Impact Of Image Quality Research Articles

The Impact of Balloon Use during Endobronchial Ultrasound: A Randomized Pilot Study

Introduction: During endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA), a saline-filled balloon placed over the tip of the bronchoscope is used to improve coupling of the ultrasound transducer to the airway wall. However, it is unknown whether it objectively improves image quality or affects procedural outcomes. Our aim was to establish whether the use of a balloon during EBUS impacts image quality, diagnostic yield, procedure duration or complications. Methods: A pilot randomized control trial of patients undergoing EBUS-TBNA of mediastinal lymph nodes was performed at a single academic center. Patients were randomized to use a saline-filled balloon (versus no balloon use) during EBUS-TBNA of the right lower paratracheal lymph node (station 4R). The EBUS videos were recorded and scored on a 4-point Likert scale by three blinded external reviewers. The primary outcome of this study was ultrasound image quality. Secondary outcomes included diagnostic yield, procedure duration, and complications. Results: Forty-six patients were randomized. In the balloon group, 61% of patients had an image quality score of “excellent” or “good,” compared to 47% in the no balloon group (p = 0.009). There was no significant difference in diagnostic yield, procedure duration, or complications between the groups. Conclusion: Inflation of the balloon during EBUS-TBNA at the right lower paratracheal lymph node improves ultrasound image quality but does not impact diagnostic yield, procedure duration, or safety. This study provides a framework for additional studies with complete nodal assessment to determine if balloon use has a clinically meaningful benefit to procedural outcomes.

Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment

PurposeRespiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all the major issues caused by RM, i.e., PET resolution loss, attenuation correction artifacts, and PET-CT misalignment.MethodsIn a retrospective study, 737 patients underwent [18F]FDG PET/CT scans using the uMI Panorama PET/CT scanner. Ninety-nine patients, who also had respiration monitoring device (VSM), formed the validation set. The remaining data of the 638 patients were used to train neural networks used in the uRMC. The uRMC primarily consists of three key components: (1) data-driven respiratory signal extraction, (2) attenuation map generation, and (3) PET-CT alignment. SUV metrics were calculated within 906 lesions for three approaches, i.e., data-driven uRMC (proposed), VSM-based uRMC, and OSEM without motion correction (NMC). RM magnitude of major organs were estimated.ResultsuRMC enhanced diagnostic capabilities by revealing previously undetected lesions, sharpening lesion contours, increasing SUV values, and improving PET-CT alignment. Compared to NMC, uRMC showed increases of 10% and 17% in SUVmax and SUVmean across 906 lesions. Sub-group analysis showed significant SUV increases in small and medium-sized lesions with uRMC. Minor differences were found between VSM-based and data-driven uRMC methods, with the SUVmax was found statistically marginal significant or insignificant between the two methods. The study observed varied motion amplitudes in major organs, typically ranging from 10 to 20 mm.ConclusionA data-driven solution for respiratory motion in PET/CT has been developed, validated and evaluated. To the best of our knowledge, this is the first unified solution that compensates for the motion blur within PET, the attenuation mismatch artifacts caused by PET-CT misalignment, and the misalignment between PET and CT.

The Assessment of Retinal Image Quality Using a Non-Mydriatic Fundus Camera in a Teleophthalmologic Platform.

This study assesses the quality of retinal images captured using a non-mydriatic fundus camera within a teleophthalmologic platform in Taiwan. The objective was to evaluate the effectiveness of non-mydriatic fundus cameras for remote retinal screening and identify factors impacting image quality. From June 2020 to August 2022, 629 patients from five rural infirmaries underwent ophthalmic examinations, with fundus images captured without pupil dilation. These images were reviewed by senior ophthalmologists and graded based on quality. The results indicated that approximately 70% of images were of satisfactory diagnostic quality. Risk factors for poor image quality included older age, the presence of cataracts, pseudophakia, and diabetes mellitus. This study demonstrates the feasibility of using non-mydriatic fundus cameras for teleophthalmology, highlighting the importance of identifying and addressing factors that affect image quality to enhance diagnostic accuracy in remote settings.

NoiseNet, a fully automatic noise assessment tool that can identify non-diagnostic CCTA examinations

Image noise and vascular attenuation are important factors affecting image quality and diagnostic accuracy of coronary computed tomography angiography (CCTA). The aim of this study was to develop an algorithm that automatically performs noise and attenuation measurements in CCTA and to evaluate the ability of the algorithm to identify non-diagnostic examinations. The algorithm, “NoiseNet”, was trained and tested on 244 CCTA studies from the Swedish CArdioPulmonary BioImage Study. The model is a 3D U-Net that automatically segments the aortic root and measures attenuation (Hounsfield Units, HU), noise (standard deviation of HU, HUsd) and signal-to-noise ratio (SNR, HU/HUsd) in the aortic lumen, close to the left coronary ostium. NoiseNet was then applied to 529 CCTA studies previously categorized into three subgroups: fully diagnostic, diagnostic with excluded parts and non-diagnostic. There was excellent correlation between NoiseNet and manual measurements of noise (r = 0.948; p < 0.001) and SNR (r = 0.948; <0.001). There was a significant difference in noise levels between the image quality subgroups: fully diagnostic 33.1 (29.8–37.9); diagnostic with excluded parts 36.1 (31.5–40.3) and non-diagnostic 42.1 (35.2–47.7; p < 0.001). Corresponding values for SNR were 16.1 (14.0–18.0); 14.0 (12.4–16.2) and 11.1 (9.6–14.0; p < 0.001). ROC analysis for prediction of a non-diagnostic study showed an AUC for noise of 0.73 (CI 0.64–0.83) and for SNR of 0.80 (CI 0.71–0.89). In conclusion, NoiseNet can perform noise and SNR measurements with high accuracy. Noise and SNR impact image quality and automatic measurements may be used to identify CCTA studies with low image quality.

Generalized 3D registration algorithm for enhancing retinal optical coherence tomography images.

Optical coherence tomography (OCT) has emerged as the standard of care for diagnosing and monitoring the treatment of various ocular disorders due to its noninvasive nature and in vivo volumetric acquisition capability. Despite its widespread applications in ophthalmology, motion artifacts remain a challenge in OCT imaging, adversely impacting image quality. While several multivolume registration algorithms have been developed to address this issue, they are often designed to cater to one specific OCT system or acquisition protocol. We aim to generate an OCT volume free of motion artifacts using a system-agnostic registration algorithm that is independent of system specifications or protocol. We developed a B-scan registration algorithm that removes motion and corrects for both translational eye movements and rotational angle differences between volumes. Tests were carried out on various datasets obtained from two different types of custom-built OCT systems and one commercially available system to determine the reliability of the proposed algorithm. Additionally, different system specifications were used, with variations in axial resolution, lateral resolution, signal-to-noise ratio, and real-time motion tracking. The accuracy of this method has further been evaluated through mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM). The results demonstrate improvements in the overall contrast of the images, facilitating detailed visualization of retinal vasculatures in both superficial and deep vasculature plexus. Finer features of the inner and outer retina, such as photoreceptors and other pathology-specific features, are discernible after multivolume registration and averaging. Quantitative analyses affirm that increasing the number of averaged registered volumes will decrease MSE and increase MS-SSIM as compared to the reference volume. The multivolume registered data obtained from this algorithm offers significantly improved visualization of the retinal microvascular network as well as retinal morphological features. Furthermore, we have validated that the versatility of our methodology extends beyond specific OCT modalities, thereby enhancing the clinical utility of OCT for the diagnosis and monitoring of ocular pathologies.

A Sparse SAR Imaging Method for Low-Oversampled Staggered Mode via Compound Regularization

High-resolution wide-swath (HRWS) imaging is the research focus of the modern spaceborne synthetic-aperture radar (SAR) imaging field, with significant relevance and vast application potential. Staggered SAR, as an innovative imaging system, mitigates blind areas across the entire swath by periodically altering the radar pulse repetition interval (PRI), thereby extending the swath width to multiples of that achievable by conventional systems. However, the staggered mode introduces inherent challenges, such as nonuniform azimuth sampling and echo data loss, leading to azimuth ambiguities and substantially impacting image quality. This paper proposes a sparse SAR imaging method for the low-oversampled staggered mode via compound regularization. The proposed method not only effectively suppresses azimuth ambiguities arising from nonuniform sampling without necessitating the restoration of missing echo data, but also incorporates total variation (TV) regularization into the sparse reconstruction model. This enhances the accurate reconstruction of distributed targets within the scene. The efficacy of the proposed method is substantiated through simulations and real data experiments from spaceborne missions.

Unified Noise-aware Network for Low-count PET Denoising with Varying Count Levels.

As PET imaging is accompanied by substantial radiation exposure and cancer risk, reducing radiation dose in PET scans is an important topic. However, low-count PET scans often suffer from high image noise, which can negatively impact image quality and diagnostic performance. Recent advances in deep learning have shown great potential for recovering underlying signal from noisy counterparts. However, neural networks trained on a specific noise level cannot be easily generalized to other noise levels due to different noise amplitude and variances. To obtain optimal denoised results, we may need to train multiple networks using data with different noise levels. But this approach may be infeasible in reality due to limited data availability. Denoising dynamic PET images presents additional challenge due to tracer decay and continuously changing noise levels across dynamic frames. To address these issues, we propose a Unified Noise-aware Network (UNN) that combines multiple sub-networks with varying denoising power to generate optimal denoised results regardless of the input noise levels. Evaluated using large-scale data from two medical centers with different vendors, presented results showed that the UNN can consistently produce promising denoised results regardless of input noise levels, and demonstrate superior performance over networks trained on single noise level data, especially for extremely low-count data.

Synthetic rain image generation via CycleGAN

In the domain of autonomous driving and computer vision, the formidable challenge of adverse weather conditions, particularly rainy weather, profoundly impacts image quality and visibility. Rain streaks pose a significant impediment to accurate object detection, especially concerning pedestrians and vehicles on the road. While existing solutions have focused on training rain removal models on paired images, acquiring such data with congruent backgrounds has been a substantial hurdle. Three distinct images, each featuring a diverse background, were deliberately chosen for the rain removal experiment. The deliberate selection of these varied backgrounds allowed us to rigorously assess the efficacy of our rain removal method across diverse environmental contexts. The compelling results obtained from this experiment affirm the method's ability to effectively mitigate rain line across a spectrum of backgrounds, thus establishing its robustness and versatility in real-world scenarios. The resulting images are then employed to train our rain removal model, marking a significant advancement in our endeavor. Our innovative approach stands poised to revolutionize rain removal techniques, aligning synthetic and authentic rain images more closely in real-world scenarios.

Detection of Pneumonia Using A Hybrid Approach Consisting of MobileNetV2 and Squeeze-and-Excitation Network

Pneumonia is a global health concern, responsible for a significant number of deaths. Its diagnostic challenge arises from visual similarities it shares with various respiratory diseases, such as tuberculosis, complicating accurate identification. Furthermore, the variability in acquiring and processing chest X-ray (CXR) images can impact image quality, posing a hurdle for dependable algorithm development. To address this, resilient data-centric algorithms, trained on comprehensive datasets and validated through diverse imaging methods and radiology expertise, are imperative. This study presents a deep learning approach designed to distinguish between normal and pneumonia cases. The model, a hybrid of MobileNetV2 and the Squeeze-and-Excitation (SE) block, aims to reduce learnable parameters while enhancing feature extraction and classification. Integration of the SE block enhances classification performance, despite a slight parameter increase. The model was trained and tested on a dataset of 5856 CXR images from Kaggle's medical imaging challenge. Results demonstrated the model's exceptional performance, achieving an accuracy of 98.81%, precision of 98.79%, recall rate of 98.24%, and F1-score of 98.51%. Comparative analysis with various Convolutional neural network-based pre-trained models and recent literature studies confirmed its superiority, solidifying its potential as a robust tool for pneumonia detection, thus addressing a critical healthcare need.

Design of an Achromatic Graphene Oxide Metalens with Multi-Wavelength for Visible Light

Dispersion control is a critical aspect in nano-optical systems. Moreover, chromatic aberration significantly impacts image quality. Despite metasurfaces being a novel approach to tackle chromatic aberration in diffractive lenses, numerous challenges hinder their practical implementation due to the complexity of 3D fabrication techniques and high manufacturing costs. In contrast, ultra-thin graphene oxide lenses are simpler and less expensive to manufacture. The optical performance of graphene oxide lenses, such as high focusing efficiency, large depth of field, wide bandwidth, and zooming capability, depends on the design of the positional arrangement of reduced graphene oxide regions. In this study, we utilized the self-constructed datasets to train machine learning models based on the structure of the graphene oxide lens and combined it with intelligent optimization algorithms. This approach facilitated the design of the graphene oxide achromatic lens in multi-wavelengths with high-performance. Experimental results substantiate that the designed ultra-thin graphene oxide lens, with a thickness of ~200 nm, effectively controls dispersion across multiple incident wavelengths (450, 550, and 650 nm) and achieves super resolution with consistent intensity at the focal point. Our graphene oxide lens holds the potential for integration into micro-optical systems that demand dispersion control, providing broad applications in optical imaging, optical communication, the biomedical field, and beyond.

Comparison of image quality and standardized uptake values (SUV) of two PET/CT imaging system using F-18 and Ga-68

Both the Gemini TOF PET/CT and Biograph mCT flow PET/CT scanners are advanced imaging systems used in clinical settings for a range of applications, including oncology, neurology, and cardiology. Both the Gemini TOF PET/CT and Biograph mCT flow PET/CT scanners are advanced imaging systems used in clinical settings for a range of applications, including oncology, neurology, and cardiology. Both systems are capable of producing high quality images with good spatial resolution and contrast. However, there are some differences between the two systems that may impact image quality. The recovery coefficient (RC) is an important parameter that is used to evaluate the image quality and the accuracy and precision of PET/CT imaging systems. In the other hand, Standardized uptake value (SUV) is A frequently utilized quantitative measure in PET/CT imaging is the estimation of radiotracer uptake within a specified region of interest (ROI). SUV measurements are possible to be used to assess tumor metabolism and monitor treatment response. Several studies have evaluated the SUV and RC measurements using different radiotracers and scanner has been found to exhibit good accuracy and precision for both radiotracers. In this work we investigated the SUV and RC values of the Gemini TOF PET/CT and Biograph mCT flow PET/CT scanners by using F18 and Ga-68 and compared their values. Methods in this work, we analyzed images acquired using two distinct PET-CT scanners from two different manufacturers; Gemini TOF PET/CT (Philips Medical Systems) system and biograph mCT flow PET/CT system (Siemens Healthineers). we assessed the SUV values by using the PET-CT cylindrical image quality NEMA phantom that was filled with F-18 and Ga-68 while the image quality measured by employing the NEMA/International Electrotechnical Commission (IEC) Torso Phantom. Results We revealed that the RC values of F-18 using Gemini TOF PET/CT ranged from 0.02 to 0.03 for different sphere sizes, where they ranged from 0.005 to 0.015 for Ga-68 and when used Biograph mCT flow PET/CT scanner RC values of F-18 ranged from 0.025 to 0.05 and for Ga-68 ranged from 0.02 to 0.042. The first section in your paper

Effect of the Relationship between Respiratory Interval and Temporal Resolution on Image Quality in Free-breathing Abdominal MR Imaging.

To evaluate how the relationship between respiratory interval (RI) and temporal resolution (TR) impacts image quality in free-breathing abdominal MRI (FB-aMRI) using golden-angle radial sparse parallel (GRASP). Ten healthy volunteers (25.9 ± 2.5 years, four women) underwent 2 mins free-breathing fat-suppression T1-weighted imaging using GRASP at RIs of 3 and 5s (RI3 and RI5, respectively) and retrospectively reconstructed at TR of 1.8, 2.9, 4.8, and 7.7s (TR1.8, TR2.9, TR4.8, and TR7.7, respectively) in each patient. The standard deviation (SD) under the diaphragm was measured using SD maps showing the discrepancy for each horizontal section at all TRs. Two radiologists evaluated image quality (visualization of the right hepatic vein at the confluence of the inferior vena cava, posterior segment branch of portal vein, pancreas, left kidney, and artifacts) at all TRs using a 5-point scale. The SD was significantly higher at TR1.8 compared to TR4.8 (P < 0.01) and TR7.7 (P < 0.001), as well as at TR2.9 compared to TR7.7 (P < 0.01) for both RIs. The SD between TR4.8 and TR7.7 did not differ for both RIs. For all visual assessment metrics, the TR1.8 scores were significantly lower than the TR4.8 and TR7.7 scores for both RIs. The pancreas and left kidney scores at TR2.9 were significantly lower than those at TR7.7 (P < 0.05) for RI5. Additionally, the left kidney score at TR1.8 was lower than that at TR2.9 (P < 0.05) for RI3. All scores at TR2.9, TR4.8, and TR7.7 were similar for RI3, while those at TR4.8 and TR7.7 were similar for RI5. Prolonging the TRs compared to RIs enhances image quality in FB-aMRI using GRASP.

Size characterization of x-ray tube source with sphere encoded imaging method.

In x-ray imaging, the size of the x-ray tube light source significantly impacts image quality. However, existing methods for characterizing the size of the x-ray tube light source do not meet measurement requirements due to limitations in processing accuracy and mechanical precision. In this study, we introduce a novel method for accurately characterizing the size of the x-ray tube light source using spherical encoded imaging technology. This method effectively mitigates blurring caused by system tilting, making system alignment and assembly more manageable. We employ the Richardson-Lucy algorithm to iteratively deconvolve the image and recover spatial information about the x-ray tube source. Unlike traditional coded imaging methods, spherical coded imaging employs high-Z material spheres as coding elements, replacing the coded holes used in traditional approaches. This innovation effectively mitigates blurring caused by system tilting, making system alignment and assembly more manageable. In addition, the mean square error is reduced to 0.008. Our results demonstrate that spherical encoded imaging technology accurately characterizes the size of the x-ray tube light source. This method holds significant promise for enhancing image quality in x-ray imaging.

Implantable Micro-optics for label-free non-linear imaging

Non-linear excitation microscopy offers superior in-vivo imaging but faces challenges in deep tissue. High numerical aperture beams suffer spherical aberrations, while tissue scattering impacts image quality. To address this, we propose implantable microlenses for precise focusing below the skin in lab animals. By using low numerical aperture lasers, we avoid spherical aberrations induced by high NA objectives. Our study presents various microlens designs differing in size, shape, and fabrication methods, all on glass or organo-hybrid ceramic substrates. This approach shows promise for enhancing deep tissue imaging, facilitating better understanding of biological processes in vivo.

How to Improve Clinical Photography Image Quality in Craniomaxillofacial and Reconstructive Surgery

Objective: The aim of this paper is to describe the most common variables impacting image quality and discuss tips to improve clinical perioperative photographic skills in craniomaxillofacial and reconstructive surgery. Methods: We conducted a comprehensive review of the literature to describe the basic concepts of photography and its applications in medical perioperative photography. Data sources included peer-reviewed articles and clinical studies on medical photography. Results: Medical photography in craniomaxillofacial and reconstructive surgery is an indispensable tool for obtaining precise documentation of conditions and surgical procedures. To understand the basics of medical photography, it is necessary to be familiar with the fundamental concepts of photography, such as exposure, aperture, shutter speed, ISO, and composition, as well as basic camera specifications, lenses, and flash equipment. Perioperative medical photography can be divided into pre-operative, intra-operative, and post-operative photography. While the basic concepts of photography are transferrable among the different phases of perioperative photographic documentation, some tips can be applied to improve the quality of the images at each stage. Conclusions: Enhancing the quality of perioperative photography is essential for the comprehensive documentation and communication of surgical procedures. The standardization and consistency in photographic tools, a clean surgical field with good framing, lighting, and focus with the appropriate aperture and shutter speed will produce better and sharper images.

A Coded Excitation Framework for High SNR Transcranial Ultrasound Imaging.

Robust transcranial ultrasound imaging is difficult due to poor image quality. In particular, low signal-to-noise ratio (SNR) limits sensitivity to blood flow and has hindered clinical translation of transcranial functional ultrasound neuroimaging thus far. In this work, we present a coded excitation framework to increase SNR in transcranial ultrasound without negatively impacting frame rate or image quality. We applied this coded excitation framework in phantom imaging and showed SNR gains as large as 24.78 dB and signal-to-clutter ratio gains as high as 10.66 dB with a 65 bit code. We also analyzed how imaging sequence parameters can impact image quality and showed how coded excitation sequences can be designed to maximize image quality for a given application. In particular, we show that considering the number of active transmit elements and the transmit voltage is critical for coded excitation with long codes. Finally, we applied our coded excitation technique in transcranial imaging of ten adult subjects and showed an average SNR gain of 17.91 ± 0.96 dB without a significant increase in clutter using a 65 bit code. We also performed transcranial power Doppler imaging in three adult subjects and showed contrast and contrast-to-noise ratio improvements of 27.32 ± 8.08 dB and 7.25 ± 1.61 dB, respectively with a 65 bit code. These results show that transcranial functional ultrasound neuroimaging may be possible using coded excitation.

Kualitas Informasi Anatomi Radiograf pada Pemeriksaan Sternum dengan Variasi Penyudutan Arah Sinar

Background: The sternum's radiographic examination involves the use of Right Anterior Oblique (RAO) and Lateral projections. However, if a patient cannot undergo RAO projection, Left Posterior Oblique (LPO) projection in the supine position and adjusting the central ray angle are alternatives. On another note, deviations from perpendicular alignment among the central ray, image receptor, and object can lead to distortions in shape, impacting image quality. Hence, a study is conducted to investigate the impact of beam angle variations on the quality of anatomical information in sternum radiographs to identify the optimal angle that yields the highest quality anatomical information in sternum radiographs. Methods: This research was quantitative research employing an experimental methodology. Data collection involved employing a single-body phantom, which underwent three exposures at each of the specified angle variations Results: Based on the results of the Kruskal Wallis test, there were significant differences in the quality of radiographic anatomic information (p value 0.05). The angle with the most optimal quality of radiographic anatomical information was an angle of 25° to the left with a mean rank value of 11.00. Conclusions: There was a significant difference in the quality of radiographic anatomic information (p value 0.05). The angle with the most optimal quality of radiographic anatomic information was the angle with the highest mean rank value, which was an angle of 25° to the left.

Enhancing infrared imaging systems with temperature-dependent nonuniformity correction via single-frame and inter-frame structural similarity.

Temperature-dependent nonuniformity in infrared images significantly impacts image quality, necessitating effective solutions for intensity nonuniformity. Existing variational models primarily rely on gradient prior constraints from single-frame images, resulting in limitations due to insufficient exploitation of intensity characteristics in both single-frame and inter-frame images. This paper introduces what we believe to be a novel variational model for nonuniformity correction (NUC) that leverages single-frame and inter-frame structural similarity (SISB). This approach capitalizes on the structural similarities between the corrected image, intensity bias map, and degraded image, facilitating efficient suppression of intensity nonuniformity in real-world scenarios. The proposed method diverges fundamentally from existing strategies and demonstrates superior performance in comparison with state-of-the-art correction models.

Fully automated computational measurement of noise in positron emission tomography

ObjectivesTo introduce an automated computational algorithm that estimates the global noise level across the whole imaging volume of PET datasets.Methods[18F]FDG PET images of 38 patients were reconstructed with simulated decreasing acquisition times (15–120 s) resulting in increasing noise levels, and with block sequential regularized expectation maximization with beta values of 450 and 600 (Q.Clear 450 and 600). One reader performed manual volume-of-interest (VOI) based noise measurements in liver and lung parenchyma and two readers graded subjective image quality as sufficient or insufficient. An automated computational noise measurement algorithm was developed and deployed on the whole imaging volume of each reconstruction, delivering a single value representing the global image noise (Global Noise Index, GNI). Manual noise measurement values and subjective image quality gradings were compared with the GNI.ResultsIrrespective of the absolute noise values, there was no significant difference between the GNI and manual liver measurements in terms of the distribution of noise values (p = 0.84 for Q.Clear 450, and p = 0.51 for Q.Clear 600). The GNI showed a fair to moderately strong correlation with manual noise measurements in liver parenchyma (r = 0.6 in Q.Clear 450, r = 0.54 in Q.Clear 600, all p < 0.001), and a fair correlation with manual noise measurements in lung parenchyma (r = 0.52 in Q.Clear 450, r = 0.33 in Q.Clear 600, all p < 0.001). Classification performance of the GNI for subjective image quality was AUC 0.898 for Q.Clear 450 and 0.919 for Q.Clear 600.ConclusionAn algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets.Clinical relevance statementAn automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking of clinical PET imaging within and across institutions.Key Points• Noise is an important quantitative marker that strongly impacts image quality of PET images.• An automated computational noise measurement algorithm provides an accurate and meaningful estimation of the global noise level encountered in clinical PET imaging datasets.• An automated computational approach that measures the global noise level of PET imaging datasets may facilitate quality standardization and benchmarking as well as protocol harmonization.

Interlacing in Atomic Resolution Scanning Transmission Electron Microscopy.

Fast frame rates are desirable in scanning transmission electron microscopy for a number of reasons: controlling electron beam dose, capturing in situ events, or reducing the appearance of scan distortions. While several strategies exist for increasing frame rates, many impact image quality or require investment in advanced scan hardware. Here, we present an interlaced imaging approach to achieve minimal loss of image quality with faster frame rates that can be implemented on many existing scan controllers. We further demonstrate that our interlacing approach provides the best possible strain precision for a given electron dose compared with other contemporary approaches.