Comparing CT-Like Images Based on Ultra-Short Echo Time and Gradient Echo T1-Weighted MRI Sequences for the Assessment of Vertebral Disorders Using Histology and True CT as the Reference Standard.

BACKGROUND: Several magnetic resonance (MR) techniques have been suggested for radiation-free imaging of osseous structures. PURPOSE: To compare the diagnostic value of ultra-short echo time and gradient echo T1-weighted MRI for the assessment of vertebral pathologies using histology and computed tomography (CT) as the reference standard. STUDY TYPE: Prospective. SUBJECTS: Fifty-nine lumbar vertebral bodies harvested from 20 human cadavers (donor age 73 ± 13 years; 9 male). FIELD STRENGTH/SEQUENCE: Ultra-short echo time sequence optimized for both bone (UTEb) and cartilage (UTEc) imaging and 3D T1-weighted gradient-echo sequence (T1GRE) at 3 T; susceptibility-weighted imaging (SWI) gradient echo sequence at 1.5 T. CT was performed on a dual-layer dual-energy CT scanner using a routine clinical protocol. ASSESSMENT: Histopathology and conventional CT were acquired as standard of reference. Semi-quantitative and quantitative morphological features of degenerative changes of the spines were evaluated by four radiologists independently on CT and MR images independently and blinded to all other information. Features assessed were osteophytes, endplate sclerosis, visualization of cartilaginous endplate, facet joint degeneration, presence of Schmorl's nodes, and vertebral dimensions. Vertebral disorders were assessed by a pathologist on histology. STATISTICAL TESTS: Agreement between T1GRE, SWI, UTEc, and UTEb sequences and CT imaging and histology as standard of reference were assessed using Fleiss' κ and intra-class correlation coefficients, respectively. RESULTS: For the morphological assessment of osteophytes and endplate sclerosis, the overall agreement between SWI, T1GRE, UTEb, and UTEc with the reference standard (histology combined with CT) was moderate to almost perfect for all readers (osteophytes: SWI, κ range: 0.68-0.76; T1GRE: 0.92-1.00; UTEb: 0.92-1.00; UTEc: 0.77-0.85; sclerosis: SWI, κ range: 0.60-0.70; T1GRE: 0.77-0.82; UTEb: 0.81-0.92; UTEc: 0.61-0.71). For the visualization of the cartilaginous endplate, UTEc showed the overall best agreement with the reference standard (histology) for all readers (κ range: 0.85-0.93). DATA CONCLUSIONS: Morphological assessment of vertebral pathologies was feasible and accurate using the MR-based bone imaging sequences compared to CT and histopathology. T1GRE showed the overall best performance for osseous changes and UTEc for the visualization of the cartilaginous endplate. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.

Artificial intelligence support in MR imaging of incidental renal masses: an early health technology assessment.

OBJECTIVE: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up. MATERIALS AND METHODS: For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis. RESULTS: Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI. CONCLUSIONS: This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future. CLINICAL RELEVANCE STATEMENT: These results hint at AI's the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics. KEY POINTS: • This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions. • MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions. • The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.

Longitudinal MR-based proton-density fat fraction (PDFF) and T2* for the assessment of associations between bone marrow changes and myelotoxic chemotherapy.

OBJECTIVES: MR imaging-based proton density fat fraction (PDFF) and T2* imaging has shown to be useful for the evaluation of degenerative changes in the spine. Therefore, the aim of this study was to investigate the influence of myelotoxic chemotherapy on the PDFF and T2* of the thoracolumbar spine in comparison to changes in bone mineral density (BMD). METHODS: In this study, 19 patients were included who had received myelotoxic chemotherapy (MC) and had received a MR imaging scan of the thoracolumbar vertebrates before and after the MC. Every patient was matched for age, sex, and time between the MRI scans to two controls without MC. All patients underwent 3-T MR imaging including the thoracolumbar spine comprising chemical shift encoding-based water-fat imaging to extract PDFF and T2* maps. Moreover, trabecular BMD values were determined before and after chemotherapy. Longitudinal changes in PDFF and T2* were evaluated and compared to changes in BMD. RESULTS: Absolute mean differences of PDFF values between scans before and after MC were at 8.7% (p = 0.01) and at -0.5% (p = 0.57) in the control group, resulting in significantly higher changes in PDFF in patients with MC (p = 0.008). BMD and T2* values neither showed significant changes in patients with nor in those without myelotoxic chemotherapy (p = 0.15 and p = 0.47). There was an inverse, yet non-significant correlation between changes in PDFF and BMD found in patients with myelotoxic chemotherapy (r = -0.41, p = 0.12). CONCLUSION: Therefore, PDFF could be a useful non-invasive biomarker in order to detect changes in the bone marrow in patients receiving myelotoxic therapy. CLINICAL RELEVANCE STATEMENT: Using PDFF as a non-invasive biomarker for early bone marrow changes in oncologic patients undergoing myelotoxic treatment may help enable more targeted countermeasures at commencing states of bone marrow degradation and reduce risks of possible fragility fractures. KEY POINTS: Quantifying changes in bone marrow fat fraction, as well as T2* caused by myelotoxic pharmaceuticals using proton density fat fraction, is feasible. Proton density fat fraction could potentially be established as a non-invasive biomarker for early bone marrow changes in oncologic patients undergoing myelotoxic treatment.

Automated assessment of cardiac pathologies on cardiac MRI using T1-mapping and late gadolinium phase sensitive inversion recovery sequences with deep learning.

BACKGROUND: A deep learning (DL) model that automatically detects cardiac pathologies on cardiac MRI may help streamline the diagnostic workflow. To develop a DL model to detect cardiac pathologies on cardiac MRI T1-mapping and late gadolinium phase sensitive inversion recovery (PSIR) sequences were used. METHODS: Subjects in this study were either diagnosed with cardiac pathology (n = 137) including acute and chronic myocardial infarction, myocarditis, dilated cardiomyopathy, and hypertrophic cardiomyopathy or classified as normal (n = 63). Cardiac MR imaging included T1-mapping and PSIR sequences. Subjects were split 65/15/20% for training, validation, and hold-out testing. The DL models were based on an ImageNet pretrained DenseNet-161 and implemented using PyTorch and fastai. Data augmentation with random rotation and mixup was applied. Categorical cross entropy was used as the loss function with a cyclic learning rate (1e-3). DL models for both sequences were developed separately using similar training parameters. The final model was chosen based on its performance on the validation set. Gradient-weighted class activation maps (Grad-CAMs) visualized the decision-making process of the DL model. RESULTS: The DL model achieved a sensitivity, specificity, and accuracy of 100%, 38%, and 88% on PSIR images and 78%, 54%, and 70% on T1-mapping images. Grad-CAMs demonstrated that the DL model focused its attention on myocardium and cardiac pathology when evaluating MR images. CONCLUSIONS: The developed DL models were able to reliably detect cardiac pathologies on cardiac MR images. The diagnostic performance of T1 mapping alone is particularly of note since it does not require a contrast agent and can be acquired quickly.

Imaging of traumatic mandibular fractures in young adults using CT-like MRI: a feasibility study.

OBJECTIVES: To assess and compare the diagnostic performance of CT-like images based on a three- dimensional (3D) T1-weighted spoiled gradient-echo sequence (3D T1 GRE) with CT in patients with acute traumatic fractures of the mandible. MATERIALS AND METHODS: Subjects with acute mandibular fractures diagnosed on conventional CT were prospectively recruited and received an additional 3 T MRI with a CT-like 3D T1 GRE sequence. The images were assessed by two radiologists with regard to fracture localization, degree of dislocation, and number of fragments. Bone to soft tissue contrast, diagnostic confidence, artifacts, and overall image quality were rated using a five-point Likert-scale. Agreement of measurements was assessed using an independent t-test. RESULTS: Fourteen subjects and 22 fracture sites were included (26 ± 3.9 years; 4 females, 10 males). All traumatic fractures were accurately detected on CT-like MRI (n = 22, κ 1.00 (95% CI 1.00-1.00)). There was no statistically significant difference in the assessment of the fracture dislocation (axial mean difference (MD) 0.06 mm, p = 0.93, coronal MD, 0.08 mm, p = 0.89 and sagittal MD, 0.04 mm, p = 0.96). The agreement for the fracture classification as well as the inter- and intra-rater agreement was excellent (range κ 0.92-0.98 (95% CI 0.96-0.99)). CONCLUSION: Assessment of mandibular fractures was feasible and accurate using CT-like MRI based on a 3D T1 GRE sequence and is comparable to conventional CT. CLINICAL RELEVANCE: For the assessment of acute mandibular fractures, CT-like MRI might become a useful alternative to CT in order to reduce radiation exposure particularly in young patients.

Qualitative and Quantitative Assessment of Emphysema Using Dark-Field Chest Radiography.

Background Dark-field chest radiography allows for assessment of lung alveolar structure by exploiting wave optical properties of x-rays. Purpose To evaluate the qualitative and quantitative features of dark-field chest radiography in participants with pulmonary emphysema as compared with those in healthy control subjects. Materials and Methods In this prospective study conducted from October 2018 to October 2020, participants aged at least 18 years who underwent clinically indicated chest CT were screened for participation. Inclusion criteria were an ability to consent to the procedure and stand upright without help. Exclusion criteria were pregnancy, serious medical conditions, and any lung condition besides emphysema that was visible on CT images. Participants were examined with a clinical dark-field chest radiography prototype that simultaneously acquired both attenuation-based radiographs and dark-field chest radiographs. Dark-field coefficients were tested for correlation with each participant's CT-based emphysema index using the Spearman correlation test. Dark-field coefficients of adjacent groups in the semiquantitative Fleischner Society emphysema grading system were compared using a Wilcoxon Mann-Whitney U test. The capability of the dark-field coefficient to enable detection of emphysema was evaluated with receiver operating characteristics curve analysis. Results A total of 83 participants (mean age, 65 years ± 12 [standard deviation]; 52 men) were studied. When compared with images from healthy participants, dark-field chest radiographs in participants with emphysema had a lower and inhomogeneous dark-field signal intensity. The locations of focal signal intensity loss on dark-field images corresponded well with emphysematous areas found on CT images. The dark-field coefficient was negatively correlated with the quantitative CT-based emphysema index (r = -0.54, P < .001). Participants with Fleischner Society grades of mild, moderate, confluent, or advanced destructive emphysema exhibited a lower dark-field coefficient than those without emphysema (eg, 1.3 m-1 ± 0.6 for participants with confluent or advanced destructive emphysema vs 2.6 m-1 ± 0.4 for participants without emphysema; P < .001). The area under the receiver operating characteristic curve for detection of mild emphysema was 0.79. Conclusion Pulmonary emphysema leads to reduced signal intensity on dark-field chest radiographs, showing the technique has potential as a diagnostic tool in the assessment of lung diseases. © RSNA, 2022 See also the editorial by Hatabu and Madore in this issue.

X-ray Dark-Field CT for Early Detection of Radiation-induced Lung Injury in a Murine Model.

Online supplemental material is available for this article.

Additional MRI for initial M-staging in pancreatic cancer: a cost-effectiveness analysis.

OBJECTIVE: Pancreatic cancer is portrayed to become the second leading cause of cancer-related death within the next years. Potentially complicating surgical resection emphasizes the importance of an accurate TNM classification. In particular, the failure to detect features for non-resectability has profound consequences on patient outcomes and economic costs due to incorrect indication for resection. In the detection of liver metastases, contrast-enhanced MRI showed high sensitivity and specificity; however, the cost-effectiveness compared to the standard of care imaging remains unclear. The aim of this study was to analyze whether additional MRI of the liver is a cost-effective approach compared to routinely acquired contrast-enhanced computed tomography (CE-CT) in the initial staging of pancreatic cancer. METHODS: A decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Model input parameters were assessed based on evidence from recent literature. The willingness-to-pay (WTP) was set to $100,000/QALY. To evaluate model uncertainty, deterministic and probabilistic sensitivity analyses were performed. RESULTS: In the base-case analysis, the model yielded a total cost of $185,597 and an effectiveness of 2.347 QALYs for CE-MR/CT and $187,601 and 2.337 QALYs for CE-CT respectively. With a net monetary benefit (NMB) of $49,133, CE-MR/CT is shown to be dominant over CE-CT with a NMB of $46,117. Deterministic and probabilistic survival analysis showed model robustness for varying input parameters. CONCLUSION: Based on our results, combined CE-MR/CT can be regarded as a cost-effective imaging strategy for the staging of pancreatic cancer. KEY POINTS: • Additional MRI of the liver for initial staging of pancreatic cancer results in lower total costs and higher effectiveness. • The economic model showed high robustness for varying input parameters.

Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

OBJECTIVES: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. METHODS: In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant (n = 213, 24.2%) or benign (n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. RESULTS: The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. CONCLUSIONS: An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents. KEY POINTS: • The developed machine learning model could differentiate benign from malignant bone tumors using radiography with an AUC of 0.90 on the external test set. • Machine learning models that used radiomic features or demographic information alone performed worse than those that used both radiomic features and demographic information as input, highlighting the importance of building comprehensive machine learning models. • An artificial neural network that combined both radiomic and demographic information achieved the best performance and its performance was compared to radiology readers on an external test set.

X-ray Dark-Field Chest Imaging: Qualitative and Quantitative Results in Healthy Humans.

Background X-ray dark-field radiography takes advantage of the wave properties of x-rays, with a relatively high signal in the lungs due to the many air-tissue interfaces in the alveoli. Purpose To describe the qualitative and quantitative characteristics of x-ray dark-field images in healthy human subjects. Materials and Methods Between October 2018 and January 2020, patients of legal age who underwent chest CT as part of their diagnostic work-up were screened for study participation. Inclusion criteria were a normal chest CT scan, the ability to consent, and the ability to stand upright without help. Exclusion criteria were pregnancy, serious medical conditions, and changes in the lung tissue, such as those due to cancer, pleural effusion, atelectasis, emphysema, infiltrates, ground-glass opacities, or pneumothorax. Images of study participants were obtained by using a clinical x-ray dark-field prototype, recently constructed and commissioned at the authors' institution, to simultaneously acquire both attenuation-based and dark-field thorax radiographs. Each subject's total dark-field signal was correlated with his or her lung volume, and the dark-field coefficient was correlated with age, sex, weight, and height. Results Overall, 40 subjects were included in this study (average age, 62 years ± 13 [standard deviation]; 26 men, 14 women). Normal human lungs have high signal, while the surrounding osseous structures and soft tissue have very low and no signal, respectively. The average dark-field signal was 2.5 m-1 ± 0.4 of examined lung tissue. There was a correlation between the total dark-field signal and the lung volume (r = 0.61, P < .001). No difference was found between men and women (P = .78). Also, age (r = -0.18, P = .26), weight (r = 0.24, P = .13), and height (r = 0.01, P = .96) did not influence dark-field signal. Conclusion This study introduces qualitative and quantitative values for x-ray dark-field imaging in healthy human subjects. The quantitative x-ray dark-field coefficient is independent from demographic subject parameters, emphasizing its potential in diagnostic assessment of the lung. ©RSNA, 2021 See also the editorial by Hatabu and Madore in this issue.

Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. Materials and Methods This retrospective study analyzed bone tumors on radiographs acquired prior to treatment and obtained from patient data from January 2000 to June 2020. Benign or malignant bone tumors were diagnosed in all patients by using the histopathologic findings as the reference standard. By using split-sample validation, 70% of the patients were assigned to the training set, 15% were assigned to the validation set, and 15% were assigned to the test set. The final performance was evaluated on an external test set by using geographic validation, with accuracy, sensitivity, specificity, and 95% CIs being used for classification, the intersection over union (IoU) being used for bounding box placements, and the Dice score being used for segmentations. Results Radiographs from 934 patients (mean age, 33 years ± 19 [standard deviation]; 419 women) were evaluated in the internal data set, which included 667 benign bone tumors and 267 malignant bone tumors. Six hundred fifty-four patients were in the training set, 140 were in the validation set, and 140 were in the test set. One hundred eleven patients were in the external test set. The multitask DL model achieved 80.2% (89 of 111; 95% CI: 72.8, 87.6) accuracy, 62.9% (22 of 35; 95% CI: 47, 79) sensitivity, and 88.2% (67 of 76; CI: 81, 96) specificity in the classification of bone tumors as malignant or benign. The model achieved an IoU of 0.52 ± 0.34 for bounding box placements and a mean Dice score of 0.60 ± 0.37 for segmentations. The model accuracy was higher than that of two radiologic residents (71.2% and 64.9%; P = .002 and P < .001, respectively) and was comparable with that of two musculoskeletal fellowship-trained radiologists (83.8% and 82.9%; P = .13 and P = .25, respectively) in classifying a tumor as malignant or benign. Conclusion The developed multitask deep learning model allowed for accurate and simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Carrino in this issue.

18F FDG PET/MRI with hepatocyte-specific contrast agent for M staging of rectal cancer: a primary economic evaluation.

PURPOSE: Rectal cancer is one of the most frequent causes of cancer-related morbidity and mortality in the world. Correct identification of the TNM state in primary staging of rectal cancer has critical implications on patient management. Initial evaluations revealed a high sensitivity and specificity for whole-body PET/MRI in the detection of metastases allowing for metastasis-directed therapy regimens. Nevertheless, its cost-effectiveness compared with that of standard-of-care imaging (SCI) using pelvic MRI + chest and abdominopelvic CT is yet to be investigated. Therefore, the aim of this study was to analyze the cost-effectiveness of whole-body 18F FDG PET/MRI as an alternative imaging method to standard diagnostic workup for initial staging of rectal cancer. METHODS: For estimation of quality-adjusted life years (QALYs) and lifetime costs of diagnostic modalities, a decision model including whole-body 18F FDG PET/MRI with a hepatocyte-specific contrast agent and pelvic MRI + chest and abdominopelvic CT was created based on Markov simulations. For obtaining model input parameters, review of recent literature was performed. Willingness to pay (WTP) was set to $100,000/QALY. Deterministic sensitivity analysis of diagnostic parameters and costs was applied, and probabilistic sensitivity was determined using Monte Carlo modeling. RESULTS: In the base-case scenario, the strategy whole-body 18F FDG PET/MRI resulted in total costs of $52,186 whereas total costs of SCI were at $51,672. Whole-body 18F FDG PET/MRI resulted in an expected effectiveness of 3.542 QALYs versus 3.535 QALYs for SCI. This resulted in an incremental cost-effectiveness ratio of $70,291 per QALY for PET/MRI. Thus, from an economic point of view, whole-body 18F FDG PET/MRI was identified as an adequate diagnostic alternative to SCI with high robustness of results to variation of input parameters. CONCLUSION: Based on the results of the analysis, use of whole-body 18F FDG PET/MRI was identified as a feasible diagnostic strategy for initial staging of rectal cancer from a cost-effectiveness perspective.

Soft tissue masses: distribution of entities and rate of malignancy in small lesions.

BACKGROUND: Small soft tissue masses are often falsely assumed to be benign and resected with failure to achieve tumor-free margins. Therefore, this study retrospectively investigated the distribution of histopathologic diagnosis to be encountered in small soft tissue tumors (≤ 5 cm) in a large series of a tertiary referral center. METHODS: Patients with a soft tissue mass (STM) with a maximum diameter of 5 cm presenting at our institution over a period of 10 years, who had undergone preoperative Magnetic resonance imaging and consequent biopsy or/and surgical resection, were included in this study. A final histopathological diagnosis was available in all cases. The maximum tumor diameter was determined on MR images by one radiologist. Moreover, tumor localization (head/neck, trunk, upper extremity, lower extremity, hand, foot) and depth (superficial / deep to fascia) were assessed. RESULTS: In total, histopathologic results and MR images of 1753 patients were reviewed. Eight hundred seventy patients (49.63%) showed a STM ≤ 5 cm and were therefore included in this study (46.79 +/- 18.08 years, 464 women). Mean maximum diameter of the assessed STMs was 2.88 cm. Of 870 analyzed lesions ≤ 5 cm, 170 (19.54%) were classified as superficial and 700 (80.46%) as deep. The malignancy rate of all lesions ≤ 5 cm was at 22.41% (superficial: 23.53% / deep: 22.14%). The malignancy rate dropped to 16.49% (20.79% / 15.32%) when assessing lesions ≤ 3 cm (p = 0.007) and to 15.0% (18.18% / 13.79%) when assessing lesions ≤ 2 cm (p = 0.006). Overall, lipoma was the most common benign lesion of superficial STMs (29.41%) and tenosynovial giant cell tumor was the most common benign lesion of deep STMs (23.29%). Undifferentiated pleomorphic sarcoma was the most common malignant diagnosis among both, superficial (5.29%) and deep (3.57%) STMs. CONCLUSIONS: The rate of malignancy decreased significantly with tumor size in both, superficial and deep STMs. The distribution of entities was different between superficial and deep STMs, yet there was no significant difference found in the malignancy rate.

Calcium scoring using virtual non-contrast images from a dual-layer spectral detector CT: comparison to true non-contrast data and evaluation of proportionality factor in a large patient collective.

OBJECTIVE: Determination of coronary artery calcium scoring (CACS) in non-contrast computed tomography (CT) images has been shown to be an important prognostic factor in coronary artery disease (CAD). The objective of this study was to evaluate the accuracy of CACS from virtual non-contrast (VNC) imaging generated from spectral data in comparison to standard (true) non-contrast (TNC) imaging in a representative patient cohort with clinically approved software. METHODS: One hundred three patients referred to coronary CTA with suspicion of CAD were investigated on a dual-layer spectral detector CT (SDCT) scanner. CACS was calculated from both TNC and VNC images by software certified for medical use. Patients with a CACS of 0 were excluded from analysis. RESULTS: The mean age of the study population was 61 ± 11 years with 48 male patients (67%). Inter-quartile range of clinical CACS was 22-282. Correlation of measured CACS from true- and VNC images was high (0.95); p < 0.001. The slope was 3.83, indicating an underestimation of VNC CACS compared to TNC CACS by that factor. Visual analysis of the Bland-Altman plot of CACS showed good accordance with both methods after correction of VNC CACS by the abovementioned factor. CONCLUSIONS: In clinical diagnostics of CAD, the determination of CACS is feasible using VNC images generated from spectral data obtained on a dual-layer spectral detector CT. When multiplied by a correction factor, results were in good agreement with the standard technique. This could enable radiation dose reductions by obviating the need for native scans typically used for CACS. KEY POINTS: • Calcium scoring is feasible from contrast-enhanced CT images using a dual-layer spectral detector CT scanner. • When multiplied by a correction factor, calcium scoring from virtual non-contrast images shows good agreement with the standard technique. • Omitting native scans for calcium scoring could enable radiation dose reduction.

CT-like images based on T1 spoiled gradient-echo and ultra-short echo time MRI sequences for the assessment of vertebral fractures and degenerative bone changes of the spine.

OBJECTIVES: To evaluate the performance of 3D T1w spoiled gradient-echo (T1SGRE) and ultra-short echo time (UTE) MRI sequences for the detection and assessment of vertebral fractures and degenerative bone changes compared with conventional CT. METHODS: Fractures (n = 44) and degenerative changes (n = 60 spinal segments) were evaluated in 30 patients (65 ± 14 years, 18 women) on CT and 3-T MRI, including CT-like images derived from T1SGRE and UTE. Two radiologists evaluated morphological features on both modalities: Genant and AO/Magerl classifications, anterior/posterior vertebral height, fracture age; disc height, neuroforaminal diameter, grades of spondylolisthesis, osteophytes, sclerosis, and facet joint degeneration. Diagnostic accuracy and agreement between MRI and CT and between radiologists were assessed using crosstabs, weighted κ, and intraclass correlation coefficients. Image quality was graded on a Likert scale. RESULTS: For fracture detection, sensitivity, specificity, and accuracy were 0.95, 0.98, and 0.97 for T1SGRE and 0.91, 0.96, and 0.95 for UTE. Agreement between T1SGRE and CT was substantial to excellent (e.g., Genant: κ, 0.92 [95% confidence interval, 0.83-1.00]; AO/Magerl: κ, 0.90 [0.76-1.00]; osteophytes: κ, 0.91 [0.82-1.00]; sclerosis: κ, 0.68 [0.48-0.88]; spondylolisthesis: ICCs, 0.99 [0.99-1.00]). Agreement between UTE and CT was lower, ranging from moderate (e.g., sclerosis: κ, 0.43 [0.26-0.60]) to excellent (spondylolisthesis: ICC, 0.99 [0.99-1.00]). Inter-reader agreement was substantial to excellent (0.52-1.00), respectively, for all parameters. Median image quality of T1SGRE was rated significantly higher than that of UTE (p < 0.001). CONCLUSIONS: Morphologic assessment of bone pathologies of the spine using MRI was feasible and comparable to CT, with T1SGRE being more robust than UTE. KEY POINTS: • Vertebral fractures and degenerative bone changes can be assessed on CT-like MR images, with 3D T1w spoiled gradient-echo-based images showing a high diagnostic accuracy and agreement with CT. • This could enable MRI to precisely assess bone morphology, and 3D T1SGRE MRI sequences may substitute additional spinal CT examinations in the future. • Image quality and robustness of T1SGRE sequences are higher than those of UTE MRI for the assessment of bone structures.

Spectral computed tomography angiography using a gadolinium-based contrast agent for imaging of pathologies of the aorta.

OBJECTIVES: Especially patients with aortic aneurysms and multiple computed tomography angiographies (CTA) might show medical conditions which oppose the use of iodine-based contrast agents. CTA using monoenergetic reconstructions from dual layer CT and gadolinium (Gd-)based contrast agents might be a feasible alternative in these patients. Therefore, the purpose of this study was to evaluate the feasibility of clinical spectral CTA with a Gd-based contrast agent in patients with aortic aneurysms. METHODS: Twenty-one consecutive scans in 15 patients with and without endovascular aneurysm repair showing contraindications for iodine-based contrast agents were examined using clinical routine doses (0.2 mmol/kg) of Gd-based contrast agent with spectral CT. Monoenergetic reconstructions of the spectral data set were computed. RESULTS: There was a significant increase in the intravascular attenuation of the aorta between pre- and post-contrast images for the MonoE40 images in the thoracic and the abdominal aorta (p < 0.001 for both). Additionally, the ratio between pre- and post-contrast images was significantly higher in the MonoE40 images as compared to the conventional images with a factor of 6.5 ± 4.5 vs. 2.4 ± 0.5 in the thoracic aorta (p = 0.003) and 4.1 ± 1.8 vs. 1.9 ± 0.5 in the abdominal aorta (p < 0.001). CONCLUSIONS: To conclude, our study showed that Gd-CTA is a valid and reliable alternative for diagnostic imaging of the aorta for clinical applications. Monoenergetic reconstructions of computed tomography angiographies using gadolinium based contrast agents may be a useful alternative in patients with aortic aneurysms and contraindications for iodine based contrast agents.

Deep-learning-based image quality enhancement of CT-like MR imaging in patients with suspected traumatic shoulder injury.

PURPOSE: To evaluate the diagnostic performance of CT-like MR images reconstructed with an algorithm combining compressed sense (CS) with deep learning (DL) in patients with suspected osseous shoulder injury compared to conventional CS-reconstructed images. METHODS: Thirty-two patients (12 women, mean age 46 ± 14.9 years) with suspected traumatic shoulder injury were prospectively enrolled into the study. All patients received MR imaging of the shoulder, including a CT-like 3D T1-weighted gradient-echo (T1 GRE) sequence and in case of suspected fracture a conventional CT. An automated DL-based algorithm, combining CS and DL (CS DL) was used to reconstruct images of the same k-space data as used for CS reconstructions. Two musculoskeletal radiologists assessed the images for osseous pathologies, image quality and visibility of anatomical landmarks using a 5-point Likert scale. Moreover, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. RESULTS: Compared to CT, all acute fractures (n = 23) and osseous pathologies were detected accurately on the CS only and CS DL images with almost perfect agreement between the CS DL and CS only images (κ 0.95 (95 %confidence interval 0.82-1.00). Image quality as well as the visibility of the fracture lines, bone fragments and glenoid borders were overall rated significantly higher for the CS DL reconstructions than the CS only images (CS DL range 3.7-4.9 and CS only range 3.2-3.8, P = 0.01-0.04). Significantly higher SNR and CNR values were observed for the CS DL reconstructions (P = 0.02-0.03). CONCLUSION: Evaluation of traumatic shoulder pathologies is feasible using a DL-based algorithm for reconstruction of high-resolution CT-like MR imaging.

Dark-field X-ray imaging for the assessment of osteoporosis in human lumbar spine specimens.

Background: Dark-field imaging is a novel imaging modality that allows for the assessment of material interfaces by exploiting the wave character of x-ray. While it has been extensively studied in chest imaging, only little is known about the modality for imaging other tissues. Therefore, the purpose of this study was to evaluate whether a clinical X-ray dark-field scanner prototype allows for the assessment of osteoporosis. Materials and methods: In this prospective study we examined human cadaveric lumbar spine specimens (vertebral segments L2 to L4). We used a clinical prototype for dark-field radiography that yields both attenuation and dark-field images. All specimens were scanned in lateral orientation in vertical and horizontal position. All specimens were additionally imaged with CT as reference. Bone mineral density (BMD) values were derived from asynchronously calibrated quantitative CT measurements. Correlations between attenuation signal, dark-field signal and BMD were assessed using Spearman's rank correlation coefficients. The capability of the dark-field signal for the detection of osteoporosis/osteopenia was evaluated with receiver operating characteristics (ROC) curve analysis. Results: A total of 58 vertebrae from 20 human cadaveric spine specimens (mean age, 73 years ±13 [standard deviation]; 11 women) were studied. The dark-field signal was positively correlated with the BMD, both in vertical (r = 0.56, p < .001) and horizontal position (r = 0.43, p < .001). Also, the dark-field signal ratio was positively correlated with BMD (r = 0.30, p = .02). No correlation was found between the signal ratio of attenuation signal and BMD (r = 0.14, p = .29). For the differentiation between specimens with and without osteoporosis/osteopenia, the area under the ROC curve (AUC) was 0.80 for the dark-field signal in vertical position. Conclusion: Dark-field imaging allows for the differentiation between spine specimens with and without osteoporosis/osteopenia and may therefore be a potential biomarker for bone stability.