International Journal of Biomedical Imaging最新文献

{"title":"The Blood-Brain Barrier in Both Humans and Rats: A Perspective From 3D Imaging.","authors":"Aiwen Chen, Gavin Volpato, Alice Pong, Emma Schofield, Jun Huang, Zizhao Qiu, George Paxinos, Huazheng Liang","doi":"10.1155/2024/4482931","DOIUrl":"10.1155/2024/4482931","url":null,"abstract":"<p><p><b>Background:</b> The blood-brain barrier (BBB) is part of the neurovascular unit (NVU) which plays a key role in maintaining homeostasis. However, its 3D structure is hardly known. The present study is aimed at imaging the BBB using tissue clearing and 3D imaging techniques in both human brain tissue and rat brain tissue. <b>Methods:</b> Both human and rat brain tissue were cleared using the CUBIC technique and imaged with either a confocal or two-photon microscope. Image stacks were reconstructed using Imaris. <b>Results:</b> Double staining with various antibodies targeting endothelial cells, basal membrane, pericytes of blood vessels, microglial cells, and the spatial relationship between astrocytes and blood vessels showed that endothelial cells do not evenly express CD31 and Glut1 transporter in the human brain. Astrocytes covered only a small portion of the vessels as shown by the overlap between GFAP-positive astrocytes and Collagen IV/CD31-positive endothelial cells as well as between GFAP-positive astrocytes and CD146-positive pericytes, leaving a big gap between their end feet. A similar structure was observed in the rat brain. <b>Conclusions:</b> The present study demonstrated the 3D structure of both the human and rat BBB, which is discrepant from the 2D one. Tissue clearing and 3D imaging are promising techniques to answer more questions about the real structure of biological specimens.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11368551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142120879","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":"Presegmenter Cascaded Framework for Mammogram Mass Segmentation.","authors":"Urvi Oza, Bakul Gohel, Pankaj Kumar, Parita Oza","doi":"10.1155/2024/9422083","DOIUrl":"10.1155/2024/9422083","url":null,"abstract":"<p><p>Accurate segmentation of breast masses in mammogram images is essential for early cancer diagnosis and treatment planning. Several deep learning (DL) models have been proposed for whole mammogram segmentation and mass patch/crop segmentation. However, current DL models for breast mammogram mass segmentation face several limitations, including false positives (FPs), false negatives (FNs), and challenges with the end-to-end approach. This paper presents a novel two-stage end-to-end cascaded breast mass segmentation framework that incorporates a saliency map of potential mass regions to guide the DL models for breast mass segmentation. The first-stage segmentation model of the cascade framework is used to generate a saliency map to establish a coarse region of interest (ROI), effectively narrowing the focus to probable mass regions. The proposed presegmenter attention (PSA) blocks are introduced in the second-stage segmentation model to enable dynamic adaptation to the most informative regions within the mammogram images based on the generated saliency map. Comparative analysis of the Attention U-net model with and without the cascade framework is provided in terms of dice scores, precision, recall, FP rates (FPRs), and FN outcomes. Experimental results consistently demonstrate enhanced breast mass segmentation performance by the proposed cascade framework across all three datasets: INbreast, CSAW-S, and DMID. The cascade framework shows superior segmentation performance by improving the dice score by about 6% for the INbreast dataset, 3% for the CSAW-S dataset, and 2% for the DMID dataset. Similarly, the FN outcomes were reduced by 10% for the INbreast dataset, 19% for the CSAW-S dataset, and 4% for the DMID dataset. Moreover, the proposed cascade framework's performance is validated with varying state-of-the-art segmentation models such as DeepLabV3+ and Swin transformer U-net. The presegmenter cascade framework has the potential to improve segmentation performance and mitigate FNs when integrated with any medical image segmentation framework, irrespective of the choice of the model.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11329304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001040","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":"An End-to-End CRSwNP Prediction with Multichannel ResNet on Computed Tomography.","authors":"Shixin Lai, Weipiao Kang, Yaowen Chen, Jisheng Zou, Siqi Wang, Xuan Zhang, Xiaolei Zhang, Yu Lin","doi":"10.1155/2024/4960630","DOIUrl":"10.1155/2024/4960630","url":null,"abstract":"<p><p>Chronic rhinosinusitis (CRS) is a global disease characterized by poor treatment outcomes and high recurrence rates, significantly affecting patients' quality of life. Due to its complex pathophysiology and diverse clinical presentations, CRS is categorized into various subtypes to facilitate more precise diagnosis, treatment, and prognosis prediction. Among these, CRS with nasal polyps (CRSwNP) is further divided into eosinophilic CRSwNP (eCRSwNP) and noneosinophilic CRSwNP (non-eCRSwNP). However, there is a lack of precise predictive diagnostic and treatment methods, making research into accurate diagnostic techniques for CRSwNP endotypes crucial for achieving precision medicine in CRSwNP. This paper proposes a method using multiangle sinus computed tomography (CT) images combined with artificial intelligence (AI) to predict CRSwNP endotypes, distinguishing between patients with eCRSwNP and non-eCRSwNP. The considered dataset comprises 22,265 CT images from 192 CRSwNP patients, including 13,203 images from non-eCRSwNP patients and 9,062 images from eCRSwNP patients. Test results from the network model demonstrate that multiangle images provide more useful information for the network, achieving an accuracy of 98.43%, precision of 98.1%, recall of 98.1%, specificity of 98.7%, and an AUC value of 0.984. Compared to the limited learning capacity of single-channel neural networks, our proposed multichannel feature adaptive fusion model captures multiscale spatial features, enhancing the model's focus on crucial sinus information within the CT images to maximize detection accuracy. This deep learning-based diagnostic model for CRSwNP endotypes offers excellent classification performance, providing a noninvasive method for accurately predicting CRSwNP endotypes before treatment and paving the way for precision medicine in the new era of CRSwNP.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11178416/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332155","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":"<i>In Situ</i> Immunofluorescence Imaging of Vital Human Pancreatic Tissue Using Fiber-Optic Microscopy.","authors":"Sophia Ackermann, Maximilian Herold, Vincent Rohrbacher, Michael Schäfer, Marcell Tóth, Stefan Thomann, Thilo Hackert, Eduard Ryschich","doi":"10.1155/2024/1397875","DOIUrl":"10.1155/2024/1397875","url":null,"abstract":"<p><strong>Purpose: </strong>Surgical resection is the only curative option for pancreatic carcinoma, but disease-free and overall survival times after surgery are limited due to early tumor recurrence, most often originating from local microscopic tumor residues (R1 resection). The intraoperative identification of microscopic tumor residues within the resection margin <i>in situ</i> could improve surgical performance. The aim of this study was to evaluate the effectiveness of fiber-optic microscopy for detecting microscopic residues in vital pancreatic cancer tissues. <i>Experimental Design</i>. Fresh whole-mount human pancreatic tissues, histological tissue slides, cell culture, and chorioallantoic membrane xenografts were analyzed. Specimens were stained with selected fluorophore-conjugated antibodies and studied using conventional wide-field and self-designed multicolor fiber-optic fluorescence microscopy instruments.</p><p><strong>Results: </strong>Whole-mount vital human tissues and xenografts were stained and imaged using an <i>in situ</i> immunofluorescence protocol. Fiber-optic microscopy enabled the detection of epitope-based fluorescence in vital whole-mount tissue using fluorophore-conjugated antibodies and enabled visualization of microvascular, epithelial, and malignant tumor cells. Among the selected antigen-antibody pairs, antibody clones WM59, AY13, and 9C4 were the most promising for fiber-optic imaging in human tissue samples and for endothelial, tumor and epithelial cell detection.</p><p><strong>Conclusions: </strong>Fresh dissected whole-mount tissue can be stained using direct exposure to selected antibody clones. Several antibody clones were identified that provided excellent immunofluorescence imaging of labeled structures, such as endothelial, epithelial, or EGFR-expressing cells. The combination of <i>in situ</i> immunofluorescence staining and fiber-optic microscopy visualizes structures in vital tissues and could be proposed as an useful tool for the <i>in situ</i> identification of residual tumor mass in patients with a high operative risk for incomplete resection.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11178408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332196","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":"COVID-19 Detection from Computed Tomography Images Using Slice Processing Techniques and a Modified Xception Classifier.","authors":"Kenan Morani, Esra Kaya Ayana, Dimitrios Kollias, Devrim Unay","doi":"10.1155/2024/9962839","DOIUrl":"10.1155/2024/9962839","url":null,"abstract":"<p><p>This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224 × 224) were input into an Xception transfer learning model with a modified output. Both Xception's architecture and pretrained weights were leveraged in the method. A big and rigorously annotated database of CT images was used to verify the method. The number of patients/subjects in the dataset is more than 5000, and the number and shape of the slices in each CT scan varies greatly. Verification was made both on the validation partition and on the test partition of unseen images. Results on the COV19-CT database showcased not only improvement from our previous solution and the baseline but also comparable performance to the highest-achieving methods on the same dataset. Further validation studies could explore the scalability and adaptability of the developed methodologies across diverse healthcare settings and patient populations. Additionally, investigating the integration of advanced image processing techniques, such as automated region of interest detection and segmentation algorithms, could enhance the efficiency and accuracy of COVID-19 diagnosis.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11178392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332156","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":"Swin Transformer and the Unet Architecture to Correct Motion Artifacts in Magnetic Resonance Image Reconstruction.","authors":"Md Biddut Hossain, Rupali Kiran Shinde, Shariar Md Imtiaz, F M Fahmid Hossain, Seok-Hee Jeon, Ki-Chul Kwon, Nam Kim","doi":"10.1155/2024/8972980","DOIUrl":"10.1155/2024/8972980","url":null,"abstract":"<p><p>We present a deep learning-based method that corrects motion artifacts and thus accelerates data acquisition and reconstruction of magnetic resonance images. The novel model, the Motion Artifact Correction by Swin Network (MACS-Net), uses a Swin transformer layer as the fundamental block and the Unet architecture as the neural network backbone. We employ a hierarchical transformer with shifted windows to extract multiscale contextual features during encoding. A new dual upsampling technique is employed to enhance the spatial resolutions of feature maps in the Swin transformer-based decoder layer. A raw magnetic resonance imaging dataset is used for network training and testing; the data contain various motion artifacts with ground truth images of the same subjects. The results were compared to six state-of-the-art MRI image motion correction methods using two types of motions. When motions were brief (within 5 s), the method reduced the average normalized root mean square error (NRMSE) from 45.25% to 17.51%, increased the mean structural similarity index measure (SSIM) from 79.43% to 91.72%, and increased the peak signal-to-noise ratio (PSNR) from 18.24 to 26.57 dB. Similarly, when motions were extended from 5 to 10 s, our approach decreased the average NRMSE from 60.30% to 21.04%, improved the mean SSIM from 33.86% to 90.33%, and increased the PSNR from 15.64 to 24.99 dB. The anatomical structures of the corrected images and the motion-free brain data were similar.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11081754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140899883","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":"ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection.","authors":"N I Md Ashafuddula, Rafiqul Islam","doi":"10.1155/2024/6347920","DOIUrl":"10.1155/2024/6347920","url":null,"abstract":"<p><p>Brain tumors are critical neurological ailments caused by uncontrolled cell growth in the brain or skull, often leading to death. An increasing patient longevity rate requires prompt detection; however, the complexities of brain tissue make early diagnosis challenging. Hence, automated tools are necessary to aid healthcare professionals. This study is particularly aimed at improving the efficacy of computerized brain tumor detection in a clinical setting through a deep learning model. Hence, a novel thresholding-based MRI image segmentation approach with a transfer learning model based on contour (ContourTL-Net) is suggested to facilitate the clinical detection of brain malignancies at an initial phase. The model utilizes contour-based analysis, which is critical for object detection, precise segmentation, and capturing subtle variations in tumor morphology. The model employs a VGG-16 architecture priorly trained on the \"ImageNet\" collection for feature extraction and categorization. The model is designed to utilize its ten nontrainable and three trainable convolutional layers and three dropout layers. The proposed ContourTL-Net model is evaluated on two benchmark datasets in four ways, among which an unseen case is considered as the clinical aspect. Validating a deep learning model on unseen data is crucial to determine the model's generalization capability, domain adaptation, robustness, and real-world applicability. Here, the presented model's outcomes demonstrate a highly accurate classification of the unseen data, achieving a perfect sensitivity and negative predictive value (NPV) of 100%, 98.60% specificity, 99.12% precision, 99.56% <i>F</i>1-score, and 99.46% accuracy. Additionally, the outcomes of the suggested model are compared with state-of-the-art methodologies to further enhance its effectiveness. The proposed solution outperforms the existing solutions in both seen and unseen data, with the potential to significantly improve brain tumor detection efficiency and accuracy, leading to earlier diagnoses and improved patient outcomes.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11074715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140877608","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":"A Deep Learning Approach to Classify Fabry Cardiomyopathy from Hypertrophic Cardiomyopathy Using Cine Imaging on Cardiac Magnetic Resonance.","authors":"Wei-Wen Chen, Ling Kuo, Yi-Xun Lin, Wen-Chung Yu, Chien-Chao Tseng, Yenn-Jiang Lin, Ching-Chun Huang, Shih-Lin Chang, Jacky Chung-Hao Wu, Chun-Ku Chen, Ching-Yao Weng, Siwa Chan, Wei-Wen Lin, Yu-Cheng Hsieh, Ming-Chih Lin, Yun-Ching Fu, Tsung Chen, Shih-Ann Chen, Henry Horng-Shing Lu","doi":"10.1155/2024/6114826","DOIUrl":"https://doi.org/10.1155/2024/6114826","url":null,"abstract":"<p><p>A challenge in accurately identifying and classifying left ventricular hypertrophy (LVH) is distinguishing it from hypertrophic cardiomyopathy (HCM) and Fabry disease. The reliance on imaging techniques often requires the expertise of multiple specialists, including cardiologists, radiologists, and geneticists. This variability in the interpretation and classification of LVH leads to inconsistent diagnoses. LVH, HCM, and Fabry cardiomyopathy can be differentiated using T1 mapping on cardiac magnetic resonance imaging (MRI). However, differentiation between HCM and Fabry cardiomyopathy using echocardiography or MRI cine images is challenging for cardiologists. Our proposed system named the MRI short-axis view left ventricular hypertrophy classifier (MSLVHC) is a high-accuracy standardized imaging classification model developed using AI and trained on MRI short-axis (SAX) view cine images to distinguish between HCM and Fabry disease. The model achieved impressive performance, with an <i>F</i>1-score of 0.846, an accuracy of 0.909, and an AUC of 0.914 when tested on the Taipei Veterans General Hospital (TVGH) dataset. Additionally, a single-blinding study and external testing using data from the Taichung Veterans General Hospital (TCVGH) demonstrated the reliability and effectiveness of the model, achieving an <i>F</i>1-score of 0.727, an accuracy of 0.806, and an AUC of 0.918, demonstrating the model's reliability and usefulness. This AI model holds promise as a valuable tool for assisting specialists in diagnosing LVH diseases.</p>","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11068448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140867764","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":"In Vivo Detection of Staphylococcus aureus Infections Using Radiolabeled Antibodies Specific for Bacterial Toxins","authors":"M. I. Gonzalez, M. González-Arjona, L. Cussó, Miguel Ángel Morcillo, J. Aguilera-Correa, Jaime Esteban, M. Kestler, Daniel Calle, Carlos Cerón, Marta Cortes-Canteli, Patricia Muñoz, Emilio Bouza, Manuel Desco, Beatriz Salinas","doi":"10.1155/2024/3655327","DOIUrl":"https://doi.org/10.1155/2024/3655327","url":null,"abstract":"Purpose The Gram-positive Staphylococcus aureus bacterium is one of the leading causes of infection in humans. The lack of specific noninvasive techniques for diagnosis of staphylococcal infection together with the severity of its associated complications support the need for new specific and selective diagnostic tools. This work presents the successful synthesis of an immunotracer that targets the α-toxin released by S. aureus. Methods [89Zr]Zr-DFO-ToxAb was synthesized based on radiolabeling an anti-α-toxin antibody with zirconium-89. The physicochemical characterization of the immunotracer was performed by high-performance liquid chromatography (HPLC), radio-thin layer chromatography (radio-TLC), and electrophoretic analysis. Its diagnostic ability was evaluated in vivo by positron emission tomography/computed tomography (PET/CT) imaging in an animal model of local infection-inflammation (active S. aureus vs. heat-killed S. aureus) and infective osteoarthritis. Results Chemical characterization of the tracer established the high radiochemical yield and purity of the tracer while maintaining antibody integrity. In vivo PET/CT image confirmed the ability of the tracer to detect active foci of S. aureus. Those results were supported by ex vivo biodistribution studies, autoradiography, and histology, which confirmed the ability of [89Zr]Zr-DFO-ToxAb to detect staphylococcal infectious foci, avoiding false-positives derived from inflammatory processes. Conclusions We have developed an immuno-PET tracer capable of detecting S. aureus infections based on a radiolabeled antibody specific for the staphylococcal alpha toxins. The in vivo assessment of [89Zr]Zr-DFO-ToxAb confirmed its ability to selectively detect staphylococcal infectious foci, allowing us to discern between infectious and inflammatory processes.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140686921","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":"Super High Contrast USPIO-Enhanced Cerebrovascular Angiography Using Ultrashort Time-to-Echo MRI","authors":"Liam Timms, Tianyi Zhou, J. Qiao, Codi A. Gharagouzloo, Vishala Mishra, R. Lahoud, John W. Chen, Mukesh Harisinghani, Srinivas Sridhar","doi":"10.1155/2024/9763364","DOIUrl":"https://doi.org/10.1155/2024/9763364","url":null,"abstract":"Background Ferumoxytol (Ferahame, AMAG Pharmaceuticals, Waltham, MA) is increasingly used off-label as an MR contrast agent due to its relaxivity and safety profiles. However, its potent T2∗ relaxivity limits achievable T1-weighted positive contrast and leads to artifacts in standard MRI protocols. Optimization of protocols for ferumoxytol deployment is necessary to realize its potential. Methods We present first-in-human clinical results of the Quantitative Ultrashort Time-to-Echo Contrast Enhanced (QUTE-CE) MRA technique using the superparamagnetic iron oxide nanoparticle agent ferumoxytol for vascular imaging of the head/brain in 15 subjects at 3.0T. The QUTE-CE MRA method was implemented on a 3T scanner using a stack-of-spirals 3D Ultrashort Time-to-Echo sequence. Time-of-flight MRA and standard TE T1-weighted (T1w) images were also collected. For comparison, gadolinium-enhanced blood pool phase images were obtained retrospectively from clinical practice. Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and intraluminal signal heterogeneity (ISH) were assessed and compared across approaches with Welch's two-sided t-test. Results Fifteen volunteers (54 ± 17 years old, 9 women) participated. QUTE-CE MRA provided high-contrast snapshots of the arterial and venous networks with lower intraluminal heterogeneity. QUTE-CE demonstrated significantly higher SNR (1707 ± 226), blood-tissue CNR (1447 ± 189), and lower ISH (0.091 ± 0.031) compared to ferumoxytol T1-weighted (551 ± 171; 319 ± 144; 0.186 ± 0.066, respectively) and time-of-flight (343 ± 104; 269 ± 82; 0.190 ± 0.016, respectively), with p < 0.001 in each comparison. The high CNR increased the depth of vessel visualization. Vessel lumina were captured with lower heterogeneity. Conclusion Quantitative Ultrashort Time-to-Echo Contrast-Enhanced MR angiography provides approximately 5-fold superior contrast with fewer artifacts compared to other contrast-enhanced vascular imaging techniques using ferumoxytol or gadolinium, and to noncontrast time-of-flight MR angiography, for clinical vascular imaging. This trial is registered with NCT03266848.","PeriodicalId":47063,"journal":{"name":"International Journal of Biomedical Imaging","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140707204","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}