Image Quality Evaluation Research Articles

Feasibility of very low iodine dose aortoiliac CT angiography using dual-source photon-counting detector CT.

To evaluate the feasibility of aortoiliac CT-Angiography (CTA) using dual-source photon-counting detector (PCD)-CT with minimal iodine dose. This IRB-approved, single-center prospective study enrolled patients with indications for aortoiliac CTA from December 2022 to March 2023. All scans were performed using a first-generation dual-source PCD-CT. Images were acquired with fast pitch and full spectral capabilities (collimation 144×0.4mm). The contrast protocol included a mixture of sodium chloride and iodinated contrast agent (Iopromide, total iodine dose: 9.5-9.8g). Virtual monoenergetic images (VMIs) were reconstructed at 40, 50, 60, and 68keV. Two blinded radiologists evaluated image quality on a 4-point scale. Attenuation was measured across eight regions in the aorta and iliac arteries, and contrast-to-noise ratio (CNR) was calculated. Statistical comparisons were performed using repeated measures ANOVA and Bonferroni post-hoc tests. The final cohort consisted of 39 subjects (mean age: 69.6±9.6years; 30.8% female). VMI at 40keV provided significantly higher attenuation: 478±114 HU, compared to 50keV (331±74 HU), 60keV (241±51 HU), and 68keV (190±48 HU) (p<0.01). This translated in increased CNR for 40keV reconstructions (11.8±3.9), followed by 50keV (9.1±3.0), 60keV (7.0±2.3), and 68keV (6.1±1.9) (p<0.01). Subjective image quality was rated excellent at 40keV (4 [3,4]), though associated with highest noise (38±7.4 HU, p=0.02). Aortoiliac CTA using dual-source PCD-CT at 40keV achieved high attenuation and CNR, enabling effective imaging with only 9.8g of iodine.

The Clinical Value of the MAR+ Metal Artifact Reduction Algorithm for Postoperative Assessment of Lumbar Internal Fixation.

With the widespread use of lumbar pedicle screws for internal fixation, the morphology of the screws and the surrounding tissues should be evaluated. The metal artifact reduction (MAR) technique can reduce the artifacts caused by pedicle screws, improve the quality of computed tomography (CT) images after pedicle fixation, and provide more imaging information to the clinic. To explore whether the MAR+ method, a projection-based algorithm for correcting metal artifacts through multiple iterative operations, can reduce metal artifacts and have an impact on the structure of the surrounding metal. A total of 57 patients who underwent lumbar spine CT examination after lumbar internal fixation from January to December 2023 in our hospital were retrospectively enrolled. The CT images were reconstructed using MAR+ and non-MAR+ techniques and were subdivided into MAR+ and non-MAR+ groups. The CT number (in Hounsfield units) and the SD noise values of the spinal canal, vertebral body, psoas major muscle, and adjacent fat were measured in the 2 groups of CT images, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The subjective score was evaluated by two diagnostic radiologists using a double-blind method for image quality evaluation of the MAR+ group and the non-MAR+ group, and the image quality was classified on a 5-point scale. The rank-sum test was utilized to compare the subjective and objective scores of the 2 groups. The SD values of the spinal canal (Z=-4.12, P<0.01), vertebral body (Z=-3.81, P<0.01), and psoas major muscle (Z=-3.87, P<0.01) in the MAR+ group were significantly lower than those in the non-MAR+ group (P<0.05). However, the SD values of the adjacent fat (Z=-2.03, P=0.42) in the MAR+ group, although smaller than those in the non-MAR+ group, were not statistically significant. The CNR values of vertebral canal (Z=-2.67, P=0.008) and fat (Z=-2.60, P=0.009) were higher in the MAR+ group than in the non-MAR+ group, whereas the CNR values of the vertebral body (Z=-6.74, P<0.01) in the MAR+ group were smaller than those in the non-MAR+ group, and the difference of all of them was statistically significant (P<0.05). Furthermore, for both CT and SNR values, the MAR group's values were all less than those of the non-MAR group and were statistically significant (P<0.05). The subjective scores of the measurement points were all higher in the MAR+ group than in the non-MAR+ group. The MAR+ technique has a noise reduction effect on different tissues and artifacts are significantly reduced. Although the artifacts caused by metal screws were not completely eliminated, the MAR+ technique was able to reduce the interference of artifacts in the diagnosis of CT images, thus improving their diagnostic quality.

Processing of Eddy Current Infrared Thermography and Magneto-Optical Imaging for Detecting Laser Welding Defects

Infrared (IR) magneto-optical (MO) bi-imaging is an innovative method for detecting weld defects, and it is important to process both IR thermography and MO imaging characteristics of weld defects. IR thermography and MO imaging can not only run simultaneously but can also run separately in special welding processes. This paper studies the sensing processing of eddy current IR thermography and MO imaging for detecting weld defects of laser spot welding and butt joint laser welding, respectively. To address the issues of high-level noise and low contrast in eddy current IR detection thermal images interfering with defect detection and recognition, a method based on least squares and Gaussian-adaptive bilateral filtering is proposed for denoising eddy current IR detection thermal images of laser spot welding cracks and improving the quality of eddy current IR detection thermal images. Meanwhile, the image gradient is processed by Gaussian-adaptive bilateral filtering, and then the filter is embedded in the least squares model to smooth and denoise the image while preserving defect information. Additionally, MO imaging for butt joint laser welding defects is researched. For the acquired MO images of welding cracks, pits, incomplete fusions, burn-outs, and weld bumps, the MO image processing method that includes median filtering, histogram equalization, and Wiener filtering was used, which could eliminate the noise in an image, enhance its contrast, and highlight the weld defect features. The experimental results show that the proposed image processing method can eliminate most of the noise while retaining the weld defect features, and the contrast between the welding defect area and the normal area is greatly improved. The denoising effect using the Natural Image Quality Evaluator (NIQE) and the Blind Image Quality Index (BIQI) has been evaluated, further demonstrating the effectiveness of the proposed method. The differences among weld defects could be obtained by analyzing the gray values of the weld defect MO images, which reflect the weld defect information. The MO imaging method can be used to investigate the magnetic distribution characteristics of welding defects, and its effectiveness has been verified by detecting various butt joint laser welding weldments.

Usefulness of four-dimensional noise reduction filtering using a similarity algorithm in low-dose dynamic computed tomography for the evaluation of breast cancer: a preliminary study.

To evaluate the effects of four-dimensional noise reduction filtering using a similarity algorithm (4D-SF) on the image quality and tumor visibility of low-dose dynamic computed tomography (CT) in evaluating breast cancer. Thirty-four patients with 38 lesions who underwent low-dose dynamic breast CT and were pathologically diagnosed with breast cancer were enrolled. Dynamic CT images were reconstructed using iterative reconstruction alone or in combination with 4D-SF. We selected the peak enhancement phase image of breast cancer for each patient for quantitative and qualitative evaluations of image quality and measurement of the maximum diameter of breast cancer. The signal-to-noise and contrast-to-noise ratios were calculated for quantitative evaluation. The maximum diameters of the breast cancer were measured from the images obtained with and without 4D-SF (4D-SF ±) (size-4D-SF + and size-4D-SF-) and for the pathological specimen (size-PS) and compared. The median and interquartile ranges of the signal-to-noise ratio [4D-SF-: 3.03 (2.54-4.17) vs 4D-SF + : 5.52 (4.75-6.66)] and contrast-to-noise ratio [4D-SF-: 2.88 (2.00-3.60) vs 4D-SF + : 7.84 (4.65-10.35)] were significantly higher for 4D-SF + than for 4D-SF- (p < 0.001). The overall image quality (Observer 1, p < 0.001; Observer 2, p < 0.001) and tumor margin sharpness scores (Observer 1, p = 0.003; Observer 2, p < 0.001) were significantly higher for 4D-SF + than for 4D-SF-. The tumor contrast scores for 4D-SF + and 4D-SF- were not significantly different (Observers 1, 2; p = 0.083). Size-4D-SF- was significantly smaller than size-PS (p < 0.001); size-4D-SF + was also smaller than size-PS, but the difference was not significant (p = 0.088). The Spearman's rank correlation coefficient was 0.65 for size-PS and size-4D-SF- and 0.77 for size-PS and size-4D-SF + . The 4D-SF can improve the image quality and tumor visibility of low-dose dynamic CT in evaluating breast cancer extent due to noise reduction.

Gadopiclenol Enables Reduced Gadolinium Dose While Maintaining Quality of Pulmonary Arterial Enhancement for Pulmonary MRA: An Opportunity for Improved Safety and Sustainability.

Pulmonary magnetic resonance angiography (MRA) is an imaging method with proven utility for the exclusion of pulmonary embolism and avoids the need for ionizing radiation and iodinated contrast agents. High-relaxivity gadolinium-based contrast agents (GBCAs), such as gadopiclenol, can be used to reduce the required gadolinium dose for pulmonary MRA. The aim of this study was to compare the contrast enhancement performance of gadopiclenol with an established gadobenate dimeglumine-enhanced pulmonary MRA protocol. In this retrospective single-center study, data from 152 patients who underwent pulmonary MRA at 1.5 T were analyzed. Imaging was performed with either 0.05 mmol/kg gadopiclenol (n = 75) or 0.1 mmol/kg gadobenate dimeglumine (n = 77), using dedicated multiphasic imaging protocols with precontrast, pulmonary arterial phase, immediate delayed phase, and a low flip-angle T1-weighted spoiled gradient echo acquisition. Subjective image quality evaluation was performed blinded by 2 radiologists on a 5-point Likert scale. For the estimation of interrater reliability, Cohen weighted κ was calculated. For semiquantitative assessment, signal intensities were measured in the pulmonary arteries, and relative signal enhancement was calculated. Data from groups were compared with Mann-Whitney U tests using Bonferroni corrections. Signal enhancement relative to precontrast in the first-pass pulmonary arterial phase was higher with 0.05 mmol/kg gadopiclenol compared with 0.1 mmol/kg gadobenate dimeglumine (20.0-fold ± 5.6-fold vs 17.8-fold ± 5.8-fold; P = 0.015). Readers observed no difference in subjective rating in terms of intravascular contrast, peripheral vessel depiction, and diagnostic confidence with substantial interrater reliability (Cohen κ = 0.73 [95% confidence interval: 0.57-0.89], 0.65 [0.55-0.75], and 0.74 [0.65-0.84], all P's < 0.001). No severe adverse events were recorded for any clinical MRA examination. The high-relaxivity contrast agent gadopiclenol can facilitate a reduction in gadolinium dose by 50% without compromising contrast enhancement for pulmonary MRA. This approach may enhance the safety and sustainability of pulmonary MRA in the long term.

Remote sensing image Super-resolution reconstruction by fusing multi-scale receptive fields and hybrid transformer

To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resolution, substantially enhancing reconstruction quality and detail recovery. The discriminator combines multi-scale convolution, global Transformer, and hierarchical feature discriminators, providing a comprehensive and refined evaluation of image quality. Finally, the model incorporates a Charbonnier loss function and total variation (TV) loss function, which significantly improve training stability and accelerate convergence. Experimental results demonstrate that the proposed method, compared to the SRGAN algorithm, achieves average improvements of approximately 3.61 dB in Peak Signal-to-Noise Ratio (PSNR), 0.070 (8.2%) in Structural Similarity Index (SSIM), and 0.030 (3.1%) in Feature Similarity Index (FSIM) across multiple datasets, showing significant performance gains.

Automatic image quality evaluation in digital radiography using a modified version of the IAEA radiography phantom allowing multiple detection tasks: A multi detection task IAEA radiography phantom.

To evaluate image quality (IQ) of for-processing (raw) and for-presentation (clinical) radiography images, under different exposure conditions and digital image post-processing algorithms, using a phantom that enables multiple detection tasks. A modified version of the radiography phantom described in the IAEA Human Health Series No. 39 publication was constructed, incorporating six additional Aluminum (Al) targets of thicknesses both smaller and larger than the standard one. Raw and clinical images were simultaneously acquired using two digital radiography units from different manufacturers, various exposure parameters and different examination protocols. The phantom images were read using the free IAEA software (ATIA) that estimates automatically various IQ-metrics in images of the original phantom. Since in the modified phantom had seven Al targets, images were read seven times, one for each different Al thickness. The IQ-metrics' values obtained were analyzed to investigate their dependence on incident air kerma on the image receptor, tube potential, examination protocol, image type (raw or clinical), Al-target thicknesses, and manufacturer. The IQ-metric values calculated using the modified IAEA phantom images can be radically different between raw and clinical images, and between different manufacturers, irrespectively whether only the standard or all the different Al-target thicknesses are considered. The modulation transfer function (MTF) and the signal-to-noise-ratio (SNR) dependence on exposure conditions and post-processing algorithms do not always follow the same trends for raw and clinical images and/or different manufacturers, while the signal-difference-to-noise-ratio (SDNR) and the detectability index (d'), despite their differences, seem more appropriate to characterize IQ. However, the d' metric, which also considers both MTF and the normalized noise power spectrum (NNPS) should be considered more complete IQ-metric than SDNR. Though theoretically d' values should be calculated using raw images, clinical images can be also used, at least for constancy tests.

Optimizing hip MRI: enhancing image quality and elevating inter-observer consistency using deep learning-powered reconstruction

BackgroundConventional hip joint MRI scans necessitate lengthy scan durations, posing challenges for patient comfort and clinical efficiency. Previously, accelerated imaging techniques were constrained by a trade-off between noise and resolution. Leveraging deep learning-based reconstruction (DLR) holds the potential to mitigate scan time without compromising image quality.MethodsWe enrolled a cohort of sixty patients who underwent DL-MRI, conventional MRI, and No-DL MRI examinations to evaluate image quality. Key metrics considered in the assessment included scan duration, overall image quality, quantitative assessments of Relative Signal-to-Noise Ratio (rSNR), Relative Contrast-to-Noise Ratio (rCNR), and diagnostic efficacy. Two experienced radiologists independently assessed image quality using a 5-point scale (5 indicating the highest quality). To gauge interobserver agreement for the assessed pathologies across image sets, we employed weighted kappa statistics. Additionally, the Wilcoxon signed rank test was employed to compare image quality and quantitative rSNR and rCNR measurements.ResultsScan time was significantly reduced with DL-MRI and represented an approximate 66.5% reduction. DL-MRI consistently exhibited superior image quality in both coronal T2WI and axial T2WI when compared to both conventional MRI (p < 0.01) and No-DL-MRI (p < 0.01). Interobserver agreement was robust, with kappa values exceeding 0.735. For rSNR data, coronal fat-saturated(FS) T2WI and axial FS T2WI in DL-MRI consistently outperformed No-DL-MRI, with statistical significance (p < 0.01) observed in all cases. Similarly, rCNR data revealed significant improvements (p < 0.01) in coronal FS T2WI of DL-MRI when compared to No-DL-MRI. Importantly, our findings indicated that DL-MRI demonstrated diagnostic performance comparable to conventional MRI.ConclusionIntegrating deep learning-based reconstruction methods into standard clinical workflows has the potential to the promise of accelerating image acquisition, enhancing image clarity, and increasing patient throughput, thereby optimizing diagnostic efficiency.Trial registrationRetrospectively registered.

Development of an image quality evaluation system for bedside chest X-ray images using scatter correction processing.

In plain radiography, scattered X-ray correction processing (Virtual Grid: VG) is used to estimate and correct scattered rays in images. We developed an objective evaluation system for bedside chest X-ray images using VG and investigated its usefulness. First, we trained the blind/referenceless image spatial quality evaluator (BRISQUE) on 200 images obtained by portable chest radiography. We then evaluated optimal chest phantom VG images as well as those that deviated from the VG setting conditions using BRISQUE. Furthermore, we conducted a subjective evaluation using the mean opinion score (MOS) and established an objective evaluation system for VG images. Finally, the degree of agreement between the MOS subjectively evaluated by 14 radiological technologists and that determined by the objective evaluation system for 100 clinical images obtained by portable chest radiography was calculated using Cohen's kappa coefficient. The correlation coefficient between the BRISQUE score and MOS for chest phantom images was - 0.96 (p < 0.05). The two scores showed a very high linear correlation, indicating the potential of the BRISQUE score as an alternative to MOS. The Cohen's kappa coefficient for the objective evaluation system using the optimal conversion table was 0.42. Conversely, there was a very high detection rate of 82.86% for poor-quality images. An objective evaluation system for bedside chest X-ray images using VG that uses no-reference image quality evaluation helps provide proper image quality. Furthermore, such a system can be constructed with a small amount of training data, which increases the possibility of introducing it to a variety of facilities.

Staging of prostate Cancer with ultra-fast PSMA-PET scans enhanced by AI.

PSMA-PET is a reference standard examination for patients with prostate cancer, but even using recently introduced digital PET detectors image acquisition with standard field-of-view scanners is still in the range of 20min. This may cause limited access to examination slots because of the growing demand for PSMA-PET. Ultra-fast PSMA-PET may enhance throughput but comes at the cost of poor image quality. The aim of this manuscript is to evaluate the accuracy of AI-enhanced ultra-fast PSMA-PET for staging of patients with prostate cancer. A total number of 357 whole-body [68Ga]Ga-PSMA-11 PET datasets were included. Patients underwent two digital PET scans, one at standard and one at ultra-fast speed (table speed: 0.6-1.2mm/s vs. 50mm/s). A modified pix2pixHD generative adversarial network to enhance the ultra-fast images was trained with 286 datasets and evaluated with the remaining 71 datasets. The staging accuracy of ultra-fast PSMA-PET and AI-enhanced ultra-fast PET was compared with the reference standard PET separately for miTNM regions proposed by PROMISE V2.0. The AI-network significantly improved the visual image quality and detection rate in most miTNM regions compared with the non-enhanced image data (T: 69.6% vs. 43.5%, p < 0.05; N: 46.3% vs. 27.8%, p < 0.01; M1a 64.4% vs. 47.5%, p < 0.01; M1b: 85.7% vs. 72.1%, p < 0.01). However, improvement was not significant for the M1c category (42.9 vs. 28.6%, p > 0.05). Missed lesions had a smaller SUVmax and lesion size compared with detected lesions (exemplary for N: 9.5 vs. 26.5 SUVmax; 4 vs. 10mm). SUVmax values of lesions were significantly different in all miTNM regions between the ultra-fast and reference standard PET, but only in the T-region between the AI-enhanced and reference standard PET. The AI-based image enhancement improved image quality and region detection rates by a mean of 17.9%. As the sensitivity of synthetic PET for small and low-uptake lesions was limited, a potential clinical use case could be disease monitoring in patients with high tumor volume and PSMA uptake undergoing PSMA radioligand therapy. The improvement in detection rate of distant metastases was not significant. This indicates that more training data is needed to ensure robust results also for lesions that have lower appearance frequency. Future studies on accelerated PSMA-PET seem warranted.

Image-Based Detection and Classification of Malaria Parasites and Leukocytes with Quality Assessment of Romanowsky-Stained Blood Smears.

Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification. Using a dataset of 1000 clinically diagnosed images, we applied feature extraction techniques, including histogram bins and texture analysis with the gray level co-occurrence matrix (GLCM), alongside support vector machines (SVMs), for image quality assessment. Leukocyte detection employed optimal thresholding segmentation utility (OTSU) thresholding, binary masking, and erosion, followed by the connected components algorithm. Parasite detection used high-intensity region selection and adaptive bounding boxes, followed by a custom convolutional neural network (CNN) for candidate identification. A second CNN classified parasites into trophozoites, schizonts, and gametocytes. The system achieved an F1-score of 95% for image quality evaluation, 88.92% for leukocyte detection, and 82.10% for parasite detection. The F1-score-a metric balancing precision (correctly identified positives) and recall (correctly detected instances out of actual positives)-is especially valuable for assessing models on imbalanced datasets. In parasite stage classification, CNN achieved F1-scores of 85% for trophozoites, 88% for schizonts, and 83% for gametocytes. This study introduces a robust and scalable automated system that addresses critical challenges in malaria diagnosis by integrating advanced image quality assessment and deep learning techniques for parasite detection and classification. This system's adaptability to low-resource settings underscores its potential to improve malaria diagnostics globally.

Reduced contrast agent volume using a heart-rate dependent and free-breathing scanning protocol in coronary computed tomography angiography (CTA) for patients with chronic obstructive pulmonary disease (COPD)

BackgroundThe personalized, free-breathing, heart rate-dependent computed tomography angiography (CTA) protocol can significantly reduce the utilization of contrast medium (CM). This proves especially beneficial for patients with chronic obstructive pulmonary disease (COPD) undergoing coronary artery CTA examinations.ObjectiveThe aim of this study was to evaluate the feasibility of a personalized CT scanning protocol that was tailored to patients’ heart rate and free-breathing for coronary CTA of patients with COPD.MethodsA total of 400 patients with COPD who need to undergo the coronary CTA were prospectively randomized into two groups (patients with vascular occlusion were excluded). Group A (n = 200) underwent CTA following a traditional protocol (70mL). The timing of the scans in Group B (n = 200) was determined according to the patient’s HR and free-breathing (30mL).ResultsNo difference was found between the two groups in the CT values of RCA, LA, or LCX; (p = 0.131, 0.195 and 0.116). Subjective ratings of image quality (Table 2) were not statistically different between the two groups (p = 0.825).ConclusionBy adopting a heart-rate dependent and free-breathing protocol, the contrast medium volume were reduced in coronary CTA for patients with COPD, while the image quality was remained comparable to those acquired with routine CTA protocol.

First Clinical Feasibility and Safety Study of a Novel Multimodality Fallopian Tube Imaging Endoscope.

We present the results of the first feasibility and safety study of a novel multi-modality falloposcope, in 19 volunteers. The falloposcope incorporated multispectral fluorescence imaging (MFI) and optical coherence tomography (OCT) for evaluation of the fallopian tubes (FT). Nineteen females undergoing elective salpingectomy were recruited in this IRB-approved study. During a 15-min pause in standard of care surgery, a novel falloposcope was inserted under hysteroscopic guidance and advanced into the FTs. The furthest cannulated segment (proximal, middle, distal) was noted, and acquisition of MFI and OCT images was attempted at each segment in a retrograde fashion. Surgical discard samples from each segment of both FTs were obtained per pathology discretion for ex vivo imaging. All samples were processed for histology to evaluate any device-related injury. The falloposcope was inserted into the FTs of 12 patients and successfully cannulated the entire length of the tube to the distal segment in one-third (4/12) of attempts. OCT and/or MFI images were obtained of all but one cannulated FT. Cannulation failure was attributed to proximal anatomy or obstruction, tortuosity of the FT, and limited study time. There were no adverse events related to the falloposcopy procedure. Focal epithelial cell denudation in the proximal segments of three patients, without no stromal involvement, was observed by histological examination. OCT images acquired in vivo showed characteristic tissue microstructures but were more susceptible to artifacts compared to ex vivo images. The results of this first-in-human study suggest that falloposcopy with a novel multimodality imaging endoscope is feasible and safe. Future procedural and design modifications will be implemented to improve image quality and cannulation success rates.

Image Quality Evaluation for Brain Soft Tissue in Neuroendovascular Treatment by Dose-Reduction Mode of Dual-Axis "Butterfly" Scan.

Flat panel conebeam CT (CBCT) is essential for detecting hemorrhagic complications during neuroendovascular treatments. Despite its superior image quality and trajectory over conventional CBCT (circular scan), the dual-axis butterfly scan has a slightly higher radiation dose relative to conventional CBCT. This study evaluates the image quality in dose-reduction mode to uncover the appropriate radiation dose for the butterfly scan. We prospectively included patients who were scheduled for neuroendovascular treatment and underwent conventional CBCT and the dose-reduction mode of the butterfly scan. Two reduced radiation dose modes were used for the butterfly scan: medium-dose butterfly scan (70% of the original dose, 45 mGy) or low-dose butterfly scan (50% of the original dose, 30 mGy). The enrolled patients were assigned alternately to undergo either the medium- or low-dose butterfly scan. We evaluated and compared artifacts, contrast, and discrimination of the corticomedullary junction between conventional CBCT and one of the dose-reduction modes of the butterfly scan, with a 5-point scale scoring system. Twenty patients were enrolled in each of the medium- and low-dose groups, totaling 40 patients. Compared with conventional CBCT, the medium-dose butterfly group exhibited reduced artifacts, enhanced contrast, and corticomedullary junction discrimination (except in the occipital lobe). While the low-dose butterfly group exhibited markedly reduced artifacts and improved contrast (except in the occipital lobe), a significant improvement in corticomedullary junction discrimination was not observed. Even with dose reduction, the specialized trajectory of the butterfly scan enables artifact reduction, contrast improvement, and enhanced corticomedullary junction discrimination. However, the impact of the reduced dose was more noticeable, particularly in the occipital region where susceptibility to bone interference resulted in decreased contrast and compromised corticomedullary junction discrimination.

COMPARISON BETWEEN MAGNETIC RESONANCE CHOLANGIO-PANCREATOGRAPHY AND ULTRASOUND FOR DIAGNOSING PATIENTS WITH PANCREATIC TUMOR

Background: Pancreatic cancer poses a significant challenge due to its late diagnosis and poor prognosis. Early and precise detection is essential to improving outcomes. Magnetic Resonance Cholangiopancreatography (MRCP) and Ultrasound (US) are widely used imaging modalities, each with distinct advantages and limitations. Comparative studies focusing on their diagnostic accuracy, sensitivity, and specificity for pancreatic cancers remain limited, necessitating further evaluation to determine their relative effectiveness in clinical practice. Objective: To evaluate and compare the diagnostic performance of MRCP and US in the detection of pancreatic cancers, with a focus on their relative accuracy, sensitivity, and specificity. Methods: A descriptive cross-sectional study was conducted in General Hospital, Services Hospital, and Jinnah Hospital in Lahore, involving 160 patients with suspected pancreatic cancers who underwent MRCP and US. Data were collected using a structured questionnaire and analyzed using SPSS version 27. Descriptive and inferential statistics, including the chi-square test, were used to assess associations between imaging, clinical, and demographic variables, with significance set at p &lt; 0.05. Diagnostic accuracy, patient comfort, and cost were compared across modalities. Results: MRCP demonstrated higher diagnostic accuracy, detecting pancreatic tumors in 46.88% of cases (mean detection score 1.21, SD ± 0.41) compared to US, which detected tumors in 46.88% of cases (mean detection score 1.74, SD ± 0.44). MRCP was particularly effective in visualizing tumors in the pancreatic head (34.38%), with superior sensitivity (99%) and specificity (99%) compared to US (sensitivity 80%, specificity 99%). MRCP had higher image quality ratings (mean 3.83, SD ± 1.83) compared to US (mean 1.73, SD ± 1.55). However, US scored better on patient comfort (mean 1.73, SD ± 0.44) versus MRCP (mean 1.68, SD ± 1.5). Cost analysis showed MRCP was more expensive (10,000 PKR) than US (1,500–2,000 PKR). Significant associations were found between imaging modality, tumor detection accuracy (p = 0.017), and patient comfort (p = 0.014). Conclusion: MRCP demonstrated superior diagnostic accuracy, particularly for small or complex lesions, while US was effective for initial screening due to accessibility and patient comfort. A multimodal approach combining US for screening and MRCP for confirmation is recommended. Future studies should explore integrating MRCP with advanced imaging techniques like Endoscopic Ultrasonography (EUS) and involve larger, more diverse populations to enhance diagnostic precision and clinical utility.

Evaluation of Image Quality and Scan Time Efficiency in Accelerated 3D T1-Weighted Pediatric Brain MRI Using Deep Learning-Based Reconstruction

Evaluation of Image Quality and Scan Time Efficiency in Accelerated 3D T1-Weighted Pediatric Brain MRI Using Deep Learning-Based Reconstruction

Ultrafast MRI for Pediatric Brain Assessment in Routine Clinical Practice.

To assess the feasibility of ultrafast brain magnetic resonance imaging (MRI) in pediatric patients. We retrospectively reviewed 194 pediatric patients aged 0 to 19 years (median 10.2 years) who underwent both ultrafast and conventional brain MRI between May 2019 and August 2020. Ultrafast MRI sequences included T1 and T2-weighted images (T1WI and T2WI), fluid-attenuated inversion recovery (FLAIR), T2*-weighted image (T2*WI), and diffusion-weighted image (DWI). Qualitative image quality and lesion evaluations were conducted on 5-point Likert scales by two blinded radiologists, with quantitative assessment of lesion count and size on T1WI, T2WI, and FLAIR sequences for each protocol. Wilcoxon signed-rank tests and intraclass correlation coefficient (ICC) analyses were used for comparison. The total scan times for equivalent image contrasts were 1 minute 44 seconds for ultrafast MRI and 15 minutes 30 seconds for conventional MRI. Overall, image quality was lower in ultrafast MRI than in conventional MRI, with mean quality scores ranging from 2.0 to 4.8 for ultrafast MRI and 4.8 to 5.0 for conventional MRI across sequences (P < 0.001 for T1WI, T2WI, FLAIR, and T2*WI for both readers; P = 0.018 [reader 1] and 0.031 [reader 2] for DWI). Lesion detection rates on ultrafast MRI relative to conventional MRI were as follows: T1WI, 97.1%; T2WI, 99.6%; FLAIR, 92.9%; T2*WI, 74.1%; and DWI, 100%. The ICC (95% confidence interval) for lesion size measurements between ultrafast and conventional MRI was as follows: T1WI, 0.998 (0.996-0.999); T2WI, 0.998 (0.997-0.999); and FLAIR, 0.99 (0.985-0.994). Ultrafast MRI significantly reduces scan time and provides acceptable results, albeit with slightly lower image quality than conventional MRI, for evaluating intracranial abnormalities in pediatric patients.

AI-Assisted Compressed Sensing Enables Faster Brain MRI for the Elderly: Image Quality and Diagnostic Equivalence with Conventional Imaging.

Conventional brain MRI protocols are time-consuming, which can lead to patient discomfort and inefficiency in clinical settings. This study aims to assess the feasibility of using artificial intelligence-assisted compressed sensing (ACS) to reduce brain MRI scan time while maintaining image quality and diagnostic accuracy compared to a conventional imaging protocol. Seventy patients from the department of neurology underwent brain MRI scans using both conventional and ACS protocols, including axial and sagittal T2-weighted fast spin-echo sequences and T2-fluid attenuated inversion recovery (FLAIR) sequence. Two radiologists independently evaluated image quality based on avoidance of artifacts, boundary sharpness, visibility of lesions, and overall image quality using a 5-point Likert scale. Pathological features, including white matter hyperintensities, lacunar infarcts, and enlarged perivascular spaces, were also assessed. The interchangeability of the two protocols was determined by calculating the 95% confidence interval (CI) for the individual equivalence index. Additionally, Cohen's weighted kappa statistic was used to assess inter-protocol intra-observer agreement. The ACS images demonstrated superior quality across all qualitative features compared to the conventional ones. Both protocols showed no significant difference in detecting pathological conditions. The 95% CI for the individual equivalence index was below 5% for all variables except enlarged perivascular spaces, indicating the interchangeability of the conventional and ACS protocols in most cases. The inter-rater reliability between the two radiologists was strong, with kappa values of 0.78, 0.74, 0.70 and 0.86 for image quality evaluation and 0.74, 0.80 and 0.70 for diagnostic performance, indicating good-to-excellent agreement in their evaluations. The ACS technique reduces brain MRI scan time by 29.2% while achieving higher image quality and equivalent diagnostic accuracy compared to the conventional protocol. This suggests that ACS could be potentially adopted for routine clinical use in brain MRI.

Qualitative and Quantitative Evaluation of the Image Quality of MDCT Multiphasic Liver Scans in HCC Patients.

Background: The quality of CT images obtained from hepatocellular carcinoma (HCC) patients is complex, affecting diagnostic accuracy, precision, and radiation dose assessment due to increased exposure risks. Objectives: The study evaluated image quality qualitatively and quantitatively by comparing quality levels with an effective radiation dose to ensure acceptable quality accuracy. Materials and Methods: This study retrospectively reviewed 100 known HCC patients (Li-RADS-5) who underwent multidetector computed tomography (MDCT) multiphasic scans for follow-up of their health condition between January and October 2023. The evaluation involved quantitative and qualitative analyses of parameters such as SD, SNR, and CNR, as well as a qualitative assessment by two radiology consultants. The outcomes were compared, and the effective dose was calculated and compared with both quantitative and qualitative assessments of image quality. Results: ROC curve analysis revealed significant differences in CT image quality, with high to moderate specificity and sensitivity across all the quantitative parameters. However, multivariate examination revealed decreasing importance levels, except for CNR (B, 0.203; p = 0.001) and SD BG (B, 0.330; p = 0.002), which increased in B. The CNR and SD BG remained independent variables for CT image quality prediction, but no statistically significant relationship was found between the effective dose and image quality, either quantitatively or qualitatively. Conclusion: This study underscores the vital role of both quantitative and qualitative assessments of CT images in evaluating their quality for patients with HCC and highlights the predictive importance of CNR, SNR, and SD. These findings emphasize the value of these devices in assessing and predicting outcomes to minimize the effective dose.

Deep learning-driven ultrasound equipment quality assessment with ATS-539 phantom data

IntroductionUltrasound equipment provides real-time visualization of internal organs, essential for early disease detection and diagnosis. However, poor-quality ultrasound images can compromise diagnostic accuracy and increase the risk of misdiagnosis. Quality assessments are often subjective, relying on the evaluator's experience and interpretation, which can vary. MethodsThis study introduces a two-stage deep learning framework designed to objectively assess ultrasound image quality using phantom data across three key parameters: ‘Dead zone’, ‘Axial/lateral resolution’, and ‘Gray scale and dynamic range’. Stage 1 automatically extracts regions of interest for each parameter, while Stage 2 employs detection or classification models to evaluate image quality within these regions. To generate an overall equipment quality score, a logistic regression model combines the weighted results from each parameter. ResultsThe classification model demonstrated high performance across datasets, achieving AUC scores of 98.6% for ‘Dead zone’, 87.7% for ‘Axial/lateral resolution’, and 96.0% for ‘Gray scale and dynamic range’. Further analysis using guideline-compliant images of individual devices showed AUC scores of 98.2%, 92.8%, and 100%, respectively. These findings highlight deep learning's potential for quantitative and objective assessments of ultrasound image quality. Ultimately, this framework provides a streamlined approach to quality management, enabling consistent quality control and efficient scoring-based evaluation of ultrasound equipment.