High-quality Images Research Articles

3D-HoloNet: fast, unfiltered, 3D hologram generation with camera-calibrated network learning

Computational holographic displays typically rely on time-consuming iterative computer-generated holographic (CGH) algorithms and bulky physical filters to attain high-quality reconstruction images. This trade-off between inference speed and image quality becomes more pronounced when aiming to realize 3D holographic imagery. This work presents 3D-HoloNet, a deep neural network-empowered CGH algorithm for generating phase-only holograms (POHs) of 3D scenes, represented as RGB-D images, in real time. The proposed scheme incorporates a learned, camera-calibrated wave propagation model and a phase regularization prior into its optimization. This unique combination allows for accommodating practical, unfiltered holographic display setups that may be corrupted by various hardware imperfections. Results tested on an unfiltered holographic display reveal that the proposed 3D-HoloNet can achieve 30 fps at full HD for one color channel using a consumer-level GPU while maintaining image quality comparable to iterative methods across multiple focused distances.

High-fidelity image reconstruction in multimode fiber imaging through the MITM-Unet framework

Using a single multimode fiber for both illumination and imaging offers notable advantages in developing ultra-thin imaging probes. However, uneven speckle illumination introduces additional noise, complicating high-precision reconstruction of complex grayscale images, which remains challenging for traditional methods. In this study, we first optimize the image reconstruction framework by combining the inverse transmission matrix approach with deep neural networks, enhancing interpretability and delivering exceptional performance in reconstructing complex images. To address the noise introduced by uneven speckle illumination, we increase the target exposure and effectively integrate information from multiple illumination conditions. Results show that our proposed Multi-speckle Illumination type Inverse Transmission Matrix-Unet (MITM-Unet) method significantly outperforms the Single-speckle illumination type (SITM-Unet). Specifically, images reconstructed with MITM-Unet achieve a structural similarity index of 0.59 and a Pearson correlation coefficient of 0.91, compared to SITM-Unet’s 0.38 and 0.77. These findings underscore the effectiveness of the MITM-Unet method in achieving high-quality imaging of complex grayscale targets, providing valuable insights into the imaging capabilities of single multimode fiber systems. This work holds promise for advancing simpler, more compact wide-field endomicroscopic imaging using multimode fibers.

Temporal multiplexing complex amplitude holography for 3D display with natural depth perception

In this Letter, we develop a temporal multiplexing complex amplitude holography for a three-dimensional (3D) display with natural depth perception. A series of band-limited random phases is optimized through the designed pseudo incoherent wave propagation model, which can be used as the initialization condition for arbitrary target images. The temporal multiplexing holograms (TMHs) are calculated according to the optical field distribution in the image plane and encoded through the complex amplitude converting method. Based on temporal multiplexing, our method overcomes the drawbacks brought by random phase and constant phase on 3D display performance and enables 3D reconstruction with high image quality as well as natural depth cues. It is expected that our method offers an efficient route toward the future high-performance holographic 3D display.

Implementation of prehospital point-of-care ultrasound using a novel continuous feedback approach in a UK helicopter emergency medical service

BackgroundThere has been increased use of prehospital point-of-care ultrasound (PoCUS) by helicopter emergency medical services (HEMS) in recent years. Lack of governance structure and evidence of benefit have been described as major barriers to its implementation. This paper describes a novel approach to implementation of prehospital PoCUS and clinical governance framework in a UK HEMS.MethodsA retrospective database review was undertaken at London’s Air Ambulance (LAA) from 1st September 2021 to 31st March 2023. All patients who had PoCUS examination were included. Scans were archived in a cloud-based server and reviewed weekly by expert clinicians. They were graded in adequacy, agreement between reviewer and clinician was recorded and fed back to the clinicians allowing continuous feedback learning. In-hospital diagnosis was sought for patients having the full Pump, Pleura and Pouring blood (PPPB) protocol. Cohen’s Kappa (ƙ) was calculated for inter-rater reliability. Sensitivity and specificity analysis was performed using 2 × 2 tables.ResultsLAA attended 3,068 missions. Our reviewers identified 701 PoCUS scanning encounters and 628 were included in the final analysis. Clinicians performed 420 scans for pneumothorax, 308 for free fluid and 305 pericardial effusions respectively. Majority of the population were male (85%) who sustained traumatic (93.5%) thoracic injuries (65%). Paramedics performed 29% of the scans. Reviewers deemed 83% of the scans of adequate quality. Inter-rater reliability between clinicians and reviewers was 0.6 for pericardial effusion, 0.67 for pneumothorax and 0.71 for free fluid respectively. A full PPPB protocol was performed in 52 patients out of which 46 were included. The sensitivity and specificity of PPPB protocol for diagnosis life-threatening injuries was 0.5 and 0.9 respectively.ConclusionIntroduction of prehospital PoCUS in a HEM service utilizing high quality training, user-friendly workflow and image archiving system, robust governance framework and continuous feedback may be feasible allowing high quality ultrasound examinations. The bespoke PPPB protocol in prehospital may improve diagnosis of life-threatening injuries.

Security applications of metasurface holograms with color print features

Dual-functional hybrid metasurfaces integrating color printing and holography generate high-quality tricolor images that enhance document security

OoCount: A Machine-Learning Based Approach to Mouse Ovarian Follicle Counting and Classification.

The number and distribution of follicles in each growth stage provides a reliable readout of ovarian health and function. Leveraging techniques for three-dimensional imaging of ovaries in toto has the potential to uncover total, accurate ovarian follicle counts. Due to the size and holistic nature of these images, counting oocytes is time consuming and difficult. The advent of machine-learning algorithms has allowed for the development of ultra-fast, automated methods to analyze microscopy images. In recent years, these pipelines have become more accessible to non-specialists. We used these tools to create OoCount, a high-throughput, open-source method for automatic oocyte segmentation and classification from fluorescent 3D microscopy images of whole mouse ovaries using a deep-learning convolutional neural network (CNN) based approach. We developed a fast tissue-clearing and imaging protocol to obtain 3D images of whole mount mouse ovaries. Fluorescently labeled oocytes from 3D images were manually annotated in Napari to develop a training dataset. This dataset was used to retrain StarDist using a CNN within DL4MicEverywhere to automatically label all oocytes in the ovary. In a second phase, we utilize Accelerated Pixel and Object Classification, a Napari plugin, to sort oocytes into growth stages. Here, we provide an end-to-end pipeline for producing high-quality 3D images of mouse ovaries and obtaining follicle counts and staging. We demonstrate how to customize OoCount to fit images produced in any lab. Using OoCount, we obtain accurate oocyte counts from each growth stage in the perinatal and adult ovary, improving our ability to study ovarian function and fertility.

Efficient Modelisation for Complex Geometries of Tumor Growth via Thermo-Elastic Diffusion Partial Differential Equations and Artificial Neural Networks

<p dir="ltr"><span>The availability of high quality image data urges the development of new conceptual techniques to realise cancer accurate prognostic and treatment. In this study we propose a boundary value problem to model tumor elasticity impacting various types of mechanical stresses on cancer cell properties, assuming isotropic and inhomogeneous condition. We show the solution of this multiscale model expressed by PDE is numerically approximated by means of an Artificial Neural Network Architecture based on generalised Fourier decomposition. The connective key step is the use of the Laplace Neural Operator, which contributes to an efficient algorithm convergence rate. While further research of this model may address critical points on machine learning optimality checks, this framework is a rational development for oncologic medical prognosis and therapy. </span></p><div><span><br /></span></div>

Prosedur Pemeriksaan MRI Cruris dengan Kontras pada Kasus Tumor

Background: This study examines the MRI Cruris contrast examination procedure for tumor cases at Dr. Moewardi Hospital. The hospital's MRI protocol includes sequences such as 3-plane localizer, Sag T1, Sag T2, Ax T1, Ax T2 STIR, Coronal T1, Coronal PD FS, Coronal T2* MERGE, 3D Coronal TRICKS, and post-contrast sequences Sag T1+C, Coronal T1+C, and Ax T1+C. The study aims to analyze the MRI Cruris contrast procedure, the rationale for using 5 ml contrast, and the importance of the Coronal 3D TRICKS sequence.Methods: A qualitative research method with a case study approach was used, involving observation, interviews, and documentation. The data was analyzed and presented in quotations to conclude.Result and Discussion: The MRI procedure begins with patient preparation, including laboratory tests for urea and creatinine levels, fasting for 4–6 hours, and filling out informed consent. Patients are positioned supine with feet first, and an air coil is placed on the leg. The use of 5 ml contrast enhances image quality, improves tumor detection, evaluates blood vessels, shows lesions, and determines tumor location and spread. The Coronal 3D TRICKS sequence provides clear MRA angiography, enabling better visualization of feeding arteries and rapid imaging of major blood vessels within 10 seconds.Conclusion: In conclusion, the MRI Cruris contrast protocol at Dr. Moewardi Hospital ensures high-quality imaging for tumor detection and diagnosis. The 5 ml contrast dose effectively enhances MRI images, and the Coronal 3D TRICKS sequence plays a crucial role in evaluating vascular structures.

Pancreaticojejunostomy Obstruction Due to Jejuno-Jejunal Intussusception in the Roux Limb: A Rare Late Complication Following Longitudinal Pancreaticojejunostomy

Abstract Lateral pancreaticojejunostomy (LPJ) is done as a decompressive procedure for chronic pancreatitis with dilated main pancreatic duct (MPD) with a success rate of up to 80% in relieving pain with low morbidity and mortality. Roux limb obstruction following Roux-en-Y LPJ is a very rare complication and it has not been described earlier. Here, we report a rare case of pancreaticojejunostomy (PJ) obstruction caused by jejuno-jejunal intussusception in the Roux limb. We present a case of a 31-year-old lady with recurrent episodes of intermittent colicky upper abdominal pain for 8 months. She underwent Roux-en-Y LPJ 20 years back for idiopathic chronic calcific pancreatitis. Her imaging revealed dilated MPD with a dilated Roux limb due to narrowing in the distal roux limb with intussusception. She underwent robotic-assisted resection of jejuno-jejunal intussusception and anastomosis along with cholecystectomy. She recovered well post-operatively and her pain subsided. PJ obstruction following pancreatoduodenectomy has been reported in the literature. However, similar presentation is rare following LPJ. This is the first ever case to be reported with Roux limb obstruction following LPJ. We present this case to emphasise the importance of high-quality imaging in diagnosing this rare case. Robotic-assisted surgery in adults for intussusception is a novel approach and not much discussed in the literature.

4D Dynamic contrast-enhanced breast CT: Phantom-based reconstruction parameter optimization for iodine quantification.

Four-dimensional dynamic contrast-enhanced breast CT (4D DCE-bCT) offers promising high-resolution spatial and temporal imaging capabilities for the characterization and monitoring of breast tumors. However, the optimal combination of parameters for iodine quantification in image space remains to bedetermined. This study aims to optimize a dedicated bCT system to perform long dynamic contrast-enhanced scans with high spatio-temporal resolution while maintaining a reasonable radiation dose. Our protocol includes the acquisition of a high-quality prior image that is reconstructed with a polychromatic iterative algorithm (IMPACT). The acquisition of the post-contrast sequence is continuous but sparse and these images are reconstructed using prior image constrained compressed sensing (PICCS). A four-step optimization process is performed using images of a physical phantom. First, the optimal tube current is selected by taking the noise level into account. Second, the optimal number of angles is selected based on the absence of streak artifacts. Third, the number of iterations in IMPACT is specified at the lowest value that achieves the highest spatial resolution. Finally, the number of iterations in PICCS is determined based on the quantitative accuracy of a range of iodine concentrations. When a high-quality prior image is available, the imaging of post-contrast images can be performed using just 40 projection angles with a tube current of 32 mA. The noise level in the post-contrast images is inherited from the prior image and no streak artifacts are visible. Mean difference between the linear attenuation coefficients of samples containing iodine reconstructed with IMPACT using all 360 projections and PICCS using 40 projections is 0.0004 at most. The spatial resolution of images reconstructed with PICCS is lower than the one of IMPACT images and is concentration dependent. The cut-off frequency at 10% modulation transfer function drops from 1.55 in the prior image to 0.9 when the target with the largest concentration is evaluated. The total mean glandular dose of the protocol does not exceed 22.5mGy. This study found the optimal acquisition and reconstruction parameters for a low-dose dynamic contrast-enhanced bCT protocol. The numerical accuracy of the proposed protocol was ensured by performing a physical phantomstudy.

Mass Spectrometry Imaging with Matrix-Enhanced SIMS: The Influence of Matrix Deposition Rate on Crystal Size and Signal Enhancement.

Matrix-enhanced secondary ion mass spectrometry and matrix-assisted laser desorption/ionization are two mass spectrometry imaging techniques used to image biological molecules in cells and tissues. Both techniques utilize an organic matrix coating to enhance the detection of molecular ions. The homogeneity and crystal size of the matrix coating are critical for obtaining high quality images. Sublimation/deposition is currently widely used to apply a matrix layer onto a sample because of its ability to produce homogeneous layers with small crystal sizes. Additionally, the absence of a solvent during matrix application reduces lateral movement of the analyte. However, vertical migration of the analyte into the matrix coating is required to observe signal enhancement, and the factors that influence this migration are poorly understood. In this work, an in-house built sublimation matrix coater with a Knudsen effusion cell and sample cooling was used for controlled deposition of the matrix alpha-cyano-4-hydroxycinnamic acid onto a section of mouse brain cerebellum. Different deposition rates were used to coat two areas of the brain section with two different crystal sizes. Macro-scan images of the brain and 3D imaging of selected brain regions were performed. The results show differences in migration through the two matrix layers but also differences in analyte migration behavior for the same analyte in different tissue types.

Common-path ghost imaging through complex media with dual polarization

The performance of ghost imaging (GI) is severely compromised by dynamic and complex scattering media in free space. In this Letter, we design a common-path GI (CPGI) setup with dual polarization in complex environments. The s-light and p-light with mutually perpendicular polarization states are generated and overlap in free space in the designed optical path to correct a series of dynamic scaling factors induced by the complex scattering media. Experimental results demonstrate that the proposed method is highly robust and can achieve high imaging quality in complex media. Compared to previous schemes, the proposed method adopts a simplified optical setup and realizes high-quality GI in complex and dynamic scattering environments without extra algorithms in order to promote the wider application of GI.

Validation of a data-driven motion-compensated PET brain image reconstruction algorithm in clinical patients using four radiotracers

PurposePatients with dementia symptoms often struggle to limit movements during PET examinations, necessitating motion compensation in brain PET imaging to ensure the high image quality needed for diagnostic accuracy. This study validates a data-driven motion-compensated (MoCo) PET brain image reconstruction algorithm that corrects head motion by integrating the detected motion frames and their associated rigid body transformations into the iterative image reconstruction. Validation was conducted using phantom scans, healthy volunteers, and clinical patients using four radiotracers with distinct tracer activity distributions.MethodsWe conducted technical validation experiments of the algorithm using Hoffman brain phantom scans during a series of controlled movements, followed by two blinded reader studies assessing image quality between standard images and MoCo images in 38 clinical patients receiving dementia scans with [18F]Fluorodeoxyglucose, [18F]N-(3-iodopro-2E-enyl)-2beta-carbomethoxy-3beta-(4’-methylphenyl)-nortropane, [18F]flutemetamol, and a research group comprising 25 elderly subjects scanned with [18F]fluoroethoxybenzovesamicol.ResultsThe Hoffman brain phantom study demonstrated the algorithm’s capability to detect and correct for even minimal movements, 1-mm translations and 1⁰ rotations, applied to the phantom. Within the clinical cohort, where standard images were deemed suboptimal or non-diagnostic, all MoCo images were classified as having acceptable diagnostic quality. In the research cohort, MoCo images consistently matched or surpassed the standard image quality even in cases with minimal head movement, and the MoCo algorithm never led to degraded image quality.ConclusionThe PET brain MoCo reconstruction algorithm was robust and worked well for four different tracers with markedly different uptake patterns. Moco images markedly improved the image quality for patients who were unable to lie still during a PET examination and obviated the need for any repeat scans. Thus, the method was clinically feasible and has the potential for improving diagnostic accuracy.

Extremely Low-resolution RFID Vision for Real-time and Visually-anonymized Action Recognition

Despite the potential of vision-based personal monitoring (e.g., healthcare), private data leakage concerns hinder its wide deployment in personal spaces (e.g., bedrooms). A body of data anonymization designs was proposed throughout image processing and federated learning. They commonly store high-quality images and videos locally, which are anonymized via post-processing before cloud upload. However, the recent IoT camera hacking and local data leakage call for anonymized data at the sensing stage. Also, continuous and pervasive monitoring without blind spots in complicated indoor spaces requires a scalable and economic system. This paper presents Mosaic , a vision-based end-to-end action recognition framework that (i) intrinsically achieves data anonymity from the sensing stage and (ii) battery-free operation for blind spot-free continuous monitoring. Mosaic leverages an extremely low resolution (eLR) Near-Infrared (NIR) image sensor with 6 × 10 pixels for video anonymity and an RFID-compliant fully-passive tag with four solar cells for real-time eLR video streaming under as low as 30 lux (e.g., deep in the shelf without direct light). This is accompanied by a lightweight action recognition neural network for real-time inference (18.4 ms on Intel(R) Core i7-8700). Mosaic achieves an average of 98% accuracy on 10 action classes, hitting the balance between data anonymity and high-precision action recognition. Taking advantage of the NIR (non-visible) frequency, Mosaic also works in the dark without disturbing sleep. Lastly, wildfire detection reaching 20 m was demonstrated, showcasing the potential for outdoor monitoring.

Correlation Properties of Color Histograms in the Case of Image Quality Decreasing

In image recognition tasks, objects are identified by examining various features such as texture, color, contour detection, and statistical or semantic descriptions. One widely used approach for extracting image attributes is the analysis of intensity histograms. While the traditional RGB color model is commonly used in digital image processing, it is often more effective to analyze color properties in HS* systems (such as HSL, HSV, and HSI) since these systems more closely resemble the spectral representation of color. A key characteristic shared by these three systems is the use of the H (Hue) coordinate, which is represented as an angular value within a cylindrical coordinate system. The paper investigates the possibility of using color histograms generated in HS* spaces for identifying images that have undergone various types of distortions. The CQ100: A High-Quality Image Dataset for Color Quantiza-tion Research was chosen for the research. The non-quantized section of the CQ100 dataset consists of 100 RGB images in PNG format, each with a resolution of 768×512 pixels and a color depth of 24 bits. The study examines how different distortions, which can occur during real-time photo and video capture, affect the color properties of images. Specifically, the re-search focuses on distortions caused by rotation, noise, blurring, and optical aberration. His-tograms were compared using the Pearson cross-correlation coefficient, and the findings re-veal that the correlation remains high for the same image despite the applied distortions. Conversely, the correlation coefficient between different images is low for most of the studied objects. These results suggest that color histograms could be effectively used for image identi-fication tasks, even when images are significantly distorted, as is common in image registra-tion processes. The applicability of correlation detection as a method for histograms com-parison is considered regardless of the relative simplicity of its calculation. This approach could contribute to the development of faster image recognition systems.

Abstract 29: Non-gated Dual-source Photon-counting CT Angiography Can Detect Cardioembolic Sources Of Acute Ischemic Stroke At The Primary Investigation: A Retrospective Consecutive Cohort Study

Introduction: Early identification of the etiology of ischemic stroke is crucial for secondary prevention. Recent publications on dedicated ECG-gated cardiac CT showcase the potential to detect sources of cardiac embolism. Photon-counting CT (PCCT) uses semiconductors to convert X-ray photons into electrical signals directly. This allows for improved image quality and lower radiation dose compared to conventional CT. Exploiting PCCT technology with a high-pitch dual-source image acquisition may render an exceptionally high temporal resolution. We hypothesized that non-ECG-gated high-pitch dual-source PCCT angiography from the diaphragm to the brain might provide added clinical value in detecting cardiac stroke sources during initial stroke imaging while maintaining optimal brain and neck image quality. Methods: Consecutive patients with a clinical suspicion of acute stroke imaged with a PCCT system between October 4 th , 2023 and April 13 th , 2024 at a Swedish comprehensive stroke center were included. Diaphragm to brain coverage was obtained in an acquisition time of 1.3 seconds; on average, yielding a dose-length product of 360 mGy*cm for the study participants (2.34 mSv). Image quality was graded using a 4-point Likert scale. Images were assessed for cardiac stroke sources. Where available, reference standard echocardiography results were collected. Results: Of 249 consecutive stroke investigations, 193 included cardiothoracic imaging, which constituted the study population. 126 scans (65.3%) were cases with imaging confirmed ischemic stroke. The median age was 74 (IQR, 61-81) years, 99 were male (51.3%). In total, 39.4% underwent follow-up echocardiography. Image quality of the heart was excellent in 19 scans (9.8%) good in 97 (50.3%), moderate in 72 (37.3%), and poor in 5 scans (2.6%). Fourteen (7.3%) certain or probable interatrial septal defects were found in the study population. In the imaging-confirmed ischemic stroke subgroup, six certain or probable cardiac thrombi were found (4.8%). Additionally, a possible cardiac thrombus was found in 31 instances (24.6%). Three aortic valve vegetations were found (2.4%), all confirmed by echocardiography. Conclusions: Non-ECG-gated high-pitch dual-source PCCT angiography typically provides images of high quality, at virtually no time expense while maintaining a reasonably low radiation exposure. Non-gated PCCT angiography is a promising technique to be used as a primary screening method for cardioembolic stroke.

Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.

Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction time using deep learning. This study focuses on accelerating the reconstruction of volumetric multi-axis spiral projection MRF, aiming for whole-brain T1 and T2 mapping, while ensuring a streamlined approach compatible with clinical requirements. To optimize reconstruction time, the traditional method is first revamped with a memory-efficient GPU implementation. Deep Learning Initialized Compressed Sensing (Deli-CS) is then introduced, which initiates iterative reconstruction with a DL-generated seed point, reducing the number of iterations needed for convergence. The full reconstruction process for volumetric multi-axis spiral projection MRF is completed in just 20min compared to over 2h for the previously published implementation. Comparative analysis demonstrates Deli-CS's efficiency in expediting iterative reconstruction while maintaining high-quality results. By offering a rapid warm start to the iterative reconstruction algorithm, this method substantially reduces processing time while preserving reconstruction quality. Its successful implementation paves the way for advanced spatio-temporal MRI techniques, addressing the challenge of extensive reconstruction times and ensuring efficient, high-quality imaging in a streamlined manner.

Improving pediatric magnetic resonance imaging safety by enhanced non-technical skills and team collaboration.

Safe pediatric magnetic resonance imaging (MRI) ideally relies on non-sedative techniques, as avoiding risky sedation is inherently safer. However, in practice, sedation often becomes unavoidable, particularly for younger children or those with anxiety, to ensure motion-free, high-quality imaging. This narrative review explores the current practices and proposes strategies to enhance safety in pediatric MRI examinations. We identified and analyzed 247 studies addressing various aspects of pediatric MRI safety, including sedation protocols, patient monitoring, and team-based management approaches. Safe sedation requires careful drug selection tailored to individual needs, continuous monitoring, and robust emergency preparedness. While efforts are underway to minimize sedation, safer drug protocols and improved monitoring technologies remain essential. Assembling dedicated MRI teams trained in both technical and non-technical skills-such as situational awareness, communication, and teamwork-supports these strategies. Structured team briefings covering monitoring procedures, emergency scenarios, response protocols, and specific resuscitation roles are also critical. Developing a strong organizational culture that promotes patient safety and continuous learning from incident reports helps ensure ongoing improvements. Achieving safe pediatric MRI examinations requires balancing the need for sedation with the goal of minimizing its use. Strengthening collaboration, refining sedation protocols, and implementing advanced safety monitoring systems are essential steps. Further advancements in imaging technologies are also necessary to reliably obtain high-quality scans without sedation, reducing risks and improving patient outcomes.

Ultra-low-dose coronary CT angiography via super-resolution deep learning reconstruction: impact on image quality, coronary plaque, and stenosis analysis.

To exploit the capability of super-resolution deep learning reconstruction (SR-DLR) to save radiation exposure from coronary CT angiography (CCTA) and assess its impact on image quality, coronary plaque quantification and characterization, and stenosis severity analysis. This prospective study included 50 patients who underwent low-dose (LD) and subsequent ultra-low-dose (ULD) CCTA scans. LD CCTA images were reconstructed with hybrid iterative reconstruction (HIR) and ULD CCTA images were reconstructed with HIR and SR-DLR. The objective parameters and subjective scores were compared. Coronary plaques were classified into three components: necrotic, fibrous or calcified content, with absolute volumes (mm3) recorded, and further characterized by percentage of calcified content. The four main coronary arteries were evaluated for the presence of stenosis. Moreover, 48 coronary segments in 9 patients were evaluated for the presence of significant stenosis, with invasive coronary angiography as a reference. Effective dose decreased by 60% from LD to ULD CCTA scans (2.01 ± 0.84 mSv vs. 0.80 ± 0.34 mSv, p < 0.001). ULD SR-DLR was non-inferior or even superior to LD HIR in terms of image quality and showed excellent agreements with LD HIR on the plaque volumes, characterization, and stenosis analysis (ICCs > 0.8). Moreover, there was no evidence of a difference in detecting significant coronary stenosis between the LD HIR and ULD SR-DLR (AUC: 0.90 vs. 0.89; p = 1.0). SR-DLR led to significant radiation dose savings from CCTA while ensuring high image quality and excellent performance in coronary plaque and stenosis analysis. Question How can radiation dose for coronary CT angiography be reduced without compromising image quality or affecting clinical decisions? Finding Super-resolution deep learning reconstruction (SR-DLR) algorithm allows for 60% dose reduction while ensuring high image quality and excellent performance in coronary plaque and stenosis analysis. Clinical relevance Dose optimization via SR-DLR has no detrimental effect on image quality, coronary plaque quantification and characterization, and stenosis severity analysis, which paves the way for its implementation in clinical practice.

Determination of the most appropriate radionuclide for knee radiosynovectomy: Assessment of radiation dose, radiation-induced cancer risk, and post-treatment imaging feasibility.

This study aims to systematically evaluate all radionuclides used in knee RSV to date and identify the most suitable radionuclide for knee RSV. To compare knee RSV with Y-90, P-32, Ho-166, Re-188, Au-198, Lu-177, Sm-153, and Re-186, we measured the radiation dose to non-target organs and the inducing of secondary malignancies in knee RSV patient, the radiation dose to family member and medical staff from the knee RSV patients, and the quality of post-treatment images. The Lifetime Attributable Risks of cancer incidence and mortality and the Relative Risks of solid cancer incidence and mortality are significantly higher for Y-90 and P-32 in both adult and pediatric patients compared to other radionuclides used in knee RSV. Knee RSV with Re-188, Ho-166, Au-198, and Sm-153 induces a negligible risk of secondary malignancies. The average absorbed dose for medical staff and family members exposed to patients treated with Y-90 is also significantly greater than for those exposed to RSV patients treated with other radionuclides. In contrast, the absorbed dose for medical staff and family members exposed to RSV patients treated with Sm-153, Ho-166, and Au-198 is negligible. Sm-153, Ho-166, and Re-188 offer high-quality images in post-RSV planar imaging. Based on the findings of this study and the therapeutic range of the radionuclides, Re-188 has been identified as a suitable alternative to Y-90 for knee RSV in adults. At the same time, Sm-153 and Ho-166 have been recognized as the most appropriate radionuclides for pediatric knee RSV.