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Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast.

International journal of biomedical imaging

Wang D, Arlinghaus LR, Yankeelov TE, Yang X, Smith DS.
PMID: 28932236
Int J Biomed Imaging. 2017;2017:7835749. doi: 10.1155/2017/7835749. Epub 2017 Aug 28.

PURPOSE: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is used in cancer imaging to probe tumor vascular properties. Compressed sensing (CS) theory makes it possible to recover MR images from randomly undersampled METHODS: We considered five ubiquitous temporal regularizers...

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Wang D, Arlinghaus LR, Yankeelov TE, et al. Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast. Int J Biomed Imaging. 2017;2017:7835749doi: 10.1155/2017/7835749.
Wang, D., Arlinghaus, L. R., Yankeelov, T. E., Yang, X., & Smith, D. S. (2017). Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast. International journal of biomedical imaging, 20177835749. https://doi.org/10.1155/2017/7835749
Wang, Dong, et al. "Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast." International journal of biomedical imaging vol. 2017 (2017): 7835749. doi: https://doi.org/10.1155/2017/7835749
Wang D, Arlinghaus LR, Yankeelov TE, Yang X, Smith DS. Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast. Int J Biomed Imaging. 2017;2017:7835749. doi: 10.1155/2017/7835749. Epub 2017 Aug 28. PMID: 28932236; PMCID: PMC5592397.
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Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer.

Journal of medical imaging (Bellingham, Wash.)

Weis JA, Flint KM, Sanchez V, Yankeelov TE, Miga MI.
PMID: 26158120
J Med Imaging (Bellingham). 2015 Jul;2(3):036001. doi: 10.1117/1.JMI.2.3.036001. Epub 2015 Jul 02.

Cancer progression has been linked to mechanics. Therefore, there has been recent interest in developing noninvasive imaging tools for cancer assessment that are sensitive to changes in tissue mechanical properties. We have developed one such method, modality independent elastography...

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Weis JA, Flint KM, Sanchez V, et al. Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer. J Med Imaging (Bellingham). 2015;2(3):036001doi: 10.1117/1.JMI.2.3.036001.
Weis, J. A., Flint, K. M., Sanchez, V., Yankeelov, T. E., & Miga, M. I. (2015). Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer. Journal of medical imaging (Bellingham, Wash.), 2(3), 036001. https://doi.org/10.1117/1.JMI.2.3.036001
Weis, Jared A, et al. "Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer." Journal of medical imaging (Bellingham, Wash.) vol. 2,3 (2015): 036001. doi: https://doi.org/10.1117/1.JMI.2.3.036001
Weis JA, Flint KM, Sanchez V, Yankeelov TE, Miga MI. Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer. J Med Imaging (Bellingham). 2015 Jul;2(3):036001. doi: 10.1117/1.JMI.2.3.036001. Epub 2015 Jul 02. PMID: 26158120; PMCID: PMC4490672.
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Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods.

International journal of biomedical imaging

Smith DS, Gore JC, Yankeelov TE, Welch EB.
PMID: 22481908
Int J Biomed Imaging. 2012;2012:864827. doi: 10.1155/2012/864827. Epub 2012 Feb 01.

Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI...

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Smith DS, Gore JC, Yankeelov TE, et al. Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods. Int J Biomed Imaging. 2012;2012:864827doi: 10.1155/2012/864827.
Smith, D. S., Gore, J. C., Yankeelov, T. E., & Welch, E. B. (2012). Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods. International journal of biomedical imaging, 2012864827. https://doi.org/10.1155/2012/864827
Smith, David S, et al. "Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods." International journal of biomedical imaging vol. 2012 (2012): 864827. doi: https://doi.org/10.1155/2012/864827
Smith DS, Gore JC, Yankeelov TE, Welch EB. Real-Time Compressive Sensing MRI Reconstruction Using GPU Computing and Split Bregman Methods. Int J Biomed Imaging. 2012;2012:864827. doi: 10.1155/2012/864827. Epub 2012 Feb 01. PMID: 22481908; PMCID: PMC3296267.
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Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.

Translational oncology

Li X, Kang H, Arlinghaus LR, Abramson RG, Chakravarthy AB, Abramson VG, Farley J, Sanders M, Yankeelov TE.
PMID: 24772203
Transl Oncol. 2014 Feb 01;7(1):14-22. doi: 10.1593/tlo.13748. eCollection 2014 Feb.

The purpose of this study is to investigate the ability of multivariate analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parametric maps, obtained early in the course of therapy, to predict which patients will achieve...

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Li X, Kang H, Arlinghaus LR, et al. Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Transl Oncol. 2014;7(1):14-22doi: 10.1593/tlo.13748.
Li, X., Kang, H., Arlinghaus, L. R., Abramson, R. G., Chakravarthy, A. B., Abramson, V. G., Farley, J., Sanders, M., & Yankeelov, T. E. (2014). Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Translational oncology, 7(1), 14-22. https://doi.org/10.1593/tlo.13748
Li, Xia, et al. "Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer." Translational oncology vol. 7,1 (2014): 14-22. doi: https://doi.org/10.1593/tlo.13748
Li X, Kang H, Arlinghaus LR, Abramson RG, Chakravarthy AB, Abramson VG, Farley J, Sanders M, Yankeelov TE. Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Transl Oncol. 2014 Feb 01;7(1):14-22. doi: 10.1593/tlo.13748. eCollection 2014 Feb. PMID: 24772203; PMCID: PMC3998687.
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Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting.

Journal of magnetic resonance imaging : JMRI

Sorace AG, Wu C, Barnes SL, Jarrett AM, Avery S, Patt D, Goodgame B, Luci JJ, Kang H, Abramson RG, Yankeelov TE, Virostko J.
PMID: 29570895
J Magn Reson Imaging. 2018 Mar 23; doi: 10.1002/jmri.26011. Epub 2018 Mar 23.

BACKGROUND: Quantitative diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) have the potential to impact patient care by providing noninvasive biological information in breast cancer.PURPOSE/HYPOTHESIS: To quantify the repeatability, reproducibility, and accuracy of apparent diffusion coefficient (ADC) and TSTUDY...

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Sorace AG, Wu C, Barnes SL, et al. Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting. J Magn Reson Imaging. 2018;doi: 10.1002/jmri.26011.
Sorace, A. G., Wu, C., Barnes, S. L., Jarrett, A. M., Avery, S., Patt, D., Goodgame, B., Luci, J. J., Kang, H., Abramson, R. G., Yankeelov, T. E., & Virostko, J. (2018). Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting. Journal of magnetic resonance imaging : JMRI, . https://doi.org/10.1002/jmri.26011
Sorace, Anna G, et al. "Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting." Journal of magnetic resonance imaging : JMRI vol. (2018). doi: https://doi.org/10.1002/jmri.26011
Sorace AG, Wu C, Barnes SL, Jarrett AM, Avery S, Patt D, Goodgame B, Luci JJ, Kang H, Abramson RG, Yankeelov TE, Virostko J. Repeatability, reproducibility, and accuracy of quantitative mri of the breast in the community radiology setting. J Magn Reson Imaging. 2018 Mar 23; doi: 10.1002/jmri.26011. Epub 2018 Mar 23. PMID: 29570895; PMCID: PMC6151298.
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Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer.

Translational oncology

McKenna MT, Weis JA, Brock A, Quaranta V, Yankeelov TE.
PMID: 29674173
Transl Oncol. 2018 Jun;11(3):732-742. doi: 10.1016/j.tranon.2018.03.009. Epub 2018 Apr 16.

Medical oncology is in need of a mathematical modeling toolkit that can leverage clinically-available measurements to optimize treatment selection and schedules for patients. Just as the therapeutic choice has been optimized to match tumor genetics, the delivery of those...

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McKenna MT, Weis JA, Brock A, et al. Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Transl Oncol. 2018;11(3):732-742doi: 10.1016/j.tranon.2018.03.009.
McKenna, M. T., Weis, J. A., Brock, A., Quaranta, V., & Yankeelov, T. E. (2018). Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Translational oncology, 11(3), 732-742. https://doi.org/10.1016/j.tranon.2018.03.009
McKenna, Matthew T, et al. "Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer." Translational oncology vol. 11,3 (2018): 732-742. doi: https://doi.org/10.1016/j.tranon.2018.03.009
McKenna MT, Weis JA, Brock A, Quaranta V, Yankeelov TE. Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Transl Oncol. 2018 Jun;11(3):732-742. doi: 10.1016/j.tranon.2018.03.009. Epub 2018 Apr 16. PMID: 29674173; PMCID: PMC6056758.
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Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies.

Journal of medical imaging (Bellingham, Wash.)

Malyarenko D, Fedorov A, Bell L, Prah M, Hectors S, Arlinghaus L, Muzi M, Solaiyappan M, Jacobs M, Fung M, Shukla-Dave A, McManus K, Boss M, Taouli B, Yankeelov TE, Quarles CC, Schmainda K, Chenevert TL, Newitt DC.
PMID: 29134189
J Med Imaging (Bellingham). 2018 Jan;5(1):011006. doi: 10.1117/1.JMI.5.1.011006. Epub 2017 Oct 30.

This paper reports on results of a multisite collaborative project launched by the MRI subgroup of Quantitative Imaging Network to assess current capability and provide future guidelines for generating a standard parametric diffusion map Digital Imaging and Communication in...

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Malyarenko D, Fedorov A, Bell L, et al. Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. J Med Imaging (Bellingham). 2017;5(1):011006doi: 10.1117/1.JMI.5.1.011006.
Malyarenko, D., Fedorov, A., Bell, L., Prah, M., Hectors, S., Arlinghaus, L., Muzi, M., Solaiyappan, M., Jacobs, M., Fung, M., Shukla-Dave, A., McManus, K., Boss, M., Taouli, B., Yankeelov, T. E., Quarles, C. C., Schmainda, K., Chenevert, T. L., & Newitt, D. C. (2018). Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. Journal of medical imaging (Bellingham, Wash.), 5(1), 011006. https://doi.org/10.1117/1.JMI.5.1.011006
Malyarenko, Dariya, et al. "Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies." Journal of medical imaging (Bellingham, Wash.) vol. 5,1 (2018): 011006. doi: https://doi.org/10.1117/1.JMI.5.1.011006
Malyarenko D, Fedorov A, Bell L, Prah M, Hectors S, Arlinghaus L, Muzi M, Solaiyappan M, Jacobs M, Fung M, Shukla-Dave A, McManus K, Boss M, Taouli B, Yankeelov TE, Quarles CC, Schmainda K, Chenevert TL, Newitt DC. Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. J Med Imaging (Bellingham). 2018 Jan;5(1):011006. doi: 10.1117/1.JMI.5.1.011006. Epub 2017 Oct 30. PMID: 29134189; PMCID: PMC5658654.
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Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric.

Frontiers in physiology

McKenna MT, Weis JA, Quaranta V, Yankeelov TE.
PMID: 31178753
Front Physiol. 2019 May 22;10:616. doi: 10.3389/fphys.2019.00616. eCollection 2019.

Treatment response assays are often summarized by sigmoidal functions comparing cell survival at a single timepoint to applied drug concentration. This approach has a limited biophysical basis, thereby reducing the biological insight gained from such analysis. In particular, drug...

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McKenna MT, Weis JA, Quaranta V, et al. Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric. Front Physiol. 2019;10:616doi: 10.3389/fphys.2019.00616.
McKenna, M. T., Weis, J. A., Quaranta, V., & Yankeelov, T. E. (2019). Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric. Frontiers in physiology, 10616. https://doi.org/10.3389/fphys.2019.00616
McKenna, Matthew T, et al. "Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric." Frontiers in physiology vol. 10 (2019): 616. doi: https://doi.org/10.3389/fphys.2019.00616
McKenna MT, Weis JA, Quaranta V, Yankeelov TE. Leveraging Mathematical Modeling to Quantify Pharmacokinetic and Pharmacodynamic Pathways: Equivalent Dose Metric. Front Physiol. 2019 May 22;10:616. doi: 10.3389/fphys.2019.00616. eCollection 2019. PMID: 31178753; PMCID: PMC6538812.
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Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.

Journal of magnetic resonance imaging : JMRI

McGarry SD, Brehler M, Bukowy JD, Lowman AK, Bobholz SA, Duenweg SR, Banerjee A, Hurrell SL, Malyarenko D, Chenevert TL, Cao Y, Li Y, You D, Fedorov A, Bell LC, Quarles CC, Prah MA, Schmainda KM, Taouli B, LoCastro E, Mazaheri Y, Shukla-Dave A, Yankeelov TE, Hormuth DA, Madhuranthakam AJ, Hulsey K, Li K, Huang W, Huang W, Muzi M, Jacobs MA, Solaiyappan M, Hectors S, Antic T, Paner GP, Palangmonthip W, Jacobsohn K, Hohenwalter M, Duvnjak P, Griffin M, See W, Nevalainen MT, Iczkowski KA, LaViolette PS.
PMID: 34767682
J Magn Reson Imaging. 2021 Nov 12; doi: 10.1002/jmri.27983. Epub 2021 Nov 12.

BACKGROUND: Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease.PURPOSE: To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI...

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McGarry SD, Brehler M, Bukowy JD, et al. Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness. J Magn Reson Imaging. 2021;doi: 10.1002/jmri.27983.
McGarry, S. D., Brehler, M., Bukowy, J. D., Lowman, A. K., Bobholz, S. A., Duenweg, S. R., Banerjee, A., Hurrell, S. L., Malyarenko, D., Chenevert, T. L., Cao, Y., Li, Y., You, D., Fedorov, A., Bell, L. C., Quarles, C. C., Prah, M. A., Schmainda, K. M., Taouli, B., LoCastro, E., Mazaheri, Y., Shukla-Dave, A., Yankeelov, T. E., Hormuth, D. A., Madhuranthakam, A. J., Hulsey, K., Li, K., Huang, W., Huang, W., Muzi, M., Jacobs, M. A., Solaiyappan, M., Hectors, S., Antic, T., Paner, G. P., Palangmonthip, W., Jacobsohn, K., Hohenwalter, M., Duvnjak, P., Griffin, M., See, W., Nevalainen, M. T., Iczkowski, K. A., & LaViolette, P. S. (2021). Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness. Journal of magnetic resonance imaging : JMRI, . https://doi.org/10.1002/jmri.27983
McGarry, Sean D, et al. "Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness." Journal of magnetic resonance imaging : JMRI vol. (2021). doi: https://doi.org/10.1002/jmri.27983
McGarry SD, Brehler M, Bukowy JD, Lowman AK, Bobholz SA, Duenweg SR, Banerjee A, Hurrell SL, Malyarenko D, Chenevert TL, Cao Y, Li Y, You D, Fedorov A, Bell LC, Quarles CC, Prah MA, Schmainda KM, Taouli B, LoCastro E, Mazaheri Y, Shukla-Dave A, Yankeelov TE, Hormuth DA, Madhuranthakam AJ, Hulsey K, Li K, Huang W, Huang W, Muzi M, Jacobs MA, Solaiyappan M, Hectors S, Antic T, Paner GP, Palangmonthip W, Jacobsohn K, Hohenwalter M, Duvnjak P, Griffin M, See W, Nevalainen MT, Iczkowski KA, LaViolette PS. Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness. J Magn Reson Imaging. 2021 Nov 12; doi: 10.1002/jmri.27983. Epub 2021 Nov 12. PMID: 34767682.
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Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells.

Communications biology

Chen YI, Chang YJ, Liao SC, Nguyen TD, Yang J, Kuo YA, Hong S, Liu YL, Rylander HG, Santacruz SR, Yankeelov TE, Yeh HC.
PMID: 35017629
Commun Biol. 2022 Jan 11;5(1):18. doi: 10.1038/s42003-021-02938-w.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to...

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Chen YI, Chang YJ, Liao SC, et al. Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells. Commun Biol. 2022;5(1):18doi: 10.1038/s42003-021-02938-w.
Chen, Y. I., Chang, Y. J., Liao, S. C., Nguyen, T. D., Yang, J., Kuo, Y. A., Hong, S., Liu, Y. L., Rylander, H. G., Santacruz, S. R., Yankeelov, T. E., & Yeh, H. C. (2022). Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells. Communications biology, 5(1), 18. https://doi.org/10.1038/s42003-021-02938-w
Chen, Yuan-I, et al. "Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells." Communications biology vol. 5,1 (2022): 18. doi: https://doi.org/10.1038/s42003-021-02938-w
Chen YI, Chang YJ, Liao SC, Nguyen TD, Yang J, Kuo YA, Hong S, Liu YL, Rylander HG, Santacruz SR, Yankeelov TE, Yeh HC. Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells. Commun Biol. 2022 Jan 11;5(1):18. doi: 10.1038/s42003-021-02938-w. PMID: 35017629.
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Disposable point-of-care portable perfusion phantom for quantitative DCE-MRI.

Medical physics

Holland MD, Morales A, Simmons S, Smith B, Misko SR, Jiang X, Hormuth DA, Christenson C, Koomullil RP, Morgan DE, Li Y, Xu J, Yankeelov TE, Kim H.
PMID: 34802148
Med Phys. 2022 Jan;49(1):271-281. doi: 10.1002/mp.15372. Epub 2021 Dec 10.

PURPOSE: To develop a disposable point-of-care portable perfusion phantom (DP4) and validate its clinical utility in a multi-institutional setting for quantitative dynamic contrast-enhanced magnetic resonance imaging (qDCE-MRI).METHODS: The DP4 phantom was designed for single-use and imaged concurrently with a...

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Holland MD, Morales A, Simmons S, et al. Disposable point-of-care portable perfusion phantom for quantitative DCE-MRI. Med Phys. 2021;49(1):271-281doi: 10.1002/mp.15372.
Holland, M. D., Morales, A., Simmons, S., Smith, B., Misko, S. R., Jiang, X., Hormuth, D. A., Christenson, C., Koomullil, R. P., Morgan, D. E., Li, Y., Xu, J., Yankeelov, T. E., & Kim, H. (2022). Disposable point-of-care portable perfusion phantom for quantitative DCE-MRI. Medical physics, 49(1), 271-281. https://doi.org/10.1002/mp.15372
Holland, Martin D, et al. "Disposable point-of-care portable perfusion phantom for quantitative DCE-MRI." Medical physics vol. 49,1 (2022): 271-281. doi: https://doi.org/10.1002/mp.15372
Holland MD, Morales A, Simmons S, Smith B, Misko SR, Jiang X, Hormuth DA, Christenson C, Koomullil RP, Morgan DE, Li Y, Xu J, Yankeelov TE, Kim H. Disposable point-of-care portable perfusion phantom for quantitative DCE-MRI. Med Phys. 2022 Jan;49(1):271-281. doi: 10.1002/mp.15372. Epub 2021 Dec 10. PMID: 34802148.
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Associations Between Dynamic Contrast Enhanced Magnetic Resonance Imaging and Clinically Relevant Histopathological Features in Breast Cancer: A Multicenter Analysis.

In vivo (Athens, Greece)

Surov A, Kim JY, Aiello M, Huang W, Yankeelov TE, Wienke A, Pech M.
PMID: 34972741
In Vivo. 2022 Jan-Feb;36(1):398-408. doi: 10.21873/invivo.12717.

BACKGROUND/AIM: To provide data regarding relationships between quantitative dynamic contrast enhanced magnetic resonance imaging (DCE MRI) and prognostic factors in breast cancer (BC).PATIENTS AND METHODS: Data from 4 Centers (200 female patients, mean age, 51.2±11.5 years) were acquired. The...

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Surov A, Kim JY, Aiello M, et al. Associations Between Dynamic Contrast Enhanced Magnetic Resonance Imaging and Clinically Relevant Histopathological Features in Breast Cancer: A Multicenter Analysis. In Vivo. 2022;36(1):398-408doi: 10.21873/invivo.12717.
Surov, A., Kim, J. Y., Aiello, M., Huang, W., Yankeelov, T. E., Wienke, A., & Pech, M. (2022). Associations Between Dynamic Contrast Enhanced Magnetic Resonance Imaging and Clinically Relevant Histopathological Features in Breast Cancer: A Multicenter Analysis. In vivo (Athens, Greece), 36(1), 398-408. https://doi.org/10.21873/invivo.12717
Surov, Alexey, et al. "Associations Between Dynamic Contrast Enhanced Magnetic Resonance Imaging and Clinically Relevant Histopathological Features in Breast Cancer: A Multicenter Analysis." In vivo (Athens, Greece) vol. 36,1 (2022): 398-408. doi: https://doi.org/10.21873/invivo.12717
Surov A, Kim JY, Aiello M, Huang W, Yankeelov TE, Wienke A, Pech M. Associations Between Dynamic Contrast Enhanced Magnetic Resonance Imaging and Clinically Relevant Histopathological Features in Breast Cancer: A Multicenter Analysis. In Vivo. 2022 Jan-Feb;36(1):398-408. doi: 10.21873/invivo.12717. PMID: 34972741.
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