Artificial Intelligence to Reduce or Eliminate the Need for Gadolinium-Based Contrast Agents in Brain and Cardiac MRI

Mallio, Carlo Augusto; Radbruch, Alexander; Deike-Hofmann, Katerina; Molen, Aart J. van der; Dekkers, Ilona A.; Zaharchuk, Greg; Parizel, Paul M; Zobel, Bruno Beomonte; Quattrocchi, Carlo Cosimo

doi:10.1097/rli.0000000000000983

Cited by 13 publications

(7 citation statements)

References 49 publications

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“…In the rapidly evolving landscape of osteoporosis diagnosis and imaging, future perspectives hold promising developments, particularly with the integration of advanced technologies such as artificial intelligence (AI). This is a branch of computer science focused on developing systems that can perform tasks that typically require human intelligence [40][41][42][43]. In various fields, AI algorithms analyze data, learn patterns, and make predictions, offering solutions ranging from image recognition and natural language processing to complex problem solving.…”

Section: Future Perspectives: Artificial Intelligencementioning

confidence: 99%

Phantomless Computed Tomography-Based Quantitative Bone Mineral Density Assessment: A Literature Review

Mallio,

Vertulli,

Bernetti

et al. 2024

Applied Sciences

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The global surge in aging populations has intensified osteoporosis challenges, necessitating improved diagnostic methods. While dual X-ray absorptiometry (DXA) is the conventional standard in assessing bone mineral density (BMD), the exploration of quantitative computed tomography (QCT) has been proposed, particularly phantomless techniques. Challenges in single-energy CT, emphasizing internal calibration standards like subcutaneous fat, are discussed. Advances in PL-QCT, notably with automatic region of interest (ROI) selection, show improved accuracy. Dual-energy CT (DECT) introduces new dimensions for musculoskeletal analysis. Despite advancements, challenges persist, including interindividual variability and patient-specific factors. Evolving single-energy CT and DECT techniques show promise in refining BMD assessment and osteoporosis diagnosis, enhancing patient care. Continued research and integration into clinical practice are vital for realizing these advancements’ full benefits. In this review, we evaluate and summarize current evidence on the feasibility and different approaches to achieve analysis of BMD with phantomless QCT.

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Section: Future Perspectives: Artificial Intelligencementioning

confidence: 99%

Phantomless Computed Tomography-Based Quantitative Bone Mineral Density Assessment: A Literature Review

Mallio,

Vertulli,

Bernetti

et al. 2024

Applied Sciences

Self Cite

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“…The fundamental idea behind AI-based contrast enhancement is mostly to reduce the amount of contrast agent or to dispense with it entirely. 4,37 Yet the technology may also enable new fields to be opened up. According to the well-known motto of Paracelsus, the dose makes the poison; attempts are made to reduce the dose of contrast media or to dispense with contrast media altogether; at the same time, both contrast media containing gadolinium and iodine have an extremely high safety profile.…”

Section: New Applicationsmentioning

confidence: 99%

“…The fundamental idea behind AI-based contrast enhancement is mostly to reduce the amount of contrast agent or to dispense with it entirely 4,37 . Yet the technology may also enable new fields to be opened up.…”

Section: The Contrast-free Questionmentioning

confidence: 99%

AI as a New Frontier in Contrast Media Research

Haubold,

Hosch,

Jost

et al. 2023

Invest Radiol

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Artificial intelligence (AI) techniques are currently harnessed to revolutionize the domain of medical imaging. This review investigates 3 major AI-driven approaches for contrast agent management: new frontiers in contrast agent dose reduction, the contrast-free question, and new applications. By examining recent studies that use AI as a new frontier in contrast media research, we synthesize the current state of the field and provide a comprehensive understanding of the potential and limitations of AI in this context. In doing so, we show the dose limits of reducing the amount of contrast agents and demonstrate why it might not be possible to completely eliminate contrast agents in the future. In addition, we highlight potential new applications to further increase the radiologist's sensitivity at normal doses. At the same time, this review shows which network architectures provide promising approaches and reveals possible artifacts of a paired image-to-image conversion. Furthermore, current US Food and Drug Administration regulatory guidelines regarding AI/machine learning–enabled medical devices are highlighted.

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“…The generation of virtual contrast-enhanced (vCE) MRI scans from unenhanced acquisitions using deep learning was first reported in brain studies (14)(15)(16)(17)(18)(19). However, several recent publications (20)(21)(22)(23)(24)(25)(26) have shown the technical feasibility of such approach for breast imaging.…”

Section: Introductionmentioning

confidence: 99%

Impact of Non-Contrast Enhanced Imaging Input Sequences on the Generation of Virtual Contrast-Enhanced Breast MRI Scans using Neural Networks

Liebert,

Schreiter,

Kapsner

et al. 2024

Preprint

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Background: Virtual contrast-enhanced (vCE) imaging techniques are an emerging topic of research in breast MRI. Purpose: To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of vCE breast MRI. Materials and Methods: The IRB-approved, retrospective study included 1064 multiparametric breast MRI scans (age:52 ±12 years) obtained from 2017-2020 (single site, two 3T MRI). Eleven independent neural networks were trained to derive vCE images from varying input combinations of T1w, T2w, and multi-b-value DWI sequences (b-value=50-1500s/mm2). Three readers evaluated the vCE images with regards to qualitative scores of diagnostic image quality, image sharpness, satisfaction with contrast/signal-to-noise-ratio, and lesion/non-mass enhancement conspicuity. Quantitative metrics (SSIM, PSNR, NRMSE, and median symmetrical accuracy) were analyzed and statistically compared between the input combinations for the full breast volume and both enhancing and non-enhancing target findings. Results: The independent test set consisted of 187 cases. The quantitative metrics significantly improved in target findings when multi-b-value DWI sequences were included during vCE training (p<.05). Non-significant effects (p>.05) were observed for the quantitative metrics on the full breast volume when comparing input combinations including T1w. Using T1w and DWI acquisitions during vCE training is necessary to achieve high satisfaction with contrast/SNR and good conspicuity of the enhancing findings. The input combination of T1w, T2w, and DWI sequences with three b-values showed the best qualitative performance. Conclusion: vCE breast MRI performance is significantly influenced by input sequences. Quantitative metrics and visual quality of vCE images significantly benefit when a multi b-value DWI is added to morphologic T1w-/T2w-sequences as input for model training.

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Artificial Intelligence to Reduce or Eliminate the Need for Gadolinium-Based Contrast Agents in Brain and Cardiac MRI

Cited by 13 publications

References 49 publications

Phantomless Computed Tomography-Based Quantitative Bone Mineral Density Assessment: A Literature Review

Phantomless Computed Tomography-Based Quantitative Bone Mineral Density Assessment: A Literature Review

AI as a New Frontier in Contrast Media Research

Impact of Non-Contrast Enhanced Imaging Input Sequences on the Generation of Virtual Contrast-Enhanced Breast MRI Scans using Neural Networks

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