Peter LaViolette, PhD | Associate Professor | Medical College of Wisconsin

Peter S. LaViolette, PhD, MS

Associate Professor of Radiology and Biomedical Engineering; Director, Quantitative Imaging Laboratory, Medical College of Wisconsin; Section of Imaging Research, Division of Imaging Sciences

Locations

Medical College of Wisconsin
Department of Radiology

Contact Information

Education

Postdoctoral Fellowship, Medical College of Wisconsin, Milwaukee, WI, 2011-2012
PhD, Biophysics, Medical College of Wisconsin, Milwaukee, WI, 2011
MA, MS, Physics, University of Massachusetts, Amherst, MA, 2005
BS, Physics, St. Norbert College, De Pere, WI, 2003

Research Interests

The overall goal of my lab’s research is creating and validating imaging techniques that improve both patient outcome and treatment efficacy. I primarily work with neurological diseases such as brain cancer and epilepsy in a translational setting, collaborating with clinicians for the betterment of patient care.

Our brain cancer research has been focused on characterizing brain tumor cellularity and vascularity through imaging methods such as diffusion and perfusion MRI. The neuro-imaging techniques we develop are used to determine whether brain tumors respond to therapy or signal when brain tumors are progressing. In addition to patient outcome studies, we study the histological features responsible for the imaging characteristics measures in-vivo. This provides us with a validation technique for assessing our imaging biomarkers.

My epilepsy research is mainly focused on development of image processing techniques meant to help clinicians better plan surgery and map out brain function.

Longitudinal molecular trajectories of diffuse glioma in adults.

(Barthel FP, Johnson KC, Varn FS, Moskalik AD, Tanner G, Kocakavuk E, Anderson KJ, Abiola O, Aldape K, Alfaro KD, Alpar D, Amin SB, Ashley DM, Bandopadhayay P, Barnholtz-Sloan JS, Beroukhim R, Bock C, Brastianos PK, Brat DJ, Brodbelt AR, Bruns AF, Bulsara KR, Chakrabarty A, Chakravarti A, Chuang JH, Claus EB, Cochran EJ, Connelly J, Costello JF, Finocchiaro G, Fletcher MN, French PJ, Gan HK, Gilbert MR, Gould PV, Grimmer MR, Iavarone A, Ismail A, Jenkinson MD, Khasraw M, Kim H, Kouwenhoven MCM, LaViolette PS, Li M, Lichter P, Ligon KL, Lowman AK, Malta TM, Mazor T, McDonald KL, Molinaro AM, Nam DH, Nayyar N, Ng HK, Ngan CY, Niclou SP, Niers JM, Noushmehr H, Noorbakhsh J, Ormond DR, Park CK, Poisson LM, Rabadan R, Radlwimmer B, Rao G, Reifenberger G, Sa JK, Schuster M, Shaw BL, Short SC, Smitt PAS, Sloan AE, Smits M, Suzuki H, Tabatabai G, Van Meir EG, Watts C, Weller M, Wesseling P, Westerman BA, Widhalm G, Woehrer A, Yung WKA, Zadeh G, Huse JT, De Groot JF, Stead LF, Verhaak RGW, GLASS Consortium.) Nature. 2019 Dec;576(7785):112-120.
Positive STAT5 Protein and Locus Amplification Status Predicts Recurrence after Radical Prostatectomy to Assist Clinical Precision Management of Prostate Cancer.

(Haddad BR, Erickson A, Udhane V, LaViolette PS, Rone JD, Kallajoki MA, See WA, Rannikko A, Mirtti T, Nevalainen MT.) Cancer Epidemiol Biomarkers Prev. 2019 Oct;28(10):1642-1651.
Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space.

(McGarry SD, Bukowy JD, Iczkowski KA, Unteriner JG, Duvnjak P, Lowman AK, Jacobsohn K, Hohenwalter M, Griffin MO, Barrington AW, Foss HE, Keuter T, Hurrell SL, See WA, Nevalainen MT, Banerjee A, LaViolette PS.) Tomography. 2019 03;5(1):127-134.
The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

(Huang W, Chen Y, Fedorov A, Li X, Jajamovich GH, Malyarenko DI, Aryal MP, LaViolette PS, Oborski MJ, O'Sullivan F, Abramson RG, Jafari-Khouzani K, Afzal A, Tudorica A, Moloney B, Gupta SN, Besa C, Kalpathy-Cramer J, Mountz JM, Laymon CM, Muzi M, Kinahan PE, Schmainda K, Cao Y, Chenevert TL, Taouli B, Yankeelov TE, Fennessy F, Li X.) Tomography. 2019 03;5(1):99-109.
An integrated iterative annotation technique for easing neural network training in medical image analysis.

(Lutnick B, Ginley B, Govind D, McGarry SD, LaViolette PS, Yacoub R, Jain S, Tomaszewski JE, Jen KY, Sarder P.) Nat Mach Intell. 2019 Feb;1(2):112-119.
Radio-pathomic Maps of Epithelium and Lumen Density Predict the Location of High-Grade Prostate Cancer.

(McGarry SD, Hurrell SL, Iczkowski KA, Hall W, Kaczmarowski AL, Banerjee A, Keuter T, Jacobsohn K, Bukowy JD, Nevalainen MT, Hohenwalter MD, See WA, LaViolette PS.) Int J Radiat Oncol Biol Phys. 2018 08 01;101(5):1179-1187.
Vascular Interventional Radiology-Guided Photothermal Therapy of Colorectal Cancer Liver Metastasis with Theranostic Gold Nanorods.

(Parchur AK, Sharma G, Jagtap JM, Gogineni VR, LaViolette PS, Flister MJ, White SB, Joshi A.) ACS Nano. 2018 07 24;12(7):6597-6611.
Split laminotomy versus conventional laminotomy: postoperative outcomes in pediatric patients.

(Arocho-Quinones EV, Kolimas A, LaViolette PS, Kaufman BA, Foy AB, Zwienenberg M, Lew SM.) J Neurosurg Pediatr. 2018 06;21(6):615-625.
Optimized -value selection for the discrimination of prostate cancer grades, including the cribriform pattern, using diffusion weighted imaging.

(Hurrell SL, McGarry SD, Kaczmarowski A, Iczkowski KA, Jacobsohn K, Hohenwalter MD, Hall WA, See WA, Banerjee A, Charles DK, Nevalainen MT, Mackinnon AC, LaViolette PS.) J Med Imaging (Bellingham). 2018 01;5(1):011004.
Host genetic modifiers of nonproductive angiogenesis inhibit breast cancer.

(Flister MJ, Tsaih SW, Stoddard A, Plasterer C, Jagtap J, Parchur AK, Sharma G, Prisco AR, Lemke A, Murphy D, Al-Gizawiy M, Straza M, Ran S, Geurts AM, Dwinell MR, Greene AS, Bergom C, LaViolette PS, Joshi A.) Breast Cancer Res Treat. 2017 Aug;165(1):53-64.
Progressing Bevacizumab-Induced Diffusion Restriction Is Associated with Coagulative Necrosis Surrounded by Viable Tumor and Decreased Overall Survival in Patients with Recurrent Glioblastoma.

(Nguyen HS, Milbach N, Hurrell SL, Cochran E, Connelly J, Bovi JA, Schultz CJ, Mueller WM, Rand SD, Schmainda KM, LaViolette PS.) AJNR Am J Neuroradiol. 2016 Dec;37(12):2201-2208.
Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy.

(McGarry SD, Hurrell SL, Kaczmarowski AL, Cochran EJ, Connelly J, Rand SD, Schmainda KM, LaViolette PS.) Tomography. 2016 Sep;2(3):223-228.

Peter LaViolette, PhD, MS