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Relevant bibliographies by topics / MRI IMAGE

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Academic literature on the topic 'MRI IMAGE'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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Journal articles on the topic "MRI IMAGE"

1

Angadi, Sanjeevkumar, Mukesh Kumar Tripathi, Chudaman Devidasrao Sukte, and Shivendra Shivendra. "Medical image registration and classification using smell agent rat swarm optimization based deep Maxout network." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 3 (2025): 1908. https://doi.org/10.11591/ijeecs.v37.i3.pp1908-1917.

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Medical image registration (MIR) is a crucial task in clinical image processing, involving the alignment of images from different modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), across various time points and subjects. Despite numerous advancements, no universal method caters to all MIR applications. This paper introduces the smell agent rat swarm optimization based deep Maxout network (SARSO-DMN) for MIR and classification. This work aims to enhance the accuracy and efficiency of medical image alignment and classification, addressing the challenges posed by

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Sanjeevkumar, Angadi Mukesh Kumar Tripathi Chudaman Devidasrao Sukte Shivendra Shivendra. "Medical image registration and classification using smell agent rat swarm optimization based deep Maxout network." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 3 (2025): 1908–17. https://doi.org/10.11591/ijeecs.v37.i3.pp1908-1917.

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Medical image registration (MIR) is a crucial task in clinical image processing, involving the alignment of images from different modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), across various time points and subjects. Despite numerous advancements, no universal method caters to all MIR applications. This paper introduces the smell agent rat swarm optimization based deep Maxout network (SARSO-DMN) for MIR and classification. This work aims to enhance the accuracy and efficiency of medical image alignment and classification, addressing the challenges posed by

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Zhang, Huixian, Hailong Li, Jonathan R. Dillman, Nehal A. Parikh, and Lili He. "Multi-Contrast MRI Image Synthesis Using Switchable Cycle-Consistent Generative Adversarial Networks." Diagnostics 12, no. 4 (2022): 816. http://dx.doi.org/10.3390/diagnostics12040816.

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Multi-contrast MRI images use different echo and repetition times to highlight different tissues. However, not all desired image contrasts may be available due to scan-time limitations, suboptimal signal-to-noise ratio, and/or image artifacts. Deep learning approaches have brought revolutionary advances in medical image synthesis, enabling the generation of unacquired image contrasts (e.g., T1-weighted MRI images) from available image contrasts (e.g., T2-weighted images). Particularly, CycleGAN is an advanced technique for image synthesis using unpaired images. However, it requires two separat

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Destyningtias, Budiani, Andi Kurniawan Nugroho, and Sri Heranurweni. "Analisa Citra Medis Pada Pasien Stroke dengan Metoda Peregangan Kontras Berbasis ImageJ." eLEKTRIKA 10, no. 1 (2019): 15. http://dx.doi.org/10.26623/elektrika.v10i1.1105.

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<p>This study aims to develop medical image processing technology, especially medical images of CT scans of stroke patients. Doctors in determining the severity of stroke patients usually use medical images of CT scans and have difficulty interpreting the extent of bleeding. Solutions are used with contrast stretching which will distinguish cell tissue, skull bone and type of bleeding. This study uses contrast stretching from the results of CT Scan images produced by first turning the DICOM Image into a JPEG image using the help of the ImageJ program. The results showed that the histogra

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Yang, Huan, Pengjiang Qian, and Chao Fan. "An Indirect Multimodal Image Registration and Completion Method Guided by Image Synthesis." Computational and Mathematical Methods in Medicine 2020 (June 30, 2020): 1–10. http://dx.doi.org/10.1155/2020/2684851.

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Multimodal registration is a challenging task due to the significant variations exhibited from images of different modalities. CT and MRI are two of the most commonly used medical images in clinical diagnosis, since MRI with multicontrast images, together with CT, can provide complementary auxiliary information. The deformable image registration between MRI and CT is essential to analyze the relationships among different modality images. Here, we proposed an indirect multimodal image registration method, i.e., sCT-guided multimodal image registration and problematic image completion method. In

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Bellam, Kiranmai, N. Krishnaraj, T. Jayasankar, N. B. Prakash, and G. R. Hemalakshmi. "Adaptive Multimodal Image Fusion with a Deep Pyramidal Residual Learning Network." Journal of Medical Imaging and Health Informatics 11, no. 8 (2021): 2135–43. http://dx.doi.org/10.1166/jmihi.2021.3763.

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Multimodal medical imaging is an indispensable requirement in the treatment of various pathologies to accelerate care. Rather than discrete images, a composite image combining complementary features from multimodal images is highly informative for clinical examinations, surgical planning, and progress monitoring. In this paper, a deep learning fusion model is proposed for the fusion of medical multimodal images. Based on pyramidal and residual learning units, the proposed model, strengthened with adaptive fusion rules, is tested on image pairs from a standard dataset. The potential of the prop

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N., Rajalakshmi, Narayanan K., and Amudhavalli P. "Wavelet-Based Weighted Median Filter for Image Denoising of MRI Brain Images." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 201–6. https://doi.org/10.11591/ijeecs.v10.i1.pp201-206.

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Preliminary diagnosing of MRI images from the hospital cannot be relied on because of the chances of occurrence of artifacts resulting in degraded quality of image, while others may be confused with pathology. Obtained MRI image usually contains limited artifacts. It becomes complex one for doctors in analyzing them. By increasing the contrast of an image, it will be easy to analyze. In order to find the tumor part efficiently MRI brain image should be enhanced properly. The image enhancement methods mainly improve the visual appearance of MRI images. The goal of denoising is to remove the noi

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Singh, Ram, and Lakhwinder Kaur. "Noise-residue learning convolutional network model for magnetic resonance image enhancement." Journal of Physics: Conference Series 2089, no. 1 (2021): 012029. http://dx.doi.org/10.1088/1742-6596/2089/1/012029.

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Abstract Magnetic Resonance Image (MRI) is an important medical image acquisition technique used to acquire high contrast images of human body anatomical structures and soft tissue organs. MRI system does not use any harmful radioactive ionized material like x-rays and computerized tomography (CT) imaging techniques. High-resolution MRI is desirable in many clinical applications such as tumor segmentation, image registration, edges & boundary detection, and image classification. During MRI acquisition, many practical constraints limit the MRI quality by introducing random Gaussian noise an

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Schramm, Georg, and Claes Nøhr Ladefoged. "Metal artifact correction strategies in MRI-based attenuation correction in PET/MRI." BJR|Open 1, no. 1 (2019): 20190033. http://dx.doi.org/10.1259/bjro.20190033.

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In hybrid positron emission tomography (PET) and MRI systems, attenuation correction for PET image reconstruction is commonly based on processing of dedicated MR images. The image quality of the latter is strongly affected by metallic objects inside the body, such as e.g. dental implants, endoprostheses, or surgical clips which all lead to substantial artifacts that propagate into MRI-based attenuation images. In this work, we review publications about metal artifact correction strategies in MRI-based attenuation correction in PET/MRI. Moreover, we also give an overview about publications inve

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Yan, Rong. "The Value of Convolutional-Neural-Network-Algorithm-Based Magnetic Resonance Imaging in the Diagnosis of Sports Knee Osteoarthropathy." Scientific Programming 2021 (July 2, 2021): 1–11. http://dx.doi.org/10.1155/2021/2803857.

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The application value of the convolutional neural network (CNN) algorithm in the diagnosis of sports knee osteoarthropathy was investigated in this study. A network model was constructed in this experiment for image analysis of magnetic resonance imaging (MRI) technology. Then, 100 cases of sports knee osteoarthropathy patients and 50 healthy volunteers were selected. Digital radiography (DR) images and MRI images of all the research objects were collected after the inclusion of the two groups. Besides, the important physiological representations were extracted from their image data graphs, an

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Dissertations / Theses on the topic "MRI IMAGE"

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Al-Abdul, Salam Amal. "Image quality in MRI." Thesis, University of Exeter, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288250.

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Cui, Xuelin. "Joint CT-MRI Image Reconstruction." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/86177.

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Modern clinical diagnoses and treatments have been increasingly reliant on medical imaging techniques. In return, medical images are required to provide more accurate and detailed information than ever. Aside from the evolution of hardware and software, multimodal imaging techniques offer a promising solution to produce higher quality images by fusing medical images from different modalities. This strategy utilizes more structural and/or functional image information, thereby allowing clinical results to be more comprehensive and better interpreted. Since their inception, multimodal imaging tec

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Carmo, Bernardo S. "Image processing in echography and MRI." Thesis, University of Southampton, 2005. https://eprints.soton.ac.uk/194557/.

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This work deals with image processing for three medical imaging applications: speckle detection in 3D ultrasound, left ventricle detection in cardiac magnetic resonance imaging (MRI) and flow feature visualisation in velocity MRI. For speckle detection, a learning from data approach was taken using pattern recognition principles and low-level image features, including signal-to-noise ratio, co-occurrence matrix, asymmetric second moment, homodyned k-distribution and a proposed specklet detector. For left ventricle detection, template matching was used. Forvortex detection, a data processing fr

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Gu, Wei Q. "Automated tracer-independent MRI/PET image registration." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29596.pdf.

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Ivarsson, Magnus. "Evaluation of 3D MRI Image Registration Methods." Thesis, Linköpings universitet, Datorseende, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139075.

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Image registration is the process of geometrically deforming a template image into a reference image. This technique is important and widely used within thefield of medical IT. The purpose could be to detect image variations, pathologicaldevelopment or in the company AMRA’s case, to quantify fat tissue in variousparts of the human body.From an MRI (Magnetic Resonance Imaging) scan, a water and fat tissue image isobtained. Currently, AMRA is using the Morphon algorithm to register and segment the water image in order to quantify fat and muscle tissue. During the firstpart of this master thesis,

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Lin, Xiangbo. "Knowledge-based image segmentation using deformable registration: application to brain MRI images." Reims, 2009. http://theses.univ-reims.fr/exl-doc/GED00001121.pdf.

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L'objectif de la thèse est de contribuer au recalage élastique d'images médicales intersujet-intramodalité, ainsi qu’à la segmentation d'images 3D IRM du cerveau dans le cas normal. L’algorithme des démons qui utilise les intensités des images pour le recalage est d’abord étudié. Une version améliorée est proposée en introduisant une nouvelle équation de calcul des forces pour résoudre des problèmes de recalages dans certaines régions difficiles. L'efficacité de la méthode est montrée sur plusieurs évaluations à partir de données simulées et réelles. Pour le recalage intersujet, une méthode or

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Soltaninejad, Mohammadreza. "Supervised learning-based multimodal MRI brain image analysis." Thesis, University of Lincoln, 2017. http://eprints.lincoln.ac.uk/30883/.

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Medical imaging plays an important role in clinical procedures related to cancer, such as diagnosis, treatment selection, and therapy response evaluation. Magnetic resonance imaging (MRI) is one of the most popular acquisition modalities which is widely used in brain tumour analysis and can be acquired with different acquisition protocols, e.g. conventional and advanced. Automated segmentation of brain tumours in MR images is a difficult task due to their high variation in size, shape and appearance. Although many studies have been conducted, it still remains a challenging task and improving a

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Daga, P. "Towards efficient neurosurgery : image analysis for interventional MRI." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1449559/.

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Interventional magnetic resonance imaging (iMRI) is being increasingly used for performing imageguided neurosurgical procedures. Intermittent imaging through iMRI can help a neurosurgeon visualise the target and eloquent brain areas during neurosurgery and lead to better patient outcome. MRI plays an important role in planning and performing neurosurgical procedures because it can provide highresolution anatomical images that can be used to discriminate between healthy and diseased tissue, as well as identify location and extent of functional areas. This is of significant clinical utility as i

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Chi, Wenjun. "MRI image analysis for abdominal and pelvic endometriosis." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:27efaa89-85cd-4f8b-ab67-b786986c42e3.

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Endometriosis is an oestrogen-dependent gynaecological condition defined as the presence of endometrial tissue outside the uterus cavity. The condition is predominantly found in women in their reproductive years, and associated with significant pelvic and abdominal chronic pain and infertility. The disease is believed to affect approximately 33% of women by a recent study. Currently, surgical intervention, often laparoscopic surgery, is the gold standard for diagnosing the disease and it remains an effective and common treatment method for all stages of endometriosis. Magnetic resonance imagin

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Hagio, Tomoe, and Tomoe Hagio. "Parametric Mapping and Image Analysis in Breast MRI." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/621809.

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Breast cancer is the most common and the second most fatal cancer among women in the U.S. Current knowledge indicates that there is a relationship between high breast density (measured by mammography) and increased breast cancer risk. However, the biology behind this relationship is not well understood. This may be due to the limited information provided by mammography which only yields information on the relative amount of fibroglandular to adipose tissue in the breast. In our studies, breast density is assessed using quantitative MRI, in which MRI-based tissue-dependent parameters are derive

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Books on the topic "MRI IMAGE"

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Brant, William E. Body MRI cases. Oxford University Press, 2013.

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Song, In-chʻan. MRI ŭi hwajil pʻyŏngka kisul kaebal =: Technology development of MRI image quality evaluation. Sikpʻum Ŭiyakpʻum Anjŏnchʻŏng, 2007.

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Afrin, Farhana. fMRI and MRI image registration and statistical mapping. VDM Verlag Dr. Müller, 2008.

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W, Bancroft Laura, and Bridges, Mellena D., M.D., eds. MRI normal variants and pitfalls. Lippincott Williams and Wilkins, 2009.

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Poldrack, Russell A. Handbook of functional MRI data analysis. Cambridge University Press, 2011.

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Ciulla, Carlo. Improved signal and image interpolation in biomedical applications: The case of magnetic resonance imaging (MRI). Medical Information Science Reference, 2009.

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Fauber, Terri L. Radiographic imaging and exposure. 2nd ed. Mosby, 2004.

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1953-, Nitz Wolfgang R., and Schmeets Stuart H. 1971-, eds. The physics of MRI taught through images. 2nd ed. Thieme, 2009.

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Wahl, Richard L. Atlas of PET/CT: With SPECT/CT. Saunders, 2008.

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Johansson, Ewa-Mari. Ewa-Mari Johansson: Image 2000-2008. Silvana editoriale, 2016.

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Book chapters on the topic "MRI IMAGE"

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Ashburner, J., and K. J. Friston. "Image Registration." In Functional MRI. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58716-0_26.

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Zeng, Gengsheng Lawrence. "MRI Reconstruction." In Medical Image Reconstruction. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-05368-9_7.

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Rajan, Sunder S. "Image Contrast and Pulse Sequences." In MRI. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1632-2_4.

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English, Philip T., and Christine Moore. "Image Production." In MRI for Radiographers. Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3403-9_4.

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English, Philip T., and Christine Moore. "Image Quality." In MRI for Radiographers. Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3403-9_5.

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English, Philip T., and Christine Moore. "Image Artifacts." In MRI for Radiographers. Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3403-9_6.

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Murray, Rachel, and Natasha Werpy. "Image interpretation and artefacts." In Equine MRI. John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118786574.ch4.

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Qu, Liangqiong, Yongqin Zhang, Zhiming Cheng, Shuang Zeng, Xiaodan Zhang, and Yuyin Zhou. "Multimodality MRI Synthesis." In Medical Image Synthesis. CRC Press, 2023. http://dx.doi.org/10.1201/9781003243458-14.

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Weishaupt, Dominik, Victor D. Köchli, and Borut Marincek. "Image Contrast." In How does MRI work? Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-07805-1_3.

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Ahrar, Kamran, and R. Jason Stafford. "MRI-Guided Biopsy." In Percutaneous Image-Guided Biopsy. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8217-8_5.

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Conference papers on the topic "MRI IMAGE"

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Yoon, Jongyeon, Elyssa M. McMaster, Chloe Cho, Kurt G. Schilling, Bennett A. Landman, and Daniel Moyer. "Tractography enhancement in clinically-feasible diffusion MRI using T1-weighted MRI and anatomical context." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047112.

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Soomro, Toufique Ahmed, Mahaveer Rathi, Shafique Ahmed Soomro, Muhammad Usman Keerio, Pardeep Kumar, and Enrique Nava Baro. "Image Enhancement Technique for MRI Brain Images." In 2024 Global Conference on Wireless and Optical Technologies (GCWOT). IEEE, 2024. https://doi.org/10.1109/gcwot63882.2024.10805684.

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Rivas, Carlos A., Jinwei Zhang, Shuwen Wei, Samuel W. Remedios, Aaron Carass, and Jerry L. Prince. "Unique MS lesion identification from MRI." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047269.

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Wang, Kun, Aobo Jin, Hongchun Guo, et al. "Low Field MRI Image Synthesis." In 2025 IEEE 4th International Conference on AI in Cybersecurity (ICAIC). IEEE, 2025. https://doi.org/10.1109/icaic63015.2025.10849188.

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S, Meharban M., Sabu M. K, and T. Santhanakrishnan. "T1W MRI to T2W MRI Image Synthesis Using SSIM-CycleGAN." In 2024 11th International Conference on Advances in Computing and Communications (ICACC). IEEE, 2024. https://doi.org/10.1109/icacc63692.2024.10845374.

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Faghihpirayesh, Razieh, Xueqi Guo, Matthias M. Wolf, Kaman Chung, and Mohammad Abdi. "Deep-learning framework for analysis of longitudinal MRI studies." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047794.

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Li, Tong, Anran Liu, David Kügler, and Martin Reuter. "Boost the adversarial learning with Fourier regulator: bias-field correction on MRI." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047299.

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Yao, Tianyuan, Zhiyuan Li, Praitayini Kanakaraj, et al. "Polyhedra encoding transformers: enhancing diffusion MRI analysis beyond voxel and volumetric embedding." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047244.

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Xu, Di, Xin Miao, Hengjie Liu, et al. "Rapid reconstruction of extremely accelerated liver 4D MRI via chained iterative refinement." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3034640.

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Yu, Tian, Yunhe Li, Michael E. Kim, et al. "Tractography with T1-weighted MRI and associated anatomical constraints on clinical quality diffusion MRI." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2024. http://dx.doi.org/10.1117/12.3006286.

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Reports on the topic "MRI IMAGE"

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DSC-MRI Consensus QIBA Profile. Chair Ona Wu, Mark Shiroishi, and Leland Hu. Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2020. https://doi.org/10.1148/qiba/20201022.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. Profile development is an evolutionary, phased process; this Profile is in the Public Comment Resolution Draft stage. The performance claims represent expert consensus and will be empirically demonstrated at a subsequent stage. Users of this Profile are encouraged to refer to the following site to understand the document’s context: http://qibawiki.rsna.org/index.php/QIBA_Profile_Stages. The Claim (Section 2) describes the biomarker performance. The Activities (Section 3) contribute to generating

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MRI-Based PDFF of the Liver, Consensus QIBA Profile. Chair Diego Hernando and Houchun (Harry) Hu. Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2024. https://doi.org/10.1148/qiba/20240619.

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A QIBA Profile is an implementation guide to generate a biomarker with an effective level of performance, mostly by reducing variability and bias in the measurement. The expected performance is expressed as Claims (Section 1.2). To achieve those claims, Actors, both human and equipment, (for example: scanners, data acquisition parameters, data reconstruction software and algorithms, image analysis tools, technologists and radiographers, medical physicists, radiologists) must meet the Checklist Requirements (Section 3) covering Periodic QA, Subject Handling, Image Data Acquisition, Image Data R

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Yang, Xiaofeng, Tian Liu, Jani Ashesh, Hui Mao, and Walter Curran. Fusion of Ultrasound Tissue-Typing Images with Multiparametric MRI for Image-guided Prostate Cancer Radiation Therapy. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada622473.

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CT Tumor Volume Change for Advanced Disease, Clinically Feasible Profile. Chair Ritu Gill, Rudresh Jarecha, and Ehsan Samei. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2022. http://dx.doi.org/10.1148/qiba/20220721.

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A QIBA Profile is an implementation guide to generate a biomarker with an effective level of performance, mostly by reducing variability and bias in the measurement. The expected performance is expressed as Claims (Section 1.2). To achieve those claims, Actors (Scanners, Technologists, Physicists, Radiologists, Reconstruction Software, and Image Analysis Tools) must meet the Checklist Requirements (Section 3) covering Periodic QA, Subject Handling, Image Data Acquisition, Image Data Reconstruction, Image QA, and Image Analysis. This Profile is at the Clinically Feasible stage (qibawiki.rsna.or

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Pepin, Kay, ed. MR Elastography of the Liver, Clinically Feasible Profile. Chair Richard Ehman and Patricia Cole. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2023. http://dx.doi.org/10.1148/qiba/20231107.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. The Claim (Section 2) describes the biomarker performance. The Activities (Section 3) contribute to generating the biomarker. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim. Assessment Procedures (Section 4) for evaluating specific requirements are defined as needed. This QIBA Profile (Magnetic Resonance Elastography of the Liver) addresses the application of Magnetic Resonance Elastography (MRE) for the quantification of liver stiffne

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MR (Diffusion-Weighted Imaging (DWI) of the Apparent Diffusion Coefficient (ADC), Clinically Feasible Profile. Chair Michael Boss, Dariya Malyarenko, and Daniel Margolis. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2022. http://dx.doi.org/10.1148/qiba/20221215.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. The Claim (Section 2) describes the biomarker performance and is derived from the body of scientific literature meeting specific requirements, in particular test-retest studies. The Activities (Section 3) contribute to generating the biomarker. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim. Assessment Procedures (Section 4) for evaluating specific requirements are defined as needed to ensure acceptable performance. Diffusion-Weighted

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Ultrasound Volume Blood Flow, Consensus QIBA Profile. Chair J. Brian Fowlkes, James Jago, and Oliver Kripfgans. American Institute of Ultrasound in Medicine (AIUM)/Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2024. https://doi.org/10.1148/qiba/20240105.

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A QIBA Profile is an implementation guide to generate a biomarker with an effective level of performance, mostly by reducing variability and bias in the measurement. The expected performance is expressed as Claims (Section 1.2). To achieve those claims, Actors (Manufacturers/Vendors/Field Service Engineers, Sonographers/Technologists, Physicians, Physicist/Clinical Engineer/QA manager, and Image Analysis Tools) must meet the Checklist Requirements (Section 3) covering Product Validation, Staff Qualification, Pre-delivery, Installation, Periodic QA, Subject Handling, Image Data Acquisition, Ima

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MR MSK Cartilage for Joint Disease, Consensus Profile. Chair Thomas Link and Xiaojuan Li. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2021. http://dx.doi.org/10.1148/qiba/20210925.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. The Claim (Section 2) describes the biomarker performance. The Activities (Section 3) contribute to generating the biomarker. Requirements are placed on the Actors that participate in those activities as necessary to achieve the Claim. Assessment Procedures (Section 4) for evaluating specific requirements are defined as needed. This QIBA Profile (MR-based cartilage compositional biomarkers (T1ρ, T2) ) addresses the application of T1ρ and T2 for the quantification of cartilage composition, which c

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Atherosclerosis Biomarkers by Computed Tomography Angiography (CTA). Chair Andrew Buckler, Luca Saba, and Uwe Joseph Schoepf. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2023. http://dx.doi.org/10.1148/qiba/20230328.

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Abstract:

The clinical application of Computed Tomography Angiography (CTA) is widely available as a technique to optimize the therapeutic approach to treating vascular disease. Evaluation of atherosclerotic arterial plaque characteristics is currently based on qualitative biomarkers. However, the reproducibility of such findings has historically been limited even among experts (1). Quantitative imaging biomarkers have been shown to have additive value above traditional qualitative imaging metrics and clinical risk scores regarding patient outcomes (2). However, many definitions and cut-offs are present

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Saba, Luca, and Uwe Joseph Schoepf. Atherosclerosis Biomarkers by Computed Tomography Angiography (CTA) - Maintenance version June 2024. Chair Andrew Buckler. Radiological Society of North America (RSNA) / Quantitative Imaging Biomarkers Alliance (QIBA), 2024. http://dx.doi.org/10.1148/qiba/202406.

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Abstract:

The clinical application of Computed Tomography Angiography (CTA) is widely available as a technique to optimize the therapeutic approach to treating vascular disease. Evaluation of atherosclerotic arterial plaque characteristics is currently based on qualitative biomarkers. However, the reproducibility of such findings has historically been limited even among experts. Quantitative imaging biomarkers have been shown to have additive value above traditional qualitative imaging metrics and clinical risk scores regarding patient outcomes. However, many definitions and cut-offs are present in the

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