Dr. Bolan is an Associate Professor in Radiology. After receiving his B.S. in Mechanical Engineering from the University of Illinois (UIUC) and post-graduate studies at UC Berkeley, Dr. Bolan spent five years working in industry as a software engineer. He joined Dr. Michael Garwood's group at the University of Minnesota in 1999 and received his Ph.D. in Biomedical Engineering in 2003 while developing methods for performing quantitative MR spectroscopy of breast cancer. He has continued at the UMN Center for Magnetic Resonance Research as a postdoc, Assistant, and Associate Professor. His research focuses on developing methods for quantitative MR imaging methods and integrating advanced imaging methods into clinical trials of cancer and obesity.
Saunders SL, Leng E, Spilseth B, Wasserman N, Metzger GJ, Bolan PJ. (2021). Training convolutional networks for prostate segmentation with limited data. IEEE access : practical innovations, open solutions, 9, 109214-109223. doi: https://doi.org/10.1109/access.2021.3100585 PubMed ID: 34527506.
Bolan PJ, Branzoli F, Di Stefano AL, Nichelli L, Valabregue R, Saunders SL, Akçakaya M, Sanson M, Lehéricy S, Marjańska M. (2020). Automated acquisition planning for magnetic resonance spectroscopy in brain cancer. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 12267, 730-739. doi: https://doi.org/10.1007/978-3-030-59728-3_71 PubMed ID: 35005744.
McKay JA, Church AL, Rubin N, Emory TH, Hoven NH, Kuehn-Hajder JE, Nelson MT, Ramanna S, Auerbach EJ, Moeller S, Bolan PJ. (2020). A comparison of methods for high-spatial-resolution diffusion-weighted imaging in breast MRI. Radiology, 297(2), 304-312. doi: https://doi.org/10.1148/radiol.2020200221 PubMed ID: 32840468.
Partridge SC, Zhang Z, Newitt DC, Gibbs JE, Chenevert TL, Rosen MA, Bolan PJ, Marques HS, Romanoff J, Cimino L, Joe BN, Umphrey H, Ojeda-Fournier H, Dogan D, Oh K, Abe H, Drukteinis J, Esserman LJ, Hylton NM. (2018). Diffusion-weighted MRI findings predict pathologic response in neoadjuvant treatment of breast cancer: The ACRIN 6698 multicenter trial. Radiology, 289(3), 618-627. doi: https://doi.org/10.1148/radiol.2018180273 PubMed ID: 30179110.
Bolan PJ, Kim E, Herman BA, Newstead GM, Rosen MA, Schnall MD, Pisano ED, Weatherall PT, Morris EA, Lehman CD, Garwood M, Nelson MT, Yee D, Polin SM, Esserman LJ, Gatsonis CA, Metzger GJ, Newitt DC, Partridge SC, Hylton NM, ACRIN Trial team ISPY-1 Investigators. (2017). MR spectroscopy of breast cancer for assessing early treatment response: Results from the ACRIN 6657 MRS trial. Journal of Magnetic Resonance Imaging, 46(1), 290-302. doi: https://doi.org/10.1002/jmri.25560 PubMed ID: 27981651.
Bolan PJ, Arentson L, Sublinvong T, Zhang Y, Moeller S, Downs LS Jr., Ghebre R, Yee D, Froelich J, Hui S, (2013). Water-fat imaging for assessing therapy-induced bone marrow damage in gynecologic cancers. Journal of Magnetic Resonance Imaging, 38(6), 1578-1584. doi: https://doi.org/10.1002/jmri.24071 PubMed ID: 23450703.
Bolan PJ, , Meisamy S, Baker EH, Lin J, Emory T, Nelson M, Everson LI, Yee D, Garwood M. (2003). In vivo quantification of choline compounds in the breast with 1H MR spectroscopy. Magnetic Resonance in Medicine, 50(6), 1134-1143. doi: https://doi.org/10.1002/mrm.10654 PubMed ID: 14648561.
Deep learning approaches for quantitative MRI
Developing and evaluating advanced diffusion-weighted imaging in breast cancer
Computational methods for MR spectroscopy acquisition and analysis