Imaging Workflow Research Articles

A novel workflow for multi-modal imaging of musculoskeletal tissues.

According to the World Health Organization (WHO) musculoskeletal conditions are a leading contributor to disability worldwide. This fact is often somewhat overlooked, since musculoskeletal conditions are less likely to be associated with mortality. Nonetheless, treatments, therapies and management of these conditions are extremely costly to national healthcare systems. As with all systemic conditions, biomedical imaging of relevant tissues plays a major role in understanding the fundamental biological processes involved in musculoskeletal health. However, the skeletal system with its relatively large proportion of dense, opaque (often mineralised) tissues can often be more challenging to image, and recently important advances have been made in imaging these complex musculoskeletal tissues. Thus, we here describe a novel workflow in which recent advanced imaging techniques have been modified and optimised for use in musculoskeletal tissues (specifically bone and cartilage). This will allow for investigations, of different phases of these tissues, at new and higher resolutions. Furthermore, the process has been designed to fit with the existing and standard processes which are typically used with these samples (i.e. μCT imaging and standard histology). The additional modalities which have been included here are second harmonic generation (SHG) imaging, tissue clearing, specifically the Passive Clear Lipid-exchanged Acrylamide-hybridised Rigid Imaging Tissue hYdrogel (CLARITY) method known as PACT, and then imaging of these tissues with confocal, multiphoton and light-sheet microscopy. This paper serves to introduce a combination of existing new methods and improvements in imaging of musculoskeletal tissues.

Quantitative Assessment of Pulmonary Fibrosis in a Murine Model via a Multimodal Imaging Workflow

Quantitative Assessment of Pulmonary Fibrosis in a Murine Model via a Multimodal Imaging Workflow

DicomOS: A Preliminary Study on a Linux-Based Operating System Tailored for Medical Imaging and Enhanced Interoperability in Radiology Workflows

In this paper, we propose a Linux-based operating system, namely, DicomOS, tailored for medical imaging and enhanced interoperability, addressing user-friendly functionality and the main critical needs in radiology workflows. Traditional operating systems in clinical settings face limitations, such as fragmented software ecosystems and platform-specific restrictions, which disrupt collaborative workflows and hinder diagnostic efficiency. Built on Ubuntu 22.04 LTS, DicomOS integrates essential DICOM functionalities directly into the OS, providing a unified, cohesive platform for image visualization, annotation, and sharing. Methods include custom configurations and the development of graphical user interfaces (GUIs) and command-line tools, making them accessible to medical professionals and developers. Key applications such as ITK-SNAP and 3D Slicer are seamlessly integrated alongside specialized GUIs that enhance usability without requiring extensive technical expertise. As preliminary work, DicomOS demonstrates the potential to simplify medical imaging workflows, reduce cognitive load, and promote efficient data sharing across diverse clinical settings. However, further evaluations, including structured clinical tests and broader deployment with a distributable ISO image, must validate its effectiveness and scalability in real-world scenarios. The results indicate that DicomOS provides a versatile and adaptable solution, supporting radiologists in routine tasks while facilitating customization for advanced users. As an open-source platform, DicomOS has the potential to evolve alongside medical imaging needs, positioning it as a valuable resource for enhancing workflow integration and clinical collaboration.

Reinforcement learning and meta-learning perspectives frameworks for future medical imaging

In the envisioned landscape of medical imaging in 2044, this research explores the integration of advanced AI techniques, specifically reinforcement learning (RL) and meta-learning, to address persistent challenges in disease diagnosis and treatment planning. Leveraging vast amounts of imaging data, deep learning models have demonstrated significant advancements in tasks such as tumor detection and organ segmentation. However, existing approaches often face limitations in adapting to evolving patient characteristics and data scarcity. By incorporating principles from RL and meta-learning, this study aims to develop dynamic, adaptive AI systems capable of optimizing imaging protocols, enhancing diagnostic accuracy, and personalizing treatment strategies for individual patients. The research conducts a comprehensive review of existing literature on RL and meta-learning in healthcare proposes novel methodologies for integrating these techniques into medical imaging workflows, and evaluates their efficacy through empirical studies and clinical validation. The ultimate goal is to contribute to the advancement of medical imaging technologies, paving the way for more personalized and efficient healthcare solutions in the future

Reverse-time migration of mobile marine vibrator data

Marine vibrators are viable alternatives to seismic air guns in ocean-bottom acquisition due to their less adverse impact on the environment, their ability to generate lower frequency content, and their suitability for simultaneous acquisition. However, mobile marine vibrators introduce a unique set of processing and imaging challenges for ocean-bottom acquisition, the main examples of which are the Doppler effect and the time-dependent source-receiver offsets, which are not features of conventional marine acquisitions. Standard seismic data processing and imaging techniques assume stationary sources and are not fully suitable for mobile marine vibrator data without modifications. Not accounting for source motion in seismic imaging introduces phase change and mispositioning of imaged structures. Further, ignoring source-motion effects in imaging workflow may lead to the estimation of inaccurate migration velocity models or imply the use of incorrect migration velocity, e.g., in seismic-to-well ties. We develop a reverse-time migration (RTM) approach that accounts for the source-motion effects and is capable of producing accurate subsurface images, closely matching stationary acquisition and imaging results. Synthetic examples illustrate the ability of the proposed method to construct accurate subsurface RTM images even for source velocities higher than those commonly used in typical marine acquisitions. Our approach is not limited to imaging mobile marine vibrator data in ocean-bottom acquisition, but is also applicable to mobile streamer data using marine vibrators or seismic air-gun sources.

CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.

This paper presents an enhanced approach to kidney segmentation using a modified CLAHE preprocessing method, aimed at improving image clarity and CNN performance on the KiTS19 dataset. To assess the impact of the modified CLAHE method, we conducted quality evaluations using the BRISQUE metric, comparing the original, standard CLAHE and modified CLAHE versions of the dataset. The BRISQUE score decreased from 28.8 in the original dataset to 21.1 with the modified CLAHE method, indicating a significant improvement in image quality. Furthermore, CNN segmentation accuracy rose from 0.951 with the original dataset to 0.996 with the modified CLAHE method, outperforming the accuracy achieved with standard CLAHE preprocessing (0.969). These results highlight the benefits of the modified CLAHE method in refining image quality and enhancing segmentation performance. This study highlights the value of adaptive preprocessing in medical imaging workflows and shows that CNN-based kidney segmentation accuracy may be greatly increased by altering conventional CLAHE. Our method provides insightful information on optimizing preprocessing for medical imaging applications, leading to more accurate and dependable segmentation results for better clinical diagnosis.

3D Investigation of Pt-Based Nanocatalyst Durability on High Surface Area Carbon by Automated Electron Tomography

Proton-exchange membrane fuel cells (PEMFCs) hold potential for hydrogen-powered heavy-duty vehicles, offering high efficiencies and comparable driving ranges and refueling times to internal combustion engines. Yet, durability challenges persist, notably in cathode catalyst degradation [1]. Understanding degradation mechanisms during accelerated stress tests (AST) is crucial. Electron tomography aids in high-resolution analysis of Pt-based nanoparticle distribution, revealing degradation rates relative to their location on or in the carbon support, and providing a possible explanation to the enhanced particles accessibility at low relative humidity after AST [2,3].In this work, we developed an automated electron tomography workflow performed in the scanning transmission electron microscopy (STEM) utilizing SerialEM for data acquisition[4]. Data were reconstructed using a model-based iterative reconstruction with adaptive regularization (MBIR-ARAR) [5], and the segmentation of the nanoparticles and carbon was performed using the commercial software Avizo.We examined Pt catalyst particle size distributions at both the beginning (BOT) and end (EOT) of the test (see Figure 1). Our results revealed a noticeable increase in particle size alongside a reduction in specific surface area (SSA), regardless of nanoparticle location. Moreover, a significant portion of particles remained within the carbon pores at EOT. In addition, there was evidence of an expanded size distribution of pores post AST, potentially due to minor carbon corrosion. These observed degradation mechanisms challenge the assumption that Pt dissolution from the interior of the carbon support and re-deposition on the surface is a predominant factor for the enhanced accessibility after AST [6] but suggests that other factors such as carbon corrosion causing pore opening and increased ionomer intrusion. Additionally, we will discuss the potential of employing low-dose cryo-tomography to mitigate damage to the catalyst, support, and particularly beam sensitive proton conducting ionomer [7,8].

Automatic patient centering in computed tomography: a systematic review and meta-analysis.

To comprehensively examine the influence of auto-patient centering technologies on positioning accuracy, radiation dose, image quality, and time efficiency of computed tomography (CT) scans. A systematic search of peer-reviewed English publications was performed between January 2000 and November 2023 in PubMed, Embase, CINAHL, Scopus, and Web of Science. Two postgraduate students and an academic lecturer independently reviewed the articles to verify adherence to the inclusion criteria. The QUADAS-2 tool was employed to evaluate study quality. We derived summary estimates on positioning accuracy, radiation dose reduction, image quality, and time efficiency using proportion and meta-analysis methodologies. Nine studies were identified comparing automatic and manual CT positioning. Automatic positioning improved accuracy by reducing vertical offsets to 7 mm and 4 mm for thorax and abdominal CTs, compared to 19 mm and 18 mm with manual methods. Most studies showed significant reductions in radiation dose, ranging from 5.71 to 31%. Image quality results were mixed, automatic methods generally produced images with less noise, but differences were minimal. Time efficiency was better, with automatic positioning reducing preparation time from 0.48 min versus 0.67 min for manual positioning. This review confirms that automatic patient-centering technologies enhance positioning accuracy and decrease preparation times in CT scans. While reductions in radiation doses and some improvements in image quality were observed, the evidence remains mixed. Findings support integrating these technologies into clinical practice to optimize patient care. Question Does automatic patient centering in CT enhance positioning accuracy, reduce radiation exposure, and improve image quality? Findings Findings indicate that automatic centering can optimize image quality, reduce examination times and contribute to overall improvements in imaging efficiency. Clinical relevance Automatic patient centering in CT improves positioning accuracy, minimizes radiation exposure, enhances image quality, and accelerates imaging workflows, contributing to safer, more efficient imaging procedures that benefit patient care.

Emerging technologies and applications in multimodality imaging for ischemic heart disease: current state and future of artificial intelligence

Ischemic heart disease (IHD) is a major global health issue, frequently resulting in myocardial infarction and ischemic cardiomyopathy. Prompt and precise diagnosis is essential to avert complications such as heart failure and sudden cardiac death. Although invasive coronary angiography remains the gold standard for high-risk patients, noninvasive multimodality imaging is becoming more prevalent for those at low-to-intermediate risk. This review evaluated the current state of multimodality imaging in IHD, including echocardiography, nuclear cardiology, cardiac magnetic resonance imaging (MRI), computed tomography (CT) angiography, and invasive coronary angiography. Each modality has distinct strengths and limitations, and their complementary use provides a comprehensive assessment of cardiac health. Integrating artificial intelligence (AI) into imaging workflows holds promise for enhancing diagnostic accuracy and efficiency. AI algorithms can optimize image acquisition, processing, and interpretation of complex imaging data. Emerging technologies like 4D flow MRI, molecular imaging, and hybrid systems [e.g., positron emission tomography (PET)/MRI, PET/CT] integrate anatomical, functional, and molecular data, providing comprehensive insights into cardiac pathology and potentially revolutionizing the management of IHD. This review also explored the clinical applications and impact of multimodality imaging on patient outcomes, emphasizing its role in improving diagnostic precision and guiding therapeutic decisions. Future directions include AI-driven decision support systems and personalized medicine approaches. Addressing regulatory and ethical challenges, such as data privacy and algorithm transparency, is crucial for the broader adoption of these advanced technologies. This review highlighted the transformative potential of AI-enhanced multimodality imaging in improving the diagnosis and management of IHD.

Expert consensus on workflow of PET/CT with long axial field-of-view.

Positron emission tomography/computed tomography (PET/CT) imaging has been widely used in clinical practice. Long axial field-of-view (LAFOV) systems have enhanced clinical practice by leveraging their technological advantages and have emerged as the new state-of-the-art PET imaging modalities. A consensus was conducted to explore expert views in this emerging field to comprehensively elucidate the proposed workflow in LAFOV PET/CT examinations and highlight the potential challenges inherent in the workflow. A multidisciplinary task group formed by 28 experts from six countries over the world discussed and approved the consensus based on the published guidelines, peer-reviewed articles of LAFOV PET/CT, and the collective experience from clinical practice. This consensus focuses on the workflow that allows for a broader range of imaging protocols of LAFOV PET/CT, catering to diverse patient needs and in line with precision medicine principles. This consensus describes the workflows and imaging protocols of LAFOV PET/CT for various imaging scenarios including routine static imaging, dynamic imaging, low-activity imaging, fast imaging, prolonged imaging, delayed imaging, and dual-tracer imaging. In addition, imaging reconstruction and reviewing specific to LAFOV PET/CT imaging, as well as the main challenges facing installation and application of LAFOV PET/CT scanner were also summarized. This consensus summarized the various imaging workflow, imaging protocol, and challenges of LAFOV PET/CT imaging, aiming to enhance the clinical and research applications of these scanners.

Long-Axial-Range Double-Helix Point Spread Functions for 3D Volumetric Super-Resolution Imaging.

Single-molecule localization microscopy (SMLM) is a powerful tool for observing structures beyond the diffraction limit of light. Combining SMLM with engineered point spread functions (PSFs) enables 3D imaging over an extended axial range, as has been demonstrated for super-resolution imaging of various cellular structures. However, super-resolving structures in 3D in thick samples, such as whole mammalian cells, remains challenging as it typically requires acquisition and postprocessing stitching of multiple slices to cover the entire sample volume or more complex analysis of the data. Here, we demonstrate how the imaging and analysis workflows can be simplified by 3D single-molecule super-resolution imaging with long-axial-range double-helix (DH)-PSFs. First, we experimentally benchmark the localization precisions of short- and long-axial-range DH-PSFs at different signal-to-background ratios by imaging fluorescent beads. The performance of the DH-PSFs in terms of achievable resolution and imaging speed was then quantified for 3D single-molecule super-resolution imaging of mammalian cells by DNA-PAINT imaging of nuclear lamina protein lamin B1 in U-2 OS cells. Furthermore, we demonstrate how the use of a deep-learning-based algorithm allows the localization of dense emitters, drastically improving the achievable imaging speed and resolution. Our data demonstrate that using long-axial-range DH-PSFs offers stitching-free, 3D super-resolution imaging of whole mammalian cells, simplifying the experimental and analysis procedures for obtaining volumetric nanoscale structural information.

Methodological aspects of correlative, multimodal, multiparametric breast cancer imaging: from data acquisition to image processing for AI-based radioproteomics in a preclinical setting

Preclinical high-field magnetic resonance imaging (MRI) systems offer a diverse array of MRI techniques, providing rich multiparametric MRI (mpMRI) platforms for studying numerous biological parameters. mpMRI platforms prove particularly indispensable when investigating tumors that exhibit profound intratumoral heterogeneity, such as breast cancer. A thoughtful comprehension of the origins of intratumoral heterogeneity is imperative for the judicious assessment of new targeted therapies and treatment interventions. Furthermore, when data from mpMRI are complemented with data from other in vivo imaging modalities, such as positron emission tomography (PET), and correlated with data from ex vivo modalities, such as matrix-assisted laser desorption imaging mass spectrometry (MALDI IMS), the in vivo parameters can be further elucidated at a molecular level and microscopic scale. Nevertheless, extracting meaningful scientific insights from such complex datasets necessitates the utilization of machine learning (ML) approaches to discern region-specific radiomic features. The development of correlative, multimodal imaging (CMI) workflows, such as one incorporating MRI, PET and MALDI IMS, is inherently challenging, given the many technological and methodological challenges related to multimodal data acquisition as well as the physiological limitations of the laboratory mice of the investigation. Standardization efforts in image acquisition and processing are required to increase the reproducibility and translatability of CMI data. To address the challenges of developing standardized CMI workflows and stimulate dialog regarding this area of need, we present a practical workflow to investigate tumor heterogeneity in breast cancer xenografts across various spatial scales. Our workflow entails simultaneous functional MRI and PET acquisitions in living mice, followed by correlation with post-imaging MALDI IMS and histologic data. Additionally, we propose data preprocessing steps for potential ML applications. We illustrate the feasibility of this workflow through two examples, showcasing its effectiveness in comparing in vivo and ex vivo images to evaluate tumor metabolism and hypoxia in mice with breast cancer xenografts.

Entropy Stacked Autoencoder based Diffusion Model (ESADM) and Fuzzy Clustering with Semi Supervised Fuzzy Graph Convolutional Network (FC-SSFGCN) for Rail Surface Defect Detection

Rails, fasteners, and other parts of railway track lines eventually develop flaws due to continuous strain from train operations and direct exposure to the environment; these faults directly affect the safety of train operations. Detecting defects on rail surfaces presents a formidable challenge due to the wide array of possible flaws and their unpredictable nature. However, Defect identification errors, massive variances, inadequate training sample availability, and weak contrast between faults and the surrounding background all contribute to the complexity of this procedure. In this work, rail surface and fastener flaws may be detected non-destructively using a multi-crack detection approach based on the Entropy Stacked Autoencoder Diffusion Model (ESADM). A novel approach, ESAFM, has been developed, combining a rail surface image encoder with a multi-layer, Stacked Autoencoder to extract latent materials from images showcasing different types of cracks. This integration reduces the need for significant processing resources by integrating easily into the conventional physically-based image workflow. Additionally, the Zero Shot-Semi Supervised Fuzzy Class Knowledge Graph (ZS-SSFCKG) method proposes Class Knowledge Graph Construction (CKGC), which constructs a CKG to elucidate the connection between defects and non-defects. Class features are learned by using a Fuzzy Clustering with Semi Supervised Fuzzy Graph Convolutional Network (FC-SSFGCN). The method employs a transformer encoder to capture distant relationships, allowing for the extraction of features from defect samples. This facilitates the acquisition of distinct defect image characteristics, as industrial defects vary in shape and size. The experimental results on the public Railway Track Fault Detection (RTFD)and Rail Surface Defect Datasets (RSDDs) with rail surface defects are collected from rail tracks surface defect detection.

Use of Virtual CT and On-Treatment MRI to Reduce Radiation Dose and Anesthesia Exposure Associated With the Adaptive Workflow in Pediatric Patients Treated With Intensity Modulated Proton Therapy

The purpose of this study was to determine whether virtual computed tomography (vCT) derived from daily cone beam computed tomography (CBCT), or on-treatment magnetic resonance imaging (MRItx) can replace quality assurance computed tomography (qCT) in our clinical workflow to minimize imaging dose and potentially anesthesia exposure in patients requiring plan adaptation. Pediatric patients (age <24 years) treated from 2020 to 2023 with intensity modulated proton therapy with at least 1 qCT during proton therapy were eligible. For cases that required plan adaptation, the dose was recalculated on vCT and compared with same-day qCT as well as the original planning computed tomography (pCT). Anatomic changes triggering plan adaptation were grouped into categories. Two pediatric radiation oncologists verified whether these changes could be detected using CBCT, qCT, and/or MRItx. A new adaptive imaging workflow was proposed to limit imaging dose and anesthesia exposure. One hundred sixty-eight pediatric patients were treated from 2020 to 2023. Across all patients, there were 517 qCT scans and 61 MRItx acquired. The median number of qCT scans per patient was 3 (range, 1-5). The treatment plans for 20 patients (12%) were adapted. In all patients requiring plan adaptation, there was a correlation between dose differences in target coverage and maximum body dose when comparing vCT with pCT and qCT with pCT (n = 20, r2 = 0.79, P < .01, and r2 = 0.32 P = .01, respectively). The most common reason for adaptation was tissue change (eg, inflammation, changes in abdominal gas, or diaphragmatic variability) in the beam path (10/20) and changes in tumor volume (6/20). All cases of weight change, tissue change in beam path, and unreproducible setup could be detected on CBCT. All cases of change in tumor volume within the brain were detected on MRItx. Replacing the qCT with the vCT was associated with an estimated median reduction of imaging dose by 50% and anesthesia exposure by 1.5 hours. vCT derived from daily CBCT only or MRItx can safely replace qCT for monitoring dosimetric changes to trigger a new pCT in our clinical workflow. This change would potentially reduce imaging dose and anesthesia exposure.

Seismic data interpolation with a recurrent inference mechanism

Abstract Seismic data interpolation is a significant procedure in seismic data processing as the highly complete data are extremely useful for the subsequent imaging and interpretation workflows. Recently, numerous deep learning-based techniques have been utilized to help improve the reconstruction quality. Supervised learning approaches can predict the results in a second, but they depend heavily on the training data, which therefore limits the generalized applications. By contrast, unsupervised learning methods are applicable to any data, but they need much more computational time and prior knowledge to be implemented. Especially, the recovery of aliased and consecutively missing data is incredibly challenging. To solve this problem, we propose a novel framework for seismic interpolation using a recurrent inference mechanism (SIRIM). Integrating the advantages of supervised and unsupervised learning paradigms, we build a specific convolutional recurrent inference network such that it can learn the dynamic priors when plugged in the physics-informed iterative algorithm, as well as directly reconstruct various types of incomplete data. We validate SIRIM in comparison with some traditional and learning-based methods. The performance on synthetic and field data illustrates the effectiveness and robustness of our proposed approach, which outperforms baseline methods in terms of aliased and consecutively missing data.

Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.

Ultrashort echo time (UTE) MRI can be a radiation-free alternative to CT for craniofacial imaging of pediatric patients. However, unlike CT, bone-specific MR imaging is limited by long scan times, relatively low spatial resolution, and a time-consuming bone segmentation workflow. A rapid, high-resolution UTE technique for brain and skull imaging in conjunction with an automatic segmentation pipeline was developed. A dual-RF, dual-echo UTE sequence was optimized for rapid scan time (3 min) and smaller voxel size (0.65 mm3). A weighted least-squares conjugate gradient method for computing the bone-selective image improves bone specificity while retaining bone sensitivity. Additionally, a deep-learning U-Net model was trained to automatically segment the skull from the bone-selective images. Ten healthy adult volunteers (six male, age 31.5 ± 10 years) and three pediatric patients (two male, ages 12 to 15 years) were scanned at 3 T. Clinical CT for the three patients were obtained for validation. Similarities in 3D skull reconstructions relative to clinical standard CT were evaluated based on the Dice similarity coefficient and Hausdorff distance. Craniometric measurements were used to assess geometric accuracy of the 3D skull renderings. The weighted least-squares method produces images with enhanced bone specificity, suppression of soft tissue, and separation from air at the sinuses when validated against CT in pediatric patients. Dice similarity coefficient overlap was 0.86 ± 0.05, and the 95th percentile Hausdorff distance was 1.77 ± 0.49 mm between the full-skull binary masks of the optimized UTE and CT in the testing dataset. An optimized MRI acquisition, reconstruction, and segmentation workflow for craniofacial imaging was developed.

A Product Evaluation of Augmented Reality Equipment Coupled to Diagnostic Medical Sonography: A Potential Equipment and Ergonomic Innovation

Objective: The aim of this product evaluation was to investigate the workflow and occupational health benefits of implementing augmented reality (AR) technology (MediView XR Inc, Cleveland, OH) paired with a portable ultrasound equipment system. Materials and Method: A diverse sample of sonography users was consented and ranged in experience levels. They were asked to complete a video training session, a hands-on demonstration, and remote instruction while using the augmented reality ultrasound (AR US) system. The duration of participants’ tasks was assessed during the use of both AR US and the 2-dimensional ultrasound (2D US) system. Participants’ postures and positions were evaluated using the Rapid Upper Limb Assessment (RULA), which assessed ergonomic risk factors, linked to body position and muscle usage. Participants also completed the System Usability Scale (SUS). Finally, participants were invited to complete a qualitative interview session, regarding the use of the AR US system. Results: A total of 20 participants provided data after using both the AR US and 2D US systems. The assessment data revealed positive SUS scores, which reflected favorably on both the product, education, and the training provided. The time spent on simulated work (20 minutes), with both imaging systems, remained consistent. The Rapid Upper Limb Assessment tool (RULA) scores for the cohort indicated a lower (better) median upper body score when using the AR US system. The cohort also had a statistically significant lower median subscore for the neck, trunk, and legs, compared with 2D US ( p &lt; 0.00). Conclusion: Although larger studies are necessary to validate these findings, this cohort identified several opportunities for integrating the AR US system. Preliminary evidence suggests that an AR US system may offer occupational health benefits, as part of the imaging workflow.

An accessible digital imaging workflow for multiplexed quantitative analysis of adult eye phenotypes in Drosophila melanogaster.

The compound eye of Drosophila melanogaster has long been a model for studying genetics, development, neurodegeneration, and heterochromatin. Imaging and morphometry of adult Drosophila and other insects is hampered by the low throughput, narrow focal plane, and small image sensors typical of stereomicroscope cameras. When data collection is distributed among many individuals or extended time periods, these limitations are compounded by inter-operator variability in lighting, sample positioning, focus, and post-acquisition processing. To address these limitations we developed a method for multiplexed quantitative analysis of adult Drosophila melanogaster phenotypes. Efficient data collection and analysis of up to 60 adult flies in a single image with standardized conditions eliminates inter-operator variability and enables precise quantitative comparison of morphology. Semi-automated data analysis using ImageJ and R reduces image manipulations, facilitates reproducibility, and supports emerging automated segmentation methods, as well as a wide range of graphical and statistical tools. These methods also serve as a low-cost hands-on introduction to imaging, data visualization, and statistical analysis for students and trainees.

Alzheimer Disease Anti-Amyloid Immunotherapies: Imaging Recommendations and Practice Considerations for Monitoring of Amyloid-Related Imaging Abnormalities.

With full FDA approval and Centers for Medicare & Medicaid Services coverage of lecanemab and donanemab, a growing number of practices are offering anti-amyloid immunotherapy to appropriate patients with cognitive impairment or mild dementia due to amyloid-positive Alzheimer disease. The goal of this article is to provide updated practical considerations for radiologists, including implementation of MR imaging protocols, workflows, and reporting and communication practices relevant to anti-amyloid immunotherapy and monitoring for amyloid-related imaging abnormalities (ARIA). On the basis of consensus discussion within an expanded American Society of Neuroradiology (ASNR) Alzheimer, ARIA, and Dementia Study Group, our purpose is the following: 1) summarize the FDA guidelines for the evaluation of radiographic ARIA; 2) review the 3 key MRI sequences for ARIA monitoring and standardized imaging protocols on the basis of ASNR-industry collaborations; 3) provide imaging recommendations for 3 key patient scenarios; 4) highlight the role of the radiologist in the care team for this population; 5) discuss implementation of MRI protocols to detect ARIA in diverse practice settings; and 6) present the results of the 2023 ASNR international neuroradiologist practice survey on dementia and ARIA imaging.

Effect of the cone-beam CT acquisition trajectory on image quality in spine surgery: experimental cadaver study

BACKGROUNDIntraoperative 3D imaging with cone-beam CT (CBCT) improves assessment of implant position and reduces complications in spine surgery. It is also used for image-guided surgical techniques, resulting in improved quality of care. However, in some cases, metal artifacts can reduce image quality and make it difficult to assess pedicle screw position and reduction. PURPOSEThe objective of this study was to investigate whether a change in CBCT acquisition trajectory in relation to pedicle screw position during dorsal instrumentation can reduce metal artifacts and consequently improve image quality and clinical assessability. STUDY DESIGNExperimental cadaver study. METHODSA human cadaver was instrumented with pedicle screws in the thoracic and lumbar spine region (Th11 to L5). Then, the acquisition trajectory of the CBCT (Cios Spin, Siemens, Germany) to the pedicle screws was systematically changed in 5° steps in angulation (−30° to +30°) and swivel (−25° to +25°). Subsequently, radiological evaluation was performed by 3 blinded, qualified raters on image quality using 9 questions (including anatomical structures, implant position, appearance of artifacts) with a score (1–5 points). For statistical evaluation, the image quality of the different acquisition trajectories was compared to the standard acquisition trajectory and checked for significant differences. RESULTSThe angulated acquisition trajectory significantly increased the score for subjective image quality (p<.001) as well as the clinical assessability of pedicle screw position (p<.001) with particularly strong effects on subjective image quality in the vertebral pedicle region (d=1.61). Swivel of the acquisition trajectory significantly improved all queried domains of subjective image quality (p<.001) as well as clinical assessability of pedicle screw position (p<.001). CONCLUSIONSIn this cadaver study, the angulation as well as the swivel of the acquisition trajectory led to a significantly improved image quality in intraoperative 3D imaging (CBCT) with a constant isocenter. The data show that maximizing the angulation/swivel angle towards 30°/25° provides the best tested subjective image quality and enhances clinical assessability. Therefore, a correct adjustment of the acquisition trajectory can help to make intraoperative revision decisions more reliably. CLINICAL SIGNIFICANCEThe knowledge of enhanced image quality by changing the acquisition trajectory in intraoperative 3D imaging can be used for the assessment of critical screw positions in spine surgery. The implementation of this knowledge requires only a minor change of the current intraoperative imaging workflow without additional technical equipment and could further reduce the need for revision surgery.