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

BACKGROUND: The consistency of pancreatic apparent diffusion coefficient (ADC) values and intravoxel incoherent motion (IVIM) parameter values across different magnetic resonance imaging (MRI) devices significantly impacts the patient\'s diagnosis and treatment.
OBJECTIVE: To explore consistency in image quality, ADC values, and IVIM parameter values among different MRI devices in pancreatic examinations.
METHODS: This retrospective study was approved by the local ethics committee, and informed consent was obtained from all participants. In total, 22 healthy volunteers (10 males and 12 females) aged 24-61 years (mean, 28.9 ± 2.3 years) underwent pancreatic diffusion-weighted imaging using 3.0T MRI equipment from three vendors. Two independent observers subjectively scored image quality and measured the pancreas\'s overall ADC values and signal-to-noise ratios (SNRs). Subsequently, regions of interest (ROIs) were delineated for the IVIM parameters (true diffusion coefficient, pseudo-diffusion coefficient, and perfusion fraction) using post-processing software. These ROIs were on the head, body, and tail of the pancrease. The subjective image ratings were assessed using the kappa consistency test. Intraclass correlation coefficients (ICCs) and mixed linear models were used to evaluate each device\'s quantitative parameter values. Finally, a pairwise analysis of IVIM parameter values across each device was performed using Bland-Altman plots.
RESULTS: The Kappa value for the subjective ratings of the different observers was 0.776 (P < 0.05). The ICC values for inter-observer and intra-observer agreements for the quantitative parameters were 0.803 [95% confidence interval (CI): 0.684-0.880] and 0.883 (95%CI: 0.760-0.945), respectively (P < 0.05). The ICCs for the SNR between different devices was comparable (P > 0.05), and the ICCs for the ADC values from different devices were 0.870, 0.707, and 0.808, respectively (P < 0.05). Notably, only a few statistically significant inter-device agreements were observed for different IVIM parameters, and among those, the ICC values were generally low. The mixed linear model results indicated differences (P < 0.05) in the f-value for the pancreas head, D-value for the pancreas body, and D-value for the pancreas tail obtained using different MRI machines. The Bland-Altman plots showed significant variability at some data points.
CONCLUSIONS: ADC values are consistent among different devices, but the IVIM parameters\' repeatability is moderate. Therefore, the variability in the IVIM parameter values may be associated with using different MRI machines. Thus, caution should be exercised when using IVIM parameter values to assess the pancreas.

Purpose: To compare image quality and radiation exposure between super- and ultra-high-resolution helical and super-high-resolution volumetric CT of the temporal bone. Methods: Six cadaveric temporal bone specimens were used to evaluate key temporal bone structures using the following CT reconstruction and acquisition modes: helical and single-volume acquisition modes in super-high resolution (0.25-mm slice thickness, 10242 matrix), and helical mode in ultra-high resolution (0.25-mm slice thickness, 20482 matrix). Two observers performed 5 previously described preoperative measurements, measured noise and signal-to-noise ratios for air, and noise for bone, and rated the visualization of 5 anatomical structures on a 4-point scale, for each reconstruction mode. Radiation dose exposure was recorded for each examination. Results: There was no significant difference between any of the quantitative or qualitative measurements in any of the reconstruction and acquisition modes. There was a slight increase in noise and a decrease in signal-to-noise ratio in the air using the single-volume mode (115 ± 13.1 HU and 8.37 ± 0.91, respectively) compared to the helicoidal super-high-resolution (92.4 ± 11.8 HU and 10.8 ± 1.26, respectively) and helicoidal ultra-high-resolution (91.1 ± 10.7 HU and 10.9 ± 1.39, respectively) modes (P < .002). The volumic CT dose index was 50.9 mGy with helical acquisition and 29.8 mGy with single-volume acquisition mode (P < .0001). Conclusion: The single-volume super-high-resolution acquisition mode allows a reduction in radiation dose exposure without compromising image quality compared to helical scanning, but with a slightly lower signal-to-noise ratio in air with the single-volume mode, while there was no difference in image quality between the helical super- and ultra-high-resolution modes.

OBJECTIVE: To benchmark image quality and corresponding radiation doses for acute abdominal CT examination across different laboratories and CT manufacturers.
METHODS: An anthropomorphic phantom was scanned once with local abdominal CT protocols at 40 CT scanners, from four vendors, in thirty-three sites. Quantitative image quality was evaluated by CNR and SNR in the liver and kidney parenchyma. Qualitative image quality was assessed by visual grading analysis performed by three experienced radiologists using a five-point Likert scale to score thirteen image quality criteria. The CTDIvol was recorded for each scan. Pearson\'s correlation coefficient was calculated for the continuous variables, and the intraclass correlation coefficient was used to investigate interrater reliability between the radiologists.
RESULTS: CTDIvol ranged from 3.5 to 12 mGy (median 5.3 mGy, third quartile 6.7 mGy). SNR in liver parenchyma ranged from 4.4 to 14.4 (median 8.5), and CNR ranged from 2.7 to 11.2 (median 6.1). A weak correlation was found between CTDIvol and CNR (r = 0.270, p = 0.092). Variations in CNR across scanners at the same dose level CTDIvol were observed. No significant difference in CTDIvol or CNR was found based on scanner installation year. The oldest scanners had a 15 % higher median CTDIvol and a 12 % lower median CNR. The ICC showed acceptable agreement for all dose groups: low (ICC=0.889), medium (ICC=0.767), high (ICC=0.847), and in low (ICC=0.803) and medium (ICC=0.811) CNR groups.
CONCLUSIONS: There was large variation in radiation dose and image quality across the different CT scanners. Interestingly, the weak correlation between CTDIvol and CNR indicates that higher doses do not consistently improve CNR, indicating a need for systematic assessment and optimization of image quality and radiation doses for the abdominal CT examination.

UNASSIGNED: To assess image quality of a deep learning reconstruction (DLR) algorithm across dose levels using a semi-anthropomorphic upper-abdominal phantom, and compare with filtered back projection (FBP) and hybrid iterative reconstruction (IR).
UNASSIGNED: CT scans obtained at five dose levels (CTDIvol 5, 10, 15, 20 and 25 mGy) were reconstructed with FBP, hybrid IR (IR50, IR70 and IR90) and DLR of low (DLL), medium (DLM) and high strength (DLH) in 0.625 mm and 2.5 mm slices. CT number, homogeneity, noise, contrast, contrast-to-noise ratio (CNR), noise texture deviation (NTD; a measure of IR-specific artifacts), noise power spectrum (NPS) and task-based transfer function (TTF) were compared between reconstruction algorithms.
UNASSIGNED: CT numbers were highly consistent across reconstruction algorithms. Image noise was significantly reduced with higher levels of DLR. Noise texture (NPS and NTD) was with DLR maintained at comparable levels to FBP, contrary to increasing levels of hybrid IR. Images reconstructed with DLR of low and high strength in 0.625 mm slices showed similar noise characteristics to 2.5 mm slice FBP and IR50, respectively. Dose-reduction potential based on image noise with IR50 as reference was estimated to 35% for DLM and 74% for DLH.
UNASSIGNED: The novel DLR algorithm demonstrates robust noise reduction with maintained noise texture characteristics despite higher algorithm strength, and may have overcome important limitations of IR. There may be potential for dose reduction and additional benefit from thin-slice reconstruction.

OBJECTIVE: To evaluate the image quality and PI-RADS scoring performance of prostate T2-weighted imaging (T2WI) based on AI-assisted compressed sensing (ACS).
METHODS: In this prospective study, adult male urological outpatients or inpatients underwent prostate MRI, including T2WI, diffusion-weighted imaging and apparent diffusion coefficient maps. Three accelerated scanning protocols using parallel imaging (PI) and ACS: T2WIPI, T2WIACS1 and T2WIACS2 were evaluated through comparative analysis. Quantitative analysis included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), slope profile, and edge rise distance (ERD). Image quality was qualitatively assessed using a five-point Likert scale (ranging from 1 = non-diagnostic to 5 = excellent). PI-RADS scores were determined for the largest or most suspicious lesions in each patient. The Friedman test and one-way ANOVA with post hoc tests were utilized for group comparisons, with statistical significance set at P < 0.05.
RESULTS: This study included 40 participants. Compared to PI, ACS reduced acquisition time by over 50%, significantly enhancing the CNR of sagittal and axial T2WI (P < 0.05), significantly improving the image quality of sagittal and axial T2WI (P < 0.05). No significant differences were observed in slope profile, ERD, and PI-RADS scores between groups (P > 0.05).
CONCLUSIONS: ACS reduced prostate T2WI acquisition time by half while improving image quality without affecting PI-RADS scores.

BACKGROUND: The overall reject rate (RR) of our newly set up Radiology department was an average of 14%, higher than the recommended 8% target and 10% threshold set by the American Association of Physicists in Medicine (AAPM). An analysis done to identify potential causes of a high RR suggested that radiographers might have been rejecting images of diagnostic value. A lack of consistency in the definition of a diagnostic value image amongst radiographers may be a possible cause in the higher overall RR. This study aims to investigate potential discrepancies among radiographers in defining a diagnostic radiograph.
METHODS: An online survey composed of an image bank with a questionnaire was created, participants grade each image as either accepted or rejected. Fleiss Kappa was used to determine the level of agreement between the radiographers in accepting or rejecting the images in the image bank.
RESULTS: Twenty radiographers with varying years of experience participated in this study. There was fair agreement amongst the radiographers\' judgements, k=.277 (95% CI, .277 to .278), p < .005. Individual kappa for the \"Accept\" and \"Reject\" categories were both 0.277. There is no significant difference in the agreement level across the junior (k=.278), intermediate (k=.371) and senior (k=.275) radiographers.
CONCLUSIONS: The result suggests that there is discrepancy in the radiographers\' definition of a diagnostic radiograph and this misalignment of radiographers\' perception might be one of the underlying causes of high RR.
CONCLUSIONS: This study has provided the researchers with a better insight on the underlying cause of the department high RR. By calibrating the radiographers\' definition of a diagnostic radiograph, it will help realign the radiographer\'s agreement on when a radiograph should be rejected. This will reduce the overall RR and patient\'s overall dose. A lower RR translates to a more efficient turnaround time in General Radiography services, ensuring quality service is provided without further strain on our limited resources.

UNASSIGNED: Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blotchy appearance and decreased resolution for subtle contrasts. The deep-learning image reconstruction (DLIR) algorithm, which integrates a convolutional neural network (CNN) into the reconstruction process, generates high-quality images with minimal noise. Hence, the objective of this study was to assess the IQ of the Precise Image (DLIR) and the IR technique (iDose 4) for the NCCT brain.
UNASSIGNED: This is a prospective study. Thirty patients who underwent NCCT brain were included. The images were reconstructed using DLIR-standard and iDose 4. Qualitative IQ analysis parameters, such as overall image quality (OQ), subjective image noise (SIN), and artifacts, were measured. Quantitative IQ analysis parameters such as Computed Tomography (CT) attenuation (HU), image noise (IN), posterior fossa index (PFI), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in the basal ganglia (BG) and centrum-semiovale (CSO) were measured. Paired t-tests were performed for qualitative and quantitative IQ analyses between the iDose 4 and DLIR-standard. Kappa statistics were used to assess inter-observer agreement for qualitative analysis.
UNASSIGNED: Quantitative IQ analysis showed significant differences (p<0.05) in IN, SNR, and CNR between the iDose 4 and DLIR-standard at the BG and CSO levels. IN was reduced (41.8-47.6%), SNR (65-82%), and CNR (68-78.8%) were increased with DLIR-standard. PFI was reduced (27.08%) the DLIR-standard. Qualitative IQ analysis showed significant differences (p<0.05) in OQ, SIN, and artifacts between the DLIR standard and iDose 4. The DLIR standard showed higher qualitative IQ scores than the iDose 4.
UNASSIGNED: DLIR standard yielded superior quantitative and qualitative IQ compared to the IR technique (iDose4). The DLIR-standard significantly reduced the IN and artifacts compared to iDose 4 in the NCCT brain.

Low-iodine-dose computed tomography (CT) protocols have emerged to mitigate the risks associated with contrast injection, often resulting in decreased image quality.
To evaluate the image quality of low-iodine-dose CT combined with an artificial intelligence (AI)-based contrast-boosting technique in abdominal CT, compared to a standard-iodine-dose protocol in children.
This single-center retrospective study included 35 pediatric patients (mean age 9.2 years, range 1-17 years) who underwent sequential abdominal CT scans-one with a standard-iodine-dose protocol (standard-dose group, Iobitridol 350 mgI/mL) and another with a low-iodine-dose protocol (low-dose group, Iohexol 240 mgI/mL)-within a 4-month interval from January 2022 to July 2022. The low-iodine CT protocol was reconstructed using an AI-based contrast-boosting technique (contrast-boosted group). Quantitative and qualitative parameters were measured in the three groups. For qualitative parameters, interobserver agreement was assessed using the intraclass correlation coefficient, and mean values were employed for subsequent analyses. For quantitative analysis of the three groups, repeated measures one-way analysis of variance with post hoc pairwise analysis was used. For qualitative analysis, the Friedman test followed by post hoc pairwise analysis was used. Paired t-tests were employed to compare radiation dose and iodine uptake between the standard- and low-dose groups.
The standard-dose group exhibited higher attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of organs and vessels compared to the low-dose group (all P-values < 0.05 except for liver SNR, P = 0.12). However, noise levels did not differ between the standard- and low-dose groups (P = 0.86). The contrast-boosted group had increased attenuation, CNR, and SNR of organs and vessels, and reduced noise compared with the low-dose group (all P < 0.05). The contrast-boosted group showed no differences in attenuation, CNR, and SNR of organs and vessels (all P > 0.05), and lower noise (P = 0.002), than the standard-dose group. In qualitative analysis, the contrast-boosted group did not differ regarding vessel enhancement and lesion conspicuity (P > 0.05) but had lower noise (P < 0.05) and higher organ enhancement and artifacts (all P < 0.05) than the standard-dose group. While iodine uptake was significantly reduced in low-iodine-dose CT (P < 0.001), there was no difference in radiation dose between standard- and low-iodine-dose CT (all P > 0.05).
Low-iodine-dose abdominal CT, combined with an AI-based contrast-boosting technique exhibited comparable organ and vessel enhancement, as well as lesion conspicuity compared to standard-iodine-dose CT in children. Moreover, image noise decreased in the contrast-boosted group, albeit with an increase in artifacts.

UNASSIGNED: Super-resolution deep-learning-based reconstruction: SR-DLR is a newly developed and clinically available deep-learning-based image reconstruction method that can improve the spatial resolution of CT images. The image quality of the output from non-linear image reconstructions, such as DLR, is known to vary depending on the structure of the object being scanned, and a simple phantom cannot explicitly evaluate the clinical performance of SR-DLR. This study aims to accurately investigate the quality of the images reconstructed by SR-DLR by utilizing a structured phantom that simulates the human anatomy in coronary CT angiography.
UNASSIGNED: The structural phantom had ribs and vertebrae made of plaster, a left ventricle filled with dilute contrast medium, a coronary artery with simulated stenosis, and an implanted stent graft. By scanning the structured phantom, we evaluated noise and spatial resolution on the images reconstructed with SR-DLR and conventional reconstructions.
UNASSIGNED: The spatial resolution of SR-DLR was higher than conventional reconstructions; the 10 % modulation transfer function of hybrid IR (HIR), DLR, and SR-DLR were 0.792-, 0.976-, and 1.379 cycle/mm, respectively. At the same time, image noise was lowest (HIR: 21.1-, DLR: 19.0-, and SR-DLR: 13.1 HU). SR-DLR could accurately assess coronary artery stenosis and the lumen of the implanted stent graft.
UNASSIGNED: SR-DLR can obtain CT images with high spatial resolution and lower noise without special CT equipments, and will help diagnose coronary artery disease in CCTA and other CT examinations that require high spatial resolution.