Objectives: Cold ischemia during kidney transplantation induces ischemia-reperfusion injury with endothelial dysfunction, capillary leak, and impaired perfusion. Its duration critically determines graft outcome. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables noninvasive assessment of renal microcirculation and may indicate ischemic injury. We evaluated the impact of ischemia duration on DCE-MRI-derived perfusion parameters in renal transplants in mice.
Materials and methods: Procedures were approved by the local institutional animal care and use committee. A total of 15 C57BL/6 mice underwent kidney transplantation and were assigned to a short or prolonged cold ischemia group. DCE-MRI was performed to assess renal perfusion. Imaging was conducted at a mean of 268 ± 30 days (mean ± standard deviation) after transplantation. Perfusion parameters were calculated using the Patlak model, which provides the plasma volume fraction (vp), reflecting renal blood volume and perfusion, and the volume transfer constant (Ktrans), characterizing the rate of contrast agent extravasation from capillaries into the extravascular extracellular space.
Results: Significant differences were observed in the Ktrans parameter of transplanted kidneys between groups. The median Ktrans (mL/100 mL/min) was significantly higher in the 16-h group (2.87, interquartile range 2.45-3.03) versus the 30-min group (0.91, 0.90-1.42; p = 0.008). Median vp (mL/100 mL/min) was non-significantly lower in the 16-h group (21.89, 17.28-23.22) versus the 30-min group (29.02, 24.99-37.15; p = 0.151).
Conclusion: Cold ischemia with 16-h duration was associated with significantly higher Ktrans values in kidney transplants, reflecting significantly increased vascular permeability. DCE-MRI provides a sensitive tool for detecting ischemia-induced microvascular dysfunction.
Relevance statement: Quantitative DCE-MRI detects microvascular injury after 16-h cold ischemia in kidney transplants in mice, supporting its potential as a noninvasive tool to assess graft integrity and guide interventions aimed at improving long-term transplant outcomes.
Key points: The duration of ischemia critically affects endothelial integrity and perfusion characteristics in a mouse kidney transplant model. Prolonged 16-h ischemia leads to increased vascular permeability, indicating more severe endothelial and microcirculatory injury in transplanted kidneys. DCE-MRI enables sensitive detection of subtle ischemia-related microvascular alterations, supporting its value for noninvasive graft assessment.
Objective: We investigated the transport of gadolinium-based contrast agent (GBCA) across the blood-brain barrier (BBB) along the perivascular spaces as part of the glymphatic drainage in patients with iatrogenic BBB disruption following digital subtraction angiography (DSA).
Materials and methods: A retrospective analysis was conducted on patients who underwent DSA for diagnosis and/or treatment of intracranial aneurysms and received a 3-T magnetic resonance imaging (MRI) within the following day. Exclusion criteria included states with a suggested impairment of BBB integrity, such as neurodegenerative diseases or suspected glymphatic impairment. BBB disruption was assessed using a pre- and post-contrast three-dimensional T1-weighted volume-isotropic turbo spin-echo sequence. Patterns of GBCA distributions were described. The localization of GBCA-extravasation was correlated with perivascular spaces visualized on the coregistered T2-weighted sequences. Fisher's exact test and logistic regression were used.
Results: Out of 43 patients, 30 (69.8%) exhibited visible BBB disruption. BBB disruption was significantly more often observed after therapeutic DSA (p = 0.004). GBCA-enhancement patterns indicated a localized pial enhancement in 96.7% of affected patients, with additional parenchymal enhancement along the perivascular spaces in 56.7%. Enhancement was predominantly located in the downstream territories of probed vessels, suggesting a potential association with glymphatic transport. An illustrative case with serial MRI examinations is presented, demonstrating time-dependent GBCA-enhancement patterns.
Conclusion: The study provides in vivo evidence of GBCA transport patterns following iatrogenic BBB disruption, which may correspond to parts of the proposed glymphatic pathways. Our results indicate a sequential progression of contrast enhancement, initially manifesting at the brain surface and subsequently extending along perivascular spaces to the subarachnoid space.
Relevance statement: Understanding BBB disruption and glymphatic transport with MRI imaging methods may improve neurovascular disease management.
Key points: BBB disruption post-DSA may facilitate GBCA transport via glymphatic pathways, offering novel and hypothesis-generating insights into brain clearance mechanisms. GBCA enhancement followed a chronological and spatial pattern, suggesting an organized cerebrospinal-interstitial exchange system relevant for brain clearance. Findings highlight potential implications for BBB integrity in neurovascular health with prospective implications for diagnostic imaging.
Objective: We compared a super-resolution deep learning image reconstruction (SR-DLR) algorithm with a normal-resolution (NR)-DLR algorithm according to radiation dose for abdominal computed tomography (CT).
Materials and methods: An image-quality phantom was scanned with an energy-integrating detectors CT unit at three volume CT dose index radiation dose levels (12.7, 5.9, and 3 mGy). Images were reconstructed using a 1,0242 matrix for SR-DLR and a 5122 matrix for NR-DLR, for three DLR levels (level-1, level-2, and level-3). Noise power spectrum (NPS) and task-based transfer function (TTF) for iodine and Solid Water® inserts were computed; TTF values at 50% (f50, mm-1) were used to quantify spatial resolution. The detectability index (d') was computed for two simulated lesions.
Results: Noise magnitude values were lower with SR-DLR than with NR-DLR for level-2 (-27.6 ± 3.8%) and level-3 (-43.5 ± 1.4%), the opposite for level-1. Average NPS spatial frequency was higher with SR-DLR than with NR-DLR for all radiation dose levels for level-1 (55.9 ± 16.7%) and level-2 (20.1 ± 13.9%) and the opposite for level-3, except at 12.7 mGy. For both inserts, f50 was higher with SR-DLR than with NR-DLR at each radiation dose and DLR level. For simulated lesions and all DLR levels, d' values were higher with SR-DLR than with NR-DLR (level-1, 6.0 ± 2.0%; level-2, 45.7 ± 5.0%; level-3, 75.2 ± 7.3%).
Conclusion: Compared to NR-DLR, SR-DLR improved spatial resolution and detectability of simulated abdominal lesions; image noise was reduced with SR-DLR only for level-2 and level-3, while image texture was better for level-1 and level-2.
Relevance statement: Super-resolution DLR with a 1,0242 matrix size improved spatial resolution and detectability of simulated abdominal lesions compared to normal-resolution DLR. Validation in clinical settings is necessary before translation into routine practice.
Key points: The performance of a new deep learning super-resolution image reconstruction algorithm (SR-DLR) was compared to a normal-resolution (NR)-DLR algorithm using an image-quality phantom for an abdominal energy-integrating detector CT protocol. SR-DLR with a 1,0242 matrix improved spatial resolution and detectability of simulated abdominal lesions compared to NR-DLR with a 5122 matrix. Using SR-DLR, therefore, presents numerous prospects for improving abdominal CT images and a high potential for reducing the radiation doses.
Objective: Abdominal aortic aneurysm (AAA) remains a life-threatening condition with few large-animal disease models. We aimed to develop a fully endovascular porcine AAA model for radiology research, reducing surgical trauma and improving reproducibility versus laparotomy-based models.
Materials and methods: Fourteen female German Landrace swine (n = 14, 30-40 kg) underwent angiography-guided intervention. The animals' infrarenal aorta was dilated by ~30% via balloon catheter, then collagenase (6,000 IU), elastase (500 IU), and 25% calcium chloride (0.5 mL) were locally incubated to weaken the vessel wall. Eight animals were included in the study; group 1 (n = 4) was euthanized at 2 weeks, and group 2 (n = 4) at 4 weeks. Aortic diameter was measured weekly by ultrasound; ex vivo histology, immunofluorescence, and western blot assessed remodeling and inflammation.
Results: Progressive aneurysm expansion was observed, with diameters of 1.32 ± 0.08 cm (mean ± standard deviation) at 1 week post-intervention, 1.59 ± 0.06 cm at 2 weeks, 1.81 ± 0.10 cm at 3 weeks, and 1.94 ± 0.19 cm at 4 weeks (baseline: 0.74 ± 0.08 cm; p < 0.001). Experimental groups' macrophages increased (group 1, 15.12 ± 3.88%; group 2, 16.65 ± 5.27%) compared to control (0.66 ± 0.27%, p = 0.012 and p = 0.021, respectively). Vascular smooth muscle cells were reduced across interventional groups (45.97 ± 17.26% versus control 80.94 ± 14.26%, p = 0.005).
Conclusions: This porcine AAA model replicates human disease features with a fully endovascular workflow, offering a valuable platform for evaluation of novel imaging techniques and interventional therapies.
Relevance statement: This study presents a fully endovascular porcine model of abdominal aortic aneurysm for translational research in interventional radiology and imaging. By enabling aneurysm induction entirely through catheter-based techniques, the model could provide a clinically relevant platform for future evaluation of novel endovascular devices and intraluminal therapeutics.
Key points: This study established a fully endovascular, translational porcine model of abdominal aortic aneurysm. The model exhibited a significant mean aneurysmal dilation of about 161% at 4 weeks and 107% at 2 weeks. Serial ultrasound confirmed consistent aneurysm expansion and reproducible growth patterns in surviving animals. Ex vivo analyses demonstrated inflammation and extracellular-matrix damage, mirroring key features of human abdominal aortic aneurysm pathology. This fully catheter-based workflow provides a practical preclinical platform for evaluating imaging techniques and endovascular therapies.
Artificial intelligence (AI) and large language models (LLMs) are increasingly integrated into radiology, offering new possibilities for advanced imaging techniques, including cardiovascular magnetic resonance (CMR). This proof-of-concept study assessed four high-performing LLMs (Gemini 2.5 Pro, ChatGPT 4.1, DeepSeek V3, and Claude Opus 4) on their ability to generate CMR protocols for 140 hypothetical cardiac cases. AI-generated protocols were compared against a reference standard established by a consensus between two experienced cardiovascular radiologists, following the Society for Cardiovascular Magnetic Resonance (SCMR) recommendations. Descriptive statistics were used to quantify the concordance of LLM-generated sequences with the SCMR guidelines. Statistical agreement was measured using Cohen and Fleiss κ statistics. Gemini 2.5 Pro achieved the highest concordance, aligning with the SCMR guidelines in 71.5% of all evaluated scenarios. Overall, LLMs showed moderate agreement with the SCMR protocols, with Gemini 2.5 Pro again performing best (Cohen κ = 0.55). Agreement was substantial for mandatory CMR sequences (Fleiss κ ∈ [0.69, 0.74]) and predominantly fair for optional sequences. The tested LLMs demonstrate a potential to generate efficient and pathology-adapted CMR protocols. Under expert supervision, this capability could streamline the imaging workflow and help extend CMR to primary healthcare centers through protocol automation. RELEVANCE STATEMENT: The potential of Gemini 2.5 Pro, ChatGPT 4.1, DeepSeek V3, and Claude Opus 4 to suggest pathology-adapted CMR protocols could improve imaging throughput and help to expand access to advanced cardiac diagnostics in primary healthcare centers. KEY POINTS: The tested large language models show potential for generating CMR protocols. Substantial agreement on mandatory CMR sequences promises more efficient examinations. Automation of CMR protocols could help to improve access to this advanced technique outside major medical institutions.
Background: Chemical exchange saturation transfer (CEST)-magnetic resonance imaging (MRI), particularly amide proton transfer-weighted (APTw)-CEST and 2-deoxy-D-glucose-CEST, holds promise for noninvasive molecular breast cancer (BC) characterization. However, quantification remains challenging due to field inhomogeneities, overlapping exchange pools, and the limited robustness of conventional metrics such as the magnetization transfer ratio asymmetry (MTRasym). This study evaluates four CEST postprocessing metrics-MTRasym, Lorentzian amplitudes, MTR relaxation exchange (MTRREX), and apparent exchange-dependent relaxation (AREX)-for their diagnostic performance in differentiating BC subtypes using endogenous APTw-CEST and exogenous 2-deoxy-D-glucose-CEST in a murine BC xenograft model of Luminal A, human epidermal growth factor receptor 2 (HER2)+, and triple-negative tumors.
Materials and methods: Metabolic CEST-MRI was performed in vitro on protein and 2-deoxy-D-glucose phantoms and in vivo in a murine BC model. Imaging was conducted at 9.4 T with 120 frequency offsets from +6 to -6 ppm. MTRREX and AREX were derived via Lorentzian fitting using tailored five-pool models. Statistical comparisons across subtypes were performed per metric.
Results: In APTw-CEST, MTRREX and AREX significantly distinguished Luminal A from HER2+ (p ≤ 0.027) and Luminal A from triple-negative (p ≤ 0.006) tumors. Lorentzian amplitudes differentiated Luminal A from triple-negative (p = 0.019), while MTRasym showed no separation. In 2-deoxy-D-glucose-CEST, only AREX distinguished Luminal A from HER2+ tumors (p = 0.017).
Conclusion: Advanced metrics, particularly MTRREX and AREX, improve metabolic CEST-MRI for BC subtyping in a murine preclinical model, while MTRasym is inadequate for this purpose.
Relevance statement: Our findings underscore the importance of applying advanced postprocessing metrics to metabolic CEST-MRI for improved noninvasive BC characterization in a murine preclinical model.
Key points: Advanced multimetabolic APTw-CEST and 2-deoxy-D-glucose-CEST postprocessing metrics allowed adequate preclinical murine BC subtyping. AREX showed potential for 2-deoxy-D-glucose-CEST in tumor characterization; however, APTw-CEST remains superior. MTRasym failed to distinguish between tumor subtypes in CEST-MRI.
Objectives: To develop a nomogram based on low-dose one-stop dual-energy and perfusion computed tomography (LD-DE&PCT) for predicting the efficacy of transcatheter arterial chemoembolization (TACE) combined with lenvatinib and immune checkpoint inhibitors (TACE-LEN-ICIs) in unresectable hepatocellular carcinoma (uHCC) patients.
Materials and methods: This prospective, multicenter study included uHCC patients who underwent LD-DE&PCT scanning. The relationships between quantitative LD-DE&PCT-derived parameters and the efficacy of TACE-LEN-ICIs were analyzed using logistic regression analysis. A nomogram incorporating the independent predictors was constructed, and its predictive performance was evaluated by the area under the receiver operating characteristic curve (AUROC).
Results: A total of 125 lesions from 71 uHCC patients were enrolled, with 71 lesions (56.8%) classified as the objective response (ObR) group and 54 lesions (43.2%) as the non-response (NR) group. Univariate analysis revealed significant differences in tumor size, corona enhancement, tumor location, iodine concentration in the arterial phase (IC-AP), normalized iodine concentration in the arterial phase (NIC-AP), effective atomic number in the arterial phase (Zeff-AP), slope of spectral HU curve in the arterial phase (λHU-AP), and permeability surface area product (PS) between ObR and NR groups. Among these, NIC-AP exhibited the highest predictive value (AUROC = 0.770; 95% confidence interval [CI]: 0.682‒0.858). Multivariate analysis identified tumor size, NIC-AP, and PS as independent predictors. The nomogram showed excellent performance (AUROC = 0.913; 95% CI: 0.858-0.968). The total radiation dose was 19.02 ± 5.39 mSv.
Conclusion: The LD-DE&PCT-based nomogram can accurately predict the response to TACE-LEN-ICIs in uHCC patients.
Relevance statement: Low-dose one-stop dual-energy and perfusion CT provides a noninvasive method to predict response to TACE combined with lenvatinib and immune checkpoint inhibitors in unresectable HCC.
Key points: Predicting response to TACE-LEN-ICIs in uHCC helps treatment decision-making. NIC-AP and PS from LD-DE&PCT, and tumor size were independent predictive biomarkers. NIC-AP was the best parameter for predicting response to TACE-LEN-ICIs in uHCC.
Objectives: This study aims to evaluate the actual energy consumption of two generations of 1.5-T magnetic resonance imaging (MRI) scanners, quantify the benefits in terms of primary energy savings resulting from technological replacement, and compare field estimates of primary energy consumption with those reported in environmental product declarations (EPDs).
Materials and methods: Two 1.5-T MRI scanner models, the old model version and its new model replacement, were monitored using a power quality analyzer connected to the electrical cabinet. Electrical power consumption data were collected over 2-week periods, both before and after the scanner replacement. Primary energy consumption was projected over 10 years, and the resulting values were compared with those reported in the EPDs for the two scanners.
Results: Over 10 years, cumulative energy consumption is estimated to be 1,010.4 MWh for the new unit versus 1,206.7 MWh for the old unit, corresponding to a 16.3% reduction. Considering the range of European primary energy factors (PEFs), energy savings varied from 235.6 to 687.1 MWh. Comparison with EPDs revealed significant discrepancies (± 40%) depending on the national PEF used, demonstrating that EPDs can both overestimate and underestimate actual energy consumption.
Conclusion: Replacement of an old MRI model resulted in measurable energy savings, particularly in non-productive phases. However, EPDs do not always reflect clinical operation or the impact of national energy mixes. While energy efficiency is central to sustainable radiology, it should not be the sole driver for equipment replacement, which must remain primarily guided by clinical and diagnostic criteria.
Relevance statement: For a radiology department focused on more sustainable practices, it is essential to have accurate data on the environmental performance of medical imaging equipment, which should not be based solely on EPDs, but on real data based on usage patterns and national energy mixes.
Key points: Replacing the old MRI scanner reduced energy consumption by 16.3%, mainly due to lower use in non-productive modes. Over a 10-year operational period, the primary energy consumption savings varied from 235.6 to 687.1 MWh. A discrepancy emerged between EPD-reported and real-world measurements, highlighting the importance of on-site validation for sustainability assessments.
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