Safety of non-ionic contrast media in CT examinations for out-patients: retrospective multicenter analysis of 473,482 patients.

OBJECTIVES: This study aimed to explore the incidence of and potential risk factors for adverse drug reactions (ADRs) after non-ionic iodinated contrast media (NICM) administration for CT exams in out-patient settings in China. MATERIALS AND METHODS: A total of 473,482 out-patients who underwent intravenous NICM between January 1st, 2017, and Dec 31st, 2021, were retrospectively enrolled from three institutions. The occurrence of ADRs and clinical information were recorded. Chi-square test, Poisson regression, and logistic regression analyses were used to evaluate potential ADR risk factors and correlation with demographics, season, and NICM type. RESULTS: Among the 473,482 patients (mean age 55.22 ± 14.85; 253,499 male) who received intravenous NICM, the overall ADR incidence was 0.110% (522 of 473,482), with 0.099% acute-related drug reactions (469 of 473,482) and 0.0004% serious ADRs (two of 473,482). Iopromide was associated with a higher risk of acute ADRs. Late ADRs were more frequently observed with iodixanol 320. Multi-level logistic regression of patients with acute ADRs and a control group (matched 1:1 for age, gender, NICM, prescriber department, and institution) showed that summer (adjusted OR = 1.579; p = 0.035) and autumn (adjusted OR = 1.925; p < 0.001) were risk factors of acute ADRs. However, underlying disease and scanned body area were not related to a higher ADR incidence. CONCLUSION: The use of NICM for out-patients is in general safe with a low ADR incidence. The type of contrast medium (iopromide) and the seasons (summer and autumn) were associated with a higher risk of acute ADRs. Late ADRs were more often observed with iodixanol. CLINICAL RELEVANCE STATEMENT: In comparison to in-patients, out-patients may be exposed to higher risk due to a lack of extensive risk screening, less nursing care, and higher throughput pressure. Safety data about NICM from a large population may complement guidelines and avoid ambiguity. KEY POINTS: • The incidence and risk factors for adverse events after using non-ionic iodinated contrast media are complex in out-patients. • Non-ionic iodinated contrast media are safe for out-patients and the overall incidence of adverse drug reactions was 0.110%. • There is a higher risk of acute adverse drug reactions in summer and autumn.

Early left ventricular microvascular dysfunction in diabetic pigs: a longitudinal quantitative myocardial perfusion CMR study.

BACKGROUND: Microvascular pathology is one of the main characteristics of diabetic cardiomyopathy; however, the early longitudinal course of diabetic microvascular dysfunction remains uncertain. This study aimed to investigate the early dynamic changes in left ventricular (LV) microvascular function in diabetic pig model using the cardiac magnetic resonance (CMR)-derived quantitative perfusion technique. METHODS: Twelve pigs with streptozotocin-induced diabetes mellitus (DM) were included in this study, and longitudinal CMR scanning was performed before and 2, 6, 10, and 16 months after diabetic modeling. CMR-derived semiquantitative parameters (upslope, maximal signal intensity, perfusion index, and myocardial perfusion reserve index [MPRI]) and fully quantitative perfusion parameters (myocardial blood flow [MBF] and myocardial perfusion reserve [MPR]) were analyzed to evaluate longitudinal changes in LV myocardial microvascular function. Pearson correlation was used to analyze the relationship between LV structure and function and myocardial perfusion function. RESULTS: With the progression of DM duration, the upslope at rest showed a gradually increasing trend (P = 0.029); however, the upslope at stress and MBF did not change significantly (P > 0.05). Regarding perfusion reserve function, both MPRI and MPR showed a decreasing trend with the progression of disease duration (MPRI, P = 0.001; MPR, P = 0.042), with high consistency (r = 0.551, P < 0.001). Furthermore, LV MPR is moderately associated with LV longitudinal strain (r = - 0.353, P = 0.022), LV remodeling index (r = - 0.312, P = 0.033), fasting blood glucose (r = - 0.313, P = 0.043), and HbA1c (r = - 0.309, P = 0.046). Microscopically, pathological results showed that collagen volume fraction increased gradually, whereas no significant decrease in microvascular density was observed with the progression of DM duration. CONCLUSIONS: Myocardial microvascular reserve function decreased gradually in the early stage of DM, which is related to both structural (but not reduced microvascular density) and functional abnormalities of microvessels, and is associated with increased blood glucose, reduced LV deformation, and myocardial remodeling.

Image feature index: A novel metric for quantifying chest radiographic image quality.

BACKGROUND: Radiographic X-ray imaging is a common clinical examination. Current objective methods for quantifying image quality for radiographs struggle to capture the combined impact of factors throughout the imaging chain on the perceived image quality. Therefore, there is a need to further develop metrics that correlate with image quality as perceived by the observer. OBJECTIVES: We proposed the image feature index (IFI) to comprehensively quantify radiographic X-ray image quality. We also aimed to study the correlation between IFI and observer-perceived image quality for chest radiographs. MATERIALS AND METHODS: The IFI algorithm was developed, which measured the amount of information, textural features, and noise in the image. A total of 70 chest phantom radiographs were generated under 60-120 kV and 0.2-80 mAs. A vendor-proprietary exposure index (EI) and dose area product (DAP) were extracted from the DICOM header in addition to calculating IFI for each image to investigate the relationships between IFI, EI and DAP. The quality of the images was rated by three observers, and the correlation between IFI and subjective score of image quality was tested. Next, a retrospective study using a random sample of 50 clinical chest radiographs was performed, and the correlation between IFI and subjective score was tested. The correlation was determined by the Spearman test. RESULTS: The curves of IFI versus DAP and IFI versus EI both demonstrated a similar three-stage form where IFI is above zero: in the first stage, IFI increases rapidly with increased DAP or EI, whereas in the second stage, the slope of the curves decreased towards an asymptote, that is, minimal gain in IFI with increased DAP or EI-until they hit the inflection point and then descended sharply in the third stage. For both phantom and clinical chest images, IFI demonstrated good correlation with subjective score (r = 0.9084 for phantom images, r = 0.8153 for clinical images). CONCLUSIONS: IFI is a feasible and efficient descriptor for image quality for chest radiographs. Future studies with larger sample sizes and sample types are needed to confirm the feasibility of IFI for other exam types and anatomical views, thus fulfilling and extending the potential applications of IFI in quality control and radiation dose reduction.

Rapid 3D breath-hold MR cholangiopancreatography using deep learning-constrained compressed sensing reconstruction.

OBJECTIVES: To compare the image quality of three-dimensional breath-hold magnetic resonance cholangiopancreatography with deep learning-based compressed sensing reconstruction (3D DL-CS-MRCP) to those of 3D breath-hold MRCP with compressed sensing (3D CS-MRCP), 3D breath-hold MRCP with gradient and spin-echo (3D GRASE-MRCP) and conventional 2D single-shot breath-hold MRCP (2D MRCP). METHODS: In total, 102 consecutive patients who underwent MRCP at 3.0 T, including 2D MRCP, 3D GRASE-MRCP, 3D CS-MRCP, and 3D DL-CS-MRCP, were prospectively included. Two radiologists independently analyzed the overall image quality, background suppression, artifacts, and visualization of pancreaticobiliary ducts using a five-point scale. The signal-to-noise ratio (SNR) of the common bile duct (CBD), contrast-to-noise ratio (CNR) of the CBD and liver, and contrast ratio between the periductal tissue and CBD were measured. The Friedman test was performed to compare the four protocols. RESULTS: 3D DL-CS-MRCP resulted in improved SNR and CNR values compared with those in the other three protocols, and better contrast ratio compared with that in 3D CS-MRCP and 3D GRASE-MRCP (all, p < 0.05). Qualitative image analysis showed that 3D DL-CS-MRCP had better performance for second-level intrahepatic ducts and distal main pancreatic ducts compared with 3D CS-MRCP (all, p < 0.05). Compared with 2D MRCP, 3D DL-CS-MRCP demonstrated better performance for the second-order left intrahepatic duct but was inferior in assessing the main pancreatic duct (all, p < 0.05). Moreover, the image quality was significantly higher in 3D DL-CS-MRCP than in 3D GRASE-MRCP. CONCLUSION: 3D DL-CS-MRCP has superior performance compared with that of 3D CS-MRCP or 3D GRASE-MRCP. Deep learning reconstruction also provides a comparable image quality but with inferior main pancreatic duct compared with that revealed by 2D MRCP. KEY POINTS: • 3D breath-hold MRCP with deep learning reconstruction (3D DL-CS-MRCP) demonstrated improved image quality compared with that of 3D MRCP with compressed sensing or GRASE. • Compared with 2D MRCP, 3D DL-CS-MRCP had superior performance in SNR and CNR, better visualization of the left second-level intrahepatic bile ducts, and comparable overall image quality, but an inferior main pancreatic duct.

[Feasibility Study of Ultra-High-Resolution Low-Dose Temporal Bone CT with 1 024×1 024 Reconstruction Matrix Size].

OBJECTIVE: To investigate the feasibility of low-dose CT scan of the temporal bone combined with reconstruction matrix size of 1 024×1 024 and the effect of the reconstruction matrix size on image quality. METHODS: Normal-dose and low-dose bilateral temporal bone CT scans were performed on twelve adult male cadaveric skull specimens using the 160-slice multi-detector CT scanning of United Imaging Healthcare. Normal-dose CT images were reconstructed with matrix sizes of 512×512 and 1 024×1 024, while low-dose CT images were reconstructed with the matrix size of 1 024×1 024. CT value, noise, signal-to-noise ratio, contrast-to-noise ratio, the visualization scoring of 15 anatomical structures of the temporal bone, and the result of three-dimensional reconstruction of the ossicular chain were compared among the three groups. RESULTS: The radiation dose of low-dose CT scanning was reduced by about 50% compared with that of normal-dose CT. There was no significant difference in CT values of air, soft tissues and bones among the three groups. Low-dose temporal bone CT with the matrix size of 1 024×1 024 had higher noise, but much better visualization of temporal bone structure than the normal-dose temporal bone CT with matrix size of 512×512. Both the three-dimensional reconstructions of normal-dose and low-dose 1 024×1 024 matrix images were satisfactory and showed no significant difference. The morphology, size and relative position of malleus, incus, stapes, cochlea, and labyrinth, as well as the location of the ossicular chain in the cranium were all clearly displayed. CONCLUSION: Low-dose temporal bone CT with the matrix size of 1 024×1 024 can be used to effectively reduce the radiation dose and significantly improve the spatial resolution and the visualization of the temporal bone anatomical structures compared with the normal-dose temporal bone CT with a matrix size of 512×512.

[Application of Deep Learning Reconstruction Algorithm in Low-Dose Thin-Slice Liver CT of Healthy Volunteers].

OBJECTIVE: To explore the clinical feasibility of applying deep learning (DL) reconstruction algorithm in low-dose thin-slice liver CT examination of healthy volunteers by comparing the reconstruction algorithm based on DL, filtered back projection (FBP) reconstruction algorithm and iterative reconstruction (IR) algorithm. METHODS: A standard water phantom with a diameter of 180 mm was scanned, using the 160 slice multi-detector CT scanning of United Imaging Healthcare, to compare the noise power spectrums of DL, FBP and IR algorithms. 100 healthy volunteers were prospectively enrolled, with 50 assigned to the normal dose group (ND) and 50 to the low dose group (LD). IR algorithm was used in the ND group to reconstruct images, while DL, FBP and IR algorithms were used in the LD group to reconstruct images. One-way analysis of variance was used to compare the liver CT values, the liver noise, liver signal-to-noise ratio (SNR), contrast noise ratio (CNR) and figure of merit (FOM) of the images of ND-IR, LD-FBP, LD-IR and LD-DL. The Kruskal-Wallis test was used to analyse subjective scores of anatomical structures. RESULTS: The DL algorithm had the lowest average peak value of noise power spectrum, and its shape was similar to that of medium-level IR algorithm. Liver CT values of ND-IR, LD-FBP, LD-IR and LD-DL did not show statistically significant difference. The noise of LD-DL was lower than that of LD-FBP, LD-IR and ND-IR ( P<0.05), and the SNR, CNR and FOM of LD-DL were higher than those of LD-FBP, LD-IR and ND-IR ( P<0.05). The subjective scores of anatomical structures of LD-DL did not show significant difference compared to those of ND-IR ( P >0.05), and were higher than those of LD-FBP and LD-IR. The radiation dose of the LD group was reduced by about 50.2% compared with that of the ND group. CONCLUSION: The DL algorithm with noise shape similar to the medium iterative grade IR commonly used in clinical practice showed higher noise reduction ability than IR did. Compared with FBP, the DL algorithm had smoother noise shape, but much better noise reduction ability. The application of DL algorithm in low-dose thin-slice liver CT of healthy volunteers can help achieve the standard image quality of liver CT.

[Noise Reduction Effect of Deep-learning-based Image Reconstruction Algorithms in Thin-section Chest CT].

OBJECTIVE: To evaluate the noise reduction effect of deep learning-based reconstruction algorithms in thin-section chest CT images by analyzing images reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASIR), and deep learning image reconstruction (DLIR) algorithms. METHODS: The chest CT scan raw data of 47 patients were included in this study. Images of 0.625 mm were reconstructed using six reconstruction methods, including FBP, ASIR hybrid reconstruction (ASIR50%, ASIR70%), and deep learning low, medium and high modes (DL-L, DL-M, and DL-H). After the regions of interest were outlined in the aorta, skeletal muscle and lung tissue of each group of images, the CT values, SD values and signal-to-noise ratio (SNR) of the regions of interest were measured, and two radiologists evaluated the image quality. RESULTS: CT values, SD values and SNR of the images obtained by the six reconstruction methods showed statistically significant difference ( P<0.001). There were statistically significant differences in the image quality scores of the six reconstruction methods ( P<0.001). Images reconstruced with DL-H have the lowest noise and the highest overall quality score. CONCLUSION: The model based on deep learning can effectively reduce the noise of thin-section chest CT images and improve the image quality. Among the three deep-learning models, DL-H showed the best noise reduction effect.

[The Application Value of Artificial Intelligence-based Filtering and Interpolated Image Reconstruction Algorithm in Abdominal Magnetic Resonance Image Denoising].

OBJECTIVE: To compare the noise reduction performance of conventional filtering and artificial intelligence-based filtering and interpolation (AIFI) and to explore for optimal parameters of applying AIFI in the noise reduction of abdominal magnetic resonance imaging (MRI). METHODS: Sixty patients who underwent upper abdominal MRI examination in our hospital were retrospectively included. The raw data of T1-weighted image (T1WI), T2-weighted image (T2WI), and dualecho sequences were reconstructed with two image denoising techniques, conventional filtering and AIFI of different levels of intensity. The difference in objective image quality indicators, peak signal-to-noise ratio (pSNR) and image sharpness, of the different denoising techniques was compared. Two radiologists evaluated the image noise, contrast, sharpness, and overall image quality. Their scores were compared and the interobserver agreement was calculated. RESULTS: Compared with the original images, improvement of varying degrees were shown in the pSNR and the sharpness of the images of the three sequences, T1W1, T2W2, and dual echo sequence, after denoising filtering and AIFI were used (all P<0.05). In addition, compared with conventional filtering, the objective quality scores of the reconstructed images were improved when conventional filtering was combined with AIFI reconstruction methods in T1WI sequence, AIFI level≥3 was used in T2WI and echo1 sequence, and AIFI level≥4 was used in echo2 sequence (all P<0.05). The subjective scores given by the two radiologists for the image noise, contrast, sharpness, and overall image quality in each sequence of conventional filtering reconstruction, AIFI reconstruction (except for AIFI level=1), and two-method combination reconstruction were higher than those of the original images (all P<0.05). However, the image contrast scores were reduced for AIFI level=5. There was good interobserver agreement between the two radiologists (all r>0.75, P<0.05). After multidimensional comparison, the optimal parameters of using AIFI technique for noise reduction in abdominal MRI were conventional filtering+AIFI level=3 in the T1WI sequence and AIFI level=4 in the T2WI and dualecho sequences. CONCLUSION: AIFI is superior to filtering in imaging denoising at medium and high levels. It is a promising noise reduction technique. The optimal parameters of using AIFI for abdominal MRI are Filtering+AIFI level=3 in the T1WI sequence and AIFI level=4 in T2WI and dualecho sequences.

[The Clinical Effectiveness of Neural Network-based Boundary Recognition of Upper Abdominal Organs on CT Images].

OBJECTIVE: To assess the clinical effectiveness of boundary recognition of upper abdomen organs on CT images based on neural network model and the combination of different slices. METHODS: A total of 2 000 patients who underwent upper abdomen enhanced CT scans from March 2018 to March 2019 were included in the study. The quality of the CT images met the requirements for clinical diagnosis. Eight boundary layers (the upper and lower edge of liver, the upper and lower edge of spleen, the lower edge of left kidney, the lower edge of right kidney, the lower edge of the stomach and the lower edge of the gallbladder) of the main organs in the upper abdomen were labeled. The model training (training set, verification set and test set) based on different neural network methods and combinations of different slices were then performed to assess the accuracy of boundary recognition. Furthermore, clinical data from 50 cases were used as test group for assessing the accuracy and clinical effectiveness of this model. RESULTS: The fusion model created by integrating the two models according to different weight ratios yielded the highest accuracy, and then followed the EfficientNet-b3 model, with the Xception model showing the lowest accuracy. In each model, the boundary recognition accuracy of 5-slice image is higher than that of 3-silce image, and that of 1-slice image is the lowest. The recognition accuracy of fusion model of the 5-continuous-slice image for upper edge of liver, lower edge of liver, upper edge of spleen, lower edge of spleen, lower edge of left kidney, lower edge of right kidney, lower edge of stomach and lower edge of gallbladder was 91%, 87%, 92%, 85%, 92%, 95%, 76% and 74%, respectively. The fusion model was checked with the effectiveness data of 50 cases, yielding 88%, 86%, 88%, 80%, 82%, 80%, 69%, and 65% accuracy for 8-slice image, respectively, and the accuracy of meeting clinical application requirement was as high as 98%, 98%, 95%, 98%, 99%, 98%, 80% and 77%, respectively. CONCLUSION: By increasing boundary change logics in the continuous slices, the fusion model integrating different weight proportions demonstrates the highest accuracy for identifying the boundary of upper abdominal organs on CT images, achieving high examination effectiveness in clinical practice.

[Application of 3.0T Time-of-flight Magnetic Resonance Angiography with Sparse Undersampling and Iterative Reconstruction in the Diagnosis of Unruptured Intracranial Aneurysms].

OBJECTIVE: To evaluate the diagnostic value of 3.0T time-of-flight MR angiography with sparse undersampling and iterative reconstruction (TOFu-MRA) for unruptured intracranial aneurysms (UIAs) on the basis of using digital subtraction angiography (DSA) as the reference standard. METHODS: A total of 65 patients with suspected UIAs were prospectively enrolled and all patients underwent TOFu-MRA and DSA. Relying on DSA as the reference standard, the sensitivity (SEN), specificity (SPE), positive predictive value (PPV) and negative predictive value (NPV) of using TOFu-MRA in UIA diagnosis were calculated, and the inter-observer agreement between two doctors was determined. Comparison of maximum intensity projection (MIP) and volume rendering (VR) image datasets was made to evaluate the agreement between DSA results and TOFu-MRA in the measurement of UIA morphological parameters, including the neck width (D neck), height (H) , and width (D width) of UIAs. RESULTS: The study covered 55 UIAs from 46 patients. The SEN, SPE, PPV and NPV of the two doctors using TOFu-MRA in UIA diagnosis were as follows: (95.7%, 95.7%), (94.7%, 94.7%), (97.8%, 97.8%) and (90.0%, 90.0%), respectively for patient-based assessment; (96.4%, 94.5%), (94.7%, 94.7%), (98.1%, 98.1%) and (90.0%, 85.7%), respectively, for aneurysm-based assessment. There is a strong inter-observer agreement (Kappa=0.93 for patient-based assessment and 0.96 for aneurysm-based assessment) between the two doctors. Moreover, Bland-Altman analysis showed that more than 95% points fell within the limits of agreement (LoA), suggesting strong agreement between the two examination methods for the measurement of UIAs morphological parameters. CONCLUSION: TOFu-MRA showed good diagnostic efficacy for UIAs and the results were in good agreement with those of DSA, the reference standard, for assessing UIA morphological parameter. TOFu-MRA can be used as a first choice for noninvasive diagnostic evaluation of UIAs.

Deep learning trained algorithm maintains the quality of half-dose contrast-enhanced liver computed tomography images: Comparison with hybrid iterative reconstruction: Study for the application of deep learning noise reduction technology in low dose.

PURPOSE: This study compares the image and diagnostic qualities of a DEep Learning Trained Algorithm (DELTA) for half-dose contrast-enhanced liver computed tomography (CT) with those of a commercial hybrid iterative reconstruction (HIR) method used for standard-dose CT (SDCT). METHODS: This study enrolled 207 adults, and they were divided into two groups: SDCT and low-dose CT (LDCT). SDCT was reconstructed using the HIR method (SDCTHIR), and LDCT was reconstructed using both the HIR method (LDCTHIR) and DELTA (LDCTDL). Noise, Hounsfield unit (HU) values, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between three image series. Two radiologists assessed the noise, artefacts, overall image quality, visualisation of critical anatomical structures and lesion detection, characterisation and visualisation. RESULTS: The mean effective doses were 5.64 ±â€¯1.96 mSv for SDCT and 2.87 ±â€¯0.87 mSv for LDCT. The noise of LDCTDL was significantly lower than that of SDCTHIR and LDCTHIR. The SNR and CNR of LDCTDL were significantly higher than those of the other two groups. The overall image quality, visualisation of anatomical structures and lesion visualisation between LDCTDL and SDCTHIR were not significantly different. For lesion detection, the sensitivities and specificities of SDCTHIR vs. LDCTDL were 81.9 % vs. 83.7 % and 89.1 % vs. 86.3 %, respectively, on a per-patient basis. SDCTHIR showed 75.4 % sensitivity and 82.6 % specificity for lesion characterisation on a per-patient basis, whereas LDCTDL showed 73.5 % sensitivity and 82.4 % specificity. CONCLUSIONS: LDCT with DELTA had approximately 49 % dose reduction compared with SDCT with HIR while maintaining image quality on contrast-enhanced liver CT.