European Radiology Experimental最新文献

Background: We aimed to evaluate longitudinal changes in ultrashort echo time (UTE) two-component biomarkers reflecting graft ligamentization after anterior cruciate ligament (ACL) reconstruction and to identify associated clinical factors.

Materials and methods: Patients who underwent ACL reconstruction were prospectively included to perform 3-T three-dimensional double-echo UTE sequence at 3, 6, and 12 months postoperatively. Mean values of short T2* (T2*_s), long T2* (T2*_l), and fast fraction (FF), i.e., the signal proportion attributed to the T2*_s component, were calculated by fitting a biexponential model. Changes were analyzed using repeated measures analysis of variance-ANOVA. Multiple linear regression was used to assess associations between clinical factors and UTE parameters at 12 months.

Results: Forty-two patients (20 males), aged 32.7 ± 15.0 years (mean ± standard deviation), were enrolled. T2*_s and T2*_l increased from 3 to 6 months (T2*_s, 5.3 to 5.7 ms; p = 0.017; T2*_l, 21.1 to 23.3 ms; p < 0.001), then decreased from 6 to 12 months (T2*_s, 5.7 to 5.0 ms; T2*_l, 23.3 to 21.1 ms; both p < 0.001). FF followed the opposite trend, decreasing from 0.29 to 0.25, then increasing to 0.30 (both p < 0.001). At 12 months, a higher body mass index (BMI) was associated with elevated T2*_s (p = 0.005), while semitendinosus-gracilis (STG) grafts (p = 0.018) and remnant preservation (p = 0.004) were associated with lower T2*_s values.

Conclusion: UTE two-component analysis captures temporal changes in graft after ACL reconstruction, suggesting collagen regeneration. Higher BMI may hinder, while STG grafts and remnant preservation may promote ligamentization.

Relevance statement: UTE two-component analysis serves as an imaging biomarker for ACL graft ligamentization, with higher BMI being associated with impaired ligamentization, while the use of STG grafts and remnant preservation may be associated with more favorable graft maturation at 12 months as assessed by UTE two-component MRI. These findings may help tailor rehabilitation protocols and guide graft selection.

Trial registration: This study was prospectively registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) under the identification number UMIN000045710 in October 2021.

Key points: Ultrashort echo time two-component analysis noninvasively evaluates ligamentization of reconstructed ACL. Graft short T2* significantly decreased between 6 and 12 months postoperatively. Body mass index, graft type, and remnant status may influence graft maturation at 12 months.

Recent advances in magnetic resonance imaging (MRI) hardware and software have renewed interest in low-field MRI, challenging the long-held notion that such systems are inherently inferior to high-field counterparts. Traditionally dismissed due to lower signal-to-noise ratios and reduced image quality, low-field MRI was primarily relegated to cost-sensitive or resource-limited settings. However, modern low-field systems now integrate advanced reconstruction algorithms, refined imaging techniques, and improved hardware design, significantly narrowing the performance gap. In some scenarios, these systems offer distinct advantages, such as reduced susceptibility artifacts and improved safety of metallic implants. Their portability, lower operational costs, and reduced infrastructure demands make them especially valuable in point-of-care, remote, or intraoperative environments. This review examines the physical principles of low-field MRI, traces its technological evolution, and evaluates its current and emerging clinical applications. By highlighting both its strengths and limitations, we aim to clarify the growing role of low-field MRI in contemporary diagnostic imaging and underscore its potential in expanding global access to high-quality radiological care. RELEVANCE STATEMENT: Low-field and portable MRI systems offer a cost-effective, accessible, and safer imaging alternative that may expand diagnostic capabilities in underserved, point-of-care, and intraoperative settings, thereby improving global access to essential radiologic services. KEY POINTS: Advanced image reconstruction improves low-field MRI image quality and diagnostic utility. Reduced susceptibility artifacts enhance imaging near metallic hardware and air-tissue interfaces. Low-field systems enable cost-effective, portable imaging in constrained clinical environments.

Background: Aortic dissection (AD) is a life-threatening condition. We developed an artificial intelligence (AI) algorithm capable of robust, accurate, and automated AD detection and sub-classification.

Materials and methods: Based on 2010-2023 data from Mannheim University Medical Centre, heterogeneous internal training cases with confirmed AD (n = 70) were manually segmented and, together with non-AD cases (n = 87), used for training of a convolutional neural network (CNN; U-Net architecture) configured using the nnU-Net framework. Internal test dataset was composed of 106 cases. The external test was performed on a public dataset: 100 AD cases from ImageTBAD, Guangdong Provincial People's Hospital, China, and 38 non-AD cases from the AVT dataset (multiple sources). Model performance was evaluated by area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, specificity, precision, and F1-score, and by investigating performance on different subsets of cases. Confidence intervals were determined using DeLong's method and bootstrapping.

Results: The best-performing algorithm achieved an AUROC of 98.7% (95% CI: 96.1-100.0%) and an AUPRC of 98.9% (96.0-100.0%) on the internal test dataset, 97.0% (94.7-99.3%) and 99.06% (98.0-99.7%) on the external test datasets, respectively. In the internal test dataset, of 15 unsuspected AD cases, 14 (93.3%) were successfully detected by the algorithm. On the external test dataset, sensitivity, specificity, precision, and F1-score were 92.0%, 100.0%, 100.0%, and 95.8%, respectively.

Conclusion: The developed AI pipeline highlighted the capability of optimized CNNs to reliably detect AD across heterogeneous multicenter datasets. The resulting tool will be made publicly available for further scientific evaluation.

Relevance statement: Artificial Intelligence demonstrated promising potential to detect AD on heterogeneous thoracic CT imaging data.

Key points: Early detection of aortic dissection (AD) is crucial for timely treatment. A modern convolutional neural network (CNN) achieved 93.5% sensitivity and 100.0% specificity for AD detection on multicenter, heterogeneous CT data. These results demonstrate the potential of streamlined, optimized CNNs for robust AD detection on CT, supporting fast clinical response.

Background: Computed tomography (CT) is widely used to diagnose peritoneal metastases (PM), with debated accuracy. Dual-energy CT (DECT) may improve accuracy, yet its diagnostic performance is still unknown. We explored the potential of DECT for PM detection and quantification.

Materials and methods: We retrospectively included patients undergoing staging DECT for cancers with a high risk of peritoneal involvement, followed by staging laparoscopy/laparotomy, which served as the reference standard. Nine readers with varying experience levels (three expert, three intermediate, and three inexpert) reviewed two sets of images, separated by ≥ 60 days, considering the presence/absence of PM, abdominal region(s) involved, and calculated the radiological peritoneal cancer index (PCI). The first set included contrast-enhanced delayed-DECT scans reconstructed as virtual 120-kVp images; the second set also included virtual monoenergetic, 40-keV images and iodine maps. Performance metrics, receiver operating characteristic (ROC) analysis, McNemar, DeLong, and Wilcoxon tests were applied.

Results: Twenty patients (mean age 64.2 years; 12 females) were included, 10 with PM. At per-patient analysis, the addition of monoenergetic 40-keV images and iodine maps slightly increased the performance and improved inter-reader agreement, with significant benefit for inexperienced readers only (p = 0.010). Per-region analysis demonstrated a significant advantage with an area under the ROC curve ranging from 0.709 to 0.766 (p < 0.001), confirmed for each reader group; in addition, the inter-reader agreement significantly improved. Quantitative analysis showed a reduction in the differences between CT results and surgical PCI by DECT (4 ± 12 versus 2 ± 9, p < 0.001).

Conclusion: DECT-derived reconstructions in the delayed-phase enhanced PM detection and quantification.

Relevance statement: Delayed-phase DECT reconstruction showed superior accuracy over conventional CT in detecting and quantifying peritoneal metastases. These findings could help establish a new standard CT protocol for malignancies with peritoneal tropism.

Key points: CT is the most widely used technique for assessing peritoneal metastases. The accuracy of CT for peritoneal metastases is debated; dual-energy CT shows promise. In our study, delayed-phase dual-energy CT provided significant advantages for all readers.

Background: To determine the feasibility of diffusion-relaxation correlation spectroscopic imaging in identifying tumoral differentiation profile and predicting cervical lymph node metastasis (CLNM) in oral tongue squamous cell carcinoma (OTSCC).

Materials and methods: This prospective study enrolled fifty-seven OTSCC patients who underwent preoperative head and neck magnetic resonance imaging (MRI). Scans with multi b-values (0-1500 s/mm²) and multi-echo times (7-150 ms) were performed to generate normalized diffusion-T2 spectra. Tumor maximal diameter and depth of invasion were measured. Tumors were segmented into five compartments (V_A to V_E) with metrics compared across normal controls, CLNM-, and CLNM+ groups. Pathological parameters such as tumor-stroma ratio (TSR), perineural invasion, Ki-67, tumor p53 protein, and cyclin-dependent kinase inhibitor p16 were evaluated. Correlations between MRI metrics and pathological parameters were assessed. Predictors of CLNM+ were identified using logistic regression analysis, and the predictive performance was evaluated using receiver operating characteristic analysis.

Results: Thirty-four patients were assigned to the CLNM+ group and 23 to the CLNM- group. CLNM+ patients showed larger tumor maximal diameters, deeper invasion, increased V_B and V_D, and decreased V_A compared to CLNM- patients. V_B exhibited strong positive correlations with perineural invasion and depth of invasion, while V_D correlated positively with TSR. Moreover, V_B and depth of invasion were independent prognostic factors for CLNM+, and their combined model achieved the highest predictive performance.

Conclusion: Diffusion-relaxation correlation spectroscopic imaging marked a significant advancement in the diagnostic and prognostic assessment of OTSCC, offering detailed tumor characterization and improving CLNM+ prediction, with great potential for accurate and non-invasive evaluation.

Relevance statement: Diffusion-relaxation correlation spectroscopic imaging metrics (V_B and V_D) characterized tumor heterogeneity and correlated with pathological biomarkers, making it a promising non-invasive tool for enhancing preoperative decisions and reducing unnecessary lymph node dissections in clinical workflows.

Key points: Tumoral components and heterogeneity of oral tongue cancer were investigated on MRI. Advanced diffusion-relaxation imaging delineated the tumoral differential profile and predicted metastasis. We provided a non-invasive tool for preoperative decision-making in clinical workflows.

Background: Maximum intensity projections (MIPs) facilitate rapid lesion detection both for contrast-enhanced (CE) and diffusion-weighted imaging (DWI) breast magnetic resonance imaging (MRI). We evaluated the feasibility of AI-based virtual CE subtraction MIPs as a reading approach.

Materials and methods: This Institutional Review Board-approved retrospective study includes 540 multi-parametric breast MRI examinations (performed from 2017 to 2020), including multi-b-value DWI (50, 750, and 1,500 s/mm²). A 2D U-Net was trained using unenhanced (UnE) images as inputs to generate virtual abbreviated CE (VAbCE) subtractions. Two radiologists evaluated lesion suspicion, image quality, and artifacts for UnE, VACE, and abbreviated CE (AbCE) images. Lesion conspicuity was compared between VAbCE and AbCE MIPs.

Results: Cancer detection rates for UE, VAbCE, and AbCE MIPs were 90.0%, 91.4%, and 94.3%, respectively. Single-slice reading demonstrated sensitivities of 88.6% (UnE), 91.4% (VAbCE), and 94.3% (AbCE). Inter-rater agreement (Cohen κ) for lesion suspicion scores was higher for VAbCE (0.53) than UnE alone (0.39) and comparable to AbCE (0.58). No significant difference in mean lesion conspicuity was observed for VACE MIPs compared to ACE (p ≥ 0.670). No significant difference could be observed for quality (p ≥ 0.108), and reading time (p = 1.000) between methods. Fewer visually significant artifacts could be observed in VAbCE than in AbCE MIPs (p ≤ 0.001).

Conclusion: VAbCE breast MRI improved inter-rater agreement and allowed for slightly improved sensitivity compared to UnE images, while AbCE still provided the overall highest sensitivity. Further research is necessary to investigate the diagnostic potential of VAbCE breast MRI.

Relevance statement: VAbCE breast MRI generated by neural networks allowed the derivation of MIPs for rapid visual assessment, showing a way for screening applications.

Key points: Virtual abbreviated contrast-enhanced (VAbCE) MIPs provided comparable sensitivity to MIPs of unenhanced high b-value DWI and were slightly lower than AbCE MIPs. Adding VAbCE to unenhanced high b-value DWI significantly improved interrater agreement for lesion suspicion scoring. Single-slice evaluation of VAbCE MIPs provided a sensitivity comparable to unenhanced high b-value DWI MIPs.

The study aimed to evaluate the feasibility of using cone-beam computed tomography (CBCT) for gastric volume assessment before and after endoscopic sleeve gastroplasty (ESG), focusing on patient radiation dose. Ten patients scheduled for ESG were prospectively enrolled. Each patient underwent three CBCT scans under anesthesia: two scans following CO₂ insufflation of the gastric lumen-one pre-ESG and one post-ESG-and a third scan post-ESG after gastric distension with Gastrografin. Image quality was evaluated for its adequacy in generating three-dimensional reconstructions and calculating gastric volumes. Dose-area product was recorded for each scan and used to estimate the effective dose (ED) via Monte Carlo simulations using the PCXMC rotational model. Although image quality did not match conventional computed tomography (CT), it was sufficient for three-dimensional reconstruction and gastric volume measurements. The median ED was 4.2 mSv for pre-ESG scans with CO₂ insufflation, 4.2 mSv for post-ESG scans with CO2 insufflation, and 4.8 mSv for post-ESG scans with Gastrografin. CBCT provided satisfactory image quality for gastric volumetry at relatively low radiation doses, with ED being approximately 50% of that of conventional CT. This preliminary feasibility study suggests that CBCT could be a useful tool for planning ESG and assessing post-procedural outcomes. RELEVANCE STATEMENT: Low-dose CBCT provided sufficient image quality for gastric volumetry in a small cohort of patients undergoing ESG, reducing radiation exposure by approximately 50% compared to conventional CT. This investigational technique enables seamless intraoperative imaging that could improve planning and evaluation of endoscopic bariatric procedures. KEY POINTS: CBCT allowed gastric volumetric assessment at a relatively low radiation dose. Scans with CO₂ insufflation delivered lower radiation doses than scans with Gastrografin. Scans with CO₂ insufflation showed superior image quality compared to scans with Gastrografin. CBCT could be a valuable tool for planning ESG and evaluating outcomes.

Background: We investigated the influence of different kernel types and sharpness levels on in vitro and in vivo coronary stenosis quantification in high-pitch photon-counting detector coronary CT angiography (PCD-CCTA).

Materials and methods: Coronary stenoses were evaluated in a phantom containing two stenosis grades (25% and 50%), and in a retrospective cohort of 30 patients who underwent high-pitch PCD-CCTA. Scans were reconstructed as virtual monoenergetic images at 55 keV using three different kernels (Br, Bv, and Qr) and four sharpness levels (36, 40, 44, and 48). Percent diameter stenosis (PDS) values were compared. In vitro measurements were additionally compared with the stenosis reference value. Two readers independently assessed the in vivo measurements.

Results: In vitro, PDS values of all stenoses showed no difference among various kernel types and sharpness levels (p ≥ 0.412). However, PDS measurements using kernel Bv40 showed the smallest cumulative deviation from the ground truth. In vivo, a total of 53 stenoses were identified in 30 patients, aged 63 ± 13 years (mean ± standard deviation), 8/30 (27%) females. There was no significant difference in PDS measurements among reconstructions, either when analyzed per stenosis or stratified by different plaque types (p = 1.000). Bv kernels showed higher interobserver reliability (intraclass correlation coefficient: Bv 0.91; Qr 0.88; Br 0.85).

Conclusion: With comparable diagnostic accuracy, different kernel types and sharpness levels can be used in high-pitch PCD-CCTA. Due to the in vivo advantage in interobserver reliability and the in vitro observed lowest cumulative deviation from ground truth, reconstruction with kernel Bv40 should be preferred.

Relevance statement: For image reconstruction in PCD-CCTA with high-pitch mode, kernel Bv40 should be considered to obtain the best diagnostic performance and reliability of stenosis quantification.

Key points: High-pitch PCD-CCTA images can be reconstructed with different kernels. Reconstructions with different kernels showed comparable accuracy on coronary stenosis quantification. In vitro, Bv40 reconstructions showed superior measurement accuracy to the reference. In vivo, reconstructions with the Bv kernel had the highest interobserver reliability. Reconstruction with kernel Bv40 should be considered in high-pitch PCD-CCTA.

Background: Periodontitis is characterized by the inflammatory destruction of tooth-supporting alveolar bone. Dental magnetic resonance imaging (MRI) using dynamic contrast-enhanced perfusion can potentially detect vascular inflammatory responses. This study aims to assess the feasibility of perfusion dental MRI and characterize periodontal lesions with perfusion profiles.

Materials and methods: In this prospective study, 19 patients with severe periodontitis underwent pretreatment 3-T dental MRI with T2-weighted, high-resolution dynamic contrast-enhanced T1-weighted perfusion protocol, and contrast-enhanced T1-weighted fat-suppressed sequences as well as cone-beam computed tomography (CBCT). Periodontal bone lesions were segmented semiautomatically using a multistep threshold-based algorithm, guided by T1-weighted contrast enhancement, T2-weighted hyperintensity, as well as CBCT-based bone loss. Volumetric analyses and clinical data were compared with perfusion parameters.

Results: In all 95 assessed periodontal lesions, perfusion parameter elevations were significantly different when compared to normal distant bone (p < 0.001 to 0.026). Moreover, structurally normal-appearing bone adjacent to T2-hyperintense/T1-contrast-enhancing signal alterations exhibited increased permeability (p = 0.036-006) but showed no significant change in blood flow (p = 0.270) compared to bone control areas. Lesions with bleeding showed higher vascular permeability and blood flow markers than lesions without bleeding (p = 0.004-0.006). Additionally, lesions with excessive edema and areas of bone loss exhibited significantly elevated permeability and blood flow parameters (p = 0.001-0.028).

Conclusion: Perfusion dental MRI for periodontal lesion assessment is feasible. Permeability/perfusion parameters elevations are related to clinical signs of inflammation and CBCT-based bone loss, with the potential for detecting early inflammatory responses.

Relevance statement: Perfusion dental MRI effectively characterizes periodontal disease by detecting inflammation-related vascular changes beyond structural imaging on CBCT and conventional MR, offering potential for improved diagnosis, monitoring, and treatment evaluation. Longitudinal studies are needed.

Key points: Perfusion dental MRI detects increased blood flow and vascular permeability in periodontal lesions. Increased permeability in adjacent bone suggests early inflammatory changes before structural loss. Dental MRI perfusion metrics could aid early lesion detection and monitoring of periodontitis.

Background: Manufacturer-defined AI thresholds for chest x-ray (CXR) often lack customization options. Threshold optimization strategies utilizing users' clinical real-world data along with pathology-enriched validation data may better address subgroup-specific and user-specific needs.

Materials and methods: A pathology-enriched dataset (study cohort, 563 (CXRs)) with pleural effusions, consolidations, pneumothoraces, nodules, and unremarkable findings was analysed by an AI system and six reference radiologists. The same AI model was applied to a routine dataset (clinical cohort, 15,786 consecutive routine CXRs). Iterative receiver operating characteristic analysis linked achievable sensitivities (study cohort) to resulting AI alert rates in clinical routine inpatient or outpatient subgroups. "Optimized" thresholds (OTs) were defined by a 1% sensitivity increase leading to more than a 1% rise in AI alert rates. Threshold comparisons (OTs versus AI vendor's default thresholds (AIDT) versus Youden's thresholds) were based on 400 clinical cohort cases with expert radiologists' reference.

Results: AIDTs, OTs, and Youden's thresholds varied across scenarios, with OTs differing based on tailoring for inpatient or outpatient CXRs. AIDT lowering most reasonably improved sensitivity for pleural effusion, with increases from 46.8% (AIDT) to 87.2% (OT) for outpatients and from 76.3% (AIDT) to 93.5% (OT) for inpatients; similar trends appeared for consolidations. Conversely, regarding inpatient nodule detection, increasing the threshold improved accuracy from 69.5% (AIDT) to 82.5% (OT) without compromising sensitivity. Graphical analysis supports threshold selection by illustrating estimated sensitivities and clinical routine AI alert rates.

Conclusion: An innovative, subgroup-specific AI threshold optimization is proposed, automatically implemented and transferable to other AI algorithms and varying clinical subgroup settings.

Relevance statement: Individually customizing thresholds tailored to specific medical experts' needs and patient subgroup characteristics is promising and may enhance diagnostic accuracy and the clinical acceptance of diagnostic AI algorithms.

Key points: Customizing AI thresholds individually addresses specific user/patient subgroup needs. The presented approach utilizes pathology-enriched and real-world subgroup data for optimization. Potential is shown by comparing individualized thresholds with vendor defaults. Distinct thresholds for in- and outpatient CXR AI analysis may improve perception. The automated pipeline methodology is transferable to other AI models or subgroups.