Image Quality Measures Research Articles

Underwater image enhancement by using amalgamation of colour correction, contrast-enhancing and dehazing

Underwater images can be captured either with the help of light waves or sound waves. Images that are taken underwater typically are not of optimum quality as they suffer from issues such as low contrast, blurring of detail, colour distortion, and greenish tones. Several physical processes that take place in the aquatic environment, such as light absorption, refraction, and scattering, are responsible for the existence of such degradation in underwater images. To address these challenges, numerous researchers have put forth a range of cutting-edge techniques for enhancing and restoring such degraded underwater images, with the aim of addressing these issues. These techniques primarily focus on improving visibility and enhancing the level of detail. To achieve this, we propose a method that performs White Balancing in the LAB colour space to remove the bluish-greenish tones present in the image. Next, we enhance the contrast by first converting the RGB image into HSV and HLS colour spaces and then by using the S & V channels in HSV and L & S colour channels in HLS, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE). To control the brightness of the enhanced image, we apply Gamma Correction. Lastly, by using the method Dark Channel Prior (DCP), we separate the image’s red channel from the RGB colour space and perform the dehazing operation to get the final enhanced image. We have conducted a comprehensive qualitative analysis of our proposed approach as well as existing techniques, evaluating them objectively and subjectively through metrics such as peak signal-to-noise ratio (PSNR), root-mean-square error (RMSE), structural similarity (SSIM), and the underwater colour image quality evaluation metric (UCIQE) and underwater image quality measure (UIQM). Since our proposed approach uses traditional image processing methods, it is computationally less expensive and quicker as compared to deep learning or frequency domain-based methods. With this, it can be adapted for using in real-time applications such as underwater navigation, examination of the behavior of marine ecosystems and other scientific research.

Comparing Strain Assessment in Compressed Sensing and Conventional Cine MRI.

The aim of this study is to assess the feasibility of compressed sensing (CS) acceleration methods compared to conventional segmented cine (Seg) cardiac magnetic resonance (CMR) for evaluating left ventricular (LV) function and strain by feature tracking (FT). In this prospective study, 45 healthy volunteers underwent CMR imaging used Seg, threefold (CS3), fourfold (CS4), and eightfold (CS8) CS acceleration. Cine images were scored for quality (1-5 scale). LV volumetric and functional parameters and global longitudinal (GLS), circumferential (GCS), and radial strains (GRS) were quantified. LV volumetric and functional parameters exhibited no differences between Seg and all CS cines (all P > 0.05). The strains were similar for Seg, CS3, and CS4 (all P > 0.05). Similarly, no significant differences were observed in GRS and GCS between Seg and CS8 (all P > 0.05), but the global longitudinal strain was significantly lower for CS8 versus Seg (P < 0.001). Image quality declined with CS acceleration, especially in long-axis views with CS8. CS cine MRI at acceleration factor 4 maintained good image quality and accurate measurements of LV function and strain, although there was a slight reduction in the quality of long-axis images and GLS with CS8. CS acceleration up to a factor of 4 enabled fast CMR evaluations, making it suitable for clinical use.

Wavelet-based information theory in quantitative assessment of AFM images’ quality

The quantitative assessment of the image quality produced by atomic force microscopy (AFM) is an ongoing and challenging task. In our study, we demonstrate Shannon’s application of information theory for measuring image quality. Specifically, we propose quantifying the loss of image information due to the various distortion processes by exploring the relationship between image information based on the information channel capacity (ICC), spectral image representation, and visual quality. Since the ideal image is unavailable, the power and noise spectrum, the critical input information for the image quality evaluation, must be robustly estimated in the proposed method. The classical, most popular Welch method for spectral estimation uses an average of several windowed periodograms and can produce biased spectrum estimates. Therefore, in our work, we discuss an alternative technique based on the wavelet transform that can be applied to solve this challenging problem, specifically in the case of noisy, uncertain AFM measurements. Finally, we validate the performance of the enhanced ICC-wavelet-based algorithm with noisy measurement AFM data.

Screen content image quality measurement based on multiple features

Screen content image quality measurement based on multiple features

Non-contrast free-breathing 2D CINE compressed SENSE T1-TFE cardiovascular MRI at 3T in sedated young children for assessment of congenital heart disease.

Cardiac MRI is a crucial tool for assessing congenital heart disease (CHD). However, its application remains challenging in young children when performed at 3T. The aim of this retrospective single center study was to compare a non-contrast free-breathing 2D CINE T1-weighted TFE-sequence with compressed sensing (FB 2D CINE CS T1-TFE) with 3D imaging for diagnostic accuracy of CHD, image quality, and vessel diameter measurements in sedated young children. FB 2D CINE CS T1-TFE was compared with a 3D non-contrast whole-heart sequence (3D WH) and 3D contrast-enhanced MR angiography (3D CE-MRA) at 3T in 37 CHD patients (20♂, 1.5±1.4 years). Two radiologists independently assessed image quality, type of CHD, and diagnostic confidence. Diameters and measures of contrast and sharpness of the aorta and pulmonary vessels were determined. A non-parametric multi-factorial approach was used to estimate diagnostic accuracy for the diagnosis of CHD. Linear mixed models were calculated to compare contrast and vessel sharpness. Krippendorff's alpha was determined to quantify vessel diameter agreement. FB 2D CINE CS T1-TFE was rated superior regarding image quality, diagnostic confidence, and diagnostic sensitivity for both intra- and extracardiac pathologies compared to 3D WH and 3D CE-MRA (all p<0.05). FB 2D CINE CS T1-TFE showed superior contrast and vessel sharpness (p<0.001) resulting in the highest proportion of measurable vessels (740/740; 100%), compared to 3D WH (530/620; 85.5%) and 3D CE-MRA (540/560; 96.4%). Regarding vessel diameter measurements, FB 2D CINE CS T1-TFE revealed the closest inter-reader agreement (Krippendorff's alpha: 0.94-0.96; 3D WH: 0.78-0.94; 3D CE-MRA: 0.76-0.93). FB 2D CINE CS T1-TFE demonstrates robustness at 3T and delivers high-quality diagnostic results to assess CHD in sedated young children. Its ability to function without contrast injection and respiratory compensation enhances ease of use and could encourage widespread adoption in clinical practice.

Prospective Comparison of Free-Breathing Accelerated Cine Deep Learning Reconstruction Versus Standard Breath-Hold Cardiac MRI Sequences in Patients With Ischemic Heart Disease.

BACKGROUND. Cine cardiac MRI sequences require repeated breath-holds, which can be difficult for patients with ischemic heart disease (IHD). OBJECTIVE. The purpose of the study was to compare a free-breathing accelerated cine sequence using deep learning (DL) reconstruction and a standard breath-hold cine sequence in terms of image quality and left ventricular (LV) measurements in patients with IHD undergoing cardiac MRI. METHODS. This prospective study included patients undergoing 1.5- or 3-T cardiac MRI for evaluation of IHD between March 15, 2023, and June 21, 2023. Examinations included an investigational free-breathing cine short-axis sequence with DL reconstruction (hereafter, cine-DL sequence). Two radiologists (reader 1 [R1] and reader 2 [R2]), in blinded fashion, independently assessed left ventricular ejection fraction (LVEF), left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), and subjective image quality for the cine-DL sequence and a standard breath-hold balanced SSFP sequence; R1 assessed artifacts. RESULTS. The analysis included 26 patients (mean age, 64.3 ± 11.7 [SD] years; 14 men, 12 women). Acquisition was shorter for the cine-DL sequence than the standard sequence (mean ± SD, 0.6 ± 0.1 vs 2.4 ± 0.6 minutes; p < .001). The cine-DL sequence, in comparison with the standard sequence, showed no significant difference for LVEF for R1 (mean ± SD, 51.7% ± 14.3% vs 51.3% ± 14.7%; p = .56) or R2 (53.4% ± 14.9% vs 52.8% ± 14.6%; p = .53); significantly greater LVEDV for R2 (mean ± SD, 171.9 ± 51.9 vs 160.6 ± 49.4 mL; p = .01) but not R1 (171.8 ± 53.7 vs 165.5 ± 52.4 mL; p = .16); and no significant difference in LVESV for R1 (mean ± SD, 88.1 ± 49.3 vs 86.0 ± 50.5 mL; p = .45) or R2 (85.2 ± 48.1 vs 81.3 ± 48.2 mL; p = .10). The mean bias between the cine-DL and standard sequences by LV measurement was as follows: LVEF, 0.4% for R1 and 0.7% for R2; LVEDV, 6.3 mL for R1 and 11.3 mL for R2; and LVESV, 2.1 mL for R1 and 3.9 mL for R2. Subjective image quality was better for cine-DL sequence than the standard sequence for R1 (mean ± SD, 2.3 ± 0.5 vs 1.9 ± 0.8; p = .02) and R2 (2.2 ± 0.4 vs 1.9 ± 0.7; p = .02). R1 reported no significant difference between the cine-DL and standard sequences for off-resonance artifacts (3.8% vs 23.1% examinations; p = .10) and parallel imaging artifacts (3.8% vs 19.2%; p = .19); blurring artifacts were more frequent for the cine-DL sequence than the standard sequence (42.3% vs 7.7% examinations; p = .008). CONCLUSION. A free-breathing cine-DL sequence, in comparison with a standard breath-hold cine sequence, showed very small bias for LVEF measurements and better subjective quality. The cine-DL sequence yielded greater LV volumes than the standard sequence. CLINICAL IMPACT. A free-breathing cine-DL sequence may yield reliable LVEF measurements in patients with IHD unable to repeatedly breath-hold. TRIAL REGISTRATION. ClinicalTrials.gov NCT05105984.

Construction and validation of an infant chest phantom for paediatric computed tomography

Paediatric imaging protocols should be carefully optimised to maintain the desired image quality while minimising the delivered patient dose. A paediatric chest phantom was designed, constructed and evaluated to optimise chest CT examinations for infants. The phantom was designed to enable dosimetry and image quality measurements within the anthropomorphic structure. It was constructed using tissue equivalent materials to mimic thoracic structures of infants, aged 0–6 months. The phantom materials were validated across a range of diagnostic tube voltages with resulting CT numbers found equivalent to paediatric tissues observed via a survey of clinical paediatric chest studies. The phantom has been successfully used to measure radiation dose and evaluate various image quality parameters for paediatric specific protocols.

Improving the Safety of Computed Tomography Through Automated Quality Measurement: A Radiologist Reader Study of Radiation Dose, Image Noise, and Image Quality.

The Centers for Medicare and Medicaid Services funded the development of a computed tomography (CT) quality measure for use in pay-for-performance programs, which balances automated assessments of radiation dose with image quality to incentivize dose reduction without compromising the diagnostic utility of the tests. However, no existing quantitative method for assessing CT image quality has been validated against radiologists' image quality assessments on a large number of CT examinations. Thus to develop an automated measure of image quality, we tested the relationship between radiologists' subjective ratings of image quality with measurements of radiation dose and image noise. Board-certified, posttraining, clinically active radiologists rated the image quality of 200 diagnostic CT examinations from a set of 734, representing 14 CT categories. Examinations with significant distractions, motion, or artifact were excluded. Radiologists rated diagnostic image quality as excellent, adequate, marginally acceptable, or poor; the latter 2 were considered unacceptable for rendering diagnoses. We quantified the relationship between ratings and image noise and radiation dose, by category, by analyzing the odds of an acceptable rating per standard deviation (SD) increase in noise or geometric SD (gSD) in dose. One hundred twenty-five radiologists contributed 24,800 ratings. Most (89%) were acceptable. The odds of an examination being rated acceptable statistically significantly increased per gSD increase in dose and decreased per SD increase in noise for most categories, including routine dose head, chest, and abdomen-pelvis, which together comprise 60% of examinations performed in routine practice. For routine dose abdomen-pelvis, the most common category, each gSD increase in dose raised the odds of an acceptable rating (2.33; 95% confidence interval, 1.98-3.24), whereas each SD increase in noise decreased the odds (0.90; 0.79-0.99). For only 2 CT categories, high-dose head and neck/cervical spine, neither dose nor noise was associated with ratings. Radiation dose and image noise correlate with radiologists' image quality assessments for most CT categories, making them suitable as automated metrics in quality programs incentivizing reduction of excessive radiation doses.

Improving Data Embedding Capacity in LSB Steganography Utilizing LSB2 and Zlib Compression

In an increasingly advanced era, the exchange of information through digital tools has become a common practice. With easy access and advancing facilities, securely and covertly exchanging data has become a challenging task. Therefore, the technique of steganography can be used as a solution for data hiding and protection, enabling safer data exchanges. Steganography is a method to conceal data within a transmission object, which can be an image, video, audio, and more. In this research, steganography will be performed using images as the transmission object. This study is done to offer a modification of the Least Significant Bit (LSB) steganography technique by utilizing the LSB-2 method, along with the utilization of the Zlib compression algorithm. The modification and use of the Zlib compression algorithm aim to increase the message capacity that can be embedded in the transmission object while preserving the image quality. The results of the experiments will be presented in tabular form by comparing the original image with the steganography-processed image using metrics such as Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) as measures of image quality. The experiments conducted results in an increase of capacity of approximately 36.54%, an increase in PSNR value of approximately 4.72%, accompanied by a decrease in MSE value in average of 49.19%, and SSIM values constantly at 0,99999 thus proving the proposed method successfully increased the embedded massage capacity while preserving even enhance the quality of the stego image produced by the embedding process

A motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease

BackgroundMRI is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for 3D whole-heart acquisition involve long and unpredictable scan times and methods that accelerate scans via k-space undersampling often rely on long iterative reconstructions. Deep-learning-based reconstruction methods have recently attracted much interest due to their capacity to provide fast reconstructions whilst often outperforming existing state-of-the-art methods. In this study, we sought to adapt and validate a non-rigid motion-corrected model-based deep-learning (MoCo-MoDL) reconstruction framework for 3D whole-heart MRI in a CHD patient cohort. MethodsThe previously proposed deep-learning reconstruction framework MoCo-MoDL, which incorporates a non-rigid motion-estimation network and a denoising regularisation network within an unrolled iterative reconstruction, was trained in an end-to-end manner using 39 CHD patient datasets. Once trained, the framework was evaluated in 8 CHD patient datasets acquired with seven-fold prospective undersampling. Reconstruction quality was compared with the state-of-the-art non-rigid motion-corrected patch-based low-rank reconstruction method (NR-PROST) and against reference images (acquired with three-or-four-fold undersampling and reconstructed with NR-PROST). ResultsSeven-fold undersampled scan times were 2.1 ± 0.3 minutes and reconstruction times were ~ 30 seconds, approximately 240 times faster than an NR-PROST reconstruction. Image quality comparable to the reference images was achieved using the proposed MoCo-MoDL framework, with no statistically significant differences found in any of the assessed quantitative or qualitative image quality measures. Additionally, expert image quality scores indicated the MoCo-MoDL reconstructions were consistently of a higher quality than the NR-PROST reconstructions of the same data, with the differences in 12 of the 22 scores measured for individual vascular structures found to be statistically significant. ConclusionThe MoCo-MoDL framework was applied to an adult CHD patient cohort, achieving good quality 3D whole-heart images from ~ 2-minute scans with reconstruction times of ~ 30 seconds.

Study the Effect of Texture Extending Direction in Inpainting Missing Areas of Digital Images Using Modified Isotropic Model

The modified isotropic model was used to recover the missing regions of the colored stripes image, and two models (the isotropic model and the modified isotropic model) were applied to them. Comparing the results obtained from the two models show that the modified model achieved better results than the results of the old model. A study was also conducted on the effect of the texture direction in the missing area on the success of the modified model. Use a number of image quality measures, including MSE, PSNR, and SSIM.

Two-center validation of Pilot Tone based cardiac triggering of a comprehensive cardiovascular magnetic resonance examination.

The electrocardiogram (ECG) signal is prone to distortions from gradient and radiofrequency interference and the magnetohydrodynamic effect during cardiovascular magnetic resonance imaging (CMR). Although Pilot Tone Cardiac (PTC) triggering has the potential to overcome these limitations, effectiveness across various CMR techniques has yet to be established. To evaluate the performance of PTC triggering in a comprehensive CMR exam. Fifteen volunteers and 20 patients were recruited at two centers. ECG triggered images were collected for comparison in a subset of sequences. The PTC trigger accuracy was evaluated against ECG in cine acquisitions. Two experienced readers scored image quality in PTC-triggered cine, late gadolinium enhancement (LGE), and T1- and T2-weighted dark-blood turbo spin echo (DB-TSE) images. Quantitative cardiac function, flow, and parametric mapping values obtained using PTC and ECG triggered sequences were compared. Breath-held segmented cine used for trigger timing analysis was collected in 15 volunteers and 14 patients. PTC calibration failed in three volunteers and one patient; ECG trigger recording failed in one patient. Out of 1987 total heartbeats, three mismatched trigger PTC-ECG pairs were found. Image quality scores showed no significant difference between PTC and ECG triggering. There was no significant difference found in quantitative measurements in volunteers. In patients, the only significant difference was found in post-contrast T1 (p = 0.04). ICC showed moderate to excellent agreement in all measurements. PTC performance was equivalent to ECG in terms of triggering consistency, image quality, and quantitative image measurements across multiple CMR applications.

Pembuatan In-House Phantom untuk Pengukuran Image Quality dan Dosimetri Sebagai Tool Optimasi Protokol Pemeriksaan CT Scan Thorak

The computed tomography scanner (CT scan) protocol used in patient examinations must be continuously reviewed and optimized. Medical physicists evaluate the quality of the resulting image using an image quality phantom and evaluate radiation dose using a dosimetry phantom separately. Procuring a dosimetry phantom and image quality phantom for CT scans separately also requires a lot of money. Therefore, developing a dosimetry phantom and image quality phantom combined in one phantom is expected to be a solution to overcome the mentioned problem. This research aims to create a phantom as a combination of a dosimetry phantom and an image quality phantom and to obtain an optimal CT scan protocol so that informative images can be obtained with minimal doses. The phantom that is made resembles the thoracic organs. The dose length product (DLP) and size-specific dose estimates (SSDE) measured from the phantom were compared with those from 100 patient images for chest CT scan examinations from January to December 2022 and measured using IndoseCT.The image quality of the phantom was measured using IndoQCT software. The estimated dosimetry data from the phantom are 122,5 mGy.cm and 10,94 mGy for DLP and SSDE, respectively, while the DLP and SSDE from patients are 143,5 mGy.cm and 9 mGy. Image quality test results for uniformity with a maximum difference in CT number of 4 HU. Maximum noise is 38,1 HU, and the minimum is 30 HU. The geometric accuracy measurement results are 0,4 mm from the actual size. Spatial Resolution of 0,8 lp/mm. The R2 value of contrast linearity is 0,9972. The Modulation Transfer Function (MTF10) measurement results are 0,63 mm--1. The results of the slice thickness test show the suitability between the measured results and the slice thickness setting on the CT Scan. An in-house phantom can be used in dosimetry and image quality measurements to optimize the thoracic CT scan protocol. Keywords: In-house phantom, Image quality, Dosimetry, Optimization

Ideal Observer Computation by Use of Markov-Chain Monte Carlo With Generative Adversarial Networks.

Medical imaging systems are often evaluated and optimized via objective, or task-specific, measures of image quality (IQ) that quantify the performance of an observer on a specific clinically-relevant task. The performance of the Bayesian Ideal Observer (IO) sets an upper limit among all observers, numerical or human, and has been advocated for use as a figure-of-merit (FOM) for evaluating and optimizing medical imaging systems. However, the IO test statistic corresponds to the likelihood ratio that is intractable to compute in the majority of cases. A sampling-based method that employs Markov-chain Monte Carlo (MCMC) techniques was previously proposed to estimate the IO performance. However, current applications of MCMC methods for IO approximation have been limited to a small number of situations where the considered distribution of to-be-imaged objects can be described by a relatively simple stochastic object model (SOM). As such, there remains an important need to extend the domain of applicability of MCMC methods to address a large variety of scenarios where IO-based assessments are needed but the associated SOMs have not been available. In this study, a novel MCMC method that employs a generative adversarial network (GAN)-based SOM, referred to as MCMC-GAN, is described and evaluated. The MCMC-GAN method was quantitatively validated by use of test-cases for which reference solutions were available. The results demonstrate that the MCMC-GAN method can extend the domain of applicability of MCMC methods for conducting IO analyses of medical imaging systems.

Hierarchical vectorization for facial images

The explosive growth of social media means portrait editing and retouching are in high demand. While portraits are commonly captured and stored as raster images, editing raster images is non-trivial and requires the user to be highly skilled. Aiming at developing intuitive and easy-to-use portrait editing tools, we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical representation. The base layer consists of a set of sparse diffusion curves (DCs) which characterize salient geometric features and low-frequency colors, providing a means for semantic color transfer and facial expression editing. The middle level encodes specular highlights and shadows as large, editable Poisson regions (PRs) and allows the user to directly adjust illumination by tuning the strength and changing the shapes of PRs. The top level contains two types of pixel-sized PRs for high-frequency residuals and fine details such as pimples and pigmentation. We train a deep generative model that can produce high-frequency residuals automatically. Thanks to the inherent meaning in vector primitives, editing portraits becomes easy and intuitive. In particular, our method supports color transfer, facial expression editing, highlight and shadow editing, and automatic retouching. To quantitatively evaluate the results, we extend the commonly used FLIP metric (which measures color and feature differences between two images) to consider illumination. The new metric, illumination-sensitive FLIP, can effectively capture salient changes in color transfer results, and is more consistent with human perception than FLIP and other quality measures for portrait images. We evaluate our method on the FFHQR dataset and show it to be effective for common portrait editing tasks, such as retouching, light editing, color transfer, and expression editing.

Photon-Counting Detector CT With Denoising for Imaging of the Osseous Pelvis at Low Radiation Doses: A Phantom Study.

BACKGROUND. Photon-counting detector (PCD) CT may allow lower radiation doses than used for conventional energy-integrating detector (EID) CT, with preserved image quality. OBJECTIVE. The purpose of this study was to compare PCD CT and EID CT, reconstructed with and without a denoising tool, in terms of image quality of the osseous pelvis in a phantom, with attention to low radiation doses. METHODS. A pelvic phantom comprising human bones in acrylic material mimicking soft tissue underwent PCD CT and EID CT at various tube potentials and radiation doses ranging from 0.05 to 5.00 mGy. Additional denoised reconstructions were generated using a commercial tool. Noise was measured in the acrylic material. Two readers performed independent qualitative assessments that entailed determining the denoised EID CT reconstruction with the lowest acceptable dose and then comparing this reference reconstruction with PCD CT reconstructions without and with denoising, using subjective Likert scales. RESULTS. Noise was lower for PCD CT than for EID CT. For instance, at 0.05 mGy and 100 kV with tin filter, noise was 38.4 HU for PCD CT versus 48.8 HU for EID CT. Denoising further reduced noise; for example, for PCD CT at 100 kV with tin filter at 0.25 mGy, noise was 19.9 HU without denoising versus 9.7 HU with denoising. For both readers, lowest acceptable dose for EID CT was 0.10 mGy (total score, 11 of 15 for both readers). Both readers somewhat agreed that PCD CT without denoising at 0.10 mGy (reflecting reference reconstruction dose) was relatively better than the reference reconstruction in terms of osseous structures, artifacts, and image quality. Both readers also somewhat agreed that denoised PCD CT reconstructions at 0.10 mGy and 0.05 mGy (reflecting matched and lower doses, respectively, with respect to reference reconstruction dose) were relatively better than the reference reconstruction for the image quality measures. CONCLUSION. PCD CT showed better-quality images than EID CT when performed at the lowest acceptable radiation dose for EID CT. PCD CT with denoising yielded better-quality images at a dose lower than lowest acceptable dose for EID CT. CLINICAL IMPACT. PCD CT with denoising could facilitate lower radiation doses for pelvic imaging.

Assessment of the Impact of Turbo Factor on Image Quality and Tissue Volumetrics in Brain Magnetic Resonance Imaging Using the Three-Dimensional T1-Weighted (3D T1W) Sequence.

The 3D T1W turbo field echo sequence is a standard imaging method for acquiring high-contrast images of the brain. However, the contrast-to-noise ratio (CNR) can be affected by the turbo factor, which could affect the delineation and segmentation of various structures in the brain and may consequently lead to misdiagnosis. This study is aimed at evaluating the effect of the turbo factor on image quality and volumetric measurement reproducibility in brain magnetic resonance imaging (MRI). Brain images of five healthy volunteers with no history of neurological diseases were acquired on a 1.5 T MRI scanner with varying turbo factors of 50, 100, 150, 200, and 225. The images were processed and analyzed with FreeSurfer. The influence of the TFE factor on image quality and reproducibility of brain volume measurements was investigated. Image quality metrics assessed included the signal-to-noise ratio (SNR) of white matter (WM), CNR between gray matter/white matter (GM/WM) and gray matter/cerebrospinal fluid (GM/CSF), and Euler number (EN). Moreover, structural brain volume measurements of WM, GM, and CSF were conducted. Turbo factor 200 produced the best SNR (median = 17.01) and GM/WM CNR (median = 2.29), but turbo factor 100 offered the most reproducible SNR (IQR = 2.72) and GM/WM CNR (IQR = 0.14). Turbo factor 50 had the worst and the least reproducible SNR, whereas turbo factor 225 had the worst and the least reproducible GM/WM CNR. Turbo factor 200 again had the best GM/CSF CNR but offered the least reproducible GM/CSF CNR. Turbo factor 225 had the best performance on EN (-21), while turbo factor 200 was next to the most reproducible turbo factor on EN (11). The results showed that turbo factor 200 had the least data acquisition time, in addition to superior performance on SNR, GM/WM CNR, GM/CSF CNR, and good reproducibility characteristics on EN. Both image quality metrics and volumetric measurements did not vary significantly (p > 0.05) with the range of turbo factors used in the study by one-way ANOVA analysis. Since no significant differences were observed in the performance of the turbo factors in terms of image quality and volume of brain structure, turbo factor 200 with a 74% acquisition time reduction was found to be optimal for brain MR imaging at 1.5 T.

A quality of experience model for live video in first-person-view drone control in cellular networks

Several upcoming 5G and 6G services will rely on unmanned aerial vehicles (UAV) sending live information to remote terminals. Thus, understanding the traffic flows that might influence end-user experience in these services is key for cellular network operators. One of these UAV-based services is first person view (FPV) drone control, consisting on the remote control of the UAV in Beyond Visual Line of Sight scenarios using only the live video visualized in a ground control station. This work focuses on the networking aspects of this service by presenting the assembly, integration and evaluation methodology of an UAV quadrotor teleoperated via FPV through a Long Term Evolution (LTE) network or WiFi radio access link. To assess system performance, three different connectivity schemes between UAV and ground control station are tested, namely server-based connection via LTE, direct LTE, and peer-to-peer WiFi connection. Then, several experiments are carried out in the testbed to characterize telemetry, control and video traffic for FPV service in the above schemes. Later, a methodology is defined to estimate Quality of Experience (QoE) for FPV service based on image quality and video latency measurements collected at network and application level. Results show that the QoE model for live video introduced in this work can be the basis of more sophisticated models for cellular FPV services.

Abstract 16514: Head-to-Head Comparison of Quantitative Image Quality on Rubidium-82 Myocardial Perfusion Positron Emission Tomography Between a Digital Silicon Photomultiplierand Conventional Scanner

Introduction: The purpose of this study was to compare rubidium-82 positron emission tomography ( 82 Rb PET) myocardial perfusion imaging (MPI) findings and quantitative image quality measures between the digital silicone photomultipliers (SiPM) scanner and a conventional scanner. Methods: We studied 24 consecutive patients (mean age 71.2±2.2 years) who underwent two clinically indicated rest/stress 82 Rb PET MPI examinations on two scanners, one using a SiPM-based PET scanner (Biograph Vision, Siemens Healthineers) and the other using a conventional photomultiplier tube (PMT)-based PET scanner (Discovery 710, GE Healthcare). Inclusion criteria were: 1) interval between the scans &lt;2 yr (median 1.1 yr, 95% CI: 0.9-1.4), 2) no interval revascularization, and 3) no interval symptom changes. Stress and imaging protocols were the same. 82 Rb doses were 925 MBq at rest and stress. Image quality was assessed using blood background coefficient-of-variation (COV), myocardium to blood signal to noise ratio (SNR) and contrast to noise ratio (CNR) calculated from the 82 Rb images. Results: Between two scanners, there were no significant differences in perfusion and functional measures, including summed stress score (SiPM: 1.6 ± 0.7 vs. PMT: 1.8 ± 0.8, p=0.21), summed rest score (SiPM: 2.7 ± 1.0 vs. PMT: 3.1 ± 1.2, p=0.34), LV function (LVEF, SiPM: 62.6 ± 2.8 vs. PMT: 61.4 ± 2.8, p=0.82) and volumes (LVEDV, SiPM: 62.6 ± 2.8 vs. PMT: 61.4 ± 2.8, p=0.51). Image quality measures significantly improved (Figure), with lower COV (SiPM: 15.3 ± 0.8 vs. PMT: 19.3 ± 1.1, p=0.006), higher SNR (SiPM; 26.4 ± 1.8 vs. PMT; 20.5 ± 1.5, p&lt;0.001) and CNR (SiPM; 14.2 ± 1.2 vs. PMT; 11.4 ± 1.1, p=0.02). Conclusions: 82 Rb PET MPI performed with SiPM-based a PET scanner showed significantly better image contrast and less noise compared to a PMT-based PET scanner. These findings suggest that the SiPM based PET scanner provides more previse evaluations for patients undergoing 82 Rb PET MPI.

Artificial CT images can enhance variation of case images in diagnostic radiology skills training

ObjectivesWe sought to investigate if artificial medical images can blend with original ones and whether they adhere to the variable anatomical constraints provided.MethodsArtificial images were generated with a generative model trained on publicly available standard and low-dose chest CT images (805 scans; 39,803 2D images), of which 17% contained evidence of pathological formations (lung nodules). The test set (90 scans; 5121 2D images) was used to assess if artificial images (512 × 512 primary and control image sets) blended in with original images, using both quantitative metrics and expert opinion. We further assessed if pathology characteristics in the artificial images can be manipulated.ResultsPrimary and control artificial images attained an average objective similarity of 0.78 ± 0.04 (ranging from 0 [entirely dissimilar] to 1[identical]) and 0.76 ± 0.06, respectively. Five radiologists with experience in chest and thoracic imaging provided a subjective measure of image quality; they rated artificial images as 3.13 ± 0.46 (range of 1 [unrealistic] to 4 [almost indistinguishable to the original image]), close to their rating of the original images (3.73 ± 0.31). Radiologists clearly distinguished images in the control sets (2.32 ± 0.48 and 1.07 ± 0.19). In almost a quarter of the scenarios, they were not able to distinguish primary artificial images from the original ones.ConclusionArtificial images can be generated in a way such that they blend in with original images and adhere to anatomical constraints, which can be manipulated to augment the variability of cases.Critical relevance statementArtificial medical images can be used to enhance the availability and variety of medical training images by creating new but comparable images that can blend in with original images.Key points• Artificial images, similar to original ones, can be created using generative networks.• Pathological features of artificial images can be adjusted through guiding the network.• Artificial images proved viable to augment the depth and broadening of diagnostic training.Graphical