Automatic Individual Arterial Input Functions Calculated From PCA Outperform Manual and Population-Averaged Approaches for the Pharmacokinetic Modeling of DCE-MR Images

[EN] Background: To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on prin- cipal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters. Methods: The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results. Results: Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61). Conclusion: The automatic PCA-based approach minimizes the variability associated to obtaining individual volume- based AIFs in DCE-MR studies of the prostate.Sanz Requena, R.; Prats-Montalbán, JM.; Marti Bonmati, L.; Alberich Bayarri, A.; García Martí, G.; Pérez, R.; Ferrer Riquelme, AJ. (2015). Automatic Individual Arterial Input Functions Calculated From PCA Outperform Manual and Population-Averaged Approaches for the Pharmacokinetic Modeling of DCE-MR Images. Journal of Magnetic Resonance Imaging. 42:477-487. doi:10.1002/jmri.24805S47748742Leach, M. O., Brindle, K. M., Evelhoch, J. L., Griffiths, J. R., Horsman, M. R., Jackson, A., … Workman, P. (2005). The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. British Journal of Cancer, 92(9), 1599-1610. doi:10.1038/sj.bjc.6602550Tofts, P. S., & Kermode, A. G. (1991). Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magnetic Resonance in Medicine, 17(2), 357-367. doi:10.1002/mrm.1910170208Parker, G. J. M., Roberts, C., Macdonald, A., Buonaccorsi, G. A., Cheung, S., Buckley, D. L., … Jayson, G. C. (2006). Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 56(5), 993-1000. doi:10.1002/mrm.21066Meng, R., Chang, S. D., Jones, E. C., Goldenberg, S. L., & Kozlowski, P. (2010). Comparison between Population Average and Experimentally Measured Arterial Input Function in Predicting Biopsy Results in Prostate Cancer. Academic Radiology, 17(4), 520-525. doi:10.1016/j.acra.2009.11.006Loveless, M. E., Halliday, J., Liess, C., Xu, L., Dortch, R. D., Whisenant, J., … Yankeelov, T. E. (2011). A quantitative comparison of the influence of individual versus population-derived vascular input functions on dynamic contrast enhanced-MRI in small animals. Magnetic Resonance in Medicine, 67(1), 226-236. doi:10.1002/mrm.22988Shukla-Dave, A., Lee, N., Stambuk, H., Wang, Y., Huang, W., Thaler, H. T., … Koutcher, J. A. (2009). Average arterial input function for quantitative dynamic contrast enhanced magnetic resonance imaging of neck nodal metastases. BMC Medical Physics, 9(1). doi:10.1186/1756-6649-9-4Wang, Y., Huang, W., Panicek, D. M., Schwartz, L. H., & Koutcher, J. A. (2008). Feasibility of using limited-population-based arterial input function for pharmacokinetic modeling of osteosarcoma dynamic contrast-enhanced MRI data. Magnetic Resonance in Medicine, 59(5), 1183-1189. doi:10.1002/mrm.21432Rijpkema, M., Kaanders, J. H. A. M., Joosten, F. B. M., van der Kogel, A. J., & Heerschap, A. (2001). Method for quantitative mapping of dynamic MRI contrast agent uptake in human tumors. Journal of Magnetic Resonance Imaging, 14(4), 457-463. doi:10.1002/jmri.1207Singh, A., Rathore, R. K. S., Haris, M., Verma, S. K., Husain, N., & Gupta, R. K. (2009). Improved bolus arrival time and arterial input function estimation for tracer kinetic analysis in DCE-MRI. Journal of Magnetic Resonance Imaging, 29(1), 166-176. doi:10.1002/jmri.21624Shi, L., Wang, D., Liu, W., Fang, K., Wang, Y.-X. J., Huang, W., … Ahuja, A. T. (2013). Automatic detection of arterial input function in dynamic contrast enhanced MRI based on affinity propagation clustering. Journal of Magnetic Resonance Imaging, 39(5), 1327-1337. doi:10.1002/jmri.24259Kim, J.-H., Im, G. H., Yang, J., Choi, D., Lee, W. J., & Lee, J. H. (2011). Quantitative dynamic contrast-enhanced MRI for mouse models using automatic detection of the arterial input function. NMR in Biomedicine, 25(4), 674-684. doi:10.1002/nbm.1784Li, X., Welch, E. B., Arlinghaus, L. R., Chakravarthy, A. B., Xu, L., Farley, J., … Yankeelov, T. E. (2011). A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer. Physics in Medicine and Biology, 56(17), 5753-5769. doi:10.1088/0031-9155/56/17/018Fedorov, A., Fluckiger, J., Ayers, G. D., Li, X., Gupta, S. N., Tempany, C., … Fennessy, F. M. (2014). A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation. Magnetic Resonance Imaging, 32(4), 321-329. doi:10.1016/j.mri.2014.01.004Lin, Y.-C., Chan, T.-H., Chi, C.-Y., Ng, S.-H., Liu, H.-L., Wei, K.-C., … Wang, J.-J. (2012). Blind estimation of the arterial input function in dynamic contrast-enhanced MRI using purity maximization. Magnetic Resonance in Medicine, 68(5), 1439-1449. doi:10.1002/mrm.24144Roberts, C., Little, R., Watson, Y., Zhao, S., Buckley, D. L., & Parker, G. J. M. (2010). The effect of blood inflow andB1-field inhomogeneity on measurement of the arterial input function in axial 3D spoiled gradient echo dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 65(1), 108-119. doi:10.1002/mrm.22593Jackson, J. E. (1991). A Use’s Guide to Principal Components. Wiley Series in Probability and Statistics. doi:10.1002/0471725331Prats-Montalbán, J. M., Sanz-Requena, R., Martí-Bonmatí, L., & Ferrer, A. (2013). Prostate functional magnetic resonance image analysis using multivariate curve resolution methods. Journal of Chemometrics, 28(8), 672-680. doi:10.1002/cem.2585Eyal, E., Bloch, B. N., Rofsky, N. M., Furman-Haran, E., Genega, E. M., Lenkinski, R. E., & Degani, H. (2010). Principal Component Analysis of Dynamic Contrast Enhanced MRI in Human Prostate Cancer. Investigative Radiology, 45(4), 174-181. doi:10.1097/rli.0b013e3181d0a02fTofts, P. S. (1997). Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. Journal of Magnetic Resonance Imaging, 7(1), 91-101. doi:10.1002/jmri.1880070113Donahue, K. M., Burstein, D., Manning, W. J., & Gray, M. L. (1994). Studies of Gd-DTPA relaxivity and proton exchange rates in tissue. Magnetic Resonance in Medicine, 32(1), 66-76. doi:10.1002/mrm.1910320110Taylor, J. S., & Reddick, W. E. (2000). Evolution from empirical dynamic contrast-enhanced magnetic resonance imaging to pharmacokinetic MRI. Advanced Drug Delivery Reviews, 41(1), 91-110. doi:10.1016/s0169-409x(99)00058-7Port, R. E., Knopp, M. V., & Brix, G. (2001). Dynamic contrast-enhanced MRI using Gd-DTPA: Interindividual variability of the arterial input function and consequences for the assessment of kinetics in tumors. Magnetic Resonance in Medicine, 45(6), 1030-1038. doi:10.1002/mrm.1137Dale, B. M., Jesberger, J. A., Lewin, J. S., Hillenbrand, C. M., & Duerk, J. L. (2003). Determining and optimizing the precision of quantitative measurements of perfusion from dynamic contrast enhanced MRI. Journal of Magnetic Resonance Imaging, 18(5), 575-584. doi:10.1002/jmri.10399Garpebring, A., Brynolfsson, P., Yu, J., Wirestam, R., Johansson, A., Asklund, T., & Karlsson, M. (2012). Uncertainty estimation in dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 69(4), 992-1002. doi:10.1002/mrm.24328Onxley, J. D., Yoo, D. S., Muradyan, N., MacFall, J. R., Brizel, D. M., & Craciunescu, O. I. (2014). Comprehensive Population-Averaged Arterial Input Function for Dynamic Contrast–Enhanced vMagnetic Resonance Imaging of Head and Neck Cancer. International Journal of Radiation Oncology*Biology*Physics, 89(3), 658-665. doi:10.1016/j.ijrobp.2014.03.006Chen, Y.-J., Chu, W.-C., Pu, Y.-S., Chueh, S.-C., Shun, C.-T., & Tseng, W.-Y. I. (2012). Washout gradient in dynamic contrast-enhanced MRI is associated with tumor aggressiveness of prostate cancer. Journal of Magnetic Resonance Imaging, 36(4), 912-919. doi:10.1002/jmri.23723Vos, E. K., Litjens, G. J. S., Kobus, T., Hambrock, T., Kaa, C. A. H. de, Barentsz, J. O., … Scheenen, T. W. J. (2013). Assessment of Prostate Cancer Aggressiveness Using Dynamic Contrast-enhanced Magnetic Resonance Imaging at 3 T. European Urology, 64(3), 448-455. doi:10.1016/j.eururo.2013.05.045Yang, C., Karczmar, G. S., Medved, M., Oto, A., Zamora, M., & Stadler, W. M. (2009). Reproducibility assessment of a multiple reference tissue method for quantitative dynamic contrast enhanced-MRI analysis. Magnetic Resonance in Medicine, 61(4), 851-859. doi:10.1002/mrm.21912McGrath, D. M., Bradley, D. P., Tessier, J. L., Lacey, T., Taylor, C. J., & Parker, G. J. M. (2009). Comparison of model-based arterial input functions for dynamic contrast-enhanced MRI in tumor bearing rats. Magnetic Resonance in Medicine, 61(5), 1173-1184. doi:10.1002/mrm.21959Orton, M. R., d’ Arcy, J. A., Walker-Samuel, S., Hawkes, D. J., Atkinson, D., Collins, D. J., & Leach, M. O. (2008). Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI. Physics in Medicine and Biology, 53(5), 1225-1239. doi:10.1088/0031-9155/53/5/005Heisen, M., Fan, X., Buurman, J., van Riel, N. A. W., Karczmar, G. S., & ter Haar Romeny, B. M. (2010). The use of a reference tissue arterial input function with low-temporal-resolution DCE-MRI data. Physics in Medicine and Biology, 55(16), 4871-4883. doi:10.1088/0031-9155/55/16/01

Prostate functional magnetic resonance image analysis using multivariate curve resolution methods

This paper discusses the potential of Multivariate Curve Resolution (MCR) models to extract physiological dynamics behaviors from Dynamic Contrast Enhanced Magnetic Resonance (DCE-MR) Imaging prostate perfusion studies for cancer diagnosis. A relationship with biomarkers ( hidden parameters for assessing the possible existence of a tumor) from pharmacokinetic models is also studied.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2011-28112-C04-02.Prats-Montalbán, JM.; Sanz Requena, R.; Marti Bonmati, L.; Ferrer, A. (2014). Prostate functional magnetic resonance image analysis using multivariate curve resolution methods. Journal of Chemometrics. 28(8):672-680. https://doi.org/10.1002/cem.2585S672680288Collins, D. J., & Padhani, A. R. (2004). Dynamic magnetic resonance imaging of tumor perfusion. IEEE Engineering in Medicine and Biology Magazine, 23(5), 65-83. doi:10.1109/memb.2004.1360410JACKSON, A. S. N., REINSBERG, S. A., SOHAIB, S. A., CHARLES-EDWARDS, E. M., JHAVAR, S., CHRISTMAS, T. J., … DEARNALEY, D. P. (2009). Dynamic contrast-enhanced MRI for prostate cancer localization. The British Journal of Radiology, 82(974), 148-156. doi:10.1259/bjr/89518905Leach, M. O., Brindle, K. M., Evelhoch, J. L., Griffiths, J. R., Horsman, M. R., Jackson, A., … Workman, P. (2005). The assessment of antiangiogenic and antivascular therapies in early-stage clinical trials using magnetic resonance imaging: issues and recommendations. British Journal of Cancer, 92(9), 1599-1610. doi:10.1038/sj.bjc.6602550Tofts, P. S., Brix, G., Buckley, D. L., Evelhoch, J. L., Henderson, E., Knopp, M. V., … Weisskoff, R. M. (1999). Estimating kinetic parameters from dynamic contrast-enhanced t1-weighted MRI of a diffusable tracer: Standardized quantities and symbols. Journal of Magnetic Resonance Imaging, 10(3), 223-232. doi:10.1002/(sici)1522-2586(199909)10:33.0.co;2-sPort, R. E., Knopp, M. V., & Brix, G. (2001). Dynamic contrast-enhanced MRI using Gd-DTPA: Interindividual variability of the arterial input function and consequences for the assessment of kinetics in tumors. Magnetic Resonance in Medicine, 45(6), 1030-1038. doi:10.1002/mrm.1137McGrath, D. M., Bradley, D. P., Tessier, J. L., Lacey, T., Taylor, C. J., & Parker, G. J. M. (2009). Comparison of model-based arterial input functions for dynamic contrast-enhanced MRI in tumor bearing rats. Magnetic Resonance in Medicine, 61(5), 1173-1184. doi:10.1002/mrm.21959Yang, C., Karczmar, G. S., Medved, M., Oto, A., Zamora, M., & Stadler, W. M. (2009). Reproducibility assessment of a multiple reference tissue method for quantitative dynamic contrast enhanced-MRI analysis. Magnetic Resonance in Medicine, 61(4), 851-859. doi:10.1002/mrm.21912Meng, R., Chang, S. D., Jones, E. C., Goldenberg, S. L., & Kozlowski, P. (2010). Comparison between Population Average and Experimentally Measured Arterial Input Function in Predicting Biopsy Results in Prostate Cancer. Academic Radiology, 17(4), 520-525. doi:10.1016/j.acra.2009.11.006Sourbron, S. P., & Buckley, D. L. (2011). Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Physics in Medicine and Biology, 57(2), R1-R33. doi:10.1088/0031-9155/57/2/r1Lüdemann, L., Prochnow, D., Rohlfing, T., Franiel, T., Warmuth, C., Taupitz, M., … Beyersdorff, D. (2009). Simultaneous Quantification of Perfusion and Permeability in the Prostate Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging with an Inversion-Prepared Dual-Contrast Sequence. Annals of Biomedical Engineering, 37(4), 749-762. doi:10.1007/s10439-009-9645-xPrats-Montalbán, J. M., de Juan, A., & Ferrer, A. (2011). Multivariate image analysis: A review with applications. Chemometrics and Intelligent Laboratory Systems, 107(1), 1-23. doi:10.1016/j.chemolab.2011.03.002Jackson, J. E. (1991). A Use’s Guide to Principal Components. Wiley Series in Probability and Statistics. doi:10.1002/0471725331Bruwer, M.-J., MacGregor, J. F., & Noseworthy, M. D. (2008). Dynamic contrast-enhanced MRI diagnostics in oncology via principal component analysis. Journal of Chemometrics, 22(11-12), 708-716. doi:10.1002/cem.1143Gujral, P., Amrhein, M., Bonvin, D., Vallée, J.-P., Montet, X., & Michoux, N. (2009). Classification of magnetic resonance images from rabbit renal perfusion. Chemometrics and Intelligent Laboratory Systems, 98(2), 173-181. doi:10.1016/j.chemolab.2009.06.004Fortuna, J., Elzibak, A. H., Fan, Z., MacGregor, J. F., & Noseworthy, M. D. (2012). Liver functional magnetic resonance imaging analysis using a latent variables approach. Journal of Chemometrics, 26(5), 170-179. doi:10.1002/cem.2430Buckley, D. L. (2002). Uncertainty in the analysis of tracer kinetics using dynamic contrast-enhancedT1-weighted MRI. Magnetic Resonance in Medicine, 47(3), 601-606. doi:10.1002/mrm.10080Henderson, E., Sykes, J., Drost, D., Weinmann, H.-J., Rutt, B. K., & Lee, T.-Y. (2000). Simultaneous MRI measurement of blood flow, blood volume, and capillary permeability in mammary tumors using two different contrast agents. Journal of Magnetic Resonance Imaging, 12(6), 991-1003. doi:10.1002/1522-2586(200012)12:63.0.co;2-1Tauler, R., Smilde, A., & Kowalski, B. (1995). Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution. Journal of Chemometrics, 9(1), 31-58. doi:10.1002/cem.1180090105Tauler, R. (1995). Multivariate curve resolution applied to second order data. Chemometrics and Intelligent Laboratory Systems, 30(1), 133-146. doi:10.1016/0169-7439(95)00047-xPiqueras, S., Duponchel, L., Tauler, R., & de Juan, A. (2011). Resolution and segmentation of hyperspectral biomedical images by Multivariate Curve Resolution-Alternating Least Squares. Analytica Chimica Acta, 705(1-2), 182-192. doi:10.1016/j.aca.2011.05.020De Juan, A., & Tauler, R. (2003). Chemometrics applied to unravel multicomponent processes and mixtures. Analytica Chimica Acta, 500(1-2), 195-210. doi:10.1016/s0003-2670(03)00724-4Jaumot, J., Gargallo, R., de Juan, A., & Tauler, R. (2005). A graphical user-friendly interface for MCR-ALS: a new tool for multivariate curve resolution in MATLAB. Chemometrics and Intelligent Laboratory Systems, 76(1), 101-110. doi:10.1016/j.chemolab.2004.12.007Windig, W., & Guilment, J. (1991). Interactive self-modeling mixture analysis. Analytical Chemistry, 63(14), 1425-1432. doi:10.1021/ac00014a016Gallagher, N. B., Shaver, J. M., Martin, E. B., Morris, J., Wise, B. M., & Windig, W. (2004). Curve resolution for multivariate images with applications to TOF-SIMS and Raman. Chemometrics and Intelligent Laboratory Systems, 73(1), 105-117. doi:10.1016/j.chemolab.2004.04.003Windig, W. (2009). Two-Way Data Analysis: Detection of Purest Variables. Comprehensive Chemometrics, 275-307. doi:10.1016/b978-044452701-1.00048-xChtioui, Y., Bertrand, D., & Barba, D. (1998). Feature selection by a genetic algorithm. Application to seed discrimination by artificial vision. Journal of the Science of Food and Agriculture, 76(1), 77-86. doi:10.1002/(sici)1097-0010(199801)76:13.0.co;2-9Wang, M., Zhou, X., King, R. W., & Wong, S. T. (2007). BMC Bioinformatics, 8(1), 32. doi:10.1186/1471-2105-8-32Sánchez, F. C., Toft, J., van den Bogaert, B., & Massart, D. L. (1996). Orthogonal Projection Approach Applied to Peak Purity Assessment. Analytical Chemistry, 68(1), 79-85. doi:10.1021/ac950496gMultivariate curve resolution homepage http://www.mcrals.info/De Juan, A., Maeder, M., Hancewicz, T., & Tauler, R. (2005). Local rank analysis for exploratory spectroscopic image analysis. Fixed Size Image Window-Evolving Factor Analysis. Chemometrics and Intelligent Laboratory Systems, 77(1-2), 64-74. doi:10.1016/j.chemolab.2004.11.006Keller, H. R., & Massart, D. L. (1991). Peak purity control in liquid chromatography with photodiode-array detection by a fixed size moving window evolving factor analysis. Analytica Chimica Acta, 246(2), 379-390. doi:10.1016/s0003-2670(00)80976-9De Juan, A., Maeder, M., Hancewicz, T., Duponchel, L., & Tauler, R. (s. f.). Chemometric Tools for Image Analysis. Infrared and Raman Spectroscopic Imaging, 65-109. doi:10.1002/9783527628230.ch2De Juan, A., Maeder, M., Hancewicz, T., & Tauler, R. (2008). Use of local rank-based spatial information for resolution of spectroscopic images. Journal of Chemometrics, 22(5), 291-298. doi:10.1002/cem.1099Orton, M. R., d’ Arcy, J. A., Walker-Samuel, S., Hawkes, D. J., Atkinson, D., Collins, D. J., & Leach, M. O. (2008). Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI. Physics in Medicine and Biology, 53(5), 1225-1239. doi:10.1088/0031-9155/53/5/005Parker, G. J. M., Roberts, C., Macdonald, A., Buonaccorsi, G. A., Cheung, S., Buckley, D. L., … Jayson, G. C. (2006). Experimentally-derived functional form for a population-averaged high-temporal-resolution arterial input function for dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 56(5), 993-1000. doi:10.1002/mrm.21066Jaumot, J., & Tauler, R. (2010). MCR-BANDS: A user friendly MATLAB program for the evaluation of rotation ambiguities in Multivariate Curve Resolution. Chemometrics and Intelligent Laboratory Systems, 103(2), 96-107. doi:10.1016/j.chemolab.2010.05.02

Analysis of contrast-enhanced medical images.

Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images

Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Review

Tumor microvasculature possesses a high degree of heterogeneity in its structure and function. These features have been demonstrated to be important for disease diagnosis, response assessment, and treatment planning. The exploratory efforts of quantifying tumor vascular heterogeneity with DCE-MRI have led to promising results in a number of studies. However, the methodological implementation in those studies has been highly variable, leading to multiple challenges in data quality and comparability. This paper reviews several heterogeneity quantification methods, with an emphasis on their applications on DCE-MRI pharmacokinetic parametric maps. Important methodological and technological issues in experimental design, data acquisition, and analysis are also discussed, with the current opportunities and efforts for standardization highlighted

A non-invasive image based system for early diagnosis of prostate cancer.

Prostate cancer is the second most fatal cancer experienced by American males. The average American male has a 16.15% chance of developing prostate cancer, which is 8.38% higher than lung cancer, the second most likely cancer. The current in-vitro techniques that are based on analyzing a patients blood and urine have several limitations concerning their accuracy. In addition, the prostate Specific Antigen (PSA) blood-based test, has a high chance of false positive diagnosis, ranging from 28%-58%. Yet, biopsy remains the gold standard for the assessment of prostate cancer, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The major limitation of the relatively small needle biopsy samples is the higher possibility of producing false positive diagnosis. Moreover, the visual inspection system (e.g., Gleason grading system) is not quantitative technique and different observers may classify a sample differently, leading to discrepancies in the diagnosis. As reported in the literature that the early detection of prostate cancer is a crucial step for decreasing prostate cancer related deaths. Thus, there is an urgent need for developing objective, non-invasive image based technology for early detection of prostate cancer. The objective of this dissertation is to develop a computer vision methodology, later translated into a clinically usable software tool, which can improve sensitivity and specificity of early prostate cancer diagnosis based on the well-known hypothesis that malignant tumors are will connected with the blood vessels than the benign tumors. Therefore, using either Diffusion Weighted Magnetic Resonance imaging (DW-MRI) or Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), we will be able to interrelate the amount of blood in the detected prostate tumors by estimating either the Apparent Diffusion Coefficient (ADC) in the prostate with the malignancy of the prostate tumor or perfusion parameters. We intend to validate this hypothesis by demonstrating that automatic segmentation of the prostate from either DW-MRI or DCE-MRI after handling its local motion, provides discriminatory features for early prostate cancer diagnosis. The proposed CAD system consists of three majors components, the first two of which constitute new research contributions to a challenging computer vision problem. The three main components are: (1) A novel Shape-based segmentation approach to segment the prostate from either low contrast DW-MRI or DCE-MRI data; (2) A novel iso-contours-based non-rigid registration approach to ensure that we have voxel-on-voxel matches of all data which may be more difficult due to gross patient motion, transmitted respiratory effects, and intrinsic and transmitted pulsatile effects; and (3) Probabilistic models for the estimated diffusion and perfusion features for both malignant and benign tumors. Our results showed a 98% classification accuracy using Leave-One-Subject-Out (LOSO) approach based on the estimated ADC for 30 patients (12 patients diagnosed as malignant; 18 diagnosed as benign). These results show the promise of the proposed image-based diagnostic technique as a supplement to current technologies for diagnosing prostate cancer

Improving Accuracy of Information Extraction from Quantitative Magnetic Resonance Imaging

Quantitative MRI offers the possibility to produce objective measurements of tissue physiology at different scales. Such measurements are highly valuable in applications such as drug development, treatment monitoring or early diagnosis of cancer. From microstructural information in diffusion weighted imaging (DWI) or local perfusion and permeability in dynamic contrast (DCE-) MRI to more macroscopic observations of the local intestinal contraction, a number of aspects of quantitative MRI are considered in this thesis. The main objective of the presented work is to provide pre-processing techniques and model modification in order to improve the reliability of image analysis in quantitative MRI. Firstly, the challenge of clinical DWI signal modelling is investigated to overcome the biasing effect due to noise in the data. Several methods with increasing level of complexity are applied to simulations and a series of clinical datasets. Secondly, a novel Robust Data Decomposition Registration technique is introduced to tackle the problem of image registration in DCE-MRI. The technique allows the separation of tissue enhancement from motion effects so that the latter can be corrected independently. It is successfully applied to DCE-MRI datasets of different organs. This application is extended to the correction of respiratory motion in small bowel motility quantification in dynamic MRI data acquired during free breathing. Finally, a new local model for the arterial input function (AIF) is proposed. The estimation of the arterial blood contrast agent concentration in DCE-MRI is augmented using prior knowledge on local tissue structure from DWI. This work explores several types of imaging using MRI. It contributes to clinical quantitative MRI analysis providing practical solutions aimed at improving the accuracy and consistency of the parameters derived from image data

Quantification of tumour heterogenity in MRI

Cancer is the leading cause of death that touches us all, either directly or indirectly. It is estimated that the number of newly diagnosed cases in the Netherlands will increase to 123,000 by the year 2020. General Dutch statistics are similar to those in the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup

Advanced perfusion quantification methods for dynamic PET and MRI data modelling

The functionality of tissues is guaranteed by the capillaries, which supply the microvascular network providing a considerable surface area for exchanges between blood and tissues. Microcirculation is affected by any pathological condition and any change in the blood supply can be used as a biomarker for the diagnosis of lesions and the optimization of the treatment. Nowadays, a number of techniques for the study of perfusion in vivo and in vitro are available. Among the several imaging modalities developed for the study of microcirculation, the analysis of the tissue kinetics of intravenously injected contrast agents or tracers is the most widely used technique. Tissue kinetics can be studied using different modalities: the positive enhancement of the signal in the computed tomography and in the ultrasound dynamic contrast enhancement imaging; T1-weighted MRI or the negative enhancement of T2* weighted MRI signal for the dynamic susceptibility contrast imaging or, finally, the uptake of radiolabelled tracers in dynamic PET imaging. Here we will focus on the perfusion quantification of dynamic PET and MRI data. The kinetics of the contrast agent (or the tracer) can be analysed visually, to define qualitative criteria but, traditionally, quantitative physiological parameters are extracted with the implementation of mathematical models. Serial measurements of the concentration of the tracer (or of the contrast agent) in the tissue of interest, together with the knowledge of an arterial input function, are necessary for the calculation of blood flow or perfusion rates from the wash-in and/or wash-out kinetic rate constants. The results depend on the acquisition conditions (type of imaging device, imaging mode, frequency and total duration of the acquisition), the type of contrast agent or tracer used, the data pre-processing (motion correction, attenuation correction, correction of the signal into concentration) and the data analysis method. As for the MRI, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a non-invasive imaging technique that can be used to measure properties of tissue microvasculature. It is sensitive to differences in blood volume and vascular permeability that can be associated with tumour angiogenesis. DCE-MRI has been investigated for a range of clinical oncologic applications (breast, prostate, cervix, liver, lung, and rectum) including cancer detection, diagnosis, staging, and assessment of treatment response. Tumour microvascular measurements by DCE-MRI have been found to correlate with prognostic factors (such as tumour grade, microvessel density, and vascular endothelial growth factor expression) and with recurrence and survival outcomes. Furthermore, DCE-MRI changes measured during treatment have been shown to correlate with outcome, suggesting a role as a predictive marker. The accuracy of DCE-MRI relies on the ability to model the pharmacokinetics of an injected contrast agent using the signal intensity changes on sequential magnetic resonance images. DCE-MRI data are usually quantified with the application of the pharmacokinetic two-compartment Tofts model (also known as the standard model), which represents the system with the plasma and tissue (extravascular extracellular space) compartments and with the contrast reagent exchange rates between them. This model assumes a negligible contribution from the vascular space and considers the system in, what-is-known as, the fast exchange limit, assuming infinitely fast transcytolemmal water exchange kinetics. In general, the number, as well as any assumption about the compartments, depends on the properties of the contrast agent used (mainly gadolinium) together with the tissue physiology or pathology studied. For this reason, the choice of the model is crucial in the analysis of DCE-MRI data. The value of PET in clinical oncology has been demonstrated with studies in a variety of cancers including colorectal carcinomas, lung tumours, head and neck tumours, primary and metastatic brain tumours, breast carcinoma, lymphoma, melanoma, bone cancers, and other soft-tissue cancers. PET studies of tumours can be performed for several reasons including the quantification of tumour perfusion, the evaluation of tumour metabolism, the tracing of radiolabelled cytostatic agents. In particular, the kinetic analysis of PET imaging has showed, in the past few years, an increasing value in tumour diagnosis, as well as in tumour therapy, through providing additional indicative parameters. Many authors have showed the benefit of kinetic analysis of anticancer drugs after labelling with radionuclide in measuring the specific therapeutic effect bringing to light the feasibility of applying the kinetic analysis to the dynamic acquisition. Quantification methods can involve visual analysis together with compartmental modelling and can be applied to a wide range of different tracers. The increased glycolysis in the most malignancies makes 18F-FDG-PET the most common diagnostic method used in tumour imaging. But, PET metabolic alteration in the target tissue can depend by many other factors. For example, most types of cancer are characterized by increased choline transport and by the overexpression of choline kinase in highly proliferating cells in response to enhanced demand of phosphatidylcholine (prostate, breast, lung, ovarian and colon cancers). This effect can be diagnosed with choline-based tracers as the 18Ffluoromethylcholine (18F-FCH), or the even more stable 18F-D4-Choline. Cellular proliferation is also imaged with 18F-fluorothymidine (FLT), which is trapped within the cytosol after being mono phosphorylated by thymidine kinase-1 (TK1), a principal enzyme in the salvage pathway of DNA synthesis. 18F-FLT has been found to be useful for noninvasive assessment of the proliferation rate of several types of cancer and showed high reproducibility and accuracy in breast and lung cancer tumours. The aim of this thesis is the perfusion quantification of dynamic PET and MRI data of patients with lung, brain, liver, prostate and breast lesions with the application of advanced models. This study covers a wide range of imaging methods and applications, presenting a novel combination of MRI-based perfusion measures with PET kinetic modelling parameters in oncology. It assesses the applicability and stability of perfusion quantification methods, which are not currently used in the routine clinical practice. The main achievements of this work include: 1) the assessment of the stability of perfusion quantification of D4-Choline and 18F-FLT dynamic PET data in lung and liver lesions, respectively (first applications in the literature); 2) the development of a model selection in the analysis of DCE-MRI data of primary brain tumours (first application of the extended shutter speed model); 3) the multiparametric analysis of PET and MRI derived perfusion measurements of primary brain tumour and breast cancer together with the integration of immuohistochemical markers in the prediction of breast cancer subtype (analysis of data acquired on the hybrid PET/MRI scanner). The thesis is structured as follows: - Chapter 1 is an introductive chapter on cancer biology. Basic concepts, including the causes of cancer, cancer hallmarks, available cancer treatments, are described in this first chapter. Furthermore, there are basic concepts of brain, breast, prostate and lung cancers (which are the lesions that have been analysed in this work). - Chapter 2 is about Positron Emission Tomography. After a brief introduction on the basics of PET imaging, together with data acquisition and reconstruction methods, the chapter focuses on PET in the clinical settings. In particular, it shows the quantification techniques of static and dynamic PET data and my results of the application of graphical methods, spectral analysis and compartmental models on dynamic 18F-FDG, 18F-FLT and 18F-D4- Choline PET data of patients with breast, lung cancer and hepatocellular carcinoma. - Chapter 3 is about Magnetic Resonance Imaging. After a brief introduction on the basics of MRI, the chapter focuses on the quantification of perfusion weighted MRI data. In particular, it shows the pharmacokinetic models for the quantification of dynamic contrast enhanced MRI data and my results of the application of the Tofts, the extended Tofts, the shutter speed and the extended shutter speed models on a dataset of patients with brain glioma. - Chapter 4 introduces the multiparametric imaging techniques, in particular the combined PET/CT and the hybrid PET/MRI systems. The last part of the chapter shows the applications of perfusion quantification techniques on a multiparametric study of breast tumour patients, who simultaneously underwent DCE-MRI and 18F-FDG PET on a hybrid PET/MRI scanner. Then the results of a predictive study on the same dataset of breast tumour patients integrated with immunohistochemical markers. Furthermore, the results of a multiparametric study on DCE-MRI and 18F-FCM brain data acquired both on a PET/CT scanner and on an MR scanner, separately. Finally, it will show the application of kinetic analysis in a radiomic study of patients with prostate cancer

Reconstruction Methods for Free-Breathing Dynamic Contrast-Enhanced MRI

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a valuable diagnostic tool due to the combination of anatomical and physiological information it provides. However, the sequential sampling of MRI presents an inherent tradeoff between spatial and temporal resolution. Compressed Sensing (CS) methods have been applied to undersampled MRI to reconstruct full-resolution images at sub-Nyquist sampling rates. In exchange for shorter data acquisition times, CS-MRI requires more computationally intensive iterative reconstruction methods. We present several model-based image reconstruction (MBIR) methods to improve the spatial and temporal resolution of MR images and/or the computational time for multi-coil MRI reconstruction. We propose efficient variable splitting (VS) methods for support-constrained MRI reconstruction, image reconstruction and denoising with non-circulant boundary conditions, and improved temporal regularization for breast DCE-MRI. These proposed VS algorithms decouple the system model and sparsity terms of the convex optimization problem. By leveraging matrix structures in the system model and sparsifying operator, we perform alternating minimization over a list of auxiliary variables, each of which can be performed efficiently. We demonstrate the computational benefits of our proposed VS algorithms compared to similar proposed methods. We also demonstrate convergence guarantees for two proposed methods, ADMM-tridiag and ADMM-FP-tridiag. With simulation experiments, we demonstrate lower error in spatial and temporal dimensions for these VS methods compared to other object models. We also propose a method for indirect motion compensation in 5D liver DCE-MRI. 5D MRI separates temporal changes due to contrast from anatomical changes due to respiratory motion into two distinct dimensions. This work applies a pre-computed motion model to perform motion-compensated regularization across the respiratory dimension and improve the conditioning of this highly sparse 5D reconstruction problem. We demonstrate a proof of concept using a digital phantom with contrast and respiratory changes, and we show preliminary results for motion model-informed regularization on in vivo patient data.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138498/1/mtle_1.pd