Yue Cao Laboratory

About Us:

Functional Imaging Group

The Functional Imaging Group in the Department of Radiation Oncology at the University of Michigan was established in 2003.

Metabolic and physiological imaging has been demonstrated as a means to customize radiation therapy (RT) based on an individual patient's prognosis or early response to treatment. However, clinical utilization is challenging due to issues such as quantification and reproducibility of physiological images, heterogeneity of the image parameters in the tumor, and lack of quantitative and automated tools to derive meaningful metrics to support clinical decision making. In addition, radiation sensitivity of normal tissue function that is at risk for damage varies from patient to patient. Assessing individual patient sensitivity to radiation using physiological imaging allows us to select patients for intensified treatment to improve outcomes while preventing organ injury. Overall goals of our group are to develop and validate quantitative functional and metabolic imaging models and methods for physiological adaptation of radiation therapy based upon early assessment and prediction of tumor and normal tissue function response to treatment. Also, we develop and optimize MRI techniques for using MRI as a sole imaging modality for RT planning.

Our research projects are funded by NIH grants (4P01CA59827 (Ten Haken), 1U01CA183848(Cao/Eisbruch), RO1 NS064973(Cao), RO1 CA132834 (Cao), and RO1 EB016079 (Balter/Cao), industrial grant (Siemens), and our institute and department funds.


Develop algorithms and models to extract the subvolume of the tumor for outcome prediction

imaging studies considered a tumor as a uniform entity by using a mean value of a physiological imaging parameter to depict the whole tumor. This approach ignores heterogeneity of the tumor pathophysiology and biology. It is plausible that heterogeneity within a tumor is responsible for observed heterogeneous tumor responses to treatment. This motivates investigations of tumor voxel-level changes longitudinally, such as from pre to after starting therapy. However, reliably determining voxel-to-voxel changes from a pair of image volumes acquired over a period depends on the voxel-level accuracy of image registration, which is a very challenging task when a tumor volume grows or shrinks over the period of follow-up. Errors in deformable image registration have a great impact on the analysis of voxel-to-voxel changes due to therapy or tumor growth. We have been developing an alternative approach that extracts an aggressive subvolume of the tumor from its heterogeneously-distributed physiological imaging parameters and assess its change after starting therapy for prediction of treatment response and outcomes (Figure 1). Our model extracts the “feature” subvolumes for therapy assessment by analyzing the imaging parameters in the feature space. Finally, to spatially guide adaptive RT, the prognostic or predictive subvolume of the tumor is required to be highly associated with the site for tumor recurrence/failure. We have been developing and validating our models in three cancer types, namely, head-and-neck cancer, brain metastasis, and intrahepatic cancer.

Illustration of fuzzy subvolume model
Figure 1. Illustration of our fuzzy subvolume model


  • P. Wang, A. Popovtzer, A. Eisbruch and Y. Cao. An Approach to Identify, from DCE MRI, Significant Subvolumes of Tumors Related to Outcomes in Advanced Head-and-Neck Cancer. Med Phys 39(8):5277-85, 2012.
  • R. Farjam, C.I. Tsien, F.Y. Feng, D. Gomez-Hassan, J.A. Hayman, T.S. Lawrence, Y. Cao. The physiological imaging-defined response-driven subvolumes of a tumor. Int J Rad Onbc Biol Phys, 85(5):1383-1390, 2013. PMID: 23257692. PMCID: PMC3638951.
  • R. Farjam, C.I. Tsien, T.S. Lawrence, Y. Cao. DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy. Med Phys, 41(1):011708, 2014. PMCID: PMC3880380.
  • H. Wang, R. Farjam, M. Feng, T. S. Lawrence, and Y. Cao. Arterial Perfusion Imaging-Defined Subvolume of Intrahepatic Cancer. Int J Rad Onbc Biol Phys.(in press) 2014.


  • (Utility) Yue Cao et al, U.S. Provisional Application No. 61/656,323, Title: Subvolume Identification for Prediction of Treatment Outcome (2013)

Develop methods to enable the use of MRI as a sole imaging modality for RT planning

Superior multiple contrasts for soft tissue and lesion characterization have made MRI to be considered as a primary imaging modality for radiation therapy (RT) treatment planning and guidance. Currently, MRI is recommended for target delineation in many forms of precise radiation treatment. In addition, MRI can provide physiological and metabolic information for evaluating and characterizing tumors and normal tissues, and their responses to radiation therapy, and for defining the radiation boost target.. However, MRI technologies have to address several challenges in order to be used as a primary imaging modality for RT planning and guidance. One is geometric accuracy required for precision RT planning. Another is that MRI does not provide electron density information that is required for RT planning. This NIH funded project, co-led by Drs Balter and Cao, aims to develop methods to create synthetic CT from MR images (Figure 2), to reconstruct undistorted MR images (Figures 3 and 4), and/or to monitor the quality of MRI geometry accuracy in individual patients.

Synthetic CT and synthetic DRR from MR images compared to CT and DRR
Figure 2. Synthetic CT and synthetic DRR (left) from MR images compared to CT and DRR (right).
Illustration of the geometric correction algorithm
Figure 3. Illustration of the geometric correction algorithm.
Illustration of geometric distortion in the liver MRI
Figure 4. Illustration of geometric distortion in the liver MRI.

Select Publications

    • H. Wang, J.M. Balter, and Y. Cao, Patient-Induced Susceptibility Effect on Geometric Distortion of Clinical Brain MRI. Phys Med Bio 58:465-477, 2013.
    • S. Hua, Y. Cao, C. Huang, M. Feng, J. M. Balter. Investigation of a method for generating synthetic CT models from MRI scans for radiation therapy. Phys. Med. Biol. 58:8419-8435(2013).
    • Antonis Matakos, J. Balter, and Y. Cao. Estimation of geometrically distorted B0 inhomogeneity maps. PMB (in press) 2014.


  • (Utility) James M Balter, Yue Cao, U.S. Provisional App. No. 61/522,366. Title: PATIENT MODELING FROM MULTISPECTRAL INPUT IMAGE VOLUMES (2012)

Develop functional Imaging Biomarkers for Neurotoxicity after Brain Irradiation

RT is a major treatment modality for malignant and benign brain tumors. The major limiting factor is radiation-induced neurotoxicity. This neurotoxicity manifests as late neurological sequelae and neurocognitive dysfunction with or without gross tissue necrosis. Late neurocognitive dysfunction presents as diminishing mental capacity for working memory, learning ability, executive function, and attention. The potential effect of RT on neurocognitive outcomes is an important factor in the determination of the risks versus benefits of treatment, which should be an integral part of clinical decision-making. Our primary goal of this NIH funded project is to develop quantitative functional imaging biomarkers for early assessment of individual sensitivity to radiation and prediction of late neurotoxicity. Quantitative functional imaging biomarkers have the potential to identify critical functional anatomic structures that are relevant to delayed and late cognitive function declines (Figure 5).Such quantitative imaging biomarkers might provide an opportunity to individualize the dose of RT and support neuroprotective therapy. Our on-going imaging studies of patients undergoing partial brain RT have showed that early changes in vascular and WM properties assessed by DCE and DT MRI correlated with delayed changes in memory and learning functions. Also, we will test and validate the clinical value of these quantitative imaging metrics for assessing cerebral vascular and tissue injury and predicting neurocognitve function declines in patients with brain metastases undergoing WBRT.

Illustration of radial diffusivity changes in the brain of patients who received whole brain radiation therapy for brain metastases.
Figure 5. Illustration of radial diffusivity changes in the brain of patients who received whole brain radiation therapy for brain metastases. White arrows point the great changes observed in fonix and cingulum that are interconnected with the hippocampus.

Select Publications

  • V. Nagesh, C. I. Tsien, T. L. Chenevert, B. D. Ross, T. S. Lawrence, L. Junck, and Y. Cao. Quantitative characterization of radiation dose dependent changes in normal appearing white matter of cerebral tumor patients using diffusion tensor imaging. Int J Rad Onc Biol Phys 70(4):1002-10, 2008. PMCID: PMC2799942
  • P.C. Sundgren, V. Nagesh, A. Elias, C. Tsien, L. Junck, D.M. Gomez-Hassan, T.S. Lawrence, T.L. Chenevert, L. Rogers, P. McKeever, Y. Cao. Metabolic alterations: a biomarker for radiation induced normal brain injury. A MR Spectroscopy study. J Magn Reson Imag, 29(2):291-297, 2009. PMCID: PMC2679518
  • Y. Cao, C.I. Tsien, P. Sundgren, V. Nagesh, D. Normolle, H. Buchtel, L. Junck, and T.S. Lawrence. DCE MRI as a biomarker for radiation-induced neurocognitive dysfunctions. Clin Cancer Res 15(5): 1747-1754, 2009. PMCID: PMC2699596
  • C.H. Chapman, V. Nagesh, P.C. Sundgren, H. Buchtel, T.L. Chenevert, L. Junck, T.S. Lawrence, C.I. Tsien, Y. Cao. Diffusion tensor imaging of normal appearing white matter as a biomarker for radiation-induced late delayed cognitive decline. Int J Rad Onc Biol Phys 82(5):2033-40, 2012. PMCID: PMC3157581.
  • M. Nazem-Zadeh, C. H. Chapman, T. L. Lawrence, C. I. Tsien, and Y. Cao. Radiation Therapy Effects on White Matter Fiber bundles in the Limbic Circuit. Med Phys, 39 (9): 5603-5613, 2012. PMCID: PMC3436921.
  • C. H. Chapman, M. Nazem-Zadeh, O. Lee, M. Schipper, C. I. Tsien, T. S. Lawrence, Y. Cao. Regional variation in brain white matter diffusion index changes following chemoradiotherapy: A prospective study using tract-based spatial statistics. PLOS ONE 8(3): e57768. doi:10.1371/journal.pone.0057768 (2013). PMCID: PMC3587621.
  • M. Nazem-Zadeh, C. H. Chapman, T. L. Lawrence, C. I. Tsien, and Y. Cao. Uncertainty in Assessment of Radiation-Induced Diffusion Index Changes in Individual Patients. Physics in Medicine and Biology, 58:4277-4296, 2013.
  • M. Nazem-Zadeh, C. H. Chapman, T. Chenevert, T. S. Lawrence, R. K. Ten Haken, C. I. Tsien, and Y. Cao. Response-driven Imaging Biomarkers for Predicting Radiation Necrosis of the Brain. Physics in Medicine and Biology(in press) 2014.

Develop Hepatic Perfusion and functional Imaging Models for Prediction of Liver Function after Irradiation

Our clinical trials have showed that high dose conformal radiation therapy to intrahepatic cancers seems to lead to better tumor local control. However, attempts to increase radiation doses are limited by the development of radiation-induced liver disease (RILD). In the past, efforts to develop models to estimate the likelihood of developing RILD have been based primarily on the planned radiation dose distribution in the normal liver. While these models have permitted the safe delivery of far higher doses of radiation than have previously been possible, they also suggest that there is a broad range of individual patient sensitivity that is not reflected by predictions made solely based on the physical dose distribution or general clinical features. If individual patient sensitivity could be better estimated before or during a course of treatment, it would permit higher doses of radiation to be delivered safely to the tumors of patients whose liver is relatively radiation resistant, thus prolonging survival without increasing complications. In this NIH funded project,we aim to develop, test and validate hepatic perfusion and functional imaging models to predict liver function outcomes based upon individual patient response to radiation doses assessed at the mid-course of RT. Also, Drs. Matuszak and Feng are exploring to use these images to redistribute radiation doses to maximize individual benefits of tumor control and minimize liver injury risks (Figure 6).

Illustration of portal venous perfusion and live function probability maps pre and post radiation therapy
Figure 6. Illustration of portal venous perfusion and live function probability maps pre and post radiation therapy. 

Select Publications

  • Y. Cao, J. F. Platt, I.R Francis, J. M. Balter, C. Pan, D. Normolle, E. Ben-Josef, R. K. Ten Haken, T. S. Lawrence. The prediction of radiation-induced liver dysfunction using a local dose and regional venous perfusion model. Med Phys 34(2):604-612, 2007.
  • Y. Cao, C. Pan, J. M. Balter, J. F. Platt, I. R. Francis, J. A. Knol, D. Normolle, E. Ben-Josef, R. K. Ten Haken, and T. S. Lawrence. Liver function after irradiation based upon CT portal vein perfusion imaging. Int J Rad Onc Biol Phys 70(1):154-160, 2008. PMCID: PMC2714771.
  • Robert Jeraj, Yue Cao, Randall K. Ten Haken, Carol Hahn, Lawrence Marks. Imaging for assessment of normal tissue effects. Int J Rad Onc Biol Phys, 76(3), S140-S144, 2010.
  • Y. Cao, H. Wang, T.D. Johnson, C. Pan, H. Hussain, J.M. Balter, Daniel. Normolle, E. Ben-Josef, R. K. Ten Haken, T.S. Lawrence, and M. Feng. Prediction of Liver function using MR-based portal venous perfusion imaging. Int J Rad Onc Biol Phys 85(1):258-63, 2013. PMCID: PMC3587621.
  • H. Wang, M. Feng, K. A. Frey, R. K. Ten Haken, T. S. Lawrence, and Y. Cao. Predictive Models for Regional Hepatic Function Based upon 99mTc-IDA SPECT and Local Radiation Dose for Physiological Adaptive RT. Int J Rad Onbc Biol Phys 86(5):1000-1006, 2013. PMCID: PMC3710542
  • M. H. Stenmark*, Y. Cao*, H. Wang, A. Jackson, E. Ben-Josef, R. K. Ten Haken, T. S. Lawrence, M. Feng. Indocyanine Green for Individualized Assessment of Functional Liver Reserve in Patients Undergoing Liver Radiotherapy. Radiotherapy and Oncology (in press) 2014. *equal contribution.

Develop MRI-based models for physiological adaptation RT in HN cancers

This project aims to address lack of quantitative and automated imaging tools to support therapy modification using metabolic and physiological imaging in HN cancers. We have developed and investigated fuzzy logic pattern recognition techniques for identifying poorly perfused subvolumes of head-and-neck (HN) cancer from heterogeneous distributions of tumor blood volume (BV) across patients and over multiple time points. Based on our findings that large poorly-perfused subvolumes of HN tumors before treatment that persist during the early course of chemo-RT have the potential to predict local failure better than the change in the mean BV in the tumor, we will further develop the subvolume definition method, extend it to diffusion-weighted (DW) MR imaging, and evaluate and validate it in a randomized phase II clinical trial of poor-prognosis HN cancers. Our aims are: (1) Develop quantitative and automated methods to extract significant subvolumes of HN tumors from dynamic contrast enhanced (DCE) and DW MRI for prediction of local and regional failure; (2) Prospectively assess variability and reproducibility of the subvolume intensity and definition extracted by our methods using test-retest data; and (3) Prospectively evaluate and validate that the method yields subvolumes predictive of local-regional failure in a randomized phase II trial of radiation dose boosting for poor-prognosis HN cancer. This project is funded by NCI QIN.

Develop image quantification and process techniques
The goals are to develop physiological image quantification and processing techniques to improve reproducibility, robustness, sensitivity, and automation of quantitative metrics for therapy assessment.


  • Y. Cao, D. Li, Z. Shen, and D. Normolle. Sensitivity of Quantitative Metrics Derived from DCE MRI and Pharmacokinetic Model to Image Quality and Acquisition Parameters. Academic Radiology, 17(4): 468-478, 2010. PMCID: PMC3932530.
  • H. Wang and Y. Cao. Correction of Arterial Input Function in Dynamic Contrast Enhanced MRI of the Liver. Journal of Magnetic Resonance Imaging, 36(2): 411-21, 2012. PMCID: PMC3371299.
  • H. Wang and Y. Cao. GPU-Accelerated Voxelwise Hepatic Perfusion Quantification. PMB 57(17):5601-16, 2012. PMCID: PMC3449322.
  • H. Wang, and Y. Cao.Spatial Regularization in T1 Estimation From Fast Multiple Flip Angles MRI. Med Phys 39(7):4319-48, 2012. PMCID: PMC3390049
  • Reza Farjam, Hemant A. Parmar, Douglas C. Noll, Tsien Christina, and Yue Cao. A New Approach for Computer-Aided Detection and Segmentation of Brain Metastases in Post-Gd T1-weighted MRI. Magnetic Resonance Imaging 30(6):824-36, 2012. PMID: 22521993. PMCID: PMC3932529.
  • H. Wang, and Y. Cao. Spatially-Resolved Assessment of Hepatic Function Using 99mTc-IDA SPECT. Med Phys. 40(9): 092501, 2013. PMCID: PMC3751955.
  • M. Nazem-Zadeh, C. H. Chapman, T. L. Lawrence, C. I. Tsien, and Y. Cao. Uncertainty in Assessment of Radiation-Induced Diffusion Index Changes in Individual Patients. Physics in Medicine and Biology, 58:4277-4296, 2013.


Group Members

Cao Lab Image 1
  • Yue Cao, PhD Professor
  • James Balter, PhDProfessor
  • Hesheng Wang, PhD, Research Investigator
  • Antonis Matakos, PhDPost-doctoral research fellow
  • Priyanka Pramanik, MS Image Analyst
  • Lianli Liu, BS Grad Student Research Asst.
  • Ross Avila, BS, Medical School Student

New Members

Cao Lab Image 2
  • Deakeung You, PhD, Post-doctoral research fellow in Sept. 2014
  • Madhava P Aryal, PhD, Post-doctoral research fellow in Sept. 2014
  • Tong Zhu, PhD, Post-doctoral research fellow (Med Phys Resident) in Sept. 2014
  • Eric Paradis, PhD, Post-doctoral research fellow in Oct. 2014
  • Adam Johansson, PhD, Candidate, Post-doctoral research fellow in Jan. 2015


  • Vijaya Nagesh, PhD, Junior faculty
  • P Wang, PhD, Post-doctoral/physics resident fellow
  • Chris Chapman, MD, Research assistant
  • Jonathan Alspaugh, MS, Undergraduate and post-graduate research assistant
  • Diana Li, MS, Undergraduate and post-graduate research assistant
  • John Hayes, Undergraduate research assistant
  • Kyle McMillan, Undergraduate research assistant
  • Orit Gutfeld, MD, Visiting clinical fellow
  • Ellen Kerkhof, Visiting doctoral student from the University of Utrecht, The Netherlands
  • Reza Farjam, PhD, Student of BME and post-doctoral research fellow
  • Colleen Fox, PhD, Post-doctoral/physics resident fellow
  • Mohammad Nazemzaseh, PhD, Post-doctoral research fellow
  • Ke Huang, PhD, Post-doctoral/physics resident fellow
  • Krithika Shanmugasundaram, Medical school student research assistant
  • Shu-Hui Hsu, PhD, Post-doctoral research fellow
  • Chris Chapman, MD, medical school student

Join Us:

Laboratory Lead

Yue Cao, PhD

Yue Cao, PhD

Professor, Radiation Oncology
Professor, Radiology