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Last Updated: 09/21/17
Quantitative Imaging Network (QIN)

University of Michigan (team#1)

ADVANCING QUANTIFICATION OF DIFFUSION MRI FOR ONCOLOGIC IMAGING

Brian Ross, Ph.D.
bdross@med.umich.edu
Grant Number: U01 CA166104

This research effort is directed towards establishing diffusion-weighted MRI (DWI) as a quantitative imaging metric for assessment of cancer patients. The studies are combined with several strategic collaborations within the NCI Quantitative Imaging Network (QIN), Imbio, LLC (industrial partner) and the National Institute of Standards and Technology (NIST). Overall the research aims are to develop a standardized platform for diffusion analysis and validation of DWI metrics for quantification of tumor diffusion values through establishment of histogram and voxel-based metrics. This QIN team also seeks to develop the next generation DWI phantom using in-situ thermometry for precise diffusion measurements over the full clinically-relevant ADC range to generate quantitative quality assurance and system performance metrics across diverse scanner platforms. In addition, the team’s efforts are also directed towards implementing retrospective correction of DW nonlinearity errors to improve the reliability of using DWI in single-site and multi-center clinical trials. Overall these research efforts are actively addressing major technical and scientific hurdles to firmly establish DWI as an imaging methods for the purpose of monitoring therapeutic response. These studies are needed in order to improve clinical management of cancer patients using DWI.

Overall, the efforts of this QIN team are directed towards advancing quantitative diffusion imaging to allow for routine use in patient care by improving its overall reliability. By effectively reducing technical variance which can confound current multi-center cancer imaging trials, this QIN team will improve the ability of clinical practice to be able to utilize diffusion weighted MRI for the detection of treatment response early in cancer patients. Ultimately, these studies will advance diagnostic, prognostic and treatment monitoring quantitative imaging technology toward more effective personalized management of cancer patients.

References

Semiautomated Workflow for Clinically Streamlined Glioma Parametric Response Mapping.
Keith L, Ross BD, Galbán CJ, Luker GD, Galbán S, Zhao B, Guo X, Chenevert TL, Hoff BA.
Tomography. 2016 Dec;2(4):267-275. doi: 10.18383/j.tom.2016.00181. PMID:28286871

QIN DAWG Validation of Gradient Nonlinearity Bias Correction Workflow for Quantitative Diffusion-Weighted Imaging in Multicenter Trials.
Malyarenko DI, Wilmes LJ, Arlinghaus LR, Jacobs MA, Huang W, Helmer KG, Taouli B, Yankeelov TE, Newitt D, Chenevert TL.
Tomography. 2016 Dec;2(4):396-405. doi: 10.18383/j.tom.2016.00214. PMID:28105469

Imaging biomarker roadmap for cancer studies.
O'Connor JP, Aboagye EO, Adams JE, Aerts HJ, Barrington SF, Beer AJ, Boellaard R, Bohndiek SE, Brady M, Brown G, Buckley DL, Chenevert TL, Clarke LP, Collette S, Cook GJ, deSouza NM, Dickson JC, Dive C, Evelhoch JL, Faivre-Finn C, Gallagher FA, Gilbert FJ, Gillies RJ, Goh V, Griffiths JR, Groves AM, Halligan S, Harris AL, Hawkes DJ, Hoekstra OS, Huang EP, Hutton BF, Jackson EF, Jayson GC, Jones A, Koh DM, Lacombe D, Lambin P, Lassau N, Leach MO, Lee TY, Leen EL, Lewis JS, Liu Y, Lythgoe MF, Manoharan P, Maxwell RJ, Miles KA, Morgan B, Morris S, Ng T, Padhani AR, Parker GJ, Partridge M, Pathak AP, Peet AC, Punwani S, Reynolds AR, Robinson SP, Shankar LK, Sharma RA, Soloviev D, Stroobants S, Sullivan DC, Taylor SA, Tofts PS, Tozer GM, van Herk M, Walker-Samuel S, Wason J, Williams KJ, Workman P, Yankeelov TE, Brindle KM, McShane LM, Jackson A, Waterton JC.
Nat Rev Clin Oncol. 2017 Mar;14(3):169-186. doi: 10.1038/nrclinonc.2016.162. Epub 2016 Oct 11. Review. PMID:27725679

Apparent diffusion coefficient is highly reproducible on preclinical imaging systems: Evidence from a seven-center multivendor study.
Doblas S, Almeida GS, Blé FX, Garteiser P, Hoff BA, McIntyre DJ, Wachsmuth L, Chenevert TL, Faber C, Griffiths JR, Jacobs AH, Morris DM, O'Connor JP, Robinson SP, Van Beers BE, Waterton JC.
J Magn Reson Imaging. 2015 Dec;42(6):1759-64. doi: 10.1002/jmri.24955. Epub 2015 May 26. PMID:26012876

Multi-site clinical evaluation of DW-MRI as a treatment response metric for breast cancer patients undergoing neoadjuvant chemotherapy.
Galbán CJ, Ma B, Malyarenko D, Pickles MD, Heist K, Henry NL, Schott AF, Neal CH, Hylton NM, Rehemtulla A, Johnson TD, Meyer CR, Chenevert TL, Turnbull LW, Ross BD.
PLoS One. 2015 Mar 27;10(3):e0122151. doi: 10.1371/journal.pone.0122151. eCollection 2015. PMID:25816249

Correction of Gradient Nonlinearity Bias in Quantitative Diffusion Parameters of Renal Tissue with Intra Voxel Incoherent Motion.
Malyarenko DI, Pang Y, Senegas J, Ivancevic MK, Ross BD, Chenevert TL.
Tomography. 2015 Dec;1(2):145-151. PMID:26811845

Demonstration of nonlinearity bias in the measurement of the apparent diffusion coefficient in multicenter trials.
Malyarenko DI, Newitt D, J Wilmes L, Tudorica A, Helmer KG, Arlinghaus LR, Jacobs MA, Jajamovich G, Taouli B, Yankeelov TE, Huang W, Chenevert TL.
Magn Reson Med. 2016 Mar;75(3):1312-23. doi: 10.1002/mrm.25754. Epub 2015 May 2. PMID:25940607

University of Michigan

URL: http://www.med.umich.edu/cmi/