Image Quality Research Articles (Page 1)

Deep Learning Denoising Algorithm for Improved Assessment of Coronary Arteries in Transcatheter Aortic Valve Implantation CT Imaging.

Deep Resolve Boost applied to accelerated acquisition (DRB-ACC) offers the potential to reduce MRI acquisition time and improve image quality. However, studies on the potential impact on artifacts, anatomical delineation, and the depiction of imaging findings are scarce. This study aims to fill this gap and evaluates the clinical performance of DRB-ACC in 2D MRI sequences. In this prospective observational study, 256 paired 2D sequences (T2/TIRM, and FLAIR) were acquired from 200 patients undergoing routine neuroradiological MRI on a 3T scanner. For each examination, both standard and DRB-ACC (predominantly high strength) were acquired in the same session using identical scanner settings, except for acceleration factor; same surface coil, sequence orientation and slice thickness. Image quality, anatomical structure delineation, artifact presence, and lesion conspicuity were independently assessed by two readers using standardized scoring. DRB-ACC sequences demonstrated good or fair image quality in 94.5% of cases, with improved or unchanged quality compared to standard acquisition in 95.7% of sequences. However, anatomical delineation was inferior in key regions such as the hippocampus, brainstem, and cerebellum. Artifacts were more pronounced in 27.3% and newly introduced in 84.8% of the accelerated DRB sequences, commonly affecting the brainstem and deep gray matter. Lesion depiction was equivalent to standard images in 91.6% of cases, with limited instances of improved (7.2%) or degraded (1.2%) delineation. DRB-ACC enables acceleration of 2D MRI while maintaining image quality and lesion visibility. At the same time, the presence of artifacts and reduced delineation of certain anatomical structures underscores the need for caution in the interpretation of image findings and selective use in clinical routine, particularly in clinical scenarios requiring high anatomical detail such as for epilepsy screening or in cases with suspected brainstem pathology. ACC＝ accelerated acquisition; DRB＝ Deep Resolve Boost.

To develop and evaluate a memory-efficient and accelerated reconstruction framework for respiratory-resolved 4D lung MRI using coil sketching and Toeplitz approximation, enabling high-quality motion-compensated low-rank (MoCo-LR) reconstructions on clinically accessible GPU hardware. Respiratory-resolved 4D MRI enables non-invasive assessment of pulmonary structure and function but is limited by computational and memory demands from large matrices and long acquisitions. We extend the coil sketching framework-previously proposed for 3D imaging-to 4D (3D + time), allowing the data consistency term of compressed-sensing objective functions to be solved with reduced GPU memory consumption and reconstruction duration while preserving image accuracy. Additionally, we implement Toeplitz approximation to accelerate repeated applications of the normal encoding operator, further reducing computational demand. Finally, we outline a MoCo-LR regularization technique that uses only forward deformations, improving reconstruction stability over many iterations. Reconstructions were performed across several regularization schemes using a 3D stack-of-spirals acquisition and evaluated for memory footprint, speed, and image accuracy. The proposed 4D coil sketching method reduced memory usage by ˜3-fold compared to fully sampled reconstructions and enabled high-resolution MoCo-LR reconstructions in < 10 min on < 48 GB GPUs. Toeplitz approximation further reduced runtime with minimal impact on image quality. Compared to conventional coil compression, coil sketching preserved parenchymal signal and structural fidelity in low-SNR regions. Coil sketching and Toeplitz approximation provide a generalizable, hardware-efficient solution for 4D lung MRI reconstruction. These methods reduce computational barriers, improve reconstruction speed, and maintain image quality, offering a path toward broader clinical adoption of respiratory-resolved lung MRI.

Abstract This paper proposes a novel dual-mode quantum image watermarking scheme based on the Generalized model of Novel Enhanced Quantum Representation (GNEQR). The scheme supports both grayscale and color images and provides two embedding modes. A key innovation is the Interleaved Tri-Symmetric Structure (ITSS) mechanism. It combines quantum image scrambling using the Quantum Baker Map (QBM) and reconstruction techniques with quantum error correction (QEC), enhancing robustness against noise and geometric attacks such as cropping and rotation. Additionally, a Quantum Parity Detection (QPD) mechanism is employed to efficiently embed and extract the watermark, substantially improving the visual quality of watermarked images. Experimental results on the USC-SIPI dataset demonstrate that the scheme achieves high visual quality, with PSNR values above 44 dB for watermarked images. Under a salt-and-pepper noise attack with density 0.05, the extracted watermark maintains a PSNR of 42 dB, outperforming the existing schemes. The proposed method also fully resists common rotation attacks and allows effective watermark extraction even when the image is cropped by 55%, confirming its strong robustness against geometric attacks. Overall, this work advances secure and efficient quantum watermarking technologies and addresses key challenges in scalability, robustness, and computational complexity for quantum image processing.

Fingerprint-sensing Keycap: A Novel Concept with Verified Image Quality for Notebook Applications

Imaging of targeted organs, such as the urinary bladder, could be transformative for preventive healthcare and early disease diagnosis when used to assess their real-time function. However, wearable and portable ultrasound imaging systems often face constraints related to power consumption, form factor, cost, and signal resolution, particularly for deep tissues like the bladder. High-accuracy platforms with large channel counts can generate data streams of up to 10 GB per second, posing significant challenges in reducing computational complexity, achieving power efficiency, and maintaining wireless connectivity. Recent advancements in wearable ultrasound sensors have demonstrated potential for low-power, unobtrusive solutions but often fail to meet the accuracy and efficiency needed in clinical settings. This work presents an algorithm-centric proof-of-concept that reconstructs missing ultrasound channels through field programmable gate array (FPGA) accelerated deep learning, effectively doubling the imaging aperture while halving analog front-end requirements. We developed a lightweight U-Net convolutional neural network (L-UNET) with 222,609 parameters, specifically optimized for sparse-array RF data reconstruction. The network is deployed on a deep learning processing unit (DPU) using mixed quantization-aware training (Mixed-QAT) that selectively applies 8-bit integer precision while preserving two critical layers at 16-bit floating point, achieving mean squared error (MSE) of 1.48×10 compared to 1.22×10 for 32-bit floating point. The FPGA implementation leverages a single-core accelerator, executing inference in 221 ms per frame with deterministic latency suitable for real-time reconstruction. By processing only odd-indexed physical channels and inferring even-indexed channels through the CNN, our approach maintains B-mode image quality (peak signal-to-noise ratio (PSNR) >18 dB, structural similarity index (SSIM) >0.5) while reducing data acquisition complexity. The system achieves 0.918 W average power consumption in a 32-channel configuration, demonstrating that CNN-based sparse-array reconstruction on embedded FPGAs offers a viable path toward fully integrated ultrasound monitoring systems.

To compare field-of-view optimized and constrained undistorted single-shot (FOCUS), multiplexed sensitivity-encoding (MUSE) and FOCUS-MUSE diffusion-weighted images (DWIs) in orbital imaging quality and staging performance for the patients with thyroid-associated ophthalmopathy (TAO). 67 TAOs underwent FOCUS, MUSE and FOCUS-MUSE DWIs. Qualitative (artifacts and geometric distortion, overall image quality, sharpness of boundaries) and quantitative parameters (geometric distortion ratio (GDR), signal-to-noise ratio (SNR), apparent diffusion coefficient (ADC) value, normalized ADC (nADC) value) were assessed. Additionally, nADC values of the extraocular muscles (EOMs) and mean nADC values were compared between active and inactive TAOs. Diagnostic performance was also evaluated. FOCUS-MUSE DWI exhibited significantly fewer artifacts and geometric distortion, superior overall image quality, enhanced sharpness of boundaries, higher SNR and lower GDR than MUSE and FOCUS DWIs (all p < 0.05). FOCUS-MUSE DWI showed significantly lower ADC values than MUSE (all p < 0.05) and FOCUS DWIs (all p < 0.05, except for that of superior EOM). The nADC values showed no significance among the three DWIs (all p > 0.05), except for that of the superior EOM. Furthermore, active TAOs showed higher nADC values than inactive TAOs in three DWIs (all p < 0.05). The mean nADC value of FOCUS-MUSE DWI (AUC, 0.890; sensitivity, 84.8%; specificity, 77.3%) performed better than that of MUSE (AUC, 0.713; sensitivity, 54.3%; specificity, 80.7%; p < 0.001) and FOCUS DWIs (AUC, 0.730; sensitivity, 47.8%; specificity, 90.9%; p < 0.001) in diagnosing active TAOs. FOCUS-MUSE DWI provides superior image quality and staging performance in assessing TAO than MUSE and FOCUS DWIs. We recommend its use for evaluating TAO patients in clinical practice. Field-of-view optimized and constrained undistorted single-shot multiplexed sensitivity-encoding DWI shows superior image quality and staging performance for thyroid-associated ophthalmopathy than other echo-planar imaging-based modified sequences. The superiority among different echo-planar imaging-based modified DWIs in thyroid-associated ophthalmopathy remains unclear. Field-of-view optimized and constrained undistorted single-shot multiplexed sensitivity-encoding (FOCUS-MUSE) DWI outperforms MUSE and FOCUS DWIs in imaging quality. Normalized apparent diffusion coefficient values derived from FOCUS-MUSE DWI improve staging performance of thyroid-associated ophthalmopathy.

Energy window selection is a critical parameter for optimizing planar gamma image quality in nuclear medicine. In this study, we developed dedicated nuclear medicine phantoms using 3D printing technology to evaluate the impact of varying energy window levels on image quality. Three types of phantoms—a Derenzo phantom with six different sphere diameters, a modified Hoffman phantom incorporating lead for attenuation, and a quadrant bar phantom with four bar thicknesses constructed from bronze filament—were fabricated using Fusion 360 and an Ultimaker S5 3D printer with PLA and bronze-based materials. Planar images were acquired using 37 MBq of Tc-99m for 60 s at energy windows centered at 122, 140, and 159 keV. Quantitative assessments included contrast-to-noise ratio (CNR), coefficient of variation (COV), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM), comparing all images with the 140 keV image as the reference. The results showed a consistent decline in image quality at 122 keV and 159 keV, with the highest CNR, lowest COV, and optimal PSNR/SSIM values obtained at 140 keV. In visual analysis using the quadrant bar phantom, thinner bars were more clearly discernible at 140 keV than at other energy levels. These findings demonstrate that the application of an appropriate energy window—particularly 140 keV for Tc-99m—substantially improves image quality in planar gamma imaging. The use of customized, material-specific 3D-printed phantoms also enables flexible, reproducible evaluation protocols for energy-dependent imaging optimization and quality assurance in clinical nuclear medicine.

To evaluate and compare the performance of diffusion-weighted imaging (DWI) using compressed sensing (CS) and DWI using CS with model-based deep learning reconstruction (DL-DWI) in detecting and differentiating liver metastases from hepatic hemangiomas. We retrospectively analyzed data from 53 patients with metastases or hemangiomas (34 men and 19 women, mean age, 65.9years) who underwent abdominal DWI. Two radiologists evaluated liver contour and distortion, artifact, noise, overall image quality, and lesion conspicuity using a five-point scale. Signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC) of the liver, as well as contras-to-noise ratio (CNR) and ADC of metastases (n = 59) and hemangiomas (n = 33) were assessed and statistically compared. A receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance of the two sequences for differentiating metastases and hemangiomas. DL-DWI provided significantly better conspicuity of metastasis than CS-DWI (p < 0.05 in both radiologists), whereas no significant difference was observed in the conspicuity of hemangioma between DL-DWI and CS-DWI. The SNR of liver parenchyma and the CNR of metastases and hemangiomas were higher in DL-DWI than in CS-DWI (p < 0.05). ADC values of liver parenchyma, metastases, and hemangiomas were lower in DL-DWI than in CS-DWI (p < 0.05). The ADC cutoff value for differentiating between metastases and hemangiomas was 1.693 × 10-3 mm2/s in DL-DWI and 1.411 × 10-3 mm2/s in CS-DWI. No significant differences were observed in the area under the ROC curve, sensitivity, and specificity between the two methods (p > 0.05). DL-DWI enhanced both qualitative and quantitative aspects of image quality in abdominal DWI. However, its diagnostic performance, including ADC cutoff values for differentiating between metastases and hemangiomas, is comparable to that of CS-DWI.

Patient-reported outcome measures (PROMs) are standardized, validated instruments that measure how patients feel and function, collected directly from the patient. Traditionally, key metrics in radiology include technical aspects such as image quality, radiation dose, and diagnostic accuracy. However, medical imaging and image-guided therapies shape patient experience in informational, emotional, physical, and logistical domains that are rarely measured. Failing to capture this information is an important gap in radiology research and practice today that needs to be addressed. This review synthesizes the science of PROMs through a radiology lens: what PROMs are; why PROMs are relevant to diagnostic imaging and interventional practice; how to select and interpret PROMs responsibly, with explicit attention to bias, conflicts of interest, and minimal important differences; and how to implement PROMs pragmatically using contemporary digital workflows. This article highlights radiology-specific frameworks for patient-centred outcomes of diagnostic tests, summarizes evidence on how electronic PROM (ePROM) programs can improve patient experience and clinical outcomes, and proposes a practical roadmap for department-level implementation. Throughout, this review aligns recommendations with current methodological and regulatory guidance, draws on Canadian implementation experience, and translates lessons from applied PROM programs in complex clinical services to radiology settings. Implemented thoughtfully, PROMs give radiologists a rigorous, low-burden way to document benefits radiology already provides, strengthen outcome and health-economic analyses, and co-design services around what patients value. Integrating PROMs alongside established technical and diagnostic metrics can extend radiology's value proposition, and make radiology's patient-centred impact visible, measurable, and improvable.

This work introduces a new magnetic particle imaging (MPI) reconstruction framework based on multi-harmonic 3D deconvolution (MH3D) of gridded portraits, offering a principled, model-driven approach to MPI imaging.&#xD;&#xD;Approach: MH3D defines a convolutional forward model using higher harmonic portraits, which are gridded images formed from filtered frequency-domain signal components. Each harmonic portrait is modeled as a convolution with a distinct PSF, closely approximated by derivatives of the Langevin function, and incorporates receive sensitivity and mesh downsampling for accurate modeling. We also introduce practical strategies for calibration, phase correction, and artifact reduction.&#xD;&#xD;Main Results: We validate the MH3D approach using analytic approximations, numerical simulations, and experimental phantom data. MH3D yields high-resolution 3D reconstructions on seconds-scale runtimes, improves image quality relative to common 3rd-harmonic-only reconstructions, and achieves image quality and resolution comparable to a generalized model-based method in simulations and phantom experiments.&#xD;&#xD;Significance: This work offers new theoretical insight into MPI signal structure, unveiling the methodological and theoretical underpinnings absent in earlier single-harmonic or heuristic methods, thereby supporting accurate and robust 3D imaging with excellent computational efficiency.

Diffusion weighted imaging (DWI) is a key component of multiparametric (mp) prostate MRI. DWI using echo-planar techniques is susceptible to distortion at the recto-prostatic air-tissue interface. This study was to determine whether prone patient positioning reduces adjacent rectal air and DW image distortion when compared with standard-of-care supine positioning. This prospective study included consecutive patients undergoing mpMRI for suspected PCa between 2023 and 2024. Prostate segmentation was performed on DW and contrast-enhanced images. DWI distortion was measured quantitatively. Qualitative image quality of DWI and T2-weighted imaging (T2WI) was evaluated using PI-QUAL version 2; a separate 5-point clinically based Likert scale was employed to evaluate the volume of rectal air adjacent to the prostate. Fifty-two patients were enrolled. In total, 58% of patients expressed a preference for supine imaging versus 20% for prone imaging. Qualitative DWI image quality improved significantly in the prone position [median: 4 (3 to 4)] versus supine [3 (1 to 4)]; P < 0.001. In contrast, prone T2WI quality [1 (1 to 1)] was significantly inferior than supine T2WI [3 (3-4)]; P < 0.001. Quantitative measures of rectal air were significantly lower for prone [1.13cm3 (0.34-2.43)] compared with supine imaging [1.96cm3 (0.47 to 5.81); P = 0.005]. There was no significant difference in distortion between prone [3.21mm (2.42 to 3.82) and supine [2.95mm (2.25 to 4.21)] positioning across all patients (P = 0.80); however, in patients with >4cm3 of supine rectal air (n = 19), distortion was significantly reduced by prone imaging [3.49mm (2.84 to 4.03)] compared with supine [4.60mm (3.17 to 5.95)]; P = 0.02. The mean additional scanning time for the necessary prone imaging was 8 minutes 18 seconds. Prone positioning significantly reduces DWI distortion artefact when rectal air is present, but consistently results in degraded T2WI quality.

Many factors, including the cornea, the lens, and the irregular shape of the eyeball, might influence the visual acuity (VA) of the eye. Ocular aberrations (OAs) are induced by these imperfections, and the normalized Zernike expansion is a typical approach for describing OAs. Customarily, estimating VA of the eye has included procedures that are subjective in nature and conducted through the eye chart. Also, it has been used conversion formulas to convert lower-order aberration to diopters. However, there is no proper approach to convert the diopter values to VA. Intending to avoid the drawbacks of subjective techniques of predicting VA and for accurating the prescribing through the OAs, unlike prior work, we propose an objective approach to determine the impact of Zernike mode(s) corresponding to an ocular aberration on VA using an objective image quality (IQ) metric which is Neural Sharpness (NS). Proposed approach leads to get mathematical relationship between VA and diopters. The Summed Square of Residuals (SSE), Root Mean Square Error (RMSE), and R2 approach confirm the accuracy and the validity of the model. This relationship can be employed to estimate the VA of any ocular aberration in terms of diopters. Also, the proposed relation can be used to assess the efficacy of refractive treatments.

This research work is targeted for image denoising and its improvement for Magnetic Resonance Imaging (MRI) images. The MRI is a special type of medical imaging technique, which shows clear structure of inner body parts like tissues and organs. Once the image gets corrupted with noise, its visual quality degraded and analysis of that noisy image becomes difficult. To improve the quality of noisy image, identification and reduction of noise is necessary. A wide variety of solutions for removal of noise from MR images have been proposed like Median filtering, Non Local Means filter, Maximum likelihood estimation and LMMSE (Minear Minimum Mean Square Error) Filtering. Most of the existing methods suffer with some drawbacks or limitations. This research proposes two novel denoising techniques for MRI images. Among available techniques, NLM filtering based techniques is very functional and possesses significant scope of improvement. The simulation results confirm the superiority of various methods as compared to existing denoising methods in removal of Gaussian as well as Rician noises.

The discrepancy in reliability and evidence conflict in multimodal data fusion for medical image prediction significantly undermines the accuracy of clinical decision-making. To address this challenge, we propose an Adaptive Evidence-Gated Fusion Network (AEGFN) based on Dempster-Shafer (DS) evidence theory. This framework models the evidence quality and cognitive uncertainty of CT images, image sequences, and clinical data using the Dirichlet distribution. We innovatively introduce an Evidence-Attention Gate (EAG) to dynamically adjust fusion weights for high-conflict modalities (conflict>0.6), enabling conflict-aware uncertainty compensation. Additionally, a hybrid loss function combining KL divergence regularization with uncertainty-weighted cross-entropy is designed to balance model confidence and generalization. Evaluated on colorectal cancer (656 cases) and radiation pneumonitis (117 cases) datasets for binary classification tasks (predicting patient death and predicting RP occurrence), AEGFN achieves classification accuracies of 95.04% (AUC 0.97) and 82.34% (AUC 0.8312), outperforming the state-of-the-art method DDEF by 0.66% and 1.94%, respectively. This work provides a robust and interpretable solution for multimodal medical prediction, enhancing the reliability of clinical decision support systems.

Skin cancer is a disease that affects people of all ages. Automated diagnosis of skin cancer reduces the rate of death by detecting the disease at primary phase. Visual inspecting at the clinical inspection of skin lesion is one of the hard procedure because the similarity between the lesions exists. In this manuscript, Optimized Auxiliary Classifier Wasserstein Generative Adversarial Network fostered Skin Cancer Classification from Dermoscopic Images (OAC-WGAN-SCC-DI) is proposed. Initially, the input Skin dermoscopic images are engaged from the dataset of Skin Lesion Images for Melanoma Classification. The Dynamic Context-Sensitive Filter was used in removing noise and increasing the quality of Skin dermoscopic image. Next, these pre-processed images are given to Classic Semantic Segmentation Algorithm for segmenting ROI region.The segmented ROI region is given into Dual-Domain Feature Extraction for extracting Radiomic features such as Grayscale statistic features and Haralick Texture features. The extracted features are given into the Auxiliary Classifier Wasserstein Generative Adversarial Network (ACWGAN) which classifies the skin cancers, like Melanocytic nevus, Basal cell carcinoma, Actinic Keratosis, Benign keratosis, Dermatofibroma, Vascular lesion including Squamous cell carcinoma. In general, ACWGAN does not show any optimization adaption methods to determine the optimum parameterto offer accurate skin cancer classification. Artificial Humming Bird Optimization Algorithm is proposed in this manuscript to optimize ACWGAN classifier that classifies skin cancer precisely. The proposed OAC-WGAN-SCC-DI is implemented using MATLAB. To classify Skin cancer, performance metrics like precision, accuracy, F1-score, Recall (Sensitivity), Matthew's correlation coefficient, specificity, Jaccard co-efficient, Error rate, ROC, computational time are considered. Performance of the OAC-WGAN-SCC-DI approach attains 13.11%, 27.12% and 18.73% high specificity, 29.13%, 23.04% and 19.51% lower computation Time, 22.29%, 5.365%, 1.551% and 3.915% higher ROC and 28.65%, 3.98%, and 17.15% higher MCC compared with existing methods such as Skin cancer classification of Convolutional Neural Network with optimized squeeze Net by Bald Eagle Search optimization (DCNN-SCC-DI) and Skin cancer detection of Convolutional Neural Network using Gray Wolf Optimization (CNN-TL-SCC-DI), Hybrid convolutional neural networkswith SVM classifier for categorization of skin cancer (SVM-SCC-DI) respectively.

ABSTRACT Magnetic Resonance Imaging (MRI) plays a pivotal role in noninvasive neurological diagnosis; however, the presence of Rician noise significantly deteriorates image quality, affecting diagnostic accuracy. This study introduces a novel and adaptive denoising framework named SGO-PPF, which synergistically integrates the Adaptive Optimum Weighted Mean Filter (AOWMF) with the Past-Present-Future (PPF) modeling approach. To optimize filter behavior, the framework leverages the Social Group Optimization (SGO) algorithm, a bio-inspired metaheuristic that dynamically tunes parameters based on the underlying noise distribution. Unlike static or pre-trained models, the proposed method adapts in real-time to spatial variations in Rician noise while preserving anatomical fidelity. Extensive experiments on the IXI brain MRI dataset across T1, T2, and PD sequences demonstrate the superiority of the proposed method in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The SGO-PPF framework achieves robust denoising performance, particularly at higher noise levels, outperforming classical filters and CNN-based models while maintaining moderate computational complexity. Its performance is further validated through 5-fold cross-validation, ensuring generalizability and stability across imaging conditions. This work contributes a computationally efficient and clinically applicable solution to high-fidelity MRI restoration.

Optical coherence tomography angiography (OCTA) provides micrometer-resolution maps of the retinal microvasculature. However, even slight ocular motion can introduce pronounced motion artifacts that degrade image quality. To overcome this, we present a split-spectrum dual-domain phase-intensity fusion algorithm. The approach mitigates low-frequency phase drift in the time domain, corrects systematic phase errors in the frequency domain, and integrates complementary amplitude-decorrelation and phase-difference information. Independent processing of spectral sub-bands further enhances the flow signal-to-noise ratio. In vivo mouse-retina imaging shows that, even without additional inter-frame registration, the method markedly improves angiographic contrast and signal fidelity while greatly reducing motion artifacts. This technique provides a reliable path to high-quality OCTA under dynamic conditions or at low acquisition frame rates.

Infant MRI is limited by motion and the frequent reliance on general anesthesia (GA), which suppresses motion but increases procedural risk, resource use, and turnover time. A sedation-free alternative is the feed-and-wrap (FW) technique-natural sleep supported by swaddling and noise control-which avoids anesthesia but can be limited by motion and variable success. Deep-learning (DL)-based image reconstruction shortens acquisitions and improves image quality, potentially strengthening the FW pathway (DL-FW) by reducing scan time and repeat sequences. Our study aimed to investigate whether, compared with GA, DL-FW reduces MRI turnover time in infants younger than 4months. In this single-center retrospective study, we included consecutive infants aged ≤4months who underwent brain MRI during the study period and met predefined criteria. Infants underwent either DL-FW or GA according to institutional practice. The primary endpoint was the turnover time of the MRI room, defined as the interval from the starting point to the end of the procedure. Times are summarized as median (IQR) and mean±SD; groups were compared using a two-sided Mann-Whitney U test (α=0.05). The between-group shift was estimated with the Hodges-Lehmann (HL) estimator and 95% confidence interval (CI). Forty-eight infants were analyzed (DL-FW n=22; GA n=26). Turnover time was shorter with DL-FW (23min [21-27], mean±SD 26.8±11.3, range 14-52) versus GA (30min [27-38], 32.6±9.8, 19-58), and the difference was significant (U=166; z= - 2.48; P=0.013). The HL estimator indicated that the GA turnover time exceeded that of DL-FW by 6min (95% CI 2-11), corresponding to a 7-min reduction in group medians (~23% relative to GA). No data were missing. In infants aged ≤4months, a DL-FW pathway with an infant MRI stabilizer was associated with significantly shorter MRI room turnover time than GA was, supporting the use of DL-FW as an anesthesia-sparing approach that may improve workflow and safety.

Chest computed tomography (CT) surpasses chest radiography (CR) in accurately assessing disease severity and detecting early structural pulmonary changes in patients with cystic fibrosis (CF). Chest CT provides detailed visualisation and quantification of CF-specific lung pathologies and can reveal these changes before they manifest clinically or become detectable on CR. The past decade has witnessed the advent and refinement of radiation-reducing techniques in CT which have enabled substantial dose reductions. Our study prospectively evaluates the efficacy of ultra-low dose CT (ULDCT) chest in identifying pulmonary changes within a paediatric patient cohort. Paediatric patients with CF, who presented for routine clinical outpatient follow-up between 01/07/2022, and 01/07/2023 underwent ULDCT and CR (if not recently performed) and image analysis was performed. Radiation dose, subjective and objective image quality and disease severity were recorded. 45 patients (mean age 10.5 years) underwent clinically indicated ULDCT chest ± CR. The mean effective dose was of ULDCT was 0.07 ± 0.01 mSv, a dose that approximates that of a frontal and lateral chest radiograph. The average ULDCT Brody II severity score across the entire cohort was 5.62, with excellent inter-rater reliability and intra-class correlation coefficient (ICC) of 0.98 (95% CI = 0.96, 0.99). The average Chrispin-Norman score on chest radiograph was 0.93 with moderate inter-rater reliability and ICC of 0.64 (95% CI = 0.19, 0.83). In light of its superior diagnostic capabilities, minimal radiation dose penalty, we advocate for ULDCT to be the preferred modality for surveillance imaging in paediatric patients with CF.