Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning

Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution methods are used to obtain clinically interpretable perfusion parameters that allow identifying brain tissue abnormalities. Deconvolution methods require the selection of two reference vascular functions as inputs to the model: the arterial input function (AIF) and the venous output function, with the AIF as the most critical model input. When manually performed, the vascular function selection is time demanding, suffers from poor reproducibility and is subject to the professionals' experience. This leads to potentially unreliable quantification of the penumbra and core lesions and, hence, might harm the treatment decision process. In this work we automatize the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable deep learning approach for estimating the vascular functions. Unlike previous methods using clustering or segmentation techniques to select vascular voxels, AIFNet is directly optimized at the vascular function estimation, which allows to better recognise the time-curve profiles. Validation on the public ISLES18 stroke database shows that AIFNet reaches inter-rater performance for the vascular function estimation and, subsequently, for the parameter maps and core lesion quantification obtained through deconvolution. We conclude that AIFNet has potential for clinical transfer and could be incorporated in perfusion deconvolution software.Comment: Preprint submitted to Elsevie

Improved reliability of perfusion estimation in dynamic susceptibility contrast MRI by using the arterial input function from dynamic contrast enhanced MRI

The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast-agent concentrations is challenging, due to uncertainty in relation to the measured (Formula presented.) -weighted signal, signal depletion at high concentration, and partial-volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE-driven AIF with perfusion coefficients determined using a DSC-based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi-automatic AIF-selection algorithm was applied to the DSC data. The amplitude and full width at half-maximum of the AIFs were compared statistically using the Wilcoxon rank-sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi-automatic DSC–AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC–AIF. Additionally, the DCE-based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE–AIFs that were compatible with previously reported values. In conclusion, DCE-based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE–AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.</p

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

Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis

Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumor-related components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment

Quantitative Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Breast Images: Optimization of the Time-to-Peak as a Diagnostic Indicator

Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in the diagnosis of breast cancer and as an aid in the management of this disease. Although DCE-MRI has a high sensitivity for the detection of malignant breast lesions, distinguishing malignant from benign lesions is more challenging for this method and may depend to some extent on how the images are analysed. Although clinical assessment of these images typically involves qualitative assessment by an expert, there is growing interest in the development of quantitative and automated methods to assist the expert assessment. This thesis involves the quantitative analysis of a particular empirical feature of the time evolution of the DCE-MRI signal known as the time-to-peak ( 7 ^ ) . In particular, this thesis investigates die feasibility of applying measures sensitive to 7 ^ heterogeneity as indicators for malignancy in breast DCE-MRI. Breast lesions in this study were automatically segmented by K-means clustering. Voxel- by-voxel 7\u27peak values were extracted using an empirical model. The / 1th percentile values (p = 10, 20...) of the 7’peak distribution within each lesion, as well as the fractional and absolute hot spot volumes were determined, where hot spot volume refers to the volume of tissue with 7 ^ less than a threshold value. Using the area under the receiver operating characteristic curve (AUC), these measures were tested as indicators for differentiating fibroadenomas from invasive lesions and from ductal carcinoma in situ, as well as for differentiating non-fibroadenoma benign lesions from these malignant lesions. For differentiating fibroadenomas from malignant lesions, low percentile values (p = 10) provided high diagnostic performance. At the optimal threshold (3 min), the hot spot volume provided high diagnostic performance. However, non-fibroadenoma benign lesions were quite difficult to distinguish from malignant lesions. This thesis demonstrates that quantitative analysis of the 7’peak distribution can be optimized for diagnostic performance providing indicators sensitive to intra-lesion r peak heterogeneity

Soft Null Hypotheses: A Case Study of Image Enhancement Detection in Brain Lesions

This work is motivated by a study of a population of multiple sclerosis (MS) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify active brain lesions. At each visit, a contrast agent is administered intravenously to a subject and a series of images is acquired to reveal the location and activity of MS lesions within the brain. Our goal is to identify and quantify lesion enhancement location at the subject level and lesion enhancement patterns at the population level. With this example, we aim to address the difficult problem of transforming a qualitative scientific null hypothesis, such as "this voxel does not enhance", to a well-defined and numerically testable null hypothesis based on existing data. We call the procedure "soft null hypothesis" testing as opposed to the standard "hard null hypothesis" testing. This problem is fundamentally different from: 1) testing when a quantitative null hypothesis is given; 2) clustering using a mixture distribution; or 3) identifying a reasonable threshold with a parametric null assumption. We analyze a total of 20 subjects scanned at 63 visits (~30Gb), the largest population of such clinical brain images