCD-CT) in terms of image quality, diagnostic confidence, and radiation dose compared with energy-integrating detector CT (EID-CT). In this prospective study, patients with known multiple myeloma underwent clinically indicated whole-body PCD-CT. The image quality of PCD-CT was assessed qualitatively by three independent radiologists for overall image quality, edge sharpness, image noise, lesion conspicuity, and diagnostic confidence using a 5-point Likert scale (5 = excellent), and quantitatively for signal homogeneity using the coefficient of variation (CV) of Hounsfield Units (HU) values and modulation transfer function (MTF) via the full width at half maximum (FWHM) in the frequency space. The results were compared with those of the current clinical standard EID-CT protocols as controls. Additionally, the radiation dose (CTDIvol) was determined. We enrolled 35 patients with multiple myeloma (mean age 69.8 ± 9.1 years; 18 [51%] males). Qualitative image analysis revealed superior scores (median [interquartile range]) for PCD-CT regarding overall image quality (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), edge sharpness (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), image noise (4.0 [4.0-4.0] vs. 3.0 [3.0-4.0]), lesion conspicuity (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]), and diagnostic confidence (4.0 [4.0-5.0] vs. 4.0 [3.0-4.0]) compared with EID-CT (P ≤ 0.004). In quantitative image analyses, PCD-CT compared with EID-CT revealed a substantially lower FWHM (2.89 vs. 25.68 cy/pixel) and a significantly more homogeneous signal (mean CV ± standard deviation [SD], 0.99 ± 0.65 vs. 1.66 ± 0.5; P < 0.001) at a significantly lower radiation dose (mean CTDIvol ± SD, 3.33 ± 0.82 vs. 7.19 ± 3.57 mGy; P < 0.001). Whole-body PCD-CT provides significantly higher subjective and objective image quality at significantly reduced radiation doses than the current clinical standard EID-CT protocols, along with readily available multi-spectral data, facilitating the potential for further advanced post-processing.

Integrated edge thresholded deep network for image denoising

The outcome of the denoising network suffers from over-smoothing effect, due to this, the texture content of the object will be lost. Lack of accurate texture properties in the image may lead to inefficient object segmentation and classification. This paper proposes an edge-preserving thresholding approach and applies it to the output of the denoised network. The thresholding approach relies on the distance and weight factors, which move the noisy components toward the mean of the subspace. This proposal is meant to treat over and under-smoothed components, where the smoothing decrement or increment is controlled by the threshold calculated with the average mean of the components in the respective subspace. The approach is compared with state-of-the-art methods in terms of image quality, and it is observed that this approach increases the quality proportionately. The result depicts that there is a significant improvement in PSNR of about 0.7~ 1 dB with the proposed integrated mechanism when compared against the conventional CNN-based image denoiser. Moreover, the edge details are better preserved with the proposed integrated mechanism.

ДИФФЕРЕНЦИАЦИЯ ПРИМЕНЕНИЯ МЕТОДОВ ДИСТАНЦИОННОГО ИССЛЕДОВАНИЯ ДОРОЖНОГО ДВИЖЕНИЯ

The existing urban trends give new demands on the transport infrastructure of modern cities. Urban development planning should be based on representative traffic information. To reduce the resource costs of network research of traffic flows, we chose the method of remote sensing of traffic by criteria of area and configuration of the street-road network of the urbane territory with the use of modern satellite systems. The most suitable satellites for this purpose in Russia are Resurs-P and Kanopus-B, while the best satellites abroad in terms of image quality and regularity of resource updates are The Landsat Programme and Maxar (USA). We set, that the spatial resolution of satellite images and the scale of the image to be interpreted are correlated with the technical capabilities of satellite systems; the critical spatial resolution of 1/14.8 m/mm is achieved at a scale of 1:450. The authors developed an algorithm for selecting a rational method of remote sensing of traffic for different spatial urban structures at micro-, meso- and macro-levels and considered it on the example of 14 urban Russian and foreign agglomerations. The article presents a feasibility study of remote sensing application based on the " cost per unit of accuracy" criterion. Its values ranged from 0.08 to 1393.94 thousand rubles depending on the selected monitoring method and the area of spatial structures of the studied objects. The satellite remote sensing method was found to be a priority in terms of cost per unit of accuracy. Taking into account the obtained data, we plan to create a software product for automation of decoding algorithms for determining traffic parameters.

An efficient one-step proximal method for EIT sparse reconstruction based on nonstationary iterated Tikhonov regularization

ABSTRACT Image reconstruction of EIT mathematically is a seriously ill-posed inverse problem, which is easily affected by measurement noise. This fact leads to a relatively low spatial resolution, particularly in the accuracy of identifying object boundaries. This paper concerns with reconstructing a finite number of simple and small inclusions equipped with the homogeneous background conductivity. The sparsity with respect to the inclusions in the spacial representation domain is apriori assumed. We try to reexamine the multi-parameter constraints on mixing between -norm and -norm for attaining the sparse reconstruction as well as simultaneously enhancing the stability. On the basis of the reference approximation reconstructed by nonstationary iterated Tikhonov regularization (NITR) method, we propose a novel one-step proximal sparsity-promoting approach by one induced proximal shrink operator, abbreviated as one-step PNITR method. The proposed one-step PNITR method consists of twofold: one first performs NITR with mth iteration to generate the reference approximation, and then performs one-step proximal shrinkage processing and one forcing constraint function on it to obtain the final sparsity-promoting reconstruction. For the latter, the former aims to not only enhance higher reliability but also guarantee the sparsity. The proposed method can benefit from the double regularization effect. It is supposed to further improve the imaging quality in terms of sharpening the edges and reducing the artefacts. To validate the advantage of the proposed method, numerical simulations with synthetic data have been carried out. Also, qualitative and quantitative comparisons are conducted. Results indicate that the proposed approach can lead to substantial improvements in EIT image resolution. The performed numerical simulations show that our method is promising.

Understanding reasons for image rejection by radiologists and radiographers.

A core element of a radiographer's role is the decision on whether a radiograph is sufficient for diagnosis, or a repeat examination is needed. Studies illustrate the disagreement on the diagnostic value of radiographs between radiographers and radiologists, which may influence repeat examinations. This study investigates if parameters contributing to image quality are possible determinants to explain the difference between professions. A total of 74 radiographers and radiologists from three different countries assessed three data sets (chest PA, hip HBL, c-spine lateral), each containing 25 radiographs. All observers scored image quality in terms of anatomical visualisation, positioning, collimation, detector exposure and judged the diagnostic value using the ACR RadLex classification. All assessments were performed on a clinically relevant display. Visual grading characteristics were used to compare image quality evaluations between groups. Radiographers scored the visualisation of anatomical structures lower than radiologists though the difference was not statistically significant. A difference in classification using the RadLex categories - with radiographers rejecting more radiographs - was demonstrated. Only the subjective evaluation of the detector exposure correlated statistically with RadLex ratings. There was no difference between radiographers and radiologists when reviewing patient positioning and collimation. Radiographers and radiologists agree on the visualisation of anatomical structures, but radiographers are more critical towards the diagnostic value. Within the criteria studied, the evaluation of anatomical structures does not explain the difference. Radiographs have a higher change of being rejected if the observer (subjectively) assessed the detector exposure as inappropriate. This correlation is stronger for radiographers.

Survey of methods and evaluation of Retinex-inspired image enhancers

Spatial color algorithms (SCAs) are computer vision procedures widely used for image enhancement and human vision modeling. The main characteristic of SCA family is that they mimic the behavior of the human vision system (HVS), achieving in this way robustness and the capability to adjust their effect according to the image content. Here, we review 35 different, popular Retinex-inspired SCAs discussing and providing a set of measures for their evaluation in terms of image quality. To this purpose, we also introduce SCA-30, a real-world color image dataset made publicly available. The algorithms considered here include and spread from well-known Retinex implementations, Retinex variants, Milano–Retinex and related inspired enhancers, illumination/decomposition approaches, and deep learning-based techniques. Data and code used for the evaluation are made freely available to the community, to pursue further analysis and comparisons.

Motion-Compensated PET Image Reconstruction via Separable Parabolic Surrogates

The effective resolution of positron emission tomography (PET) can be significantly degraded by patient motion during data acquisition. This is especially true in the thorax due to respiratory motion. This study concentrates on the improvement of motion correction algorithms both in terms of image quality and computational cost. In this paper, we present a novel motion-compensated image reconstruction (MCIR) algorithm based on a parabolic surrogate likelihood function instead of the loglikelihood function of the expectation maximization (EM) algorithm. The theoretical advantage of the parabolic surrogate algorithm lies within the fact that its loglikelihood is upper bounded by the EM loglikelihood, thus it will converge faster than EM. This is of particular importance in PET motion correction, where reconstructions are very computationally demanding. Relaxation parameters were also introduced to converge closer to the maximum likelihood (ML) solution and achieve lower noise levels. Image reconstructions with embedded relaxation parameters actually converged to better solutions than the corresponding ones without relaxation. Motion-compensated parabolic surrogates were indeed shown to accelerate convergence compared to EM, without reaching a limit cycle. Nonetheless, with the incorporation of ordered subsets in the reconstruction setting, the improvement was less evident.

Technical note: Revised projections onto convex sets reconstruction of multi-shot diffusion-weighted imaging.

High-resolution diffusion-weighted imaging (DWI) is usually achieved through multi-shot acquisitions and parallel imaging-based reconstructions. Multiple POCS (projections onto convex sets) based algorithms have been proposed for DWI reconstructions. However, the slow convergence of POCS and the suboptimal quality of the reconstructed images limit their applications. In this study, a revised POCS algorithm for multi-shot DWI reconstruction is proposed based on FISTA (fast iterative shrinkage-thresholding algorithm) to achieve faster convergence and higher accuracy. In FISTA, the next iteration is computed based on two previous iterations, instead of only the previous one, to improve the convergence speed. This scheme is adopted into the relevant POCS-based algorithms, including POCSENSE (POCS-based sensitivity-encoding), POCSMUSE (POCS-based multiplexed sensitivity-encoding), iPOCSMUSE (iterative POCSMUSE), and POCS-ICE (POCS-enhanced inherent correction of motion-induced phase errors) to address the slow convergence problem. Simulations and in vivo experiments were performed to evaluate the performance of the proposed method. Experimental results show that the proposed method enables faster convergence compared to the original POCS. For example, for a spiral DWI simulation using eight-shot interleaves and having SNR of 20dB, the iteration number needed for the revised POCS-ICE decreases by about 70% to achieve approximately the same nRMSE level as POCS-ICE. Additionally, it improves image quality in terms of fewer artifacts compared with the original POCS. The revised DWI reconstruction methods can achieve higher convergence rates than the original POCS-based algorithms and higher image quality with the same iteration numbers. As such, the proposed method can serve as a practical and efficient reconstruction method for multi-shot DWI.

Xenon-enhanced dual-energy tomosynthesis for functional imaging of respiratory disease-Concept and phantom study.

Xenon-enhanced dual-energy (DE) computed tomography (CT) and hyperpolarized noble-gas magnetic resonance imaging (MRI) provide maps of lung ventilation that can be used to detect chronic obstructive pulmonary disease (COPD) early in its development and predict respiratory exacerbations. However, xenon-enhanced DE-CT requires high radiation doses and hyper-polarized noble-gas MRI is expensive and only available at a handful of institutionsglobally. To present xenon-enhanced dual-energy tomosynthesis (XeDET) for low-dose, low-cost functional imaging of respiratory disease in an experimental phantomstudy. We propose using digital tomosynthesis to produce Xe-enhanced low-energy (LE) and high-energy (HE) coronal images. DE subtraction of the LE and HE images is used to suppress soft tissues. We used an imaging phantom to investigate image quality in terms of the area under the reciever operating characteristic curve (AUC) for the Non-PreWhitening model observer with an Eye filter and internal noise (NPWEi). The phantom simulated anatomic clutter due to lung parenchyma and attenuation due to soft tissue and lung tissue. Aluminum slats were used to simulate rib structures. A stepwedge consisting of an acrylic casing with sealed cylindrical air-filled cavities was used to simulate ventilation defects with step thicknesses of 0.5, 1, and 2cm and cylindrical radii of 0.5, 0.75, and 1cm. The phantom was ventilated with Xe and projection data were acquired using a flat-panel detector, a tube-voltage combination of 60/140kV with 1.2 mm of copper filtration on the HE spectrum and an angular range of in 1° increments. The AUC of a NPWEi observer that has access only to a single coronal slice was calculated from measurements of the three-dimensional noise power spectrum and signal template. The AUC was calculated as a function of ventilation defect thickness and radius for total patient entrance air kermas ranging from 1.42 to 2.84mGy with and without rib-simulating Al slats. For the AUC analysis, the observer internal noise level was obtained from an ad hoc calibration to a high-dose dataset. XeDET was able to suppress parenchyma-simulating clutter in coronal images enabling visualization of the simulated ventilation defects, but the limited angle acquisition resulted in residual clutter due to out-of-plane bone-mimmicking structures. The signal power of the defects increased linearly with defect radius and showed a ten-fold to fifteen-fold increase in signal power when the defect thickness increased from 0.5 to 2cm. These trends agreed with theoretical predictions. Along the depth dimension, the power of the defects decreased exponentially with distance from the center of the defects with full-width half maxima that varied from 1.85to 2.85cm depending on the defect thickness and radius. The AUCs of the 1-cm-radius defect that was 2cm in thickness ranged from good (0.8-0.9) to excellent (0.9-1.0) over the range of air kermasconsidered. Xenon-enhanced DE tomosynthesis has the potential to enable functional imaging of respiratory disease and should be further investigated as a low-cost alternative to MRI-based approaches and a low-dose alternative to CT-basedapproaches.

Die Rostocker Methode zur qualitativen und quantitativen Bewertung von Intraokularlinsen

For quantitative and qualitative evaluation of the imaging properties of IOLs, axial cross-sectional images can be obtained from the 3-dimensional light distribution by means of an optical bench, as is known from light sheet recordings in fluorescein baths. This paper presents a new image-processing algorithm to enhance the quality of generated axial cross-sectional images, and the two methods are then compared. The 3-dimensional point spread function of a diffractive trifocal IOL (AT LISA tri 839MP, Carl Zeiss Meditec AG, Jena, Germany) was recorded on an optical bench developed in Rostock for different pupil diameters. A specially adapted image processing algorithm was then applied to the measurements, allowing through-focus curves to be generated. In addition, cross-sectional images of the IOLs studied were acquired using the light sheet method in a fluorescein bath. The study clearly shows the superiority of the newly developed method over the light sheet method in terms of image quality. In addition to the individual focal points, fine focal structures as well as halos can be made visible in the cross-sectional images obtained using the new method. In the generated through-focus curves, 3 intensity peaks can be identified, which represent the near, intermediate and far focus of the tested MIOL and cannot be represented by light sheet methods. The interaction of the optical bench with the developed image processing algorithm allows a more detailed understanding of the image formation and false light phenomena of IOLs, which was restricted by the technical limitations of the existing light sheet method. In addition, other quantities such as the through-focus curve can be derived quantitatively.

Revolutionizing retinal disease screening using “intelligent” smartphones

AbstractDiabetic retinopathy (DR) and Glaucoma are among the leading causes of irreversible blindness globally. There are over 450 million Diabetics with 1 in 3 developing DR and 70 million affected with glaucoma. With the aging population the numbers are projected to increase exponentially. Early detection of DR with active screening is the key to prevent blindness secondary to advanced disease. Chronic glaucoma is frequently asymptomatic until central vision is impaired in advanced stages, resulting in a high proportion of undetected disease. Current teleophthalmology‐based screening solutions prevent scale up due to several challenges such as expensive equipment, delay in reporting and manual labour‐intensive grading process.The need of the hour for screening programs is a device that is portable, reliable, affordable with integrated AI providing rapid results. Remidio Fundus‐ on‐ phone (FOP) is one such device that has offline artificial intelligence (AI) algorithms that screens for referable diabetic retinopathy (RDR) and Glaucoma without the need for internet and cloud‐based inferencing. It utilizes smartphone technology for capturing non‐mydriatic fundus images which can be operated by minimally trained technicians with an inbuilt patient management software, auto capture and internal fixation features to improve ease‐of‐use. Further, it has the versatility to be used either as a handheld device, mounted on a slit lamp or in a stabilized version with chinrest, thus providing several options for the user. This smartphone‐based fundus camera has proven to be on par with high‐end desktop systems in terms of image quality in multiple published studies.The performance of the RDR AI has been clinically validated in multiple prospective trials. In three published studies conducted at diabetes care centres as well as in the community in India, the sensitivity ranged between 93%–100% with a specificity of 86.7%–92.5% for RDR. Additionally, it was found that the sensitivity to detect sight‐threatening disease which has a high risk of blindness, ranged between 98.7% to 100%. The AI has also been tested in several other populations in Armenia, Poland, Mexico and Caribbean islands with similar results. The novel Referable Glaucoma AI has similarly been prospectively validated against standard of care. Despite being challenged with fundus images alone the sensitivity to pick likely glaucoma was high with minimal overcall of normal subjects. Additionally, activation maps highlight areas of structural abnormality on the optic disc image, thus allowing visualization of key findings and better patient counselling.This device gives an opportunity to make a paradigm shift in the approach for screening as providers could get to the population to screen at‐risk individuals rather than relying on patients to come to clinics to be screened. It is thus an end‐to‐end solution for DR and Glaucoma screening combining two technological advances‐ smartphone technology and AI. With several upcoming algorithms to screen for other retinal conditions, this device is well positioned to be a gamechanger in the efforts aimed at reducing the burden of preventable retinal blindness across the world.

FlowRAU-Net: Accelerated 4D Flow MRI of Aortic Valvular Flows With a Deep 2D Residual Attention Network.

In this work, we propose a novel deep learning reconstruction framework for rapid and accurate reconstruction of 4D flow MRI data. Reconstruction is performed on a slice-by-slice basis by reducing artifacts in zero-filled reconstructed complex images obtained from undersampled k-space. A deep residual attention network FlowRAU-Net is proposed, trained separately for each encoding direction with 2D complex image slices extracted from complex 4D images at each temporal frame and slice position. The network was trained and tested on 4D flow MRI data of aortic valvular flow in 18 human subjects. Performance of the reconstructions was measured in terms of image quality, 3-D velocity vector accuracy, and accuracy in hemodynamic parameters. Reconstruction performance was measured for three different k-space undersamplings and compared with one state of the art compressed sensing reconstruction method and three deep learning-based reconstruction methods. The proposed method outperforms state of the art methods in all performance measures for all three different k-space undersamplings. Hemodynamic parameters such as blood flow rate and peak velocity from the proposed technique show good agreement with reference flow parameters. Visualization of the reconstructed image and velocity magnitude also shows excellent agreement with the fully sampled reference dataset. Moreover, the proposed method is computationally fast. Total 4D flow data (including all slices in space and time) for a subject can be reconstructed in 69 seconds on a single GPU. Although the proposed method has been applied to 4D flow MRI of aortic valvular flows, given a sufficient number of training samples, it should be applicable to other arterial flows.

Development and systematic evaluation of high resolution PD‐weighted, T2‐weighted, and T2 map templates of the MIITRA atlas for use in studies on older adults

AbstractBackgroundAtlas based MRI investigations involving PD‐weighted (PDw), T2‐weighted (T2w) MRI sequences on older adults typically utilized younger adult templates such as those of the ICBM. Additionally, a thorough quantitative assessment of how available standardized PDw and T2w templates perform on aging population has not yet been performed. Here a new standardized high resolution PDw, T2w and T2 map templates of the MIITRA atlas dedicated for studies on older adults was developed using concepts of super‐resolution and sparse‐representation and compared to other available standardized templates in terms of image quality, inter‐subject spatial normalization accuracy and representativeness of the older adult brain.MethodT2‐weighted multi‐echo spin‐echo brain MRI (0.5mmx0.5mmx3mm isotropic, TE=20,40,60,80,100ms) and its corresponding T1‐weighted (isotropic 1mm) data obtained from the 400 non‐demented older adults (65‐95 age range, male:female=1:1) participating in the construction of the MIITRA atlas were used in this work. Additionally, subject T2 maps were constructed by fitting the T2 decay equation. The template construction involved reconstruction of high resolution voxels of T2‐weighted data utilizing nonlocal super‐resolution upsampling technique, mapping of each of the five echo (TE=20,40,60,80,100ms) separately using combined iterative T1‐weighted and DTI spatial normalization based transformations on a super‐resolution grid followed by patch‐based sparse representation to construct our T2‐weighted templates of the MIITRA atlas (Fig.1).The first echo PD‐weighted and the third echo T2‐weighted templates were compared to other available templates in terms of image sharpness and inter‐subject spatial normalization accuracy achieved when used as references for normalization of 30 ADNI PD‐weighted and T2‐weighted participants.ResultThe new PD and T2 weighted templates of the MIITRA atlas demonstrated higher image sharpness compared to the other templates, as exhibited by larger high spatial frequency content in the normalized power spectra (Fig. 2A,B,C,D). Inter‐subject spatial normalization was higher when using the new templates compared to the other templates (Fig.3,4). The new templates required the least spatial deformation for normalizing ADNI data (Fig.5A,B).ConclusionWe conclude that the new PD‐weighted and T2‐weighted templates of the MIITRA atlas are characterized by superior image quality, require lower deformation and allow for higher spatial normalization accuracy for use in studies on older adults such as the ADNI datasets.

An evaluation of the present status of quality assurance program implementation in digital mammography facilities in a developing country

Quality assurance program (QAP) implementation in mammography is a key aspect to guarantee the acquisition of -quality images and, thus, an opportune diagnosis of breast cancer. A QAP involves planned and systematic actions, such as quality control (QC) tests that will produce consistently high-quality images, while considering low radiation exposure for stakeholders. Currently, there is poor evidence of the current QAP implementation status in developing countries, particularly in Latin America. In this work, 47 Mexican digital mammography facilities were evaluated in terms of their QAP implementation’s current status during the period October 2021 to April 2022. A survey of three key indicators of compliance/non-compliance with QAPs was carried out. Also, 17 QC test parameters were assessed in all mammography units according to an international framework. The Kruskal–Wallis test, McNemar test, and Spearman correlation were used to identify the significant correlation between the QC tests and QAP indicators. Statistical analysis showed a significant positive correlation between the QAP indicators and QC parameters that were evaluated, especially associated with an increase in image quality. Almost 90% of the facilities evaluated failed in some QAP indicators, and only 44.68% of the facilities had an updated and current QAP implemented. Successful QAP implementation directly impacted on the QC mammography test results, particularly in terms of image quality, thus conditioning a successful mammography acquisition chain. The results obtained in this work suggest that better regulations need to be implemented to guarantee QAP surveillance, which could mean an increase in opportune diagnosis and management of breast cancer, with improved radiological safety for patients and occupational exposure personnel.

A 72-channel receive array coil allows whole-heart cine MRI in two breath holds

BackgroundA new 72-channel receive array coil and sensitivity encoding, compressed (C-SENSE) and noncompressed (SENSE), were investigated to decrease the number of breath-holds (BHs) for cardiac magnetic resonance (CMR).MethodsThree-T CMRs were performed using the 72-channel coil with SENSE-2/4/6 and C-SENSE-2/4/6 accelerated short-axis cine two-dimensional balanced steady-state free precession sequences. A 16-channel coil with SENSE-2 served as reference. Ten healthy subjects were included. BH-time was kept under 15 s. Data were compared in terms of image quality, biventricular function, number of BHs, and scan times.ResultsBHs decreased from 7 with C-SENSE-2 (scan time 70 s, 2 slices/BH) to 3 with C-SENSE-4 (scan time 42 s, 4–5 slices/BH) and 2 with C-SENSE-6 (scan time 28 s, 7 slices/BH). Compared to reference, image sharpness was similar for SENSE-2/4/6, slightly inferior for C-SENSE-2/4/6. Blood-to-myocardium contrast was unaffected. C-SENSE-4/6 was given lower qualitative median scores, but images were considered diagnostically adequate to excellent, with C-SENSE-6 suboptimal. Biventricular end-diastolic (EDV), end-systolic (ESV) and stroke volumes, ejection fractions (EF), cardiac outputs, and left ventricle (LV)-mass were similar for SENSE-2/4/6 with no systematic bias and clinically appropriate limits of agreements. C-SENSE slightly underestimated LV-EDV (-6.38 ± 6.0 mL, p < 0.047), LV-ESV (-7.94 ± 6.0 mL, p < 0.030) and overestimated LV-EF (3.16 ± 3.10%; p < 0.047) with C-SENSE-4. Bland-Altman analyses revealed minor systematic biases in these variables with C-SENSE-2/4/6 and for LV-mass with C-SENSE-6.ConclusionsUsing the 72-channel coil, short-axis CMR for quantifying biventricular function was feasible in two BHs where SENSE slightly outperformed C-SENSE.

Streaking artifact reduction for CBCT-based synthetic CT generation in adaptive radiotherapy.

Cone-beam computed tomography (CBCT) is widely used for daily image guidance in radiation therapy, enhancing the reproducibility of patient setup. However, its application in adaptive radiotherapy (ART) is limited by many imaging artifacts and inaccurate Hounsfield units (HUs). The correction of CBCT image is necessary and of great value for CBCT-based ART. To explore the synthetic CT (sCT) generation from CBCT images of thorax and abdomen patients, which usually surfer from serious artifacts duo to organ state changes. In this study, a streaking artifact reduction network (SARN) is proposed to reduce artifacts and combine with cycleGAN to generate high-quality sCT images from CBCT and achieve an accurate dose calculation. The proposed SARN was trained in a self-supervised manner. Artifact-CT images were generated from planning CT by random deformation and projection replacement, and SARN was trained based on paired artifact-CT and CT images. The planning CT and CBCT images of 260 patients with cancer, including 120 thoracic and 140 abdominal CT scans, were used to train and evaluate neural networks. The CBCT images of another 12 patients in late treatment fractions, which contained large anatomy changes, were also tested by trained models. The trained models include commonly used U-Net, cycleGAN, attention-gated cycleGAN (cycAT), and cascade models combined SARN with cycleGAN or cycAT. The generated sCT images were compared in terms of image quality and dose calculation accuracy. The sCT images generated by SARN combined with cycleGAN and cycAT showed the best image quality, removed the most artifacts, and retained the normal anatomical structure. The SARN+cycleGAN performed best in streaking artifacts removal with the maximum percent integrity uniformity (PIUm ) of 91.0% and minimum standard deviation (SD) of 35.4 HU for delineated artifact regions among all models. The mean absolute error (MAE) of CBCT images in the thorax and abdomen were 71.6 and 55.2 HU, respectively, using planning CT images after deformable registration as ground truth. Compared with CBCT, the thoracic and abdominal sCT images generated by each model had significantly improved image quality with smaller MAE (p < 0.05). The SARN+cycAT obtained the minimum MAEs of 42.5 HU in the thorax while SARN+cycleGAN got the minimum MAEs of 32.0 HU in the abdomen. The sCT generated by U-Net had a remarkably lower anatomical structure accuracy compared with the other models. The thoracic and abdominal sCT images generated by SARN+cycleGAN showed optimal dose calculation accuracy with gamma passing rates (2 mm/2%) of 98.2% and 96.9%, respectively. The proposed SARN can reduce serious streaking artifacts in CBCT images. The SARN combined with cycleGAN can generate high-quality sCT images with fewer artifacts, high-accuracy HU values, and accurate anatomical structures, thus providing reliable dose calculation in ART.

Optimization of Preprocedural Full-cycle Computed Tomography in Patients Referred for Transcatheter Tricuspid Valve Repair: Test Bolus Versus Bolus Tracking.

Advancements in transcatheter mitral and tricuspid valve repair have resulted in growing demands in preprocedural computed tomography (CT) imaging. Due to the introduction of multidetector CT (MDCT), shorter acquisition times as well as high rates of heart failure and arrhythmias in this specific patient population, optimal synchronization between the passage of contrast agent and data acquisition is mandatory. There is no consensus on which acquisition technique should be used in this patient population. We aimed to optimize our preprocedural CT protocol comparing bolus tracking (BT) and test bolus (TB) techniques. We performed a retrospective analysis on 151 patients referred for full-cycle MDCT evaluation for transcatheter tricuspid valve repair comparing BT with TB (BT n=75 TB n=75). Contrast-to-noise ratios (CNR) were obtained. Demographic data, laboratory, electrocardiographic, and transthoracic echocardiography/transoesophageal echocardiography parameters were collected from electronic health records. Also, the volume of contrast agent and saline chaser and radiation dose length product and milliampere seconds were collected. BT and TB resulted in comparable CNR (BT: 0.47 [0.34 to 0.98]; TB: 0.51 [0.41 to 1.40]; P =0.1). BT was associated with a shorter scan duration (BT: 8.3min [4.1 to 24.4]; TB: 13.9min [6.2 to 41.4]; P <0.001), less radiation in terms of dose length product (BT: 1186±585; TB: 1383±679, P =0.04), and lower total volume administration (BT: 101mL [63 to 16]; TB: 114mL [71 to 154]; P <0.001). In patients with severely impaired ejection fraction (left ventricular ejection fraction [LVEF] ≤35%; n=65 [TB n=31; BT n=34]) using the TB technique yielded significantly better image quality in terms of CNR (TB=0.57 [0.41 to 1.07); BT=0.41 [0.34 to 0.65]; P =0.02). In patients with impaired LVEF (LVEF≤35%), the TB technique yielded significantly superior image quality and may be the preferred approach in this specific patient population. BT showed advantages in terms of shorter duration, less radiation, and lower contrast agent volume.