European Radiology Experimental最新文献

{"title":"In vitro and in vivo optimized reconstruction for low-keV virtual monoenergetic photon-counting detector CT angiography of lower legs.","authors":"Dirk Graafen, Willi Bart, Moritz C Halfmann, Lukas Müller, Lukas Hobohm, Yang Yang, Achim Neufang, Christine Espinola-Klein, Michael B Pitton, Roman Kloeckner, Akos Varga-Szemes, Tilman Emrich","doi":"10.1186/s41747-024-00481-x","DOIUrl":"10.1186/s41747-024-00481-x","url":null,"abstract":"<p><strong>Background: </strong>Lower extremity peripheral artery disease frequently presents with calcifications which reduces the accuracy of computed tomography (CT) angiography, especially below-the-knee. Photon-counting detector (PCD)-CT offers improved spatial resolution and less calcium blooming. We aimed to identify the optimal reconstruction parameters for PCD-CT angiography of the lower legs.</p><p><strong>Methods: </strong>Tubes with different diameters (1-5 mm) were filled with different iodine concentrations and scanned in a water container. Images were reconstructed with 0.4 mm isotropic resolution using a quantitative kernel at all available sharpness levels (Qr36 to Qr76) and using different levels of quantum iterative reconstruction (QIR-2-4). Noise and image sharpness were determined for all reconstructions. Additionally, CT angiograms of 20 patients, reconstructed with a medium (Qr44), sharp (Qr60), and ultrasharp (Qr72) kernel at QIR-2-4, were evaluated by three readers assessing noise, delineation of plaques and vessel walls, and overall quality.</p><p><strong>Results: </strong>In the phantom study, increased kernel sharpness led to higher image noise (e.g., 16, 38, 77 HU for Qr44, Qr60, Qr72, and QIR-3). Image sharpness increased with increasing kernel sharpness, reaching a plateau at the medium-high level 60. Higher QIR levels decreased image noise (e.g., 51, 38, 25 HU at QIR-2-4 and Qr60) without reducing vessel sharpness. The qualitative in vivo results confirmed these findings: the sharp kernel (Qr60) with the highest QIR yielded the best overall quality.</p><p><strong>Conclusion: </strong>The combination of a sharpness level optimized reconstruction kernel (Qr60) and the highest QIR level yield the best image quality for PCD-CT angiography of the lower legs when reconstructed at 0.4-mm resolution.</p><p><strong>Relevance statement: </strong>Using high-resolution PCD-CT angiography with optimized reconstruction parameters might improve diagnostic accuracy and confidence in peripheral artery disease of the lower legs.</p><p><strong>Key points: </strong>Effective exploitation of the potential of PCD-CT angiography requires optimized reconstruction parameters. Too soft or too sharp reconstruction kernels reduce image quality. The highest level of quantum iterative reconstruction provides the best image quality.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"89"},"PeriodicalIF":3.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294310/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876236","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":"Automated evaluation of diaphragm configuration based on chest CT in COPD patients.","authors":"Jens T Bakker, Jorine E Hartman, Karin Klooster, David A Lynch, Marieke C van der Molen, Jean-Paul Charbonnier, Michail Tsiaousis, Rozemarijn Vliegenthart, Dirk-Jan Slebos","doi":"10.1186/s41747-024-00491-9","DOIUrl":"10.1186/s41747-024-00491-9","url":null,"abstract":"<p><strong>Background: </strong>Severe chronic obstructive pulmonary disease (COPD) often results in hyperinflation and flattening of the diaphragm. An automated computed tomography (CT)-based tool for quantifying diaphragm configuration, a biomarker for COPD, was developed in-house and tested in a large cohort of COPD patients.</p><p><strong>Methods: </strong>We used the LungQ platform to extract the lung-diaphragm intersection, as direct diaphragm segmentation is challenging. The tool computed the diaphragm index (surface area/projected surface area) as a measure of diaphragm configuration on inspiratory scans in a COPDGene subcohort. Visual inspection of 250 randomly selected segmentations served as a quality check. Associations between the diaphragm index, Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages, forced expiratory volume in 1 s (FEV1) % predicted, and CT-derived emphysema scores were explored using analysis of variance and Pearson correlation.</p><p><strong>Results: </strong>The tool yielded incomplete segmentation in 9.2% (2.4% major defect, 6.8% minor defect) of 250 randomly selected cases. In 8431 COPDGene subjects (4240 healthy; 4191 COPD), the diaphragm index was increasingly lower with higher GOLD stages (never-smoked 1.83 ± 0.16; GOLD-0 1.79 ± 0.18; GOLD-1 1.71 ± 0.15; GOLD-2: 1.67 ± 0.16; GOLD-3 1.58 ± 0.14; GOLD-4 1.54 ± 0.11) (p < 0.001). Associations were found between the diaphragm index and both FEV1% predicted (r = 0.44, p < 0.001) and emphysema score (r = -0.36, p < 0.001).</p><p><strong>Conclusion: </strong>We developed an automated tool to quantify the diaphragm configuration in chest CT. The diaphragm index was associated with COPD severity, FEV1%predicted, and emphysema score.</p><p><strong>Relevance statement: </strong>Due to the hypothesized relationship between diaphragm dysfunction and diaphragm configuration in COPD patients, automatic quantification of diaphragm configuration may prove useful in evaluating treatment efficacy in terms of lung volume reduction.</p><p><strong>Key points: </strong>Severe COPD changes diaphragm configuration to a flattened state, impeding function. An automated tool quantified diaphragm configuration on chest-CT providing a diaphragm index. The diaphragm index was correlated to COPD severity and may aid treatment assessment.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"87"},"PeriodicalIF":3.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876235","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":"Intra-arterial PSMA injection using hepatic arterial infusion pump in intrahepatic cholangiocarcinoma: a proof-of-concept study.","authors":"Mara Marieke Katrien Veenstra, Erik Vegt, Marcel Segbers, Stijn Franssen, Bas Groot Koerkamp, Frederik Anton Verburg, Maarten Guillaume Josephus Thomeer","doi":"10.1186/s41747-024-00496-4","DOIUrl":"10.1186/s41747-024-00496-4","url":null,"abstract":"<p><p>Prostate-specific membrane antigen (PSMA) targeted tracers show increased uptake in several malignancies, indicating a potential for peptide radioligand therapy. Intra-arterial injection of radiotracers can increase the therapeutic window. This study aimed to evaluate the feasibility of intra-arterial injection of [⁶⁸Ga]Ga-PSMA-11 for intrahepatic cholangiocarcinoma and compare tracer uptake after intrahepatic arterial injection and intravenous injection. Three patients with intrahepatic cholangiocarcinoma received [⁶⁸Ga]Ga-PSMA-11 through a hepatic arterial infusion pump, followed by positron emission tomography/computed tomography (PET/CT). Two-three days later, patients underwent PET/CT after intravenous [⁶⁸Ga]Ga-PSMA-11 injection. All tumours showed higher uptake on the intra-arterial scan compared with the intravenous scan: the intra-arterial / intravenous standardised uptake value normalised by lean body mass ratios were 1.40, 1.46, and 1.54. Local intra-arterial PSMA injection is possible in patients with intrahepatic cholangiocarcinoma. Local injection increases tumour-to-normal tissue ratios, increasing the therapeutic window for theranostic applications. RELEVANCE STATEMENT: Intra-arterial Prostate specific membrane antigen (PSMA) injection increases the therapeutic window for potential theranostic application in intrahepatic cholangiocarcinoma. KEY POINTS: Three patients with intrahepatic cholangiocarcinoma underwent PET/CT after intra-arterial and intravenous injection of [⁶⁸Ga]Ga-PSMA-11. Intra-arterial injection showed higher uptake than intravenous injection. PSMA-targeted imaging could be valuable for a subset of intrahepatic cholangiocarcinoma patients.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"90"},"PeriodicalIF":3.7,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141876237","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":"Development of an initial training and evaluation programme for manual lower limb muscle MRI segmentation.","authors":"Jasper M Morrow, Sachit Shah, Lara Cristiano, Matthew R B Evans, Carolynne M Doherty, Talal Alnaemi, Abeer Saab, Ahmed Emira, Uros Klickovic, Ahmed Hammam, Afnan Altuwaijri, Stephen Wastling, Mary M Reilly, Michael G Hanna, Tarek A Yousry, John S Thornton","doi":"10.1186/s41747-024-00475-9","DOIUrl":"10.1186/s41747-024-00475-9","url":null,"abstract":"<p><strong>Background: </strong>Magnetic resonance imaging (MRI) quantification of intramuscular fat accumulation is a responsive biomarker in neuromuscular diseases. Despite emergence of automated methods, manual muscle segmentation remains an essential foundation. We aimed to develop a training programme for new observers to demonstrate competence in lower limb muscle segmentation and establish reliability benchmarks for future human observers and machine learning segmentation packages.</p><p><strong>Methods: </strong>The learning phase of the training programme comprised a training manual, direct instruction, and eight lower limb MRI scans with reference standard large and small regions of interest (ROIs). The assessment phase used test-retest scans from two patients and two healthy controls. Interscan and interobserver reliability metrics were calculated to identify underperforming outliers and to determine competency benchmarks.</p><p><strong>Results: </strong>Three experienced observers undertook the assessment phase, whilst eight new observers completed the full training programme. Two of the new observers were identified as underperforming outliers, relating to variation in size or consistency of segmentations; six had interscan and interobserver reliability equivalent to those of experienced observers. The calculated benchmark for the Sørensen-Dice similarity coefficient between observers was greater than 0.87 and 0.92 for individual thigh and calf muscles, respectively. Interscan and interobserver reliability were significantly higher for large than small ROIs (all p < 0.001).</p><p><strong>Conclusions: </strong>We developed, implemented, and analysed the first formal training programme for manual lower limb muscle segmentation. Large ROI showed superior reliability to small ROI for fat fraction assessment.</p><p><strong>Relevance statement: </strong>Observers competent in lower limb muscle segmentation are critical to application of quantitative muscle MRI biomarkers in neuromuscular diseases. This study has established competency benchmarks for future human observers or automated segmentation methods.</p><p><strong>Key points: </strong>• Observers competent in muscle segmentation are critical for quantitative muscle MRI biomarkers. • A training programme for muscle segmentation was undertaken by eight new observers. • We established competency benchmarks for future human observers or automated segmentation methods.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"85"},"PeriodicalIF":3.7,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11282017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141767527","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":"T1ρ relaxation mapping in osteochondral lesions of the talus: a non-invasive biomarker for altered biomechanical properties of hyaline cartilage?","authors":"Balázs Bogner, Markus Wenning, Pia M Jungmann, Marco Reisert, Thomas Lange, Marcel Tennstedt, Lukas Klein, Thierno D Diallo, Fabian Bamberg, Hagen Schmal, Matthias Jung","doi":"10.1186/s41747-024-00488-4","DOIUrl":"10.1186/s41747-024-00488-4","url":null,"abstract":"<p><strong>Background: </strong>To evaluate T1ρ relaxation mapping in patients with symptomatic talar osteochondral lesions (OLT) and healthy controls (HC) at rest, with axial loading and traction.</p><p><strong>Methods: </strong>Participants underwent 3-T ankle magnetic resonance imaging at rest and with 500 N loading and 120 N traction, without axial traction for a subcohort of 17/29 HC. We used a fast low-angle shot sequence with variable spin-lock intervals for monoexponential T1ρ fitting. Cartilage was manually segmented to extract T1ρ values.</p><p><strong>Results: </strong>We studied 29 OLT patients (age 31.7 ± 7.5 years, 15 females, body mass index [BMI] 25.0 ± 3.4 kg/m²) and 29 HC (age 25.2 ± 4.3 years, 17 females, BMI 22.5 ± 2.3 kg/m². T1ρ values of OLT (50.4 ± 3.4 ms) were higher than those of intact cartilage regions of OLT patients (47.2 ± 3.4 ms; p = 0.003) and matched HC cartilage (48.1 ± 3.3 ms; p = 0.030). Axial loading and traction induced significant T1ρ changes in the intact cartilage regions of patients (loading, mean difference -1.1 ms; traction, mean difference 1.4 ms; p = 0.030 for both) and matched HC cartilage (-2.2 ms, p = 0.003; 2.3 ms, p = 0.030; respectively), but not in the OLT itself (-1.3 ms; p = 0.150; +1.9 ms; p = 0.150; respectively).</p><p><strong>Conclusion: </strong>Increased T1ρ values may serve as a biomarker of cartilage degeneration in OLT. The absence of load- and traction-induced T1ρ changes in OLT compared to intact cartilage suggests that T1ρ may reflect altered biomechanical properties of hyaline cartilage.</p><p><strong>Trial registration: </strong>DRKS, DRKS00024010. Registered 11 January 2021, https://drks.de/search/de/trial/DRKS00024010 .</p><p><strong>Relevance statement: </strong>T1ρ mapping has the potential to evaluate compositional and biomechanical properties of the talar cartilage and may improve therapeutic decision-making in patients with osteochondral lesions.</p><p><strong>Key points: </strong>T1ρ values in osteochondral lesions increased compared to intact cartilage. Significant load- and traction-induced T1ρ changes were observed in visually intact regions and in healthy controls but not in osteochondral lesions. T1ρ may serve as an imaging biomarker for biomechanical properties of cartilage.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"83"},"PeriodicalIF":3.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11269556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753038","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":"Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms.","authors":"Quirin Bellmann, Yang Peng, Ulrich Genske, Li Yan, Moritz Wagner, Paul Jahnke","doi":"10.1186/s41747-024-00486-6","DOIUrl":"10.1186/s41747-024-00486-6","url":null,"abstract":"<p><strong>Background: </strong>Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck CT.</p><p><strong>Methods: </strong>Nine patient-mimicking neck phantoms were examined with a 320-slice scanner at six doses: 0.5, 1, 1.6, 2.1, 3.1, and 5.2 mGy. Each of eight phantoms contained one circular lesion (diameter 1 cm; contrast -30 HU to the background) in the parapharyngeal space; one phantom had no lesions. Reconstruction was made using FBP, IR, and DLR. Thirteen readers were tasked with identifying and localizing lesions in 32 images with a lesion and 20 without lesions for each dose and reconstruction algorithm. Receiver operating characteristic (ROC) and localization ROC (LROC) analysis were performed.</p><p><strong>Results: </strong>DLR improved lesion detection with ROC area under the curve (AUC) 0.724 ± 0.023 (mean ± standard error of the mean) using DLR versus 0.696 ± 0.021 using IR (p = 0.037) and 0.671 ± 0.023 using FBP (p < 0.001). Likewise, DLR improved lesion localization, with LROC AUC 0.407 ± 0.039 versus 0.338 ± 0.041 using IR (p = 0.002) and 0.313 ± 0.044 using FBP (p < 0.001). Dose reduction to 0.5 mGy compromised lesion detection in FBP-reconstructed images compared to doses ≥ 2.1 mGy (p ≤ 0.024), while no effect was observed with DLR or IR (p ≥ 0.058).</p><p><strong>Conclusion: </strong>DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used.</p><p><strong>Relevance statement: </strong>Deep learning enhances lesion detection in neck CT imaging compared to iterative reconstruction and filtered back projection, offering improved diagnostic performance and potential for x-ray dose reduction.</p><p><strong>Key points: </strong>Low-contrast lesion detectability was assessed in anatomically realistic neck CT phantoms. Deep learning reconstruction (DLR) outperformed filtered back projection and iterative reconstruction. Dose has little impact on lesion detectability against anatomical background structures.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"84"},"PeriodicalIF":3.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11269546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753037","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":"The Picasso's skepticism on computer science and the dawn of generative AI: questions after the answers to keep \"machines-in-the-loop\".","authors":"Filippo Pesapane, Renato Cuocolo, Francesco Sardanelli","doi":"10.1186/s41747-024-00485-7","DOIUrl":"10.1186/s41747-024-00485-7","url":null,"abstract":"<p><p>Starting from Picasso's quote (\"Computers are useless. They can only give you answers\"), we discuss the introduction of generative artificial intelligence (AI), including generative adversarial networks (GANs) and transformer-based architectures such as large language models (LLMs) in radiology, where their potential in reporting, image synthesis, and analysis is notable. However, the need for improvements, evaluations, and regulations prior to clinical use is also clear. Integration of LLMs into clinical workflow needs cautiousness, to avoid or at least mitigate risks associated with false diagnostic suggestions. We highlight challenges in synthetic image generation, inherent biases in AI models, and privacy concerns, stressing the importance of diverse training datasets and robust data privacy measures. We examine the regulatory landscape, including the 2023 Executive Order on AI in the United States and the 2024 AI Act in the European Union, which set standards for AI applications in healthcare. This manuscript contributes to the field by emphasizing the necessity of maintaining the human element in medical procedures while leveraging generative AI, advocating for a \"machines-in-the-loop\" approach.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"81"},"PeriodicalIF":3.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11269548/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753039","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":"Anisotropy component of DTI reveals long-term neuroinflammation following repetitive mild traumatic brain injury in rats.","authors":"Ching Cheng, Chia-Feng Lu, Bao-Yu Hsieh, Shu-Hui Huang, Yu-Chieh Jill Kao","doi":"10.1186/s41747-024-00490-w","DOIUrl":"10.1186/s41747-024-00490-w","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to investigate the long-term effects of repetitive mild traumatic brain injury (rmTBI) with varying inter-injury intervals by measuring diffusion tensor metrics, including mean diffusivity (MD), fractional anisotropy (FA), and diffusion magnitude (L) and pure anisotropy (q).</p><p><strong>Methods: </strong>Eighteen rats were randomly divided into three groups: short-interval rmTBI (n = 6), long-interval rmTBI (n = 6), and sham controls (n = 6). MD, FA, L, and q values were analyzed from longitudinal diffusion tensor imaging at days 50 and 90 after rmTBI. Immunohistochemical staining against neurons, astrocytes, microglia, and myelin was performed. Analysis of variance, Pearson correlation coefficient, and simple linear regression model were used.</p><p><strong>Results: </strong>At day 50 post-rmTBI, lower cortical FA and q values were shown in the short-interval group (p ≤ 0.038). In contrast, higher FA and q values were shown for the long-interval group (p ≤ 0.039) in the corpus callosum. In the ipsilesional external capsule and internal capsule, no significant changes were found in FA, while lower L and q values were shown in the short-interval group (p ≤ 0.028) at day 90. The q values in the external capsule and internal capsule were negatively correlated with the number of microglial cells and the total number of astroglial cells (p ≤ 0.035).</p><p><strong>Conclusion: </strong>Tensor scalar measurements, such as L and q values, are sensitive to exacerbated chronic injury induced by rmTBI with shorter inter-injury intervals and reflect long-term astrogliosis induced by the cumulative injury.</p><p><strong>Relevance statement: </strong>Tensor scalar measurements, including L and q values, are potential DTI metrics for detecting long-term and subtle injury following rmTBI; in particular, q values may be used for quantifying remote white matter (WM) changes following rmTBI.</p><p><strong>Key points: </strong>The alteration of L and q values was demonstrated after chronic repetitive mild traumatic brain injury. Changing q values were observed in the impact site and remote WM. The lower q values in the remote WM were associated with astrogliosis.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"82"},"PeriodicalIF":3.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11269550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753036","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":"Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study.","authors":"Nazanin Mobini, Davide Capra, Anna Colarieti, Moreno Zanardo, Giuseppe Baselli, Francesco Sardanelli","doi":"10.1186/s41747-024-00478-6","DOIUrl":"10.1186/s41747-024-00478-6","url":null,"abstract":"<p><strong>Introduction: </strong>Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs.</p><p><strong>Material and methods: </strong>Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F₁-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.</p><p><strong>Results: </strong>The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F₁-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images.</p><p><strong>Conclusion: </strong>Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources.</p><p><strong>Relevance statement: </strong>Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs.</p><p><strong>Key points: </strong>• We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16's superior performance in localizing BAC.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"80"},"PeriodicalIF":3.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11247067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617305","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":"Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance.","authors":"Hosamadin Assadi, Samer Alabed, Rui Li, Gareth Matthews, Kavita Karunasaagarar, Bahman Kasmai, Sunil Nair, Zia Mehmood, Ciaran Grafton-Clarke, Peter P Swoboda, Andrew J Swift, John P Greenwood, Vassilios S Vassiliou, Sven Plein, Rob J van der Geest, Pankaj Garg","doi":"10.1186/s41747-024-00477-7","DOIUrl":"10.1186/s41747-024-00477-7","url":null,"abstract":"<p><strong>Background: </strong>Cardiac magnetic resonance (CMR) in the four-chamber plane offers comprehensive insight into the volumetrics of the heart. We aimed to develop an artificial intelligence (AI) model of time-resolved segmentation using the four-chamber cine.</p><p><strong>Methods: </strong>A fully automated deep learning algorithm was trained using retrospective multicentre and multivendor data of 814 subjects. Validation, reproducibility, and mortality prediction were evaluated on an independent cohort of 101 subjects.</p><p><strong>Results: </strong>The mean age of the validation cohort was 54 years, and 66 (65%) were males. Left and right heart parameters demonstrated strong correlations between automated and manual analysis, with a ρ of 0.91-0.98 and 0.89-0.98, respectively, with minimal bias. All AI four-chamber volumetrics in repeatability analysis demonstrated high correlation (ρ = 0.99-1.00) and no bias. Automated four-chamber analysis underestimated both left ventricular (LV) and right ventricular (RV) volumes compared to ground-truth short-axis cine analysis. Two correction factors for LV and RV four-chamber analysis were proposed based on systematic bias. After applying the correction factors, a strong correlation and minimal bias for LV volumetrics were observed. During a mean follow-up period of 6.75 years, 16 patients died. On stepwise multivariable analysis, left atrial ejection fraction demonstrated an independent association with death in both manual (hazard ratio (HR) = 0.96, p = 0.003) and AI analyses (HR = 0.96, p < 0.001).</p><p><strong>Conclusion: </strong>Fully automated four-chamber CMR is feasible, reproducible, and has the same real-world prognostic value as manual analysis. LV volumes by four-chamber segmentation were comparable to short-axis volumetric assessment.</p><p><strong>Trials registration: </strong>ClinicalTrials.gov: NCT05114785.</p><p><strong>Relevance statement: </strong>Integrating fully automated AI in CMR promises to revolutionise clinical cardiac assessment, offering efficient, accurate, and prognostically valuable insights for improved patient care and outcomes.</p><p><strong>Key points: </strong>• Four-chamber cine sequences remain one of the most informative acquisitions in CMR examination. • This deep learning-based, time-resolved, fully automated four-chamber volumetric, functional, and deformation analysis solution. • LV and RV were underestimated by four-chamber analysis compared to ground truth short-axis segmentation. • Correction bias for both LV and RV volumes by four-chamber segmentation, minimises the systematic bias.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"8 1","pages":"77"},"PeriodicalIF":3.7,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11239622/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591638","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}