Loss Of Image Quality Research Articles

Аналитический обзор метрик, используемых для оценки качества мультимедийной информации

Data quality is an indicator that characterizes any transmitted information that can be measured. The very word “measure” suggests the evaluation of these data, the quality of which can be described and quantified. The metric, in turn, is essentially an objective assessment during testing, which makes it possible to determine data distortions that occur during transmission, encoding, digitization, compression, and decoding of video data. The article considers such metrics as PSNR and VQM, and analyzes the ITU-R-BT.500-8.11 standard. A brief overview of the MSU Video Quality Measurement Tool is presented, as well as a simulated video conferencing image acquisition using one of the most popular H264 codecs. The formulas for calculating the PSNR metric, defined through the mean square error (MSE), estimating the loss of image quality by comparing the received video with the downloaded (reference video) are presented. The VQM (Video Quality Metric) metric is considered, which evaluates the distortion in the transmitted video caused by the passage of network packets through the cable line of the transmission system (decoding errors or coding errors), as well as methods for subjective and objective evaluation of images in video conferencing. The advantages and disadvantages of each of the evaluation methods are considered.

Anti-noise computational imaging using unsupervised deep learning

Computational imaging enables spatial information retrieval of objects with the use of single-pixel detectors. By combining measurements and computational methods, it is possible to reconstruct images in a variety of situations that are challenging or impossible with traditional multi-pixel cameras. However, these systems typically suffer from significant loss of imaging quality due to various noises when the measurement conditions are single-photon detecting, undersampling and complicated. Here, we provide an unsupervised deep learning (UnDL) based anti-noise approach to deal with this problem. The proposed method does not require any clean experimental data to pre-train, so it effectively alleviates the difficulty of model training (especially for the biomedical imaging scene which is difficult to obtain training ground truth inherently). Our results show that an UnDL based imaging approach outperforms conventional single-pixel computational imaging methods considerably in reconstructing the target image against noise. Moreover, the well-trained model is generalized to image a real biological sample and can accurately image 64 × 64 resolution objects with a high speed of 20 fps at 5% sampling ratio. This method can be used in various solvers for general computational imaging and is expected to effectively suppress noises for high-quality biomedical imaging in generalizable complicated environments.

Accelerating BPC-PaCo through Visually Lossless Techniques

Fast image codecs are a current need in applications that deal with large amounts of images. Graphics Processing Units (GPUs) are suitable processors to speed up most kinds of algorithms, especially when they allow fine-grain parallelism. Bitplane Coding with Parallel Coefficient processing (BPC-PaCo) is a recently proposed algorithm for the core stage of wavelet-based image codecs tailored for the highly parallel architectures of GPUs. This algorithm provides complexity scalability to allow faster execution at the expense of coding efficiency. Its main drawback is that the speedup and loss in image quality is controlled only roughly, resulting in visible distortion at low and medium rates. This paper addresses this issue by integrating techniques of visually lossless coding into BPC-PaCo. The resulting method minimizes the visual distortion introduced in the compressed file, obtaining higher-quality images to a human observer. Experimental results also indicate 12% speedups with respect to BPC-PaCo.

Minimizing Image Quality Loss After Channel Count Reduction for Plane Wave Ultrasound via Deep Learning Inference.

High-frame-rate ultrasound imaging uses unfocused transmissions to insonify an entire imaging view for each transmit event, thereby enabling frame rates over 1000 frames per second (fps). At these high frame rates, it is naturally challenging to realize real-time transfer of channel-domain raw data from the transducer to the system back end. Our work seeks to halve the total data transfer rate by uniformly decimating the receive channel count by 50% and, in turn, doubling the array pitch. We show that despite the reduced channel count and the inevitable use of a sparse array aperture, the resulting beamformed image quality can be maintained by designing a custom convolutional encoder-decoder neural network to infer the radio frequency (RF) data of the nullified channels. This deep learning framework was trained with in vivo human carotid data (5-MHz plane wave imaging, 128 channels, 31 steering angles over a 30° span, and 62 799 frames in total). After training, the network was tested on an in vitro point target scenario that was dissimilar to the training data, in addition to in vivo carotid validation datasets. In the point target phantom image beamformed from inferred channel data, spatial aliasing artifacts attributed to array pitch doubling were found to be reduced by up to 10 dB. For carotid imaging, our proposed approach yielded a lumen-to-tissue contrast that was on average within 3 dB compared to the full-aperture image, whereas without channel data inferencing, the carotid lumen was obscured. When implemented on an RTX-2080 GPU, the inference time to apply the trained network was 4 ms, which favors real-time imaging. Overall, our technique shows that with the help of deep learning, channel data transfer rates can be effectively halved with limited impact on the resulting image quality.

The clinical performance of ultra-low-dose shoulder CT scans: The assessment on image and physical 3D printing models.

Evaluation of the clinical performance of ultra-low-dose computed tomography (CT) images of the shoulder joint on image-based diagnosis and three-dimensional (3D) printing surgical planning. A total of 93 patients with displaced shoulder fractures were randomly divided into standard-dose, low-dose, and ultra-low-dose groups. Three-dimensional printing models of all patients' shoulder joints were fabricated. The subjective image quality and 3D-printing model were evaluated by two senior orthopedic surgeons who were blinded to any scanning setting. A 3-point scale system was used to quantitatively assess the image quality and 3D printing model, where more than 2 points meant adequate level for clinical application. Compared with the standard dose protocol, ultra-low-dose technique reduced the radiation dose by 99.29% without loss of key image quality of fracture pattern. Regarding the subjective image quality, the assessment scores for groups of standard, low, and ultra-low doses were 3.00, 2.76, 2.00 points on scapula and humerus, and 3.00, 2.73, 2.44 points on clavicle. Scores of the three groups for the assessment of 3D printing models were 3.00, 2.80, 1.34 on scapula and humerus, and 3.00, 2.90, 2.06 on clavicle. In the ultra-low-dose group, 24 out of 33 (72.7%) 3D printing models of scapula and humerus received lower than 2 points of the evaluation score, while nearly 94% of the clavicle models reached the adequate level. An ultra-low-dose protocol is adequate for the diagnosis of either displaced or non-displaced fractures of the shoulder joint even though minor flaws of images are present. Three-dimensional printing models of shoulder joints created from ultra-low-dose CT scans can be used for surgical planning at specific bone like the clavicle but perform insufficiently in the overall surgical planning for shoulder injuries due to the significant geometric flaws.

Virtual Monochromatic Images from Dual-Energy Computed Tomography Do Not Improve the Detection of Synovitis in Hand Arthritis.

The objective of this study was to investigate subtraction images from different polychromatic and virtual monochromatic reconstructions of dual-energy computed tomography (CT) for the detection of inflammation (synovitis/tenosynovitis or peritendonitis) in patients with hand arthritis. In this IRB-approved prospective study, 35 patients with acute hand arthritis underwent contrast-enhanced dual-energy CT and musculoskeletal ultrasound (MSUS) of the clinically dominant hand. CT subtractions (CT-S) were calculated from 80 and 135 kVp source data and monochromatic 50 and 70 keV images. CT-S and MSUS were scored for synovitis and tenosynovitis/peritendonitis. Specificity, sensitivity and diagnostic accuracy were assessed by using MSUS as a reference. Parameters of objective image quality were measured. Thirty-three patients were analyzed. MSUS was positive for synovitis and/or tenosynovitis/peritendonitis in 28 patients. The 70 keV images had the highest diagnostic accuracy, with 88% (vs. 50 keV, 82%; 80 kVp, 85%; and 135 kVp, 82%), and superior sensitivity, with 96% (vs. 50 keV: 86%, 80 kVp: 93% and 135 kVp: 79%). The 80 kVp images showed the highest signal- and contrast-to-noise ratio, while the 50 keV images provided the lowest image quality. While all subtraction methods of contrast-enhanced dual-energy CT proved to be able to detect inflammation with sufficient diagnostic accuracy, virtual monochromatic images with low keV showed no significant improvement over conventional subtraction techniques and lead to a loss of image quality.

Improving ESRGAN with an additional image quality loss

Improving ESRGAN with an additional image quality loss

Impact of high kilo-voltage peak technique on radiation dose for neonates undergoing chest radiography: Experimental study

IntroductionChest radiography remains the most often utilized radiological modality in the neonatal intensive care unit. Neonates are more radiosensitive and are at higher risk of cancer than adults. In addition, they have a longer life expectancy, which increases the risk of cancer. Therefore, it is essential to protect neonates from radiation hazards. PurposeThe study aims to assess the effectiveness of using high HKVp technique on reducing radiation dose to neonates undergoing Antero-posterior (AP) chest x-rays. Materials and methodsThe experiment used a newborn anthropomorphic phantom to simulate a neonate. Chest x-ray images were acquired using different combinations of KVp and mAs. Dose area product (DAP) was recorded in the Digital Imaging and Communications in Medicine (DICOM) header. The effective dose was determined using Monte Carlo simulation. Subjective assessment of image quality was evaluated by three radiologists blindly. The Objective assessment of image quality uses the calculation of the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR). ResultsThe reduction in the DAP and effective dose at 50 KVp was 26.7% and 19.2%, respectively and for 60 KVp it was 22.9 and 20.2, respectively. While, the reduction in the DAP and effective dose at 70 KVp was 40% and 26%, respectively. There were no significant differences in the means of the DAP, SNR, CNR, and effective dose among different combinations of KVp and mAs. ConclusionThe use of the high KVp technique can play an important role in reducing radiation dose without significant loss of image quality.

Shear Wave Elastography Implementation on a Portable Research Ultrasound System: Initial Results

Ultrasound shear wave elastography (SWE) has emerged as a promising technique that enables the quantitative estimation of soft tissue stiffness. However, its practical implementation is complicated and presents a number of engineering challenges, including high-energy burst transmission, high-frame rate data acquisition and high computational requirements to process huge datasets. Therefore, to date, SWE has only been available for high-end commercial systems or bulk and expensive research platforms. In this work, we present a low-cost, portable and fully configurable 256-channel research system that is able to implement various SWE techniques. We evaluated its transmit capabilities using various push beam patterns and developed algorithms for the reconstruction of tissue stiffness maps. Three different push beam generation methods were evaluated in both homogeneous and heterogeneous experiments using an industry-standard elastography phantom. The results showed that it is possible to implement the SWE modality using a portable and cost-optimized system without significant image quality losses.

Analysis of image quality and radiation dose in routine adult brain helical and wide-volume computed tomography procedures

ObjectivesTo evaluate and compare image quality and radiation dose between the helical and wide-volume scans to determine the protocol that provides a lower radiation dose without loss in image quality. MethodsThe study was prospectively conducted on consented adult patients that presented for routine brain CT. Image quality and radiation dose were compared between the helical and wide-volume scans on the Toshiba 160-slice Aquilion Prime CT scanner. The volume computed tomography dose index (CTDIvol) and dose length product (DLP) for each scan mode were collected and compared. Image quality was quantitatively and qualitatively evaluated using the unenhanced brain CT images. The data were analysed using a statistical package for social sciences (SPSS) software version 20 for both the descriptive and inferential statistics. A significant difference in image quality and radiation dose between the helical and wide-volume scans was determined based on a p-value of <0.05. ResultsA total of 54 participants were included, with two groups of 27 participants. The CTDIvol and DLP values were significantly p < 0.05 higher in the helical scan (CTDIvol: 65 mGy; DLP: 1597 mGy.cm) compared to the wide-volume scan (CTDIvol: 54 mGy; DLP: 1133 mGy.cm). The grey and white matters show a better signal-to-noise ratio (SNR) for the helical scan. Meanwhile, the contrast-to-noise ratio (CNR) was significantly p < 0.05 higher in the wide-volume scan. The results from the visual grading methods were compared and showed superior image quality in helical over the wide-volume scan. ConclusionWide-volume provides a lower dose compared to helical and therefore, could be adopted as the routine protocol for brain CT for in house dose optimisation. Where clinical conditions warrant the need for a helical scan, the protocol should be optimised in line with the as low as reasonable achievable (ALARA) principle.

Preliminary Study of a Modular MR-Compatible Robot for Image-Guided Insertion of Multiple Needles.

Percutaneous needle-based interventions such as transperineal prostate brachytherapy require the accurate placement of multiple needles to treat cancerous lesions within the target organ. To guide needle placement, magnetic resonance imaging (MRI) offers excellent visualization of the target lesion without the need for ionizing radiation. To date, multi-needle insertion relies on a grid template, which limits the ability to steer individual needles. This work describes an MR-compatible robot designed for the sequential insertion of multiple non-parallel needles under MR guidance. The 6-DOF system is designed with an articulated arm to extend the reach of the robot. This strategy presents a novel approach enabling the robot to maneuver around existing needles while minimizing the footprint of the robot. Forward kinematics as well as optimization-based inverse kinematics are presented. The impact of the robot on image quality was tested for four sequences (T1w-TSE, T2w-TSE, THRIVE and EPI) on a 3T Philips Achieva system. Quantification of the signal-to-noise ratio showed a 46% signal loss in a gelatin phantom when the system was powered on but no further adverse effects when the robot was moving. Joint level testing showed a maximum error of 2.10 ± 0.72°s for revolute joints and 0.31 ± 0.60 mm for prismatic joints. The theoretical workspace spans the proposed clinical target surface of 10 x 10 cm. Lastly, the feasibility of multi-needle insertion was demonstrated with four needles inserted under real-time MR-guidance with no visible loss in image quality.

Deep learning reconstruction allows low-dose imaging while maintaining image quality: comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT.

We aimed to compare the radiation dose and image quality of a low-dose abdominal computed tomography (CT) protocol reconstructed with deep learning reconstruction (DLR) with those of a routine-dose protocol reconstructed with hybrid-iterative reconstruction. This retrospective study enrolled 71 patients [61 men; average age, 71.9 years; mean body mass index (BMI), 24.3 kg/m2] who underwent both low-dose abdominal CT with DLR [advanced intelligent clear-IQ engine (AiCE)] and routine-dose abdominal CT with hybrid-iterative reconstruction [adaptive iterative dose reduction 3D (AIDR 3D)]. Radiation dose parameters included volume CT dose index (CTDIvol), effective dose (ED), and size-specific dose estimate (SSDE). Mean image noise and contrast-to-noise ratio (CNR) were calculated. Image noise was measured in the hepatic parenchyma and bilateral erector spinae muscles. Moreover, subjective assessment of perceived image quality and diagnostic acceptability was performed. The low-dose protocol helped reduce the CTDIvol by 44.3%, ED by 43.7%, and SSDE by 44.9%. Moreover, the noise was significantly lower and CNR significantly higher with the low-dose protocol than with the normal-dose protocol (P<0.001). In the subjective assessment of image quality, there was no significant difference between the protocols with regard to image noise. Overall, AiCE was superior to AIDR 3D in terms of diagnostic acceptability (P=0.001). The use of AiCE can reduce overall radiation dose by more than 40% without loss of image quality compared to routine-dose abdominal CT with AIDR 3D.

Improving throughput and image quality of high-resolution 3D X-ray microscopes using deep learning reconstruction techniques

In high-resolution X-ray computed tomography (CT), also known as 3D X-ray microscopy (XRM), low photon counts can lead to extremely long data acquisition times (in the order of hours). Reducing the number of radiographic projections (Np) acquired for CT reconstruction can be a cost-efficient solution in some cases. But the risk associated with reducing Np, if analytical filteredbackprojection algorithms are used for CT reconstruction, e.g., Feldkamp-Davis-Kress (FDK), is that it may produce a significant loss of image quality. Typical Np thresholds for a faithful 3D image reconstruction, required by the Nyquist-Shannon sampling theorem, are in the order of thousand projection views with modern XRM instruments. It is now well known, however, that deep learning (DL) based algorithms for CT reconstruction can improve the scan time (throughput) and image quality capabilities of XRM. This paper proposes the use of DL-based algorithms as an option for reducing Np, even down to a few hundred projections, without a significant loss of image quality. The integration of DL-based reconstruction techniques into 3D XRM workflows is presented throughout this article. It is shown that 3D XRM data reconstructions produced by DL-based workflows can provide up to 8X and 10X throughput improvement at similar or better image quality compared to standard FDK reconstruction.

Simplifying the hardware requirements for fast neural EIT of peripheral nerves

Objective. The main objective of this study was to assess the feasibility of lowering the hardware requirements for fast neural electrical impedance tomography (EIT) in order to support the distribution of this technique. Specifically, the feasibility of replacing the commercial modules present in the existing high-end setup with compact and cheap customized circuitry was assessed. Approach. Nerve EIT imaging was performed on rat sciatic nerves with both our standard ScouseTom setup and a customized version in which commercial benchtop current sources were replaced by custom circuitry. Electrophysiological data and images collected in the same experimental conditions with the two setups were compared. Data from the customized setup was subject to a down-sampling analysis to simulate the use of a recording module with lower specifications. Main results. Compound action potentials (573 ± 287 μV and 487 ± 279 μV, p=0.28) and impedance changes (36 ± 14 μV and 31 ± 16 μV, p=0.49) did not differ significantly when measured using commercial high-end current sources or our custom circuitry, respectively. Images reconstructed from both setups showed neglibile (<1voxel, i.e. 40 μm) difference in peak location and a high degree of correlation (R 2 = 0.97). When down-sampling from 24 to 16 bits ADC resolution and from 100 to 50 KHz sampling frequency, signal-to-noise ratio showed acceptable decrease (<−20%), and no meaningful image quality loss was detected (peak location difference <1voxel, pixel-by-pixel correlation R 2 = 0.99). Significance: The technology developed for this study greatly reduces the cost and size of a fast neural EIT setup without impacting quality and thus promotes the adoption of this technique by the neuroscience research community.

Triangular fuzzy interpolation deinterlacing algorithm method for image edge detection

Edge detection is the first step in image as it can be applied in the process of feature extraction, segmentation and classification. This study proposes a new fuzzy logic based edge detection method and computationally efficient algorithm based on triangular fuzzy interpolation technique for intra field deinterlacing of images that reduces the loss in image quality at the edges. The proposed algorithm improves image deinterlacing quality with respect to missing pixels in the edge region. When interpolating the missing pixels, characteristics or features of the region around the pixel are interpolated first. The proposed algorithm is tested with five test images which have both uniform surface and edges. The subjective and objective performance of the proposed algorithm shows greater improvement in the edge quality when correlated with the traditional deinterlacing methods. The designed fuzzy rules become an attractive solution for the betterment of the quality of edges as much as possible. The result reveals that the proposed algorithm provides greater robustness to contrast and lighting variations.

ROLE OF IMAGE ENHANCEMENT IN PADDY LEAF DISEASE DETECTION : A STUDY

The detection and classification of illnesses using plant leaf photographs is required in the sector of agriculture. Finding the illnesses of paddy leaf using an image processing system will lessen the dependency on farmers in order to safeguard agricultural products. Using image processing, the study article identifies and categorises illness in paddy leaf. 2- Dimensional computerised photos are electronic images that have been made using a computer. They are mostly created utilising two-dimensional forms such as 2-Dimensional geometric shape, word, and electronic graphics, as well as ways unique to them. It is possible to use the term to refer to a branch of computer science that involves specific methodologies, or it may refer to the forms themselves. These are the most common forms of digital photographs. Initially, they are based on traditional printing and drawing technologies, such as scientific drawing, advertising, typography, mapping, and so forth. A two-dimensional image/graphic is more than simply a mirror of a real-world item in such implementations; it is an independent creation with extra textual content. Two-dimensional models are selected because they have more stringent control over images/graphics than three-dimensional electronic images. In terms of aesthetic, the approach of three Dimensional digital images is extremely similar to camera work. In this post, we used BILINEAR Interpolation to scale photos and compress them with little image quality loss. This research introduced an advanced CNN-based technique to detecting and classifying rice leaf disease. In order to diagnose illness more quickly and accurately, advanced mechanisms have used a strategy that considers picture compression, edge detection, and CNN.

Exploring Simple and Transferable Recognition-Aware Image Processing.

Recent progress in image recognition has stimulated the deployment of vision systems at an unprecedented scale. As a result, visual data are now often consumed not only by humans but also by machines. Existing image processing methods only optimize for better human perception, yet the resulting images may not be accurately recognized by machines. This can be undesirable, e.g., the images can be improperly handled by search engines or recommendation systems. In this work, we examine simple approaches to improve machine recognition of processed images: optimizing the recognition loss directly on the image processing network or through an intermediate input transformation model. Interestingly, the processing model's ability to enhance recognition quality can transfer when evaluated on models of different architectures, recognized categories, tasks, and training datasets. This makes the methods applicable even when we do not have the knowledge of future recognition models, e.g., when uploading processed images to the Internet. We conduct experiments on multiple image processing tasks paired with ImageNet classification and PASCAL VOC detection as recognition tasks. With these simple yet effective methods, substantial accuracy gain can be achieved with strong transferability and minimal image quality loss. Through a user study we further show that the accuracy gain can transfer to a black-box cloud model. Finally, we try to explain this transferability phenomenon by demonstrating the similarities of different models' decision boundaries. Code is available at https://github.com/liuzhuang13/Transferable_RA.

Impact of dose reduction and iterative model reconstruction on multi-detector CT imaging of the brain in patients with suspected ischemic stroke

Non-contrast cerebral computed tomography (CT) is frequently performed as a first-line diagnostic approach in patients with suspected ischemic stroke. The purpose of this study was to evaluate the performance of hybrid and model-based iterative image reconstruction for standard-dose (SD) and low-dose (LD) non-contrast cerebral imaging by multi-detector CT (MDCT). We retrospectively analyzed 131 patients with suspected ischemic stroke (mean age: 74.2 ± 14.3 years, 67 females) who underwent initial MDCT with a SD protocol (300 mAs) as well as follow-up MDCT after a maximum of 10 days with a LD protocol (200 mAs). Ischemic demarcation was detected in 26 patients for initial and in 64 patients for follow-up imaging, with diffusion-weighted magnetic resonance imaging (MRI) confirming ischemia in all of those patients. The non-contrast cerebral MDCT images were reconstructed using hybrid (Philips “iDose4”) and model-based iterative (Philips “IMR3”) reconstruction algorithms. Two readers assessed overall image quality, anatomic detail, differentiation of gray matter (GM)/white matter (WM), and conspicuity of ischemic demarcation, if any. Quantitative assessment included signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) calculations for WM, GM, and demarcated areas. Ischemic demarcation was detected in all MDCT images of affected patients by both readers, irrespective of the reconstruction method used. For LD imaging, anatomic detail and GM/WM differentiation was significantly better when using the model-based iterative compared to the hybrid reconstruction method. Furthermore, CNR of GM/WM as well as the SNR of WM and GM of healthy brain tissue were significantly higher for LD images with model-based iterative reconstruction when compared to SD or LD images reconstructed with the hybrid algorithm. For patients with ischemic demarcation, there was a significant difference between images using hybrid versus model-based iterative reconstruction for CNR of ischemic/contralateral unaffected areas (mean ± standard deviation: SD_IMR: 4.4 ± 3.1, SD_iDose: 3.5 ± 2.3, P < 0.0001; LD_IMR: 4.6 ± 2.9, LD_iDose: 3.2 ± 2.1, P < 0.0001). In conclusion, model-based iterative reconstruction provides higher CNR and SNR without significant loss of image quality for non-enhanced cerebral MDCT.

Accelerating compressed sensing reconstruction of subsampled radial k-space data using geometrically-derived density compensation

Objective. To accelerate compressed sensing (CS) reconstruction of subsampled radial k-space data using a geometrically-derived density compensation function (gDCF) without significant loss in image quality. Approach. We developed a theoretical framework to calculate a gDCF based on Nyquist distance along the radial and circumferential directions of a discrete polar coordinate system. Our gDCF was compared against standard DCF (e.g. ramp filter) and another commonly used DCF (modified Shepp–Logan (SL) filter). The resulting image quality produced by each DCF was quantified using normalized root-mean-square-error (NRMSE), blur metric (1 = blurriest; 0 = sharpest), and structural similarity index (SSIM; 1 = perfect match; 0 = no match) compared with the reference. For filtered backprojection (FBP) of phantom data obtained at the Nyquist sampling rate, Cartesian k-space sampling was used as the reference. For CS reconstruction of subsampled cardiac magnetic resonance imaging datasets (real-time cardiac cine data with 11 projections per frame, n = 20 patients; cardiac perfusion data with 30 projections per frame, n = 19 patients), CS reconstruction without DCF was used as the reference. Main results. The NRMSE, SSIM, and blur metrics of the phantom data were good for all DCFs, confirming that our gDCF produces uniform densities at the upper limit (Nyquist). For CS reconstruction of subsampled real-time cine and cardiac perfusion datasets, the image quality metrics (SSIM, NRMSE) were significantly (p < 0.05) higher for our gDCF than other DCFs, and the reconstruction time was significantly (p < 0.05) faster for our gDCF (reference) than no DCF (11.9%–52.9% slower), standard DCF (23.9%–57.6% slower), and modified SL filter (13.5%–34.8% slower). Significance. The proposed gDCF accelerates CS reconstruction of subsampled radial k-space data without significant loss in image quality compared with no DCF as the reference.

Exploring the use of knobology for image optimisation in final year sonography students.

Image optimisation is essential for acquisition of quality images in ultrasound and critical to the diagnostic ability of the examination. These skills are taught to sonography students early in their education, but research has found that retention of non-rehearsed knowledge decreases significantly after a year. The aim of this study was to determine which optimisation tools (knobology) final year sonography students use, how often and why they chose to adjust parameters and assess barriers to optimisation of knobology tools. A prospective study using data from an anonymous online survey of 34 final year sonography students. Survey results showed that 19/34 (55%) of students "frequently" optimise all Doppler settings and 23/34 (67%) of students "frequently" optimise basic parameter settings (depth, focus, time gain compensation). Time constraints (24/34 (70%)) and loss of gained knowledge of sonography principles and instrumentation (17/34 (50%)) were the major barriers to the use of knobology. The majority 28/34 (82%) believed they would benefit from further training. This study demonstrates that although most students are optimising settings to improve image quality, sonography principles and instrumentation knowledge loss and time constraints prevent students from maximising machine capabilities. This study supports the need for further training prior to final year clinical placement.