European Radiology Experimental最新文献

{"title":"Assessment of pulmonary vascular anatomy: comparing augmented reality by holograms versus standard CT images/reconstructions using surgical findings as reference standard.","authors":"Francesco Petrella, Stefania Maria Rita Rizzo, Cristiano Rampinelli, Monica Casiraghi, Vincenzo Bagnardi, Samuele Frassoni, Silvia Pozzi, Omar Pappalardo, Gabriella Pravettoni, Lorenzo Spaggiari","doi":"10.1186/s41747-024-00458-w","DOIUrl":"10.1186/s41747-024-00458-w","url":null,"abstract":"<p><strong>Background: </strong>We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques.</p><p><strong>Methods: </strong>Patients scheduled for lobectomy for lung cancer were prospectively included and underwent CT for staging. A patient-specific three-dimensional model was generated and visualized in an augmented reality setting. One radiologist and one thoracic surgeon evaluated CT images and holograms to count lobar arteries, having as reference standard the number of arteries recorded at surgery. The easiness of vessel identification was graded according to a Likert scale. Wilcoxon signed-rank test and κ statistics were used.</p><p><strong>Results: </strong>Fifty-two patients were prospectively included. The two doctors detected the same number of arteries in 44/52 images (85%) and in 51/52 holograms (98%). The mean difference between the number of artery branches detected by surgery and CT images was 0.31 ± 0.98, whereas it was 0.09 ± 0.37 between surgery and HGs (p = 0.433). In particular, the mean difference in the number of arteries detected in the upper lobes was 0.67 ± 1.08 between surgery and CT images and 0.17 ± 0.46 between surgery and holograms (p = 0.029). Both radiologist and surgeon showed a higher agreement for holograms (κ = 0.99) than for CT (κ = 0.81) and found holograms easier to evaluate than CTs (p < 0.001).</p><p><strong>Conclusions: </strong>Augmented reality by holograms is an effective tool for preoperative vascular anatomy assessment of lungs, especially when evaluating the upper lobes, more prone to anatomical variations.</p><p><strong>Trial registration: </strong>ClinicalTrials.gov, NCT04227444 RELEVANCE STATEMENT: Preoperative evaluation of the lung lobe arteries through augmented reality may help the thoracic surgeons to carefully plan a lobectomy, thus contributing to optimize patients' outcomes.</p><p><strong>Key points: </strong>• Preoperative assessment of the lung arteries may help surgical planning. • Lung artery detection by augmented reality was more accurate than that by CT images, particularly for the upper lobes. • The assessment of the lung arterial vessels was easier by using holograms than CT images.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"57"},"PeriodicalIF":3.8,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11082107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound-guided minimally invasive thread release of Guyon's canal: initial experience in cadaveric specimens.","authors":"Suren Jengojan, Philipp Sorgo, Gregor Kasprian, Johannes Streicher, Gerlinde Gruber, Veith Moser, Gerd Bodner","doi":"10.1186/s41747-024-00456-y","DOIUrl":"10.1186/s41747-024-00456-y","url":null,"abstract":"<p><strong>Objective: </strong>Guyon's canal syndrome is caused by compression of the ulnar nerve at the wrist, occasionally requiring decompression surgery. In recent times, minimally invasive approaches have gained popularity. The aim of this study was to assess the efficacy and safety of ultrasound-guided thread release for transecting the palmar ligament in Guyon's canal without harming surrounding structures, in a cadaveric specimen model.</p><p><strong>Methods: </strong>After ethical approval, thirteen ultrasound-guided thread releases of Guyon's canal were performed on the wrists of softly embalmed anatomic specimens. Cadavers showing injuries or prior operations at the hand were excluded. Subsequently, the specimens were dissected, and the outcome of the interventions and potential damage to adjacent anatomical structures as well as ultrasound visibility were evaluated with a score from one to three.</p><p><strong>Results: </strong>Out of 13 interventions, a complete transection was achieved in ten cases (76.9%), and a partial transection was documented in three cases (23.1%). Irrelevant lesions on the flexor tendons were observed in two cases (15.4%), and an arterial branch was damaged in one (7.7%). Ultrasound visibility varied among specimens, but essential structures were delineated in all cases.</p><p><strong>Conclusion: </strong>Ultrasound-guided thread release of Guyon's canal has shown promising first results in anatomic specimens. However, further studies are required to ensure the safety of the procedure.</p><p><strong>Relevance statement: </strong>Our study showed that minimally invasive ultrasound-guided thread release of Guyon's canal is a feasible approach in the anatomical model. The results may provide a basis for further research and refinement of this technique.</p><p><strong>Key points: </strong>• In Guyon's canal syndrome, the ulnar nerve is compressed at the wrist, often requiring surgical release. • We adapted and tested a minimally invasive ultrasound-guided thread release technique in anatomic specimens. • The technique was effective; however, in one specimen, a small anatomic branch was damaged.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"56"},"PeriodicalIF":3.8,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11076429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140877598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reproducibility of a semiautomatic lobar lung tissue assignment technique on noncontrast CT scans: a study on swine animal model.","authors":"Nile Luu, Nathan Van, Alireza Shojazadeh, Yixiao Zhao, Sabee Molloi","doi":"10.1186/s41747-024-00453-1","DOIUrl":"10.1186/s41747-024-00453-1","url":null,"abstract":"<p><strong>Background: </strong>To evaluate the reproducibility of a vessel-specific minimum cost path (MCP) technique used for lobar segmentation on noncontrast computed tomography (CT).</p><p><strong>Methods: </strong>Sixteen Yorkshire swine (49.9 ± 4.7 kg, mean ± standard deviation) underwent a total of 46 noncontrast helical CT scans from November 2020 to May 2022 using a 320-slice scanner. A semiautomatic algorithm was employed by three readers to segment the lung tissue and pulmonary arterial tree. The centerline of the arterial tree was extracted and partitioned into six subtrees for lobar assignment. The MCP technique was implemented to assign lobar territories by assigning lung tissue voxels to the nearest arterial tree segment. MCP-derived lobar mass and volume were then compared between two acquisitions, using linear regression, root mean square error (RMSE), and paired sample t-tests. An interobserver and intraobserver analysis of the lobar measurements was also performed.</p><p><strong>Results: </strong>The average whole lung mass and volume was 663.7 ± 103.7 g and 1,444.22 ± 309.1 mL, respectively. The lobar mass measurements from the initial (MLobe1) and subsequent (MLobe2) acquisitions were correlated by MLobe1 = 0.99 MLobe2 + 1.76 (r = 0.99, p = 0.120, RMSE = 7.99 g). The lobar volume measurements from the initial (VLobe1) and subsequent (VLobe2) acquisitions were correlated by VLobe1 = 0.98VLobe2 + 2.66 (r = 0.99, p = 0.160, RSME = 15.26 mL).</p><p><strong>Conclusions: </strong>The lobar mass and volume measurements showed excellent reproducibility through a vessel-specific assignment technique. This technique may serve for automated lung lobar segmentation, facilitating clinical regional pulmonary analysis.</p><p><strong>Relevance statement: </strong>Assessment of lobar mass or volume in the lung lobes using noncontrast CT may allow for efficient region-specific treatment strategies for diseases such as pulmonary embolism and chronic thromboembolic pulmonary hypertension.</p><p><strong>Key points: </strong>• Lobar segmentation is essential for precise disease assessment and treatment planning. • Current methods for segmentation using fissure lines are problematic. • The minimum-cost-path technique here is proposed and a swine model showed excellent reproducibility for lobar mass measurements. • Interobserver agreement was excellent, with intraclass correlation coefficients greater than 0.90.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"55"},"PeriodicalIF":3.8,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11070405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140855428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving image quality of sparse-view lung tumor CT images with U-Net.","authors":"Annika Ries, Tina Dorosti, Johannes Thalhammer, Daniel Sasse, Andreas Sauter, Felix Meurer, Ashley Benne, Tobias Lasser, Franz Pfeiffer, Florian Schaff, Daniela Pfeiffer","doi":"10.1186/s41747-024-00450-4","DOIUrl":"https://doi.org/10.1186/s41747-024-00450-4","url":null,"abstract":"<p><strong>Background: </strong>We aimed to improve the image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determine the best tradeoff between number of views, IQ, and diagnostic confidence.</p><p><strong>Methods: </strong>CT images from 41 subjects aged 62.8 ± 10.6 years (mean ± standard deviation, 23 men), 34 with lung metastasis, 7 healthy, were retrospectively selected (2016-2018) and forward projected onto 2,048-view sinograms. Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, 7 healthy) for a single-blinded multireader study. These slices, for all levels of subsampling, with and without U-Net postprocessing, were presented to three readers. IQ and diagnostic confidence were ranked using predefined scales. Subjective nodule segmentation was evaluated using sensitivity and Dice similarity coefficient (DSC); clustered Wilcoxon signed-rank test was used.</p><p><strong>Results: </strong>The 64-projection sparse-view images resulted in 0.89 sensitivity and 0.81 DSC, while their counterparts, postprocessed with the U-Net, had improved metrics (0.94 sensitivity and 0.85 DSC) (p = 0.400). Fewer views led to insufficient IQ for diagnosis. For increased views, no substantial discrepancies were noted between sparse-view and postprocessed images.</p><p><strong>Conclusions: </strong>Projection views can be reduced from 2,048 to 64 while maintaining IQ and the confidence of the radiologists on a satisfactory level.</p><p><strong>Relevance statement: </strong>Our reader study demonstrates the benefit of U-Net postprocessing for regular CT screenings of patients with lung metastasis to increase the IQ and diagnostic confidence while reducing the dose.</p><p><strong>Key points: </strong>• Sparse-projection-view streak artifacts reduce the quality and usability of sparse-view CT images. • U-Net-based postprocessing removes sparse-view artifacts while maintaining diagnostically accurate IQ. • Postprocessed sparse-view CTs drastically increase radiologists' confidence in diagnosing lung metastasis.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"54"},"PeriodicalIF":3.8,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11065797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140871392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in interventional radiology: state of the art","authors":"Pierluigi Glielmo, Stefano Fusco, Salvatore Gitto, Giulia Zantonelli, Domenico Albano, Carmelo Messina, Luca Maria Sconfienza, Giovanni Mauri","doi":"10.1186/s41747-024-00452-2","DOIUrl":"https://doi.org/10.1186/s41747-024-00452-2","url":null,"abstract":"<p>Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.</p><p><b>Relevance statement</b> Exploring AI’s transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.</p><p><b>Key points</b></p><p>• AI adoption in IR is more complex compared to diagnostic radiology.</p><p>• Current literature about AI in IR is in its early stages.</p><p>• AI has the potential to revolutionise every aspect of IR.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\u0000","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"13 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI","authors":"Gustav Müller-Franzes, Luisa Huck, Maike Bode, Sven Nebelung, Christiane Kuhl, Daniel Truhn, Teresa Lemainque","doi":"10.1186/s41747-024-00451-3","DOIUrl":"https://doi.org/10.1186/s41747-024-00451-3","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>To compare denoising diffusion probabilistic models (DDPM) and generative adversarial networks (GAN) for recovering contrast-enhanced breast magnetic resonance imaging (MRI) subtraction images from virtual low-dose subtraction images.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Retrospective, ethically approved study. DDPM- and GAN-reconstructed single-slice subtraction images of 50 breasts with enhancing lesions were compared to original ones at three dose levels (25%, 10%, 5%) using quantitative measures and radiologic evaluations. Two radiologists stated their preference based on the reconstruction quality and scored the lesion conspicuity as compared to the original, blinded to the model. Fifty lesion-free maximum intensity projections were evaluated for the presence of false-positives. Results were compared between models and dose levels, using generalized linear mixed models.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>At 5% dose, both radiologists preferred the GAN-generated images, whereas at 25% dose, both radiologists preferred the DDPM-generated images. Median lesion conspicuity scores did not differ between GAN and DDPM at 25% dose (5 <i>versus</i> 5, <i>p</i> = 1.000) and 10% dose (4 <i>versus</i> 4, <i>p</i> = 1.000). At 5% dose, both readers assigned higher conspicuity to the GAN than to the DDPM (3 <i>versus</i> 2, <i>p</i> = 0.007). In the lesion-free examinations, DDPM and GAN showed no differences in the false-positive rate at 5% (15% <i>versus</i> 22%), 10% (10% <i>versus</i> 6%), and 25% (6% <i>versus</i> 4%) (<i>p</i> = 1.000).</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>Both GAN and DDPM yielded promising results in low-dose image reconstruction. However, neither of them showed superior results over the other model for all dose levels and evaluation metrics. Further development is needed to counteract false-positives.</p><h3 data-test=\"abstract-sub-heading\">Relevance statement</h3><p>For MRI-based breast cancer screening, reducing the contrast agent dose is desirable. Diffusion probabilistic models and generative adversarial networks were capable of retrospectively enhancing the signal of low-dose images. Hence, they may supplement imaging with reduced doses in the future.</p><h3 data-test=\"abstract-sub-heading\">Key points</h3><p>• Deep learning may help recover signal in low-dose contrast-enhanced breast MRI.</p><p>• Two models (DDPM and GAN) were trained at different dose levels.</p><p>• Radiologists preferred DDPM at 25%, and GAN images at 5% dose.</p><p>• Lesion conspicuity between DDPM and GAN was similar, except at 5% dose.</p><p>• GAN and DDPM yield promising results in low-dose image reconstruction.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\u0000","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"105 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phantom-based analysis of variations in automatic exposure control across three mammography systems: implications for radiation dose and image quality in mammography, DBT, and CEM","authors":"Gisella Gennaro, Sara Del Genio, Giuseppe Manco, Francesca Caumo","doi":"10.1186/s41747-024-00447-z","DOIUrl":"https://doi.org/10.1186/s41747-024-00447-z","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Automatic exposure control (AEC) plays a crucial role in mammography by determining the exposure conditions needed to achieve specific image quality based on the absorption characteristics of compressed breasts. This study aimed to characterize the behavior of AEC for digital mammography (DM), digital breast tomosynthesis (DBT), and low-energy (LE) and high-energy (HE) acquisitions used in contrast-enhanced mammography (CEM) for three mammography systems from two manufacturers.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Using phantoms simulating various breast thicknesses, 363 studies were acquired using all available AEC modes 165 DM, 132 DBT, and 66 LE-CEM and HE-CEM. AEC behaviors were compared across systems and modalities to assess the impact of different technical components and manufacturers’ strategies on the resulting mean glandular doses (MGDs) and image quality metrics such as contrast-to-noise ratio (CNR).</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>For all systems and modalities, AEC increased MGD for increasing phantom thicknesses and decreased CNR. The median MGD values (interquartile ranges) were 1.135 mGy (0.772–1.668) for DM, 1.257 mGy (0.971–1.863) for DBT, 1.280 mGy (0.937–1.878) for LE-CEM, and 0.630 mGy (0.397–0.713) for HE-CEM. Medians CNRs were 14.2 (7.8–20.2) for DM, 4.91 (2.58–7.20) for a single projection in DBT, 11.9 (8.0–18.2) for LE-CEM, and 5.2 (3.6–9.2) for HE-CEM. AECs showed high repeatability, with variations lower than 5% for all modes in DM, DBT, and CEM.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>The study revealed substantial differences in AEC behavior between systems, modalities, and AEC modes, influenced by technical components and manufacturers’ strategies, with potential implications in radiation dose and image quality in clinical settings.</p><h3 data-test=\"abstract-sub-heading\">Relevance statement</h3><p>The study emphasized the central role of automatic exposure control in DM, DBT, and CEM acquisitions and the great variability in dose and image quality among manufacturers and between modalities. Caution is needed when generalizing conclusions about differences across mammography modalities.</p><h3 data-test=\"abstract-sub-heading\">Key points</h3><p>• AEC plays a crucial role in DM, DBT, and CEM.</p><p>• AEC determines the “optimal” exposure conditions needed to achieve specific image quality.</p><p>• The study revealed substantial differences in AEC behavior, influenced by differences in technical components and strategies.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\u0000","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"51 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers","authors":"Thomas Dratsch, Charlotte Zäske, Florian Siedek, Philip Rauen, Nils Große Hokamp, Kristina Sonnabend, David Maintz, Grischa Bratke, Andra Iuga","doi":"10.1186/s41747-024-00446-0","DOIUrl":"https://doi.org/10.1186/s41747-024-00446-0","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>To investigate the potential of combining compressed sensing (CS) and artificial intelligence (AI), in particular deep learning (DL), for accelerating three-dimensional (3D) magnetic resonance imaging (MRI) sequences of the knee.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Twenty healthy volunteers were examined using a 3-T scanner with a fat-saturated 3D proton density sequence with four different acceleration levels (10, 13, 15, and 17). All sequences were accelerated with CS and reconstructed using the conventional and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using seven criteria on a 5-point-Likert-scale (overall impression, artifacts, delineation of the anterior cruciate ligament, posterior cruciate ligament, menisci, cartilage, and bone). Using mixed models, all CS-AI sequences were compared to the clinical standard (sense sequence with an acceleration factor of 2) and CS sequences with the same acceleration factor.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>3D sequences reconstructed with CS-AI achieved significantly better values for subjective image quality compared to sequences reconstructed with CS with the same acceleration factor (<i>p</i> ≤ 0.001). The images reconstructed with CS-AI showed that tenfold acceleration may be feasible without significant loss of quality when compared to the reference sequence (<i>p</i> ≥ 0.999).</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>For 3-T 3D-MRI of the knee, a DL-based algorithm allowed for additional acceleration of acquisition times compared to the conventional approach. This study, however, is limited by its small sample size and inclusion of only healthy volunteers, indicating the need for further research with a more diverse and larger sample.</p><h3 data-test=\"abstract-sub-heading\">Trial registration</h3><p>DRKS00024156.</p><h3 data-test=\"abstract-sub-heading\">Relevance statement</h3><p>Using a DL-based algorithm, 54% faster image acquisition (178 s <i>versus</i> 384 s) for 3D-sequences may be possible for 3-T MRI of the knee.</p><h3 data-test=\"abstract-sub-heading\">Key points</h3><p>• Combination of compressed sensing and DL improved image quality and allows for significant acceleration of 3D knee MRI.</p><p>• DL-based algorithm achieved better subjective image quality than conventional compressed sensing.</p><p>• For 3D knee MRI at 3 T, 54% faster image acquisition may be possible.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\u0000","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"30 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperpolarized [1-13C]-pyruvate MRS evaluates immune potential and predicts response to radiotherapy in cervical cancer","authors":"Gigin Lin, Ching-Yi Hsieh, Ying-Chieh Lai, Chun-Chieh Wang, Yenpo Lin, Kuan-Ying Lu, Wen-Yen Chai, Albert P. Chen, Tzu-Chen Yen, Shu-Hang Ng, Chyong-Huey Lai","doi":"10.1186/s41747-024-00445-1","DOIUrl":"https://doi.org/10.1186/s41747-024-00445-1","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Monitoring pyruvate metabolism in the spleen is important for assessing immune activity and achieving successful radiotherapy for cervical cancer due to the significance of the abscopal effect. We aimed to explore the feasibility of utilizing hyperpolarized (HP) [1-¹³C]-pyruvate magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) to evaluate pyruvate metabolism in the human spleen, with the aim of identifying potential candidates for radiotherapy in cervical cancer.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>This prospective study recruited six female patients with cervical cancer (median age 55 years; range 39–60) evaluated using HP [1-¹³C]-pyruvate MRI/MRS at baseline and 2 weeks after radiotherapy. Proton (¹H) diffusion-weighted MRI was performed in parallel to estimate splenic cellularity. The primary outcome was defined as tumor response to radiotherapy. The Student <i>t</i>-test was used for comparing ¹³C data between the groups.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The splenic HP [1-¹³C]-lactate-to-total carbon (tC) ratio was 5.6-fold lower in the responders than in the non-responders at baseline (<i>p</i> = 0.009). The splenic [1-¹³C]-lactate-to-tC ratio revealed a 1.7-fold increase (<i>p</i> = 0.415) and the splenic [1-¹³C]-alanine-to-tC ratio revealed a 1.8-fold increase after radiotherapy (<i>p</i> = 0.482). The blood leukocyte differential count revealed an increased proportion of neutrophils two weeks following treatment, indicating enhanced immune activity (<i>p</i> = 0.013). The splenic apparent diffusion coefficient values between the groups were not significantly different.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>This exploratory study revealed the feasibility of HP [1-¹³C]-pyruvate MRS of the spleen for evaluating baseline immune potential, which was associated with clinical outcomes of cervical cancer after radiotherapy.</p><h3 data-test=\"abstract-sub-heading\">Trial registration</h3><p>ClinicalTrials.gov NCT04951921, registered 7 July 2021.</p><h3 data-test=\"abstract-sub-heading\">Relevance statement</h3><p>This prospective study revealed the feasibility of using HP ¹³C MRI/MRS for assessing pyruvate metabolism of the spleen to evaluate the patients’ immune potential that is associated with radiotherapeutic clinical outcomes in cervical cancer.</p><h3 data-test=\"abstract-sub-heading\">Key points</h3><p>• Effective radiotherapy induces abscopal effect via altering immune metabolism.</p><p>• Hyperpolarized ¹³C MRS evaluates patients’ immune potential non-invasively.</p><p>• Pyruvate-to-lactate conversion in the spleen is elevated following radiotherapy.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\u0000","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"43 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Public data homogenization for AI model development in breast cancer","authors":"Vassilis Kilintzis, Varvara Kalokyri, Haridimos Kondylakis, Smriti Joshi, Katerina Nikiforaki, Oliver Díaz, Karim Lekadir, Manolis Tsiknakis, Kostas Marias","doi":"10.1186/s41747-024-00442-4","DOIUrl":"https://doi.org/10.1186/s41747-024-00442-4","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Background</h3><p>Developing trustworthy artificial intelligence (AI) models for clinical applications requires access to clinical and imaging data cohorts. Reusing of publicly available datasets has the potential to fill this gap. Specifically in the domain of breast cancer, a large archive of publicly accessible medical images along with the corresponding clinical data is available at The Cancer Imaging Archive (TCIA). However, existing datasets cannot be directly used as they are heterogeneous and cannot be effectively filtered for selecting specific image types required to develop AI models. This work focuses on the development of a homogenized dataset in the domain of breast cancer including clinical and imaging data.</p><h3 data-test=\"abstract-sub-heading\">Methods</h3><p>Five datasets were acquired from the TCIA and were harmonized. For the clinical data harmonization, a common data model was developed and a repeatable, documented “extract-transform-load” process was defined and executed for their homogenization. Further, Digital Imaging and COmmunications in Medicine (DICOM) information was extracted from magnetic resonance imaging (MRI) data and made accessible and searchable.</p><h3 data-test=\"abstract-sub-heading\">Results</h3><p>The resulting harmonized dataset includes information about 2,035 subjects with breast cancer. Further, a platform named RV-Cherry-Picker enables search over both the clinical and diagnostic imaging datasets, providing unified access, facilitating the downloading of all study imaging that correspond to specific series’ characteristics (<i>e.g.</i>, dynamic contrast-enhanced series), and reducing the burden of acquiring the appropriate set of images for the respective AI model scenario.</p><h3 data-test=\"abstract-sub-heading\">Conclusions</h3><p>RV-Cherry-Picker provides access to the largest, publicly available, homogenized, imaging/clinical dataset for breast cancer to develop AI models on top.</p><h3 data-test=\"abstract-sub-heading\">Relevance statement</h3><p>We present a solution for creating merged public datasets supporting AI model development, using as an example the breast cancer domain and magnetic resonance imaging images.</p><h3 data-test=\"abstract-sub-heading\">Key points</h3><p>• The proposed platform allows unified access to the largest, homogenized public imaging dataset for breast cancer.</p><p>• A methodology for the semantically enriched homogenization of public clinical data is presented.</p><p>• The platform is able to make a detailed selection of breast MRI data for the development of AI models.</p><h3 data-test=\"abstract-sub-heading\">Graphical Abstract</h3>\u0000","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"50 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}