Objective Image Quality Assessment Research Articles

Optimizing coronary CT angiography quality with motion-compensated reconstruction for second-generation dual-layer spectral detector CT.

To investigate the effect of motion-compensated reconstruction (MCR) algorithm on improving the image quality of coronary computed tomography angiography (CCTA) using second-generation dual-layer spectral detector computed tomography (DLCT), and to evaluate the influence of heart rate (HR) on the motion-correction efficacy of this algorithm. We retrospectively enrolled 127 patients who underwent CCTA for suspected coronary artery disease using second-generation DLCT. We divided the patients into two subgroups according to their average HR during scanning: the "HR < 75 bpm" group and the "HR ≥ 75 bpm" group. All images were reconstructed by the standard (STD) algorithm and MCR algorithm. Subjective image quality (4-point Likert scale), interpretability, and objective image quality between the STD and MCR in the whole population and within each subgroup were compared. MCR showed significantly higher Likert scores and interpretability than STD on the per-segment (3.58 ± 0.69 vs. 2.82 ± 0.93, 98.4% vs. 91.9%), per-vessel (3.12 ± 0.81 vs. 2.12 ± 0.74, 96.3% vs. 78.7%) and per-patient (2.57 ± 0.76 vs. 1.62 ± 0.55, 90.6% vs. 59.1%) levels (all p < 0.001). In the analysis of HR subgroups on a per-vessel basis of interpretability, significant differences were observed only in the right coronary artery in the low HR group, whereas significant differences were noted in three major coronary arteries in the high HR group. For objective image quality assessment, MCR significantly improved the SNR (13.22 ± 4.06 vs. 12.72 ± 4.06) and the contrast-to-noise ratio (15.84 ± 4.82 vs. 15.39 ± 4.38) compared to STD (both p < 0.001). MCR significantly improves the subjective image quality, interpretability, and objective image quality of CCTA, especially in patients with higher HRs. The motion-compensated reconstruction algorithm of the second-generation dual-layer spectral detector computed tomography is helpful in improving the image quality of coronary computed tomography angiography in clinical practice, especially in patients with higher heart rates. Motion artifacts from cardiac movement affect the quality and interpretability of coronary computed tomography angiography (CCTA). This motion-compensated reconstruction (MCR) algorithm significantly improves the image quality of CCTA in clinical practice. Image quality improvement by using MCR was more significant in the high heart rate group.

RAD-IQMRI: A benchmark for MRI image quality assessment

Magnetic resonance imaging (MRI) is susceptible to visual artifacts that can degrade the perceptual image quality, potentially leading to inaccurate or inefficient diagnoses in clinical practice. It is critical to evaluate the perceptual image quality and build this technique into clinical solutions. In a previous study, an MRI database was created for image quality assessment (IQA), where various types of MRI artifacts with different degrees of degradation were simulated. Application specialists assessed the image quality; however, radiologists’ perception of MRI image quality remains unknown. To make IQA clinically relevant, in this paper we conduct a new subjective experiment where 13 radiologists rated the quality of images contained in the MRI database. Based on this subjective IQA benchmark named RAD-IQMRI, we evaluate the performance of state-of-the-art objective IQA models, providing insights into their application for MRI image quality assessment in clinical settings.

Is the development of objective image quality assessment methods keeping pace with technological developments?

Is the development of objective image quality assessment methods keeping pace with technological developments?

Application of fractional derivatives in image quality assessment indices

Objective image quality assessment involves the use of mathematical models to quantitatively describe image quality. FR-IQA (Full-Reference Image Quality Assessment) methods using reference images are also often used to evaluate image processing and computer vision algorithms. Quality indices often use gradient operators to express relevant visual information, such as edges. Fractional calculus has been applied in the last two decades in various fields such as signal processing, image processing, and pattern recognition. Fractional derivatives are generalizations of integer-order derivatives and can be computed using various operators such as the Riemann-Liouville, Caputo-Fabrizio, and Grünwald-Letnikov operators. In this paper, we propose a modification of the FSIMc image quality index by including fractional derivatives to extract and enhance edges. A study of the usefulness of fractional derivative in the FSIMc model was conducted by assessing Pearson, Spearman and Kendall correlations with MOS scores for images from the TID2013 and KADID-10k databases. Comparison of FD_FSIMc with the classic FSIMc shows an increase of several percent in the correlation coefficients for the modified index. The results obtained are superior to those other known approaches to FR-IQA that use fractional derivatives. The results encourage the use of fractional calculus.

Review of Image Quality Assessment Methods for Compressed Images.

The compression of images for efficient storage and transmission is crucial in handling large data volumes. Lossy image compression reduces storage needs but introduces perceptible distortions affected by content, compression levels, and display environments. Each compression method generates specific visual anomalies like blocking, blurring, or color shifts. Standardizing efficient lossy compression necessitates evaluating perceptual quality. Objective measurements offer speed and cost efficiency, while subjective assessments, despite their cost and time implications, remain the gold standard. This paper delves into essential research queries to achieve visually lossless images. The paper describes the influence of compression on image quality, appropriate objective image quality metrics (IQMs), and the effectiveness of subjective assessment methods. It also provides an overview of the existing literature, surveys, and subjective and objective image quality assessment (IQA) methods. Our aim is to offer insights, identify challenges in existing methodologies, and assist researchers in selecting the most effective assessment approach for their needs.

Hierarchical prior-guided quality assessment method for underwater images

Many underwater image enhancement (UIE) algorithms have been proposed to improve the quality of the underwater image. However, the lack of effective objective image quality assessment (IQA) methods has hindered the further development of UIE algorithms. Unlike traditional distorted images, underwater images usually contain natural distortions such as color cast and fog effects induced by light absorption and scattering. In addition, due to the complexity of underwater scenes, the enhanced results generated using existing UIE algorithms may have artificial distortions such as under-enhancement, over-enhancement, and detail loss. Thus, it is difficult for traditional IQA methods to comprehensively capture quality-related features in complicated underwater conditions. Based on this, this paper proposes a new hierarchical prior-guided quality assessment method for underwater images with the aid of deep learning technology, which first decomposes the underwater image into the base layer and detail layer. Then, the two layers are taken as input of feature extraction network for extracting the luminance information and texture information of the image, respectively. Meanwhile, two pseudo-reference images are used to guide model training so as to obtain luminance and texture features more accurately and efficiently. Finally, the two types of features are fused to obtain the quality score of the underwater image. Extensive experiments have been conducted on two publicly available underwater image datasets, and the experimental results show that the proposed method outperforms other underwater image quality assessment methods.

Thin slice photon-counting CT coronary angiography compared to conventional CT: Objective image quality and clinical radiation dose assessment.

Photon-counting CT (PCCT) is the next-generation CT scanner that enables improved spatial resolution and spectral imaging. For full spectral processing, higher tube voltages compared to conventional CT are necessary to achieve the required spectral separation. This generated interest in the potential influence of thin slice high tube voltage PCCT on overall image quality and consequently on radiation dose. This study first evaluated tube voltages and radiation doses applied in patients who underwent coronary CT angiography with PCCT and energy-integrating detector CT (EID-CT). Next, image quality of PCCT and EID-CT was objectively evaluated in a phantom study simulating different patient sizes at these tube voltages and radiation doses. We conducted a retrospective analysis of clinical doses of patients scanned on a conventional and PCCT system. Average patient water equivalent diameters for different tube voltages were extracted from the dose reports for both EID-CT and PCCT. A conical phantom made of polyethylene with multiple diameters (26/31/36cm) representing different patient sizes and containing an iodine insert was scanned with a EID-CT scanner using tube voltages and phantom diameters that match the patient scans and characteristics. Next, phantom scans were made with PCCT at a fixed tube voltage of 120kV and with CTDIVOL values and phantom diameters identical to the EID-CT scans. Clinical image reconstructions at 0.6mm slice thickness for conventional CT were compared to PCCT images with 0.4mm slice thickness. Image quality was quantified using the detectability index (d'), which estimated the visibility of a 3mm diameter contrast-enhanced coronary artery by considering noise, contrast, resolution, and human visual perception. Alongside d', noise, contrast and resolution were also individually assessed. In addition, the influence of various kernels (Bv40/Bv44/Bv48/Bv56), quantum iterative reconstruction strengths (QIR, 3/4) and monoenergetic levels (40/45/50/55keV) for PCCT on d' was investigated. In this study, 143 patients were included: 47 were scanned on PCCT (120kV) and the remaining on EID-CT (74 small-sized at 70kV, 18 medium-sized at 80kV and four large-sized at 90kV). EID-CT showed 7%-17% higher d' than PCCT with Bv40 kernel and strength four for small/medium patients. Lower monoenergetic images (40keV) helped mitigate the difference to 1%-6%. For large patients, PCCT's detectability was up to 31% higher than EID-CT. PCCT has thinner slices but similar noise levels for similar reconstruction parameters. The noise increased with lower keV levels in PCCT (≈30% increase), but higher QIR strengths reduced noise. PCCT's iodine contrast was stable across patient sizes, while EID-CT had 33% less contrast in large patients than in small-sized patients. At 120kV, thin slice PCCT enables CCTA in phantom scans representing large patients without raising radiation dose or affecting vessel detectability. However, higher doses are needed for small and medium-sized patients to obtain a similar image quality as in EID-CT. The alternative of using lower mono-energetic levels requires further evaluation in clinical practice.

Photon-Counting Detector CT Angiography Versus Digital Subtraction Angiography in Patients with Peripheral Arterial Disease

Rationale and ObjectivesThis study aims to compare the diagnostic confidence of photon-counting detector CT angiography (PCD-CTA) depending on the used vascular reformatting kernels with digital subtraction angiography (DSA) as diagnostic reference standard in peripheral arterial occlusive disease (PAOD). Material and MethodsIn 39 patients, 45 lower extremity PCD-CTA with subsequent DSA were analyzed. Advanced PAOD (Fontaine stage 4) was ascertained in 77.8% of patients. CTA post-processing comprised three vascular kernels (Bv36/48/56). Objective image quality assessment included vessel attenuation, image noise, contrast-to-noise (CNR) and signal-to-noise ratios (SNR). Subjective evaluation of calcium blooming, vessel sharpness, luminal attenuation and image noise was performed by three radiologists. Diagnostic performance and concordance to DSA were assessed. ResultsThe luminal attenuation remained kernel-independent constant. With sharper kernels, image noise increased substantially, while SNR and CNR decreased. Subjective reduction of calcium blooming and increased vessel sharpness were noted for the sharp Bv56 kernel. While sensitivity in stenosis quantification was comparable between kernels (81.6% vs. 81.5% vs. 81.0%, p=0.797), specificity increased slightly higher sharpness (71.1% vs. 76.9% vs. 79.6%, p=0.067). Diagnostic concordance of stenosis ratings compared to DSA increased likewise (Bv36 vs. Bv56, p=0.002). Severe crural vessel calcifications had no influence on sensitivity, regardless of kernel selection. Contrarily, specificity was substantially worse in severely calcified tibial vessels but could be improved by using the sharp Bv56 kernel (Bv36 vs. Bv56 p=0.024). Diagnostic confidence was highest for Bv56. ConclusionIn lower leg PCD-CTA, sharp convolution kernels increase diagnostic confidence compared to DSA by improved vessel delineation and reduced calcium blooming with acceptable image noise.

Towards safer imaging: A comparative study of deep learning-based denoising and iterative reconstruction in intraindividual low-dose CT scans using an in-vivo large animal model

PurposeComputed tomography (CT) scans are a significant source of medically induced radiation exposure. Novel deep learning-based denoising (DLD) algorithms have been shown to enable diagnostic image quality at lower radiation doses than iterative reconstruction (IR) methods. However, most comparative studies employ low-dose simulations due to ethical constraints. We used real intraindividual animal scans to investigate the dose-reduction capabilities of a DLD algorithm in comparison to IR. Materials and methodsFourteen veterinarian-sedated alive pigs underwent 2 CT scans on the same 3rd generation dual-source scanner with two months between each scan. Four additional scans ensued each time, with mAs reduced to 50 %, 25 %, 10 %, and 5 %. All scans were reconstructed ADMIRE levels 2 (IR2) and a novel DLD algorithm, resulting in 280 datasets. Objective image quality (CT numbers stability, noise, and contrast-to-noise ratio) was measured via consistent regions of interest. Three radiologists independently rated all possible dataset combinations per time point for subjective image quality (−1 = inferior, 0 = equal, 1 = superior). The points were averaged for a semiquantitative score, and inter-rater agreement was measured using Spearman's correlation coefficient and adequately corrected mixed-effects modeling analyzed objective and subjective image quality. ResultsNeither dose-reduction nor reconstruction method negatively impacted CT number stability (p > 0.999). In objective image quality assessment, the lowest radiation dose achievable by DLD when comparing noise (p = 0.544) and CNR (p = 0.115) to 100 % IR2 was 25 %. Overall, inter-rater agreement of the subjective image quality ratings was strong (r ≥ 0.69, mean 0.93 ± 0.05, 95 % CI 0.92–0.94; each p < 0.001), and subjective assessments corroborated that DLD at 25 % radiation dose was comparable to 100 % IR2 in image quality, sharpness, and contrast (p ≥ 0.281). ConclusionsThe DLD algorithm can achieve image quality comparable to the standard IR method but with a significant dose reduction of up to 75%. This suggests a promising avenue for lowering patient radiation exposure without sacrificing diagnostic quality.

Application of dual-energy CT with prospective ECG-gating in cardiac CT angiography for children: Radiation and contrast agent dose

ObjectiveThis research aimed to investigate the feasibility of utilizing dual-energy CT virtual monoenergetic images (VMI11VMI:virtual monoenergetic images.) with prospective electrocardiogram (ECG22ECG:electrocardiogram.) gating for reducing radiation and contrast agent doses in pediatric patients with congenital heart disease (CHD33CHD:congenital heart disease.). MethodsThere were 100 pediatric patients with CHD included in this study. Group A (n = 50) underwent dual-energy scanning with prospective ECG-gating, and group B (n = 50) underwent conventional scanning with retrospective ECG-gating. Comparative analysis of CT values of lumen, objective image quality assessment, subjective image quality evaluations, and diagnostic efficacy were performed. ResultsCT values, image noise, signal-to-noise ratio (SNR44SNR:signal-to-noise ratio.), and contrast-to-noise ratio (CNR55CNR:contrast-to-noise ratio.) were significantly affected by the VMI energy level, and they all increased with decreasing energy levels (P > 0.05). Combining subjective evaluation, the 45 keV VMI was considered the optimum image in group A. The 45 keV VMI exhibited higher CT values of lumen compared to conventional scanning images (P < 0.003 ∼ 0.836), but meanwhile, the image noise was also higher in the 45 keV VMI (P = 0.004). Differences between the two groups in SNR, CNR, and diagnostic accuracy were not statistically significant. Compared to group B, the 45 keV VMI showed fewer contrast-induced artifacts (P < 0.001) and higher image quality score (P = 0.037). Group A had a 64 % reduction in radiation dose and a 40 % decrease in iodine dose compared to group B. ConclusionThe combination of dual-energy CT with prospective ECG-gating reduces radiation and iodine doses in pediatric patients with CHD. The 45 keV VMI can provide clinically acceptable image quality while declining contrast agent artifacts.

Attentive Deep Image Quality Assessment for Omnidirectional Stitching

Omnidirectional images or videos are commonly generated via the stitching of multiple images or videos, and the quality of omnidirectional stitching strongly influences the quality of experience (QoE) of the generated scenes. Although there were many studies research the omnidirectional image quality assessment (IQA), the evaluation of the omnidirectional stitching quality has not been sufficiently explored. In this paper, we focus on the IQA for the omnidirectional stitching of dual fisheye images. We first establish an omnidirectional stitching image quality assessment (OSIQA) database, which includes 300 distorted images and 300 corresponding reference images generated from 12 raw scenes. The database contains a variety of distortion types caused by omnidirectional stitching, including color distortion, geometric distortion, blur distortion, and ghosting distortion, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> A subjective quality assessment study is conducted on the database and human opinion scores are collected for the distorted omnidirectional images. We then devise a deep learning based objective IQA metric termed Attentive Multi-channel IQA Net. In particular, we extend hyper-ResNet by developing a subnetwork for spatial attention and propose a spatial regularization item. Experimental results show that our proposed FR and NR models achieve the best performance compared with the state-of-the-art FR and NR IQA metrics on the OSIQA database. The OSIQA database as well as the proposed Attentive Multi-channel IQA Net will be released to facilitate future research.

Approximating the Hotelling observer with autoencoder-learned efficient channels for binary signal detection tasks.

The objective assessment of image quality (IQ) has been advocated for the analysis and optimization of medical imaging systems. One method of computing such IQ metrics is through a numerical observer. The Hotelling observer (HO) is the optimal linear observer, but conventional methods for obtaining the HO can become intractable due to large image sizes or insufficient data. Channelized methods are sometimes employed in such circumstances to approximate the HO. The performance of such channelized methods varies, with different methods obtaining superior performance to others depending on the imaging conditions and detection task. A channelized HO method using an AE is presented and implemented across several tasks to characterize its performance. The process for training an AE is demonstrated to be equivalent to developing a set of channels for approximating the HO. The efficiency of the learned AE-channels is increased by modifying the conventional AE loss function to incorporate task-relevant information. Multiple binary detection tasks involving lumpy and breast phantom backgrounds across varying dataset sizes are considered to evaluate the performance of the proposed method and compare to current state-of-the-art channelized methods. Additionally, the ability of the channelized methods to generalize to images outside of the training dataset is investigated. AE-learned channels are demonstrated to have comparable performance with other state-of-the-art channel methods in the detection studies and superior performance in the generalization studies. Incorporating a cleaner estimate of the signal for the detection task is also demonstrated to significantly improve the performance of the proposed method, particularly in datasets with fewer images. AEs are demonstrated to be capable of learning efficient channels for the HO. The resulting significant increase in detection performance for small dataset sizes when incorporating a signal prior holds promising implications for future assessments of imaging technologies.

Konx: cross-resolution image quality assessment

Scale-invariance is an open problem in many computer vision subfields. For example, object labels should remain constant across scales, yet model predictions diverge in many cases. This problem gets harder for tasks where the ground-truth labels change with the presentation scale. In image quality assessment (IQA), down-sampling attenuates impairments, e.g., blurs or compression artifacts, which can positively affect the impression evoked in subjective studies. To accurately predict perceptual image quality, cross-resolution IQA methods must therefore account for resolution-dependent discrepancies induced by model inadequacies as well as for the perceptual label shifts in the ground truth. We present the first study of its kind that disentangles and examines the two issues separately via KonX, a novel, carefully crafted cross-resolution IQA database. This paper contributes the following: 1. Through KonX, we provide empirical evidence of label shifts caused by changes in the presentation resolution. 2. We show that objective IQA methods have a scale bias, which reduces their predictive performance. 3. We propose a multi-scale and multi-column deep neural network architecture that improves performance over previous state-of-the-art IQA models for this task. We thus both raise and address a novel research problem in image quality assessment.

Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics.

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment. Despite the impressive progress in generating high-resolution, perceptually realistic images, it is not clear if modern GANs reliably learn the statistics that are meaningful to a downstream medical imaging application. In this work, the ability of a state-of-the-art GAN to learn the statistics of canonical stochastic image models (SIMs) that are relevant to objective assessment of image quality is investigated. It is shown that although the employed GAN successfully learned several basic first- and second-order statistics of the specific medical SIMs under consideration and generated images with high perceptual quality, it failed to correctly learn several per-image statistics pertinent to the these SIMs, highlighting the urgent need to assess medical image GANs in terms of objective measures of image quality.

A projection-domain correction method in CBCT reconstruction for head and neck radiotherapy using cycle-GAN and nonlocal means filter.

In radiation treatments for head and neck tumors, cone-beam computed tomography (CBCT) is employed for patient positioning and dose calculation of adaptive radiotherapy. However, the quality of CBCT is degraded by the scatter and noise, majorly impacting the accuracy of patient positioning and dose calculation. To improve the quality of CBCT for patients with head and neck cancer, a projection-domain CBCT correction method was proposed using a cycle-consistent generative adversarial network (cycle-GAN) and a nonlocal means filter based on a reference digitally reconstructed radiograph (DRR). A cycle-GAN was initially trained to learn mapping from CBCT projections to a DRR using the data obtained from 30 patients. For each patient, 671 CBCT projections were measured for CBCT reconstruction. Moreover, 360 Digital Reconstructed Radiographs (DRR) were computed from each patient's planning computed tomography (CT), whose projection angles ranged from 0° to 359° with an interval of 1°. By applying the trained generator of the cycle-GAN to the unseen CBCT projection, a synthetic DRR with considerably less scatter was obtained. However, annular artifacts were observed in the CBCT reconstructed with synthetic DRR. To address this issue, a nonlocal means filter based on reference DRR was used to further correct the synthetic DRR, which corrected the synthetic DRR using the calculated DRR as a reference image. Finally, the CBCT with no annular artifact and little noise was reconstructed with the corrected synthetic DRR. The proposed method was tested using the data of six patients. The corrected synthetic DRR and CBCT were compared with the corresponding real DRR and CT images. The structural preservation ability of the proposed method was evaluated using the Dice coefficients of the automatically extracted nasal cavity. Moreover, The image quality of CBCT corrected with the proposed method was objectively assessed with an five-point human scoring system and compared with CT, original CBCT and CBCT corrected with other strategies. The mean absolute value (MAE) of the relative error between the corrected synthetic and real DRR was <8%. The MAE between the corrected CBCT and corresponding CT was <30 HU. Moreover, the Dice coefficient of nasal cavity between the corrected CBCT image and the original image exceeded 98.8 for all the patients. Last but not least, the objective assessment of image qualityshowed the proposed method had an average score of 4.2 in overall image quality, which was higher than that of the original CBCT, CBCT reconstructed with synthetic DRR, and CBCT reconstructed with projections filtered with NLMF only. The proposed method can considerably improve the CBCT image quality with little anatomical distortion, improving the accuracy of radiotherapy for head and neck patients. This article is protected by copyright. All rights reserved.

UID2021: An Underwater Image Dataset for Evaluation of No-Reference Quality Assessment Metrics

Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing. However, the development of underwater image quality assessment (UIQA) is limited for the lack of publicly available underwater image datasets with human subjective scores and reliable objective UIQA metrics. To address this issue, we establish a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference (NR) UIQA metrics. The constructed dataset contains 60 multiply degraded underwater images collected from various sources, covering six common underwater scenes (i.e., bluish scene, blue-green scene, greenish scene, hazy scene, low-light scene, and turbid scene), and their corresponding 900 quality improved versions are generated by employing 15 state-of-the-art underwater image enhancement and restoration algorithms. Mean opinion scores with 52 observers for each image of UID2021 are also obtained by using the pairwise comparison sorting method. Both in-air and underwater-specific NR IQA algorithms are tested on our constructed dataset to fairly compare their performance and analyze their strengths and weaknesses. Our proposed UID2021 dataset enables ones to evaluate NR UIQA algorithms comprehensively and paves the way for further research on UIQA. The dataset is available at https://github.com/Hou-Guojia/UID2021 .

Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence.

Gastrointestinal endoscopy has been identified as an important tool for cancer diagnosis and therapy, particularly for treating patients with early gastric cancer (EGC). It is well known that the quality of gastroscope images is a prerequisite for achieving a high detection rate of gastrointestinal lesions. Owing to manual operation of gastroscope detection, in practice, it possibly introduces motion blur and produces low-quality gastroscope images during the imaging process. Hence, the quality assessment of gastroscope images is the key process in the detection of gastrointestinal endoscopy. In this study, we first present a novel gastroscope image motion blur (GIMB) database that includes 1,050 images generated by imposing 15 distortion levels of motion blur on 70 lossless images and the associated subjective scores produced with the manual operation of 15 viewers. Then, we design a new artificial intelligence (AI)-based gastroscope image quality evaluator (GIQE) that leverages the newly proposed semi-full combination subspace to learn multiple kinds of human visual system (HVS) inspired features for providing objective quality scores. The results of experiments conducted on the GIMB database confirm that the proposed GIQE showed more effective performance compared with its state-of-the-art peers.

Subjective Assessment of Objective Image Quality Metrics Range Guaranteeing Visually Lossless Compression.

The usage of media such as images and videos has been extensively increased in recent years. It has become impractical to store images and videos acquired by camera sensors in their raw form due to their huge storage size. Generally, image data is compressed with a compression algorithm and then stored or transmitted to another platform. Thus, image compression helps to reduce the storage size and transmission cost of the images and videos. However, image compression might cause visual artifacts, depending on the compression level. In this regard, performance evaluation of the compression algorithms is an essential task needed to reconstruct images with visually or near-visually lossless quality in case of lossy compression. The performance of the compression algorithms is assessed by both subjective and objective image quality assessment (IQA) methodologies. In this paper, subjective and objective IQA methods are integrated to evaluate the range of the image quality metrics (IQMs) values that guarantee the visually or near-visually lossless compression performed by the JPEG 1 standard (ISO/IEC 10918). A novel "Flicker Test Software" is developed for conducting the proposed subjective and objective evaluation study. In the flicker test, the selected test images are subjectively analyzed by subjects at different compression levels. The IQMs are calculated at the previous compression level, when the images were visually lossless for each subject. The results analysis shows that the objective IQMs with more closely packed values having the least standard deviation that guaranteed the visually lossless compression of the images with JPEG 1 are the feature similarity index measure (FSIM), the multiscale structural similarity index measure (MS-SSIM), and the information content weighted SSIM (IW-SSIM), with average values of 0.9997, 0.9970, and 0.9970 respectively.

First Total-Body Kinetic Modeling and Parametric Imaging of Dynamic 68Ga-FAPI-04 PET in Pancreatic and Gastric Cancer.

Fibroblast activation protein inhibitor (FAPI) is an ideal diagnostic and therapeutic target in malignant tumors. However, the knowledge of kinetic modeling and parametric imaging of 68Ga-FAPI is limited. Purpose: The purpose of this study was to explore the pharmacokinetics of 68Ga-FAPI-04 PET/CT in pancreatic cancer and gastric cancer and to conduct parametric imaging of dynamic total-body data compared with SUV imaging. Methods: Dynamic total-body 68Ga-FAPI-04 PET/CT was performed on 13 patients. The lesion time-activity curves were fitted by 3-compartment models and multigraphical models. The kinetics parameters derived from the 2-tissue reversible compartment model (2T4K) and multigraphical models were analyzed. Parametric [Formula: see text] imaging was generated using the 2T4K and Logan models, and their performances were evaluated compared with SUV images. Results: 2T4K had the lowest Akaike information criterion value, and its fitting curves matched excellently with the origin time-activity curves. Visual assessment revealed that the [Formula: see text](2T4K) images and [Formula: see text](Logan with spatial constraint [SC]) images both showed less image noise and higher lesion conspicuity compared with SUV images. Objective image quality assessment demonstrated that parametric [Formula: see text](2T4K) images and parametric [Formula: see text](Logan with SC) images had a 5.0-fold and 5.0-fold higher average signal-to-noise ratio and 3.6-fold and 4.1-fold higher average contrast-to-noise ratio compared with conventional SUV images, respectively. In addition, no significant differences in signal-to-noise ratio and contrast-to-noise of pathologic lesions were observed between parametric [Formula: see text](2T4K) images and parametric [Formula: see text](Logan with SC) images (all P > 0.05). Conclusions: The 2T4K model was the preferred compartment model. Total-body parametric imaging of 68Ga-FAPI-04 PET yielded superior quantification beyond SUV with enhanced lesion contrast, which may serve as a promising imaging method to make an early diagnosis, to better reflect tumor characterization, or to allow evaluation of treatment response. [Formula: see text](2T4K) images are comparable in image quality and consistent to [Formula: see text](Logan with SC) images in lesions conspicuity; however, [Formula: see text](Logan with SC) images presented an appealing alternative to [Formula: see text](2T4K) images because of their simplicity.

A Hybrid Method for Objective Quality Assessment of Binary Images

A Hybrid Method for Objective Quality Assessment of Binary Images