Investigative Radiology最新文献

{"title":"ADAMTS4-Specific MR Peptide Probe for the Assessment of Atherosclerotic Plaque Burden in a Mouse Model.","authors":"Dilyana B Mangarova, Jan O Kaufmann, Julia Brangsch, Avan Kader, Jana Möckel, Jennifer L Heyl, Christine Verlemann, Lisa C Adams, Antje Ludwig, Carolin Reimann, Wolfram C Poller, Peter Niehaus, Uwe Karst, Matthias Taupitz, Bernd Hamm, Michael G Weller, Marcus R Makowski","doi":"10.1097/RLI.0000000000001152","DOIUrl":"10.1097/RLI.0000000000001152","url":null,"abstract":"<p><strong>Introduction: </strong>Atherosclerosis is the underlying cause of multiple cardiovascular pathologies. The present-day clinical imaging modalities do not offer sufficient information on plaque composition or rupture risk. A disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS4) is a strongly upregulated proteoglycan-cleaving enzyme that is specific to cardiovascular diseases, inter alia, atherosclerosis.</p><p><strong>Materials and methods: </strong>Male apolipoprotein E-deficient mice received a high-fat diet for 2 (n = 11) or 4 months (n = 11). Additionally, a group (n = 11) receiving pravastatin by drinking water for 4 months alongside the high-fat diet was examined. The control group (n = 10) consisted of C57BL/6J mice on standard chow. Molecular magnetic resonance imaging was performed prior to and after administration of the gadolinium (Gd)-based ADAMTS4-specific probe, followed by ex vivo analyses of the aortic arch, brachiocephalic arteries, and carotid arteries. A P value <0.05 was considered to indicate a statistically significant difference.</p><p><strong>Results: </strong>With advancing atherosclerosis, a significant increase in the contrast-to-noise ratio was measured after intravenous application of the probe (mean precontrast = 2.25; mean postcontrast = 11.47, P < 0.001 in the 4-month group). The pravastatin group presented decreased ADAMTS4 expression. A strong correlation between ADAMTS4 content measured via immunofluorescence staining and an increase in the contrast-to-noise ratio was detected ( R2 = 0.69). Microdissection analysis revealed that ADAMTS4 gene expression in the plaque area was significantly greater than that in the arterial wall of a control mouse ( P < 0.001). Laser ablation-inductively coupled plasma-mass spectrometry confirmed strong colocalization of areas positive for ADAMTS4 and Gd.</p><p><strong>Conclusions: </strong>Magnetic resonance imaging using an ADAMTS4-specific agent is a promising method for characterizing atherosclerotic plaques and could improve plaque assessment in the diagnosis and treatment of atherosclerosis.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"499-507"},"PeriodicalIF":7.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12233170/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142978593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Reconstruction Combined With Conventional Acceleration Improves Image Quality of 3 T Brain MRI and Does Not Impact Quantitative Diffusion Metrics.","authors":"Caroline Wilpert, Maximilian Frederic Russe, Jakob Weiss, Christian Voss, Stephan Rau, Ralph Strecker, Marco Reisert, Ricardo Bedin, Horst Urbach, Maxim Zaitsev, Fabian Bamberg, Alexander Rau","doi":"10.1097/RLI.0000000000001158","DOIUrl":"10.1097/RLI.0000000000001158","url":null,"abstract":"<p><strong>Objectives: </strong>Deep learning reconstruction of magnetic resonance imaging (MRI) allows to either improve image quality of accelerated sequences or to generate high-resolution data. We evaluated the interaction of conventional acceleration and Deep Resolve Boost (DRB)-based reconstruction techniques of a single-shot echo-planar imaging (ssEPI) diffusion-weighted imaging (DWI) on image quality features in cerebral 3 T brain MRI and compared it with a state-of-the-art DWI sequence.</p><p><strong>Materials and methods: </strong>In this prospective study, 24 patients received a standard of care ssEPI DWI and 5 additional adapted ssEPI DWI sequences, 3 of those with DRB reconstruction. Qualitative analysis encompassed rating of image quality, noise, sharpness, and artifacts. Quantitative analysis compared apparent diffusion coefficient (ADC) values region-wise between the different DWI sequences. Intraclass correlations, paired sampled t test, Wilcoxon signed rank test, and weighted Cohen κ were used.</p><p><strong>Results: </strong>Compared with the reference standard, the acquisition time was significantly improved in accelerated DWI from 75 seconds up to 50% (39 seconds; P < 0.001). All tested DRB-reconstructed sequences showed significantly improved image quality, sharpness, and reduced noise ( P < 0.001). Highest image quality was observed for the combination of conventional acceleration and DL reconstruction. In singular slices, more artifacts were observed for DRB-reconstructed sequences ( P < 0.001). While in general high consistency was found between ADC values, increasing differences in ADC values were noted with increasing acceleration and application of DRB. Falsely pathological ADCs were rarely observed near frontal poles and optic chiasm attributable to susceptibility-related artifacts due to adjacent sinuses.</p><p><strong>Conclusions: </strong>In this comparative study, we found that the combination of conventional acceleration and DRB reconstruction improves image quality and enables faster acquisition of ssEPI DWI. Nevertheless, a tradeoff between increased acceleration with risk of stronger artifacts and high-resolution with longer acquisition time needs to be considered, especially for application in cerebral MRI.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"526-534"},"PeriodicalIF":7.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143370504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moving Beyond CT Body Composition Analysis: Using Style Transfer for Bringing CT-Based Fully-Automated Body Composition Analysis to T2-Weighted MRI Sequences.","authors":"Johannes Haubold, Olivia Barbara Pollok, Mathias Holtkamp, Luca Salhöfer, Cynthia Sabrina Schmidt, Christian Bojahr, Jannis Straus, Benedikt Michael Schaarschmidt, Katarzyna Borys, Judith Kohnke, Yutong Wen, Marcel Opitz, Lale Umutlu, Michael Forsting, Christoph M Friedrich, Felix Nensa, René Hosch","doi":"10.1097/RLI.0000000000001162","DOIUrl":"10.1097/RLI.0000000000001162","url":null,"abstract":"<p><strong>Objectives: </strong>Deep learning for body composition analysis (BCA) is gaining traction in clinical research, offering rapid and automated ways to measure body features like muscle or fat volume. However, most current methods prioritize computed tomography (CT) over magnetic resonance imaging (MRI). This study presents a deep learning approach for automatic BCA using MR T2-weighted sequences.</p><p><strong>Methods: </strong>Initial BCA segmentations (10 body regions and 4 body parts) were generated by mapping CT segmentations from body and organ analysis (BOA) model to synthetic MR images created using an in-house trained CycleGAN. In total, 30 synthetic data pairs were used to train an initial nnU-Net V2 in 3D, and this preliminary model was then applied to segment 120 real T2-weighted MRI sequences from 120 patients (46% female) with a median age of 56 (interquartile range, 17.75), generating early segmentation proposals. These proposals were refined by human annotators, and nnU-Net V2 2D and 3D models were trained using 5-fold cross-validation on this optimized dataset of real MR images. Performance was evaluated using Sørensen-Dice, Surface Dice, and Hausdorff Distance metrics including 95% confidence intervals for cross-validation and ensemble models.</p><p><strong>Results: </strong>The 3D ensemble segmentation model achieved the highest Dice scores for the body region classes: bone 0.926 (95% confidence interval [CI], 0.914-0.937), muscle 0.968 (95% CI, 0.961-0.975), subcutaneous fat 0.98 (95% CI, 0.971-0.986), nervous system 0.973 (95% CI, 0.965-0.98), thoracic cavity 0.978 (95% CI, 0.969-0.984), abdominal cavity 0.989 (95% CI, 0.986-0.991), mediastinum 0.92 (95% CI, 0.901-0.936), pericardium 0.945 (95% CI, 0.924-0.96), brain 0.966 (95% CI, 0.927-0.989), and glands 0.905 (95% CI, 0.886-0.921). Furthermore, body part 2D ensemble model reached the highest Dice scores for all labels: arms 0.952 (95% CI, 0.937-0.965), head + neck 0.965 (95% CI, 0.953-0.976), legs 0.978 (95% CI, 0.968-0.988), and torso 0.99 (95% CI, 0.988-0.991). The overall average Dice across body parts (2D = 0.971, 3D = 0.969, P = ns) and body regions (2D = 0.935, 3D = 0.955, P < 0.001) ensemble models indicates stable performance across all classes.</p><p><strong>Conclusions: </strong>The presented approach facilitates efficient and automated extraction of BCA parameters from T2-weighted MRI sequences, providing precise and detailed body composition information across various regions and body parts.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"552-559"},"PeriodicalIF":7.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Free-Breathing Respiratory Triggered High-Pitch Lung CT: Insights From Phantom and Patient Scans.","authors":"Annette Schwarz, Christian Hofmann, Jannis Dickmann, Arndt Simon, Andreas Maier, Frank K Wacker, Hans-Jürgen Raatschen, Stephan Gleitz, Martina Schmidbauer","doi":"10.1097/RLI.0000000000001157","DOIUrl":"10.1097/RLI.0000000000001157","url":null,"abstract":"<p><strong>Objective: </strong>Respiratory motion can affect image quality and thus affect the diagnostic accuracy of CT images by masking or mimicking relevant lung pathologies. CT examinations are often performed during deep inspiration and breath-hold to achieve optimal image quality. However, this can be challenging for certain patient groups, such as children, the elderly, or sedated patients. The study aimed to validate a dedicated triggering algorithm for initiating respiratory-triggered high-pitch computed tomography (RT-HPCT) scans in end inspiration and end expiration in complex and irregular respiratory patterns using an anthropomorphic dynamic chest phantom. Additionally, a patient study was conducted to compare the image quality and lung expansion between RT-HPCT and standard HPCT.</p><p><strong>Materials and methods: </strong>The study utilized an algorithm that processes the patient's breathing motion in real-time to determine the appropriate time to initiate a scan. This algorithm was tested on a dynamic, tissue-equivalent chest motion phantom to replicate and simulate 3-dimensional target motion using 28 breathing motion patterns taken from patient with irregular breathing. To evaluate the performance on human patients, prospective RT-HPCT was performed in 18 free-breathing patients. As a reference, unenhanced HPCT of the chest was performed in 20 patients without respiratory triggering during free-breathing. The mean CTDI was 1.73 mGy ± 0.1 mGy for HPCT and 1.68 mGy ± 0.1 mGy for RT-HPCT. For phantom tests, the deviation from the target position of the phantom inlay is known. Image quality is approximated by evaluating stationary versus moving acquisitions. For patient scans, respiratory motion artifacts and inspiration depth were analyzed using expert knowledge of lung anatomy and automated lung volume estimation. Statistical analysis was performed to compare image quality and lung volumes between conventional HPCT and RT-HPCT.</p><p><strong>Results: </strong>In phantom scans, the average deviation from the desired excursion phase was 1.6 mm ± 4.7 mm or 15% ± 24% of the phantom movement range. In patients, the overall image quality significantly improved with respiratory triggering compared with conventional HPCT ( P < 0.001). Quantitative average lung volume was 4.0 L ± 1.1 L in the RT group and 3.6 L ± 1.0 L in the control group.</p><p><strong>Conclusions: </strong>This study demonstrated the feasibility of using a patient-adaptive respiratory triggering algorithm for high-pitch lung CT in both phantom and patients. Respiratory-triggered high-pitch CT scanning significantly reduces breathing artifacts compared with conventional nontriggered free-breathing scans.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"517-525"},"PeriodicalIF":7.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143028717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differentiation of Atherosclerotic Carotid Plaque Components With Dual-Energy Computed Tomography.","authors":"Mueez Aizaz, Juul Bierens, Marion J J Gijbels, Tobien H C M L Schreuder, Narender P van Orshoven, Jan-Willem H C Daemen, Werner H Mess, Thomas Flohr, Robert J van Oostenbrugge, Alida A Postma, M Eline Kooi","doi":"10.1097/RLI.0000000000001153","DOIUrl":"10.1097/RLI.0000000000001153","url":null,"abstract":"<p><strong>Objectives: </strong>Carotid plaque vulnerability is a strong predictor of recurrent ipsilateral stroke, but differentiation of plaque components using conventional computed tomography (CT) is suboptimal. The aim of our study was to evaluate the ability of dual-energy CT (DECT) to characterize atherosclerotic carotid plaque components based on the effective atomic number and effective electron density using magnetic resonance imaging (MRI) and, where possible, histology as the reference standard.</p><p><strong>Materials and methods: </strong>Patients with recent cerebral ischemia and a ≥2-mm carotid plaque underwent computed tomography angiography and MRI. A subgroup underwent carotid endarterectomy. Trained observers delineated plaque components on histology or MRI, independent of computed tomography angiography. DECT was coregistered with MRI and/or histology. Intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous tissue, and calcifications were delineated on DECT, and ρ eff and Z eff values were determined in the derivation cohort (n = 55). Spatial separation of these components was evaluated in a ρ eff -Z eff -cluster plot. Ranges that optimally differentiate plaque features were determined. For validation, plaque components were quantified in the validation cohort (n = 29) using these ρ eff -Z eff ranges and literature-based Hounsfield unit (HU) ranges and correlated to MRI volumes.</p><p><strong>Results: </strong>Eighty-four participants (68 ± 8 years; 55 male) were evaluated. In the derivation cohort, plaque components were well separated on the cluster plot, resulting in the following ranges: IPH:ρ eff < 1.15, Z eff < 7.5, LRNC:ρ eff < 1.15, Z eff :7.5-8.75, fibrous tissue:ρ eff < 1.15, Z eff > 8.75, and calcifications: ρ eff > 1.15, Z eff > 0. In the validation cohort, significant correlations were found between ρ eff -Z eff -based and MRI plaque volumes for fibrous tissue ( r = 0.69, P < 0.001), LRNC ( r = 0.94, P < 0.001), IPH ( r = 0.35, P = 0.03), and calcifications ( r = 0.70, P < 0.001). Lower correlations were found between HU-based and MRI plaque volumes for fibrous tissue ( r = 0.40, P = 0.02), LRNC ( r = 0.86, P < 0.001), and calcifications ( r = 0.47, P = 0.005), with no correlation for IPH ( r = 0.02, P = 0.45).</p><p><strong>Conclusions: </strong>We determined ρ eff -Z eff ranges for plaque assessment. ρ eff -Z eff -based volumes showed strong-to-very strong correlations with MRI for LRNC, fibrous tissue, and calcifications and a weak correlation for IPH. ρ eff -Z eff -based volumes demonstrated superior agreement with MRI for all plaque components compared with HU-based volumes, highlighting the potential of DECT for the identification of patients with vulnerable plaques.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"508-516"},"PeriodicalIF":7.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143005256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response.","authors":"Cherry Kim, Bum Sik Tae, Do-Young Kwon, Young Hen Lee","doi":"10.1097/RLI.0000000000001190","DOIUrl":"10.1097/RLI.0000000000001190","url":null,"abstract":"","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"497-498"},"PeriodicalIF":7.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the Association Between Gadolinium-Based Contrast Agents and Parkinson Disease: Insights From the Korean National Health Insurance Service Database.","authors":"Cherry Kim, Chohee Kim, Bum Sik Tae, Do-Young Kwon, Young Hen Lee","doi":"10.1097/RLI.0000000000001155","DOIUrl":"10.1097/RLI.0000000000001155","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to investigate the association between the use of linear and macrocyclic gadolinium-based contrast agents (GBCAs) and the subsequent development of Parkinson disease (PD).</p><p><strong>Methods: </strong>In this retrospective cohort study, data were extracted from the Korean National Health Insurance Service database, comprising 1,038,439 individuals. From this population, 175,125 adults aged 40 to 60 years with no history of brain disease were identified. All patients including 3835 who were administered GBCA at least once were monitored until 2022 for the onset of PD. Propensity score (PS) matching was employed to compare the incidence of PD between those exposed to GBCAs (either linear or macrocyclic) and those not exposed (no-GBCA group).</p><p><strong>Results: </strong>The final cohort consisted of 1175 subjects exposed to linear GBCAs, 2334 exposed to macrocyclic GBCAs, and 171,616 unexposed to any GBCA (no-GBCA group). After PS matching, PD incidence was significantly higher in the linear GBCA group compared with the no-GBCA group (0.9% vs 0.0%, P = 0.002) and was also significantly higher in the macrocyclic GBCA group than in the no-GBCA group (0.5% vs 0.04%, P = 0.003). No significant difference in PD incidence was observed between the linear and macrocyclic GBCA groups.</p><p><strong>Conclusions: </strong>Exposure to GBCAs was linked to an increased risk of developing PD in this large population-based study. The risk of PD did not differ significantly between linear and macrocyclic GBCAs.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"487-492"},"PeriodicalIF":7.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143005251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodological Concerns Regarding the Association Between Gadolinium-Based Contrast Agents and Parkinson Disease.","authors":"Seong Ho Jeong, Hyungwoo Ahn, Eui Jin Hwang, Soon Ho Yoon, Jin Mo Goo","doi":"10.1097/RLI.0000000000001181","DOIUrl":"10.1097/RLI.0000000000001181","url":null,"abstract":"","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"493-494"},"PeriodicalIF":7.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143779973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Re: Assessing the Association Between Gadolinium-based Contrast Agents and Parkinson Disease: Insights from the Korean National Health Insurance Service Database.","authors":"Won-Jin Moon, Yun Jung Bae, Jong-Min Kim","doi":"10.1097/RLI.0000000000001183","DOIUrl":"10.1097/RLI.0000000000001183","url":null,"abstract":"","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"495-496"},"PeriodicalIF":7.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Signal Amplification of T1-Weighted Single-Dose Images Improves Metastasis Detection in Brain MRI.","authors":"Robert Haase, Thomas Pinetz, Erich Kobler, Zeynep Bendella, Stefan Zülow, Arndt-Hendrik Schievelkamp, Frederic Carsten Schmeel, Sarah Panahabadi, Anna Magdalena Stylianou, Daniel Paech, Martha Foltyn-Dumitru, Verena Wagner, Kai Schlamp, Gudula Heussel, Mathias Holtkamp, Claus Peter Heussel, Martin Vahlensieck, Julian A Luetkens, Heinz-Peter Schlemmer, Johannes Haubold, Alexander Radbruch, Alexander Effland, Cornelius Deuschl, Katerina Deike","doi":"10.1097/RLI.0000000000001166","DOIUrl":"10.1097/RLI.0000000000001166","url":null,"abstract":"<p><strong>Objectives: </strong>Double-dose contrast-enhanced brain imaging improves tumor delineation and detection of occult metastases but is limited by concerns about gadolinium-based contrast agents' effects on patients and the environment. The purpose of this study was to test the benefit of a deep learning-based contrast signal amplification in true single-dose T1-weighted (T-SD) images creating artificial double-dose (A-DD) images for metastasis detection in brain magnetic resonance imaging.</p><p><strong>Materials and methods: </strong>In this prospective, multicenter study, a deep learning-based method originally trained on noncontrast, low-dose, and T-SD brain images was applied to T-SD images of 30 participants (mean age ± SD, 58.5 ± 11.8 years; 23 women) acquired externally between November 2022 and June 2023. Four readers with different levels of experience independently reviewed T-SD and A-DD images for metastases with 4 weeks between readings. A reference reader reviewed additionally acquired true double-dose images to determine any metastases present. Performances were compared using Mid-p McNemar tests for sensitivity and Wilcoxon signed rank tests for false-positive findings.</p><p><strong>Results: </strong>All readers found more metastases using A-DD images. The 2 experienced neuroradiologists achieved the same level of sensitivity using T-SD images (62 of 91 metastases, 68.1%). While the increase in sensitivity using A-DD images was only descriptive for 1 of them (A-DD: 65 of 91 metastases, +3.3%, P = 0.424), the second neuroradiologist benefited significantly with a sensitivity increase of 12.1% (73 of 91 metastases, P = 0.008). The 2 less experienced readers (1 resident and 1 fellow) both found significantly more metastases on A-DD images (resident, T-SD: 61.5%, A-DD: 68.1%, P = 0.039; fellow, T-SD: 58.2%, A-DD: 70.3%, P = 0.008). They were therefore able to use A-DD images to increase their sensitivity to the neuroradiologists' initial level on regular T-SD images. False-positive findings did not differ significantly between sequences. However, readers showed descriptively more false-positive findings on A-DD images. The benefit in sensitivity particularly applied to metastases ≤5 mm (5.7%-17.3% increase in sensitivity).</p><p><strong>Conclusions: </strong>A-DD images can improve the detectability of brain metastases without a significant loss of precision and could therefore represent a potentially valuable addition to regular single-dose brain imaging.</p>","PeriodicalId":14486,"journal":{"name":"Investigative Radiology","volume":" ","pages":"543-551"},"PeriodicalIF":7.0,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143441008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}