Deterioration Of Image Quality Research Articles

Benchmarking deep learning-based low-dose CT image denoising algorithms.

Long-lasting efforts have been made to reduce radiation dose and thus the potential radiation risk to the patient for computed tomography (CT) acquisitions without severe deterioration of image quality. To this end, various techniques have been employed over the years including iterative reconstruction methods and noise reductionalgorithms. Recently, deep learning-based methods for noise reduction became increasingly popular and a multitude of papers claim ever improving performance both quantitatively and qualitatively. However, the lack of a standardized benchmark setup and inconsistencies in experimental design across studies hinder the verifiability and reproducibility of reportedresults. In this study, we propose a benchmark setup to overcome those flaws and improve reproducibility and verifiability of experimental results in the field. We perform a comprehensive and fair evaluation of several state-of-the-art methods using this standardizedsetup. Our evaluation reveals that most deep learning-based methods show statistically similar performance, and improvements over the past years have been marginal atbest. This study highlights the need for a more rigorous and fair evaluation of novel deep learning-based methods for low-dose CT image denoising. Our benchmark setup is a first and important step towards this direction and can be used by future researchers to evaluate theiralgorithms.

Design and experimental research of orbital angular momentum multiplexing holography based on optical diffraction neural network

The orbital angular momentum (OAM) multiplexing holography has the advantages of large information capacity and high security, and has important application value in holographic storage, optical encryption, and optical computing. However, as the number of multiplexing channels increases, this technology suffers from deterioration in image quality, which limits its application scope. This article proposes an innovative design that introduces an optical diffractive neural network (ODNN) into OAM multiplexing holography, establishes a scientific image quality evaluation function, applies an end-to-end optimization method, and designs OAM multiplexing holograms in parallel, significantly improving the image quality of OAM holography. The design results show that compared to classical methods, the ODNN method proposed in this paper has improved diffraction efficiency and signal-to-noise ratio by 29% and 19%, respectively, and reduced mean square error and variance by 10% and 43%, respectively. Moreover, high-quality multi-channel OAM multiplexing holography has been achieved through experiments. The design method proposed in this article provides an efficient and practical way for future OAM multiplexing holographic technology to further enhance information capacity and improve security.

UIDF-Net: Unsupervised Image Dehazing and Fusion Utilizing GAN and Encoder-Decoder.

Haze weather deteriorates image quality, causing images to become blurry with reduced contrast. This makes object edges and features unclear, leading to lower detection accuracy and reliability. To enhance haze removal effectiveness, we propose an image dehazing and fusion network based on the encoder-decoder paradigm (UIDF-Net). This network leverages the Image Fusion Module (MDL-IFM) to fuse the features of dehazed images, producing clearer results. Additionally, to better extract haze information, we introduce a haze encoder (Mist-Encode) that effectively processes different frequency features of images, improving the model's performance in image dehazing tasks. Experimental results demonstrate that the proposed model achieves superior dehazing performance compared to existing algorithms on outdoor datasets.

Sub2Full: split spectrum to boost optical coherence tomography despeckling without clean data.

Optical coherence tomography (OCT) suffers from speckle noise, causing the deterioration of image quality, especially in high-resolution modalities such as visible light OCT (vis-OCT). Here, we proposed an innovative self-supervised strategy called Sub2Full (S2F) for OCT despeckling without clean data. This approach works by acquiring two repeated B-scans, splitting the spectrum of the first repeat as a low-resolution input, and utilizing the full spectrum of the second repeat as the high-resolution target. The proposed method was validated on vis-OCT retinal images visualizing sublaminar structures in the outer retina and demonstrated superior performance over state-of-the-art Noise2Noise (N2N) and Noise2Void (N2V) schemes.

Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function

Purpose: Motion-induced magnetic resonance imaging (MRI) artifacts can deteriorate image quality and reduce diagnostic accuracy, but motion by human subjects is inevitable and can even be caused by involuntary physiological movements. Deep-learning-based motion correction methods might provide a solution. However, most studies have been based on directly applying existing models, and the trained models are rarely accessible. Therefore, we aim to develop and evaluate a deep-learning-based method (Motion Correction-Net, or MC-Net) for suppressing motion artifacts in brain MRI scans. Methods: A total of 57 subjects, providing 20,889 slices in four datasets, were used. Furthermore, 3T 3D sagittal magnetization-prepared rapid gradient-echo (MP-RAGE) and 2D axial fluid-attenuated inversion-recovery (FLAIR) sequences were acquired. The MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network to remove motion artifacts. Evaluation used simulated T1- and T2-weighted axial, coronal, and sagittal images unseen during training, as well as T1-weighted images with motion artifacts from real scans. The performance indices included the peak-signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and visual reading scores from three blinded clinical readers. A one-sided Wilcoxon signed-rank test was used to compare reader scores, with p < 0.05 considered significant. Intraclass correlation coefficients (ICCs) were calculated for inter-rater evaluations. Results: The MC-Net outperformed other methods in terms of PSNR and SSIM for the T1 axial test set. The MC-Net significantly improved the quality of all T1-weighted images for all directions (i.e., the mean SSIM of axial, sagittal, and coronal slices improved from 0.77, 0.64, and 0.71 to 0.92, 0.75, and 0.84; the mean PSNR improved from 26.35, 24.03, and 24.55 to 29.72, 24.40, and 25.37, respectively) and for simulated as well as real motion artifacts, both using quantitative measures and visual scores. However, MC-Net performed poorly for images with untrained T2-weighted contrast because the T2 contrast was unseen during training and is different from T1 contrast. Conclusion: The proposed two-stage multi-loss MC-Net can effectively suppress motion artifacts in brain MRI without compromising image quality. Given the efficiency of MC-Net (with a single-image processing time of ~40 ms), it can potentially be used in clinical settings.

Parallel- and micro-beamforming challenges in real-time, high-frame-rate, ultrasound imaging

Delay-and-sum beamforming for classic (line-by-line) ultrasound imaging is usually performed in real-time by FPGAs and GPUs. However, when a high-frame-rate (HFR) has to be achieved, beamforming becomes challenging. Here, the echoes following the transmission of multi-line focused beams, plane waves, or diverging waves, must be simultaneously beamformed along multiple view lines. Such parallel beamforming is feasible online when the scanner is endowed with high flexibility and processing power. This talk will show how the FPGAs of the ULA-OP 256, a hardware-based open scanner, were efficiently utilized to enable parallel beamforming at high speed. The talk will also discuss how the data transfer between the scanner boards impacts the frame rate, which actually achieved 4400 frames per second through a new communication topology. The talk will also examine the image quality deterioration emerging when HFR imaging sequences are implemented in probes equipped with a microbeamformer (uB) that was designed for focused beam transmission. Simulations show how the transmitted beamwidth and uB size impact the image contrast and resolution, taking into account that the uB ASIC cannot handle multiple sets of delays and apodization weights after each transmit event. Technological improvements needed in the next generation uBs will be finally discussed.

Photon-counting CT-angiography in pre-TAVR aortic annulus assessment: effects of retrospective vs. prospective ECG-synchronization on prosthesis valve selection

To compare the diagnostic value of ultrahigh-resolution CT-angiography (UHR-CTA) compared with high-pitch spiral CTA (HPS-CTA) using a first-generation, dual-source photon-counting CT (PCD-CT) scanner for preprocedural planning of transcatheter aortic valve replacement (TAVR). Clinically referred patients with severe aortic valve stenosis underwent both, retrospective ECG-gated cardiac UHR-CTA (collimation: 120 × 0.2 mm) and prospective ECG-triggered aortoiliac HPS-CTA (collimation: 144 × 0.4 mm, full spectral capabilities) for TAVR planning from August 2022 to March 2023. Radiation dose was extracted from the CT reports, and the effective dose was calculated. Two radiologists analyzed UHR-CTA and HPS-CTA datasets, assessing the image quality of the aortic annulus, with regard to the lumen visibility and margin delineation using a 4-point visual-grading scale (ranges: 4 = ”excellent” to 1 = ”poor”). Aortic annulus area (AAA) measurements were taken for valve prosthesis sizing, with retrospective UHR-CTA serving as reference standard. A total of 64 patients were included (mean age, 81 years ± 7 SD; 28 women) in this retrospective study. HPS-CTA showed a lower radiation dose, 4.1 mSv vs. 12.6 mSv (p < 0.001). UHR-CTA demonstrated higher image quality to HPS-CTA (median score, 4 [IQR, 3–4] vs. 3 [IQR, 2–3]; p < 0.001). Quantitative assessments of AAA from both CTA datasets were strongly positively correlated (mean 477.4 ± 91.1 mm2 on UHR-CTA and mean 476.5 ± 90.4 mm2 on HPS-CTA, Pearson r2 = 0.857, p < 0.001) with a mean error of 22.3 ± 24.6 mm2 and resulted in identical valve prosthesis sizing in the majority of patients (91%). Patients with lower image quality on HPS-CTA (score value 1 or 2, n = 28) were more likely to receive different sizing recommendations (82%). Both UHR-CTA and HPS-CTA acquisitions using photon-counting CT technology provided reliable aortic annular assessments for TAVR planning. While UHR-CTA offers superior image quality, HPS-CTA is associated with lower radiation exposure. However, severely impaired image quality on HPS-CTA may impact on prosthesis sizing, suggesting that immediate post-scan image evaluations may require complementary UHR-CTA scanning.

Improved limited view ultrasound tomography via machine learning.

Tomographic reconstruction is used extensively in medicine, non-destructive testing and geology. In an ideal situation where measurements are taken at all angles around an object, known as full view configuration, a full reconstruction of the object can be produced. One of the major issues faced in tomographic imaging is when measurements cannot be taken freely around the object under inspection. This may be caused by the size and geometry of the object or difficulty accessing from particular directions. The resulting limited view transducer configuration leads to a large deterioration in image quality, thus it is very beneficial to employ a compensation algorithm. At present, the most effective compensation algorithms require a large amount of computing power or a bespoke case-by case approach, often with numerous arbitrary constants which must be tuned for a specific application. This work proposes a machine learning based approach to perform the limited view compensation. The model is based around an autoencoder architecture. It is trained on an artificial dataset, taking advantage of the ability to generate arbitrary limited view images given a full view input. The approach is evaluated on ten laser-scanned corrosion maps and the results compared to positivity regularisation - a limited view compensation algorithm similar in the speed of execution and generalisation potential. The algorithms are compared for root mean squared error (RMSE) across the image, and maximum absolute error (MAE). Furthermore, they are visually compared for subjective quality. Compared to the conventional algorithm, the ML-based approach improves on the MAE in eight out of the ten cases. The conventional approach performs better on mean RMSE, which is explained by the ML outputting inaccurate background level, which is not a critical ability. Most importantly, the visual inspection of outputs shows the ML approach reconstructs the images better, especially in the case of irregular corrosion patches. Compared to limited view images, the ML method improves both the RMSE and MAE by 41%.

Gridless adult cervical spine radiography and its' effect on image quality and radiation dose: A phantom study

IntroductionImaging of the cervical spine in general radiography is most frequently performed using an anti-scatter grid. The purpose of this study was to investigate the effects of a gridless setting on image quality and radiation dose during digital radiography of the anteroposterior (AP) and lateral (LAT) cervical spine. MethodsA phantom study was performed with a variety of tube voltages (63–75 kV) with and without an anti-scatter grid. The tube current time product (mAs) and dose area product (DAP) were recorded and used to calculate effective dose (ED) and individual organ dose using PCXMC 2.0 software, as well as entrance surface dose (ESD) and objective image quality: signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Subjective visual image quality grading characteristics (VGC) was performed by five qualified radiographers. ResultsIn a gridless setting, the AP and LAT positions showed significantly lower DAP (1.6 μGym2; 61.3 % and 1.6 μGym2; 51.2%), ESD (27.6 μGy; 57.3% and 77.2 μGy; 47.2%) and ED (4.2 μSv; 61.3% and 2.3 μSv; 48.9%). In a gridless setting in the AP position, there is a slight significant deterioration in image quality. In the lateral projection, on the other hand, the image quality without the use of grid was only significantly reduced in three of six criteria and there was no difference in the objective image quality between the two settings examined. ConclusionThe results of this study show that gridless setting significantly decreases radiation dose and image quality, but the quality in the lateral projection is still acceptable for diagnostic purpose. Implications for practiceThe protocol without the use of the anti-scatter grid in cervical spine radiography leads to a reduction in the radiation dose in both projections, but the image quality in the AP is significantly reduced for all criteria examined, with a slight deterioration in image quality in the lateral projection.

Virtual plates: Getting the best out of high content screens

High content screening (HCS) is becoming widely adopted as a high throughput screening modality, using hundred-of-thousands compounds library. The use of machine learning and artificial intelligence in image analysis is amplifying this trend. Another factor is the recognition that diverse cell phenotypes can be associated with changes in biological pathways relevant to disease processes. There are numerous challenges in HCS campaigns. These include limited ability to support replicates, low availability of precious and unique cells or reagents, high number of experimental batches, lengthy preparation of cells for imaging, image acquisition time (45–60 min per plate) and image processing time, deterioration of image quality with time post cell fixation and variability within wells and batches. To take advantage of the data in HCS, cell population based rather than well-based analyses are required. Historically, statistical analysis and hypothesis testing played only a limited role in non-high content high throughput campaigns. Thus, only a limited number of standard statistical criteria for hit selection in HCS have been developed so far. In addition to complex biological content in HCS campaigns, additional variability is impacted by cell and reagent handling and by instruments which may malfunction or perform unevenly. Together these can cause a significant number of wells or plates to fail. Here we describe an automated approach for hit analysis and detection in HCS. Our approach automates HCS hit detection using a methodology that is based on a documented statistical framework. We introduce the Virtual Plate concept in which selected wells from different plates are collated into a new, virtual plate. This allows the rescue and analysis of compound wells which have failed due to technical issues as well as to collect hit wells into one plate, allowing the user easier access to the hit data.

Dose optimization of 2D X-ray image acquisition protocols in image-guided radiotherapy

PurposeIn contemporary radiotherapy, patient positioning accuracy relies on kV imaging. This study aims at optimizing planar kV image acquisition protocols regarding patient dose without degrading image quality. Materials and methodsAn image quality test-object was placed in-between PMMA plates, suitably arranged to model head or pelvis. Constructed phantoms were imaged using default protocols, the resultant image quality was assessed and the corresponding radiation dose was measured. The process was repeated using numerous kV/mAs combinations to identify those acquisition settings providing images at lower dose than the default protocols but without deterioration in image quality. Default and dose-optimized protocols were then tested on an anthropomorphic phantom and on 51 patients during two successive treatment sessions. Image quality was independently assessed by two readers. Organ and effective doses were estimated using a Monte Carlo simulation software. ResultsLow-contrast detectability exhibited a stronger dependence on kV/mAs settings, compared to high-contrast resolution. Dose-optimized protocols resulted in significant dose reductions (anteroposterior-head 48.0 %, lateral-head 30.0 %, anteroposterior-pelvis 28.4 %, lateral-pelvis 27.0 %) compared to the default ones, without compromising image quality. Optimized protocols decreased effective doses by 54 % and 29.6 % in head and pelvic acquisitions, respectively. Regarding image quality, anthropomorphic and patient images acquired using the dose-optimized protocols were subjectively evaluated equivalent to those obtained with the corresponding default settings, indicating that the proposed protocols may be routinely used. ConclusionsGiven the potentially large number of radiotherapy fractions and the pertinent image acquisitions, dose-optimized protocols could significantly reduce patient dose associated with planar imaging without compromising positioning accuracy.

The application of high-performance ultrasound probes increases anatomic depiction in obese patients

This study evaluated the impact of obesity on abdominal ultrasound diagnostics and assessed effect of high-performance ultrasound probes increased imaging quality. Lean and obese subjects (n = 40; 58% female) were categorized according to body mass index (BMI, 21 to 48 kg/m2). A highly standardized ultrasound examination of the abdomen was performed by trained examiners using three different probes in randomized order (standard probe versus two high-performance probes). Quality of B-mode and duplex ultrasound were assessed using a custom scoring approach for depiction of liver and kidney anatomy and vascularization. Across probes, imaging quality of hepatic and kidney anatomy was inversely related with BMI (P < 0.03, r < − 0.35). Age, sex, and BMI explained 51% of the variance within the ultrasound quality score, with β = − 0.35, P < 0.0001 for BMI. Compared to the standard probe, high-performance probes allowed for a better depiction of kidney and liver anatomy in subjects above BMI 35 kg/m2 (n = 20, all P < 0.05), resulting in a less pronounced deterioration of imaging quality with increased BMI (all P < 0.05). In conclusion the study shows that obesity impairs ultrasound imaging quality of abdominal anatomy. The application of high-performance probes can increase anatomic depiction in obese patients.Registration number of the German Registry of Clinical Studies: DRKS00023498.

Feasibility of In Vivo Metal Artifact Reduction in Contrast-Enhanced Dedicated Spiral Breast Computed Tomography.

Radiopaque breast markers cause artifacts in dedicated spiral breast-computed tomography (SBCT). This study investigates the extent of artifacts in different marker types and the feasibility of reducing artifacts through a metal artifact reduction (MAR) algorithm. The pilot study included 18 women who underwent contrast-enhanced SBCT. In total, 20 markers of 4 different types were analyzed for artifacts. The extent of artifacts with and without MAR was measured via the consensus of two readers. Image noise was quantitatively evaluated, and the effect of MAR on the detectability of breast lesions was evaluated on a 3-point Likert scale. Breast markers caused significant artifacts that impaired image quality and the detectability of lesions. MAR decreased artifact size in all analyzed cases, even in cases with multiple markers in a single slice. The median length of in-plain artifacts significantly decreased from 31 mm (range 11-51 mm) in uncorrected to 2 mm (range 1-5 mm) in corrected images (p ≤ 0.05). Artifact size was dependent on marker size. Image noise in slices affected by artifacts was significantly lower in corrected (13.6 ± 2.2 HU) than in uncorrected images (19.2 ± 6.8 HU, p ≤ 0.05). MAR improved the detectability of lesions affected by artifacts in 5 out of 11 cases. MAR is feasible in SBCT and improves the image quality and detectability of lesions.

Intelligent monitoring and diagnosis of telescope image quality

ABSTRACT The imaging quality of a telescope directly affects the reliability of astronomical research. Through the monitoring and diagnosis of imaging quality, the cause of the deterioration of imaging quality can be found in time, which is essential for ensuring the peaking performance of the telescope and high-quality imaging. Moreover, these operations are complex and crucial for achieving high-quality imaging of future giant telescope systems involving active optics, adaptive optics, and other advanced techniques. We propose a three-component method based on cutting-edge artificial intelligence technology to real-time monitor and efficiently diagnose the telescope image quality. The first component, an image quality monitoring system, monitors and outputs the telescope’s image quality. The second component is a query system with a knowledge graph, which outputs the node chains as the possible cause of poor image quality based on the input. The third component, a final estimator, uses the node parameter, which contains historical fault data and real-time updated data from sensors, to give the probability of each node chain. We construct and test the system in the Large Sky Area Multi-Object Fiber Spectroscopy Telescope.

An unsolvable pixel reduced authentication method for color images with grayscale invariance

This paper introduces an advanced grayscale-invariant authentication method for color images. Prior work directly utilizes the most significant bits (MSBs) of the red and blue channels for authentication code generation and embeds these codes into the least significant bits (LSBs). Concurrently, the green channel is adjusted to sustain consistent grayscale values. However, this direct utilization of MSBs often leads to a decline in image quality and results in a significant number of unsolvable pixels. To address these challenges, we present a new approach specifically designed to combat the deterioration of image quality and the prevalence of unsolvable pixels. In our method, we propose the MSB alteration (MSBA) technique, which modifies the MSBs of the red and blue channels to generate authentication codes with minimal distortion. This technique effectively reduces image distortion and the number of unsolvable pixels. Furthermore, we introduce the pixel substitution strategy (PSS) and the constraint-reduced strategy (CRS) to enhance the embedding efficiency. Experimental results demonstrate that our method outperforms prior work by converting more unsolvable pixels to solvable ones and achieving better detection results. Moreover, our method enhances image quality by approximately 1.5 dB, while maintaining the same embedding capacity.

Faster and lower dose imaging: evaluating adaptive, constant gantry velocity and angular separation in fast low-dose 4D cone beam CT imaging.

The adoption of four-dimensional cone beam computed tomography (4DCBCT) for image-guided lung cancer radiotherapy is increasing, especially for hypofractionated treatments. However, the drawbacks of 4DCBCT include long scan times (∼240s), inconsistent image quality, higher imaging dose than necessary, and streaking artifacts. With the emergence of linear accelerators that can acquire 4DCBCT scans in a short period of time (9.2s) there is a need to examine the impact that these very fast gantry rotations have on 4DCBCT image quality. This study investigates the impact of gantry velocity and angular separation between x-ray projections on image quality and its implication for fast low-dose 4DCBCT with emerging systems, such as the Varian Halcyon that provide fast gantry rotation and imaging. Large and uneven angular separation between x-ray projections is known to reduce 4DCBCT image quality through increased streaking artifacts. However, it is not known when angular separation starts degrading image quality. The study assesses the impact of constant and adaptive gantry velocity and determines the level when angular gaps impair image quality using state-of-the-art reconstruction methods. This study considers fast low-dose 4DCBCT acquisitions (60-80s, 200-projection scans). To assess the impact of adaptive gantry rotations, the angular position of x-ray projections from adaptive 4DCBCT acquisitions from a 30-patient clinical trial were analyzed (referred to as patient angular gaps). To assess the impact of angular gaps, variable and static angular gaps (20°, 30°, 40°) were introduced into evenly separated 200 projections (ideal angular separation). To simulate fast gantry rotations, which are on emerging linacs, constant gantry velocity acquisitions (9.2s, 60s, 120s, 240s) were simulated by sampling x-ray projections at constant intervals using the patient breathing traces from the ADAPT clinical trial (ACTRN12618001440213). The 4D Extended Cardiac-Torso (XCAT) digital phantom was used to simulate projections to remove patient-specific image quality variables. Image reconstruction was performed using Feldkamp-Davis-Kress (FDK), McKinnon-Bates (MKB), and Motion-Compensated-MKB (MCMKB) algorithms. Image quality was assessed using Structural Similarity-Index-Measure (SSIM), Contrast-to-Noise-Ratio (CNR), Signal-to-Noise-Ratio (SNR), Tissue-Interface-Width-Diaphragm (TIW-D), and Tissue-Interface-Width-Tumor (TIW-T). Patient angular gaps and variable angular gap reconstructions produced similar results to ideal angular separation reconstructions, while static angular gap reconstructions produced lower image quality metrics. For MCMKB-reconstructions, average patient angular gaps produced SSIM-0.98, CNR-13.6, SNR-34.8, TIW-D-1.5mm, and TIW-T-2.0mm, static angular gap 40° produced SSIM-0.92, CNR-6.8, SNR-6.7, TIW-D-5.7mm, and TIW-T-5.9mm and ideal produced SSIM-1.00, CNR-13.6, SNR-34.8, TIW-D-1.5mm, and TIW-T-2.0mm. All constant gantry velocity reconstructions produced lower image quality metrics than ideal angular separation reconstructions regardless of the acquisition time. Motion compensated reconstruction (MCMKB) produced the highest contrast images with low streaking artifacts. Very fast 4DCBCT scans can be acquired provided that the entire scan range is adaptively sampled, and motion-compensated reconstruction is performed. Importantly, the angular separation between x-ray projections within each individual respiratory bin had minimal effect on the image quality of fast low-dose 4DCBCT imaging. The results will assist the development of future 4DCBCT acquisition protocols that can now be achieved in very short time frames with emerging linear accelerators.

Influence of device-assisted suction against resistance (Mueller maneuver) on image quality in CTPA for suspected lung embolism

ObjectivesTo investigate the effect of a device-assisted suction against resistance Mueller maneuver (MM) on transient interruption of contrast (TIC) in the aorta and pulmonary trunk (PT) on computed tomography pulmonary angiogram (CTPA).MethodsIn this prospective single-center study, 150 patients with suspected pulmonary artery embolism were assigned randomly with two different breathing maneuvers (Mueller maneuver (MM) or standard end-inspiratory breath-hold command (SBC)) during routine CTPA. The MM was performed using a patented prototype (Contrast Booster™) which allows both the patient by means of visual feedback and the medical staff in the CT scanning room to monitor whether the patient is sucking sufficiently or not. Mean Hounsfield attenuation in descending aorta and PT was measured and compared.ResultsOverall, patients with MM showed an attenuation of 338.24 HU in the pulmonary trunk, compared to 313.71 HU in SBC (p = 0.157). In the aorta, the values for MM were lower compared to SBC (134.42 HU vs. 177.83 HU, p = 0.001). The TP-aortic ratio was significantly higher in the MM group at 3.86 compared to the SBC group at 2.26, p = 0.001. TIC phenomenon was absent in the MM group, whereas it was present in 9 patients (12.3%) in the SBC group (p = 0.005). Overall contrast was better on all levels for MM (p < 0.001). The presence of breathing artifacts was higher in the MM group (48.1% vs. 30.1%, p = 0.038), without clinical consequence.ConclusionsPerforming the MM with the application of the prototype is an effective way of preventing the TIC phenomenon during i.v. contrast-enhanced CTPA scanning compared to the standard end-inspiratory breathing command.Clinical relevanceCompared to standard end-inspiratory breathing command, the device-assisted Mueller maneuver (MM) improves contrast enhancement and prevents the transient interruption of contrast (TIC) phenomenon in CTPA. Therefore, it may offer optimized diagnostic workup and timely treatment for patients with pulmonary embolism.Key Points• Transient interruption of contrast (TIC) may impair image quality in CTPA.• Mueller Maneuver using a device prototype could lower the rate of TIC.• Device application in clinical routine may increase diagnostic accuracy.

SWM-DE: Statistical wavelet model for joint denoising and enhancement for multimodal medical images

Medical images are usually degraded by numerous noises during acquisition or transmission, which often causes low contrast leading to deterioration of image quality. As such, medical image denoising and enhancement has become a paramount routine task. To overcome this problem, we propose a cutting-edge joint statistical and morphological model for the denoising and enhancement operation. Firstly, we propose a statistical model in formulating the marginal distribution of the wavelet coefficients. This model is integrated into a Bayesian inference framework to develop a maximum a posterior (MAP) estimator of the noise-free coefficient. Based on the statistical model, we eliminate the need for noise level estimation, and allows the model to automatically adapts to the observed image data. Secondly, we propose an adjustable morphological reconstruction model to eliminate known and unknown noises associated with medical images, while preserving the image details. After these operations, the image is decomposed into several wavelet subbands to extract the illumination and detail components. The image is then reconstructed based on the inverse wavelet to generate the enhanced noise-free image.Experimental results show that the proposed framework obtained high EME values of 41.04, 48.81, 47.81, and 45.75 for OCTA, FFA, CT, and X-ray imaging modalities, and performs better than the state-of-the-art methods. The proposed algorithm can effectively and efficiently enhance medical images, which will assist the clinicians in disease diagnosis, monitoring, and treatment.

Deep learning-based reconstruction and 3D hybrid profile order technique for MRCP a 3T: evaluation of image quality and acquisition time.

To evaluate the image quality of the 3D hybrid profile order technique and deep-learning-based reconstruction (DLR) for 3D magnetic resonance cholangiopancreatography (MRCP) within a single breath-hold (BH) at 3T magnetic resonance imaging (MRI). This retrospective study included 32 patients with biliary and pancreatic disorders. BH images were reconstructed with and without DLR. The signal-to-noise ratio (SNR), contrast, contrast-to-noise ratio (CNR) between the common bile duct (CBD) and periductal tissues, and full width at half maximum (FWHM) of CBD on 3D-MRCP were evaluated quantitatively. Two radiologists scored image noise, contrast, artifacts, blur, and overall image quality of the three image types using a 4-point scale. Quantitative and qualitative scores were compared using the Friedman test and post hoc Nemenyi test. The SNR and CNR were not significantly different when under respiratory gating- and BH-MRCP without DLR. However, they were significantly higher under BH with DLR than under respiratory gating (SNR, p = 0.013; CNR, p = 0.027). The contrast and FWHM of MRCP under BH with and without DLR were lower than those under respiratory gating (contrast, p < 0.001; FWHM, p = 0.015). Qualitative scores for noise, blur, and overall image quality were higher under BH with DLR than those under respiratory gating (blur, p = 0.003; overall, p = 0.008). The combination of the 3D hybrid profile order technique and DLR is useful for MRCP within a single BH and does not lead to the deterioration of image quality and space resolution at 3T MRI. Considering its advantages, this sequence might become the standard protocol for MRCP in clinical practice, at least at 3.0 T. • The 3D hybrid profile order can achieve MRCP within a single breath-hold without a decrease in spatial resolution. • The DLR significantly improved the CNR and SNR of BH-MRCP. • The 3D hybrid profile order technique with DLR reduces the deterioration of image quality in MRCP within a single breath-hold.

Radiation Dose Reduction in Different Digital Radiography Systems: Impact on Assessment of Defined Bony Structures in Bearded Dragons (Pogona vitticeps).

Three different digital detector systems were used to study the effect of a defined radiation dose reduction on the image quality of digital radiographs in bearded dragons (Pogona vitticeps). A series of radiographs of seven bearded-dragon cadavers with a body mass ranging from 132 g to 499 g were taken in dorsoventral projection. The digital systems employed included two computed radiography systems (CR) (one system with a needle-based and one with a powdered-based scintillator) and one direct radiography system (DR). Three levels of the detector dose were selected: A standard dose (defined based on the recommended exposure value of the CRP, D/100%), a half dose (D/50%), and a quarter dose (D/25%). Four image criteria and one overall assessment were defined for each of four anatomic skeletal regions (femur, rib, vertebra, and phalanx) and evaluated blinded by four veterinarians using a predefined scoring system. The results were assessed for differences between reviewers (interobserver variability), radiography systems, and dosage settings (intersystem variability). The comparison of the ratings was based on visual grading characteristic (VGC) analysis. Dose reduction led to significantly lower scores in all criteria by every reviewer, indicating a linear impairment of image quality in different skeletal structures in bearded dragons. Scores did not differ significantly between the different systems used, indicating no advantage in using a computed or direct radiography system to evaluate skeletal structures in bearded dragons. The correlation was significant (p ≤ 0.05) for interobserver variability in 100% of the cases, with correlation coefficients between 0.50 and 0.59. While demonstrating the efficacy of the use of digital radiography in bearded dragons and the similar quality in using different computed or direct radiography systems, this study also highlights the importance of the appropriate level of detector dose and demonstrates the limits of post-processing algorithm to compensate for insufficient radiation doses in bearded dragons.