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8 results on '"Biancardi, Alberto"'

3. 129Xe and Free‐Breathing 1H Ventilation MRI in Patients With Cystic Fibrosis: A Dual‐Center Study

Author

Marshall, Helen, primary, Voskrebenzev, Andreas, additional, Smith, Laurie J., additional, Biancardi, Alberto M., additional, Kern, Agilo L., additional, Collier, Guilhem J., additional, Wielopolski, Piotr A., additional, Ciet, Pierluigi, additional, Tiddens, Harm A. W. M., additional, Vogel‐Claussen, Jens, additional, and Wild, Jim M., additional

Published
2022
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4. Reproducibility of 19 F‐MR ventilation imaging in healthy volunteers

Author

Pippard, Benjamin J., primary, Neal, Mary A., additional, Maunder, Adam M., additional, Hollingsworth, Kieren G., additional, Biancardi, Alberto, additional, Lawson, Rod A., additional, Fisher, Holly, additional, Matthews, John N. S., additional, Simpson, A. John, additional, Wild, Jim M., additional, and Thelwall, Peter E., additional

Published
2021
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6. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

Author

Armato, Samuel G., primary, McLennan, Geoffrey, additional, Bidaut, Luc, additional, McNitt-Gray, Michael F., additional, Meyer, Charles R., additional, Reeves, Anthony P., additional, Zhao, Binsheng, additional, Aberle, Denise R., additional, Henschke, Claudia I., additional, Hoffman, Eric A., additional, Kazerooni, Ella A., additional, MacMahon, Heber, additional, van Beek, Edwin J. R., additional, Yankelevitz, David, additional, Biancardi, Alberto M., additional, Bland, Peyton H., additional, Brown, Matthew S., additional, Engelmann, Roger M., additional, Laderach, Gary E., additional, Max, Daniel, additional, Pais, Richard C., additional, Qing, David P.-Y., additional, Roberts, Rachael Y., additional, Smith, Amanda R., additional, Starkey, Adam, additional, Batra, Poonam, additional, Caligiuri, Philip, additional, Farooqi, Ali, additional, Gladish, Gregory W., additional, Jude, C. Matilda, additional, Munden, Reginald F., additional, Petkovska, Iva, additional, Quint, Leslie E., additional, Schwartz, Lawrence H., additional, Sundaram, Baskaran, additional, Dodd, Lori E., additional, Fenimore, Charles, additional, Gur, David, additional, Petrick, Nicholas, additional, Freymann, John, additional, Kirby, Justin, additional, Hughes, Brian, additional, Vande Casteele, Alessi, additional, Gupte, Sangeeta, additional, Sallam, Maha, additional, Heath, Michael D., additional, Kuhn, Michael H., additional, Dharaiya, Ekta, additional, Burns, Richard, additional, Fryd, David S., additional, Salganicoff, Marcos, additional, Anand, Vikram, additional, Shreter, Uri, additional, Vastagh, Stephen, additional, Croft, Barbara Y., additional, and Clarke, Laurence P., additional

Published
2011
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8. Improving Xenon-129 lung ventilation image SNR with deep-learning based image reconstruction.

Author

Stewart NJ, de Arcos J, Biancardi AM, Collier GJ, Smith LJ, Norquay G, Marshall H, Brau ACS, Lebel RM, and Wild JM

Subjects
Humans, Male, Female, Middle Aged, Prospective Studies, Adult, Retrospective Studies, Aged, Feasibility Studies, Deep Learning, Xenon Isotopes, Signal-To-Noise Ratio, Magnetic Resonance Imaging methods, Lung diagnostic imaging, Pulmonary Disease, Chronic Obstructive diagnostic imaging, Image Processing, Computer-Assisted methods, Asthma diagnostic imaging
Abstract

Purpose: To evaluate the feasibility and utility of a deep learning (DL)-based reconstruction for improving the SNR of hyperpolarized 129 Xe lung ventilation MRI., Methods: 129 Xe lung ventilation MRI data acquired from patients with asthma and/or chronic obstructive pulmonary disease (COPD) were retrospectively reconstructed with a commercial DL reconstruction pipeline at five different denoising levels. Quantitative imaging metrics of lung ventilation including ventilation defect percentage (VDP) and ventilation heterogeneity index (VH I ) were compared between each set of DL-reconstructed images and alternative denoising strategies including: filtering, total variation denoising and higher-order singular value decomposition. Structural similarity between the denoised and original images was assessed. In a prospective study, the feasibility of using SNR gains from DL reconstruction to allow natural-abundance xenon MRI was evaluated in healthy volunteers., Results: 129 Xe ventilation image SNR was improved with DL reconstruction when compared with conventionally reconstructed images. In patients with asthma and/or COPD, DL-reconstructed images exhibited a slight positive bias in ventilation defect percentage (1.3% at 75% denoising) and ventilation heterogeneity index (˜1.4) when compared with conventionally reconstructed images. Additionally, DL-reconstructed images preserved structural similarity more effectively than data denoised using alternative approaches. DL reconstruction greatly improved image SNR (greater than threefold), to a level that 129 Xe ventilation imaging using natural-abundance xenon appears feasible., Conclusion: DL-based image reconstruction significantly improves 129 Xe ventilation image SNR, preserves structural similarity, and leads to a minor bias in ventilation metrics that can be attributed to differences in the image sharpness. This tool should help facilitate cost-effective 129 Xe ventilation imaging with natural-abundance xenon in the future., (© 2024 GE Healthcare and The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Published
2024
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