Research Topics
| Christos DavatzikosSummaryAffiliation: University of Pennsylvania Country: USA Publications
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Detail Information
Publications
Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalitiesChristos Davatzikos
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania Medical Center, 3600 Market Street, Philadelphia, PA 19104, USA
Arch Gen Psychiatry 62:1218-27. 2005..Frontotemporal abnormalities have been documented by using predetermined region-of-interest approaches, but deformation-based morphometry permits examination of the entire brain...- Classifying spatial patterns of brain activity with machine learning methods: application to lie detectionC Davatzikos
Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104, USA
Neuroimage 28:663-8. 2005.... 
Detection of prodromal Alzheimer's disease via pattern classification of magnetic resonance imagingChristos Davatzikos
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Neurobiol Aging 29:514-23. 2008..Detecting complex patterns of brain abnormality in very early stages of cognitive impairment has pivotal importance for the detection and management of AD...- Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classificationChristos Davatzikos
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Neurobiol Aging 32:2322.e19-27. 2011..In summary, both SPARE-AD and CSF biomarkers showed high baseline sensitivity, however, many MCI-NC had abnormal baseline SPARE-AD and CSF biomarkers. Longer follow-up will elucidate the specificity of baseline measurements... - Why voxel-based morphometric analysis should be used with great caution when characterizing group differencesChristos Davatzikos
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Neuroimage 23:17-20. 2004.... - Longitudinal progression of Alzheimer's-like patterns of atrophy in normal older adults: the SPARE-AD indexChristos Davatzikos
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Brain 132:2026-35. 2009..Future prospective studies will elucidate the temporal dynamics of spatial atrophy patterns and the emergence of clinical symptoms... - Targeted prostate biopsy using statistical image analysisYiqiang Zhan
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 26:779-88. 2007.... - Alzheimer's disease pattern of brain atrophy predicts cognitive decline in Parkinson's diseaseDaniel Weintraub
Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 3339, USA
Brain 135:170-80. 2012..In addition, an Alzheimer's disease pattern of brain atrophy may be a preclinical biomarker of cognitive decline in Parkinson's disease... - Sampling the spatial patterns of cancer: optimized biopsy procedures for estimating prostate cancer volume and Gleason ScoreYangming Ou
Section of Biomedical Image Analysis SBIA, University of Pennsylvania, Philadelphia, PA 19104, USA
Med Image Anal 13:609-20. 2009.... - Non-diffeomorphic registration of brain tumor images by simulating tissue loss and tumor growthEvangelia I Zacharaki
Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
Neuroimage 46:762-74. 2009.... - Statistical representation and simulation of high-dimensional deformations: application to synthesizing brain deformationsZhong Xue
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 8:500-8. 2005.... - Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warpingZhong Xue
Section of Biomedical Image Analysis SBIA, Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104, USA
Med Image Anal 10:740-51. 2006..This SMD-constrained deformable registration framework can potentially incorporate various registration algorithms to improve robustness and stability via statistical shape constraints... - COMPARE: classification of morphological patterns using adaptive regional elementsYong Fan
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 26:93-105. 2007..8% for female subjects and 90.8% for male subjects), but also good stability with respect to the number of features selected and the size of SVM kernel used... - Spatio-temporal analysis of brain MRI images using hidden Markov modelsYing Wang
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, USA
Med Image Comput Comput Assist Interv 13:160-8. 2010..Experimental results show this method could facilitate the early detection of pathological brain change... - Registering histologic and MR images of prostate for image-based cancer detectionYiqiang Zhan
Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
Acad Radiol 14:1367-81. 2007..The objective of this article is to develop a registration technique for aligning histological and MR images of the same prostate... - Diagnosis of brain abnormality using both structural and functional MR imagesYong Fan
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104, USA
Conf Proc IEEE Eng Med Biol Soc . 2006..8% correct classification rate using a leave-one-out cross-validation. Comparison results show the effectiveness of our method and also the importance of simultaneously using both structural and functional images for brain classification... - Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxelsZhong Xue
Section of Biomedical Image Analysis, Department of Radiology School of Medicine, University of Pennsylvania, 3600 Market ST Suite 380, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 23:1276-91. 2004..Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures... - Simulation of tissue atrophy using a topology preserving transformation modelBilge Karacali
School of Biomedical Engineering, Science, and Health Systems, Drexel University, 3120 24 Market Street, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 25:649-52. 2006..Furthermore, it provides exact correspondences between images prior and posterior to the atrophy that can be used to evaluate provisional image registration and atrophy quantification algorithms... - Deformable registration of brain tumor images via a statistical model of tumor-induced deformationAshraf Mohamed
Section of Biomedical Image Analysis, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
Med Image Anal 10:752-63. 2006..Results for a real tumor case and a number of simulated tumor cases indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration... - T(1ρ) MRI in Alzheimer's disease: detection of pathological changes in medial temporal lobeMohammad Haris
CMROI, SBIA, Center for Neurodegenerative Disease Research, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 6100, USA
J Neuroimaging 21:e86-90. 2011.... - High-dimensional pattern regression using machine learning: from medical images to continuous clinical variablesYing Wang
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Neuroimage 50:1519-35. 2010..Experimental results demonstrate that this regression scheme achieves higher estimation accuracy and better generalizing ability than Support Vector Regression (SVR)... - Dynamic Bayesian network modeling for longitudinal brain morphometryRong Chen
Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, USA
Neuroimage 59:2330-8. 2012..We found that interactions among regional volume-change rates for the mild cognitive impairment group are different from those for the normal-aging group... - Classification of brain tumor type and grade using MRI texture and shape in a machine learning schemeEvangelia I Zacharaki
Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
Magn Reson Med 62:1609-18. 2009..Multiclass classification was also performed via a one-vs-all voting scheme... - A comparative study of biomechanical simulators in deformable registration of brain tumor imagesEvangelia I Zacharaki
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104, USA
IEEE Trans Biomed Eng 55:1233-6. 2008..Thus, the computationally less expensive biomechanical simulator offers a practical alternative for registration purposes... - ORBIT: a multiresolution framework for deformable registration of brain tumor imagesEvangelia I Zacharaki
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, 3600 Market Street, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 27:1003-17. 2008.... - Topology preservation and regularity in estimated deformation fieldsBilge Karacali
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Inf Process Med Imaging 18:426-37. 2003.... - Robust computation of mutual information using spatially adaptive meshesHari Sundar
Section for Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, USA
Med Image Comput Comput Assist Interv 10:950-8. 2007..The effectiveness of the proposed method is demonstrated using both simulated MR images obtained from the BrainWeb database and clinical CT and SPECT images... - Modeling glioma growth and mass effect in 3D MR images of the brainCosmina Hogea
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Med Image Comput Comput Assist Interv 10:642-50. 2007..We test the model and the automatic optimization framework on real brain tumor data sets, achieving significant improvement in landmark prediction compared to a simplified purely mechanical approach... - Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive declineYong Fan
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, School of Medicine, Philadelphia, PA 19104, USA
Neuroimage 39:1731-43. 2008.... - Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machineZhiqiang Lao
Department of Radiology, 3600 Market Street, Suite 380, University of Pennsylvania, Philadelphia, PA 19104, USA
Acad Radiol 15:300-13. 2008..Brain lesions, especially white matter lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important... - Morphological classification of brains via high-dimensional shape transformations and machine learning methodsZhiqiang Lao
Section for Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Neuroimage 21:46-57. 2004.... - Measuring temporal morphological changes robustly in brain MR images via 4-dimensional template warpingDinggang Shen
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 2644, USA
Neuroimage 21:1508-17. 2004..The resultant deformations are smooth both in the spatial and temporal dimensions, and are shown to significantly improve warping accuracy over a series of independent 3D warpings, in longitudinal measurements... - Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNIChandan Misra
Department of Radiology, Section of Biomedical Image Analysis, University of Pennsylvania, School of Medicine, Philadelphia, PA 19104, USA
Neuroimage 44:1415-22. 2009..These pattern classification schemes, which distill spatial patterns of atrophy to a single abnormality score, offer promise as biomarkers of AD and as predictors of subsequent clinical progression, on an individual patient basis... - Diffusion tensor image registration using tensor geometry and orientation featuresJinzhong Yang
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Med Image Comput Comput Assist Interv 11:905-13. 2008..The robustness of the method makes it potentially useful for group-based analysis of DT images acquired in large studies to identify disease-induced and developmental changes... - Measuring brain lesion progression with a supervised tissue classification systemEvangelia I Zacharaki
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Med Image Comput Comput Assist Interv 11:620-7. 2008..The results show that our CAD-system achieves consistent lesion segmentation in the 4D data facilitating the disease monitoring... - Classification of structural images via high-dimensional image warping, robust feature extraction, and SVMYong Fan
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Med Image Comput Comput Assist Interv 8:1-8. 2005..8%) and steep ROC curves, but also exceptional stability with respect to the number of selected features and the SVM kernel size... 
T1rho (T1ρ) MR imaging in Alzheimer's disease and Parkinson's disease with and without dementiaMohammad Haris
Department of Radiology, Center for Magnetic Resonance and Optical Imaging, University of Pennsylvania, B1 Stellar Chance Laboratories, 422 Curie Boulevard, Philadelphia, PA 19104 6100, USA
J Neurol 258:380-5. 2011..The serial measurement of T(1ρ) in both AD and PD may provide the nature of disease progression and may contribute to their early diagnosis...- Early marker for Alzheimer's disease: hippocampus T1rho (T(1rho)) estimationMohammad Haris
MMRRCC, University of Pennsylvania, Philadelphia, Pennsylvania 19104 6100, USA
J Magn Reson Imaging 29:1008-12. 2009.... - Kernel-based manifold learning for statistical analysis of diffusion tensor imagesParmeshwar Khurd
Section of Biomedical Image Analysis, Dept of Radiology, University of Pennsylvania, Philadelphia, USA
Inf Process Med Imaging 20:581-93. 2007..We shall also present results from an application of kFDA to a DTI dataset obtained as part of a clinical study of schizophrenia... - Unaffected family members and schizophrenia patients share brain structure patterns: a high-dimensional pattern classification studyYong Fan
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, School of Medicine, Philadelphia, PA 19104, USA
Biol Psychiatry 63:118-24. 2008..This study investigates whether such endophenotypic patterns are found in FM via similar image analysis approaches... - Registering histological and MR images of prostate for image-based cancer detectionYiqiang Zhan
Sect of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
Med Image Comput Comput Assist Interv 9:620-8. 2006..This work is part of a larger effort to develop statistical atlases of prostate cancer using both imaging and histological information, and to use these atlases for optimal biopsy and therapy planning... - GRAM: A framework for geodesic registration on anatomical manifoldsJihun Hamm
Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104, USA
Med Image Anal 14:633-42. 2010..We demonstrate the advantages of the proposed framework over direct registration with both simulated and real databases of brain images... - Segmentation of prostate boundaries from ultrasound images using statistical shape modelDinggang Shen
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 22:539-51. 2003..e., coarse features first and fine features later. A number of successful experiments validate the algorithm... - DRAMMS: deformable registration via attribute matching and mutual-saliency weightingYangming Ou
Section of Biomedical Image Analysis SBIA, University of Pennsylvania, Philadelphia, PA, 19104, USA
Inf Process Med Imaging 21:50-62. 2009..The general applicability and accuracy of DRAMMS are demonstrated by experiments in simulated images, inter-subject images, single-/multi-modality images, and longitudinal images, from human and mouse brains, breast, heart, and prostate... - On analyzing diffusion tensor images by identifying manifold structure using isomapsRagini Verma
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 26:772-8. 2007..Comparisons with standard statistical analyses that rely on Euclidean, rather than geodesic distances, are also discussed... - CLASSIC: consistent longitudinal alignment and segmentation for serial image computingZhong Xue
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Inf Process Med Imaging 19:101-13. 2005..Morphological changes, such as growth or atrophy, are also estimated as part of the algorithm. Experimental results on simulated and real longitudinal MR brain images show both segmentation accuracy and longitudinal consistency... - Deriving statistical significance maps for SVM based image classification and group comparisonsBilwaj Gaonkar
Section for Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA 19104, USA
Med Image Comput Comput Assist Interv 15:723-30. 2012..Such maps are critical for understanding imaging patterns of group differences and interpreting which anatomical regions are important in determining the classifier's decision... - Multi-parametric analysis and registration of brain tumors: constructing statistical atlases and diagnostic tools of predictive valueChristos Davatzikos
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Conf Proc IEEE Eng Med Biol Soc 2011:6979-81. 2011..These methods combine machine learning, deformable registration, multi-parametric segmentation, and biophysical modeling of brain tumors... - An anatomical equivalence class based joint transformation-residual descriptor for morphological analysisSajjad Baloch
University of Pennsylvania, Philadelphia, PA, USA
Inf Process Med Imaging 20:594-606. 2007.... - Very high-resolution morphometry using mass-preserving deformations and HAMMER elastic registrationDinggang Shen
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
Neuroimage 18:28-41. 2003..The proposed method is validated by a series of experiments, with both simulated and real brain images... - Quantification of brain maturation and growth patterns in C57BL/6J mice via computational neuroanatomy of diffusion tensor imagesSajjad Baloch
Department of Radiology, University of Pennsylvania, PA 19104, USA
Cereb Cortex 19:675-87. 2009..Fiber maturation reaches steady state in about 10 days for the cortex, to 30-40 days for the corpus callosum, the hippocampus, and the internal and external capsules... - Spatiotemporal maturation patterns of murine brain quantified by diffusion tensor MRI and deformation-based morphometryRagini Verma
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104, USA
Proc Natl Acad Sci U S A 102:6978-83. 2005..This analysis provides a framework for quantifying normative maturation patterns against which phenotypes of mice of different genetic and environmental backgrounds can be contrasted... - Optimized prostate biopsy via a statistical atlas of cancer spatial distributionDinggang Shen
Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104 2644, USA
Med Image Anal 8:139-50. 2004..Experimental results using cross-validation show that the proposed method can detect cancer with a 99% success rate using seven needles, in these samples... - Deformable registration of cortical structures via hybrid volumetric and surface warpingTianming Liu
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Neuroimage 22:1790-801. 2004..Experimental results on both synthesized and real brain data demonstrate the performance of the proposed method in the registration of cortical structures across individuals... - A Bayesian morphometry algorithmEdward H Herskovits
Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 370, Room 117, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 23:723-37. 2004..e., t-test and paired t-test) fails in the nonlinear-association case... - A general and unifying framework for feature construction, in image-based pattern classificationNematollah Batmanghelich
Section of Biomedical Image Analysis, Radiology Department, University of Pennsylvania, Philadelphia, PA 19014, USA
Inf Process Med Imaging 21:423-34. 2009..A non-negative matrix factorization scheme is used, and a numerical solution with proven convergence is used for solution. Results in classification of Alzheimers patients from the ADNI study are presented... - Semi-supervised cluster analysis of imaging dataRoman Filipovych
Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Neuroimage 54:2185-97. 2011..e. cognitively stable) state. We analyze the clusters' structure, and show that clustering results obtained using our approach correlate well with clinical data... - Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI)Roman Filipovych
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, 3600 Market St, Suite 380, Philadelphia, PA 19104, USA
Neuroimage 55:1109-19. 2011.... - Anatomical equivalence class: a morphological analysis framework using a lossless shape descriptorSokratis Makrogiannis
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 26:619-31. 2007.... - Simulating deformations of MR brain images for validation of atlas-based segmentation and registration algorithmsZhong Xue
Section of Biomedical Image Analysis SBIA, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
Neuroimage 33:855-66. 2006..The code and simulated data are available through our Web site... - A robust framework for soft tissue simulations with application to modeling brain tumor mass effect in 3D MR imagesCosmina Hogea
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Phys Med Biol 52:6893-908. 2007..We illustrate the potential of our framework to simulate realistic brain tumor mass effects at reduced computational cost, for aiding the registration process towards the construction of brain tumor atlases... - Spatial normalization of diffusion tensor fieldsDongrong Xu
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104, USA
Magn Reson Med 50:175-82. 2003..Simulated experiments using this methodology are also described... - Regularized tensor factorization for multi-modality medical image classificationNematollah Batmanghelich
Section for Biomedical Image Analysis, Suite 380, 3600 Market St, 19104 Philadelphia, USA
Med Image Comput Comput Assist Interv 14:17-24. 2011..We compared this method with a publically available classification software based on SVM that has shown state-of-the-art classification rate in number of publications... - CLASSIC: consistent longitudinal alignment and segmentation for serial image computingZhong Xue
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, PA 19104, USA
Neuroimage 30:388-99. 2006..Morphological changes, such as growth or atrophy, are also estimated as part of the algorithm. Experimental results on simulated and real longitudinal MR brain images show both segmentation accuracy and longitudinal consistency... - Optimally-discriminative voxel-based analysisTianhao Zhang
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
Med Image Comput Comput Assist Interv 13:257-65. 2010..Permutation tests are finally used to obtain the statistical significance. The experiments on Mild Cognitive Impairment (MCI) study have shown the effectiveness of the framework... - Joint segmentation and deformable registration of brain scans guided by a tumor growth modelAli Gooya
Section for Biomedical Image Analysis, Suite 380, 3600 Market St, 19104 Philadelphia, USA
Med Image Comput Comput Assist Interv 14:532-40. 2011..The resulting segmentations look promising and quantitatively match well with the expert provided ground truth... - Efficient large deformation registration via geodesics on a learned manifold of imagesJihun Hamm
Department of Radiology, University of Pennsylvania, USA
Med Image Comput Comput Assist Interv 12:680-7. 2009..Furthermore, the graph representation allows us to estimate the optimal group template by minimizing geodesic distances. We demonstrate the advantages of the proposed method with synthetic 2D images and real 3D mice brain volumes... - An image-driven parameter estimation problem for a reaction-diffusion glioma growth model with mass effectsCosmina Hogea
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
J Math Biol 56:793-825. 2008..In this paper, we present the formulation, and the solution method and we conduct 1D numerical experiments for preliminary evaluation of the overall formulation/methodology... - Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR imagesRagini Verma
Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104, USA
Acad Radiol 15:966-77. 2008.... - Hierarchical active shape models, using the wavelet transformChristos Davatzikos
Section for Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 22:414-23. 2003..Examples on magnetic resonance images of the corpus callosum and hand contours demonstrate that good and fully automated segmentations can be achieved, even with as few as five training samples... - Biomechanically-constrained 4D estimation of myocardial motionHari Sundar
Section for Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, USA
Med Image Comput Comput Assist Interv 12:257-65. 2009..In these preliminary tests, we verify the implementation and conduct a parametric study to test the sensitivity of the model to material properties perturbations, model errors, and incomplete and noisy observations... - Quantification of facial expressions using high-dimensional shape transformationsRagini Verma
Department of Radiology, Section of Biomedical Image Analysis, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104, USA
J Neurosci Methods 141:61-73. 2005..A model for the average expression of specific emotions was also constructed using the RVD maps. This method can be applied in basic and clinical investigations of facial affect and its neural substrates... - Design of comprehensive Alzheimer's disease centers to address unmet national needsJohn Q Trojanowski
Institute on Aging, Philadelphia, PA, USA
Alzheimers Dement 6:150-5. 2010..The intent of this position paper is to stimulate thinking and foster the development of other or alternative models for a systematic approach to the study of dementia and movement disorders... - Diagnosis of brain abnormality using both structural and functional MR imagesYong Fan
Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
Conf Proc IEEE Eng Med Biol Soc 1:1044-7. 2006..8% correct classification rate using a leave-one-out cross-validation. Comparison results show the effectiveness of our method and also the importance of simultaneously using both structural and functional images for brain classification... - T1rho MRI of Alzheimer's diseaseArijitt Borthakur
MMRRCC, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 6100, USA
Neuroimage 41:1199-205. 2008.... - Finite element modeling of brain tumor mass-effect from 3D medical imagesAshraf Mohamed
CISST NSF Engineering Research Center, Department of Computer Science, Johns Hopkins University, USA
Med Image Comput Comput Assist Interv 8:400-8. 2005..Results indicate that the model can reproduce the real deformations with an accuracy that is similar to that of manual placement of landmark points to which the model is compared... - Deformable registration of glioma images using EM algorithm and diffusion reaction modelingAli Gooya
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 30:375-90. 2011..The results show that our method outperforms ORBIT, and the warped templates have better similarity to patient images... - HAMMER: hierarchical attribute matching mechanism for elastic registrationDinggang Shen
Center for Biomedical Image Computing, Department of Radiology, The Johns Hopkins University School of Medicine, 601 N Caroline Street, Baltimore, MD 21287, USA
IEEE Trans Med Imaging 21:1421-39. 2002..A number of experiments demonstrate that the proposed algorithm results in accurate superposition of image data from individuals with significant anatomical differences... - Automated morphometric study of brain variation in XXY malesDinggang Shen
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 380, Philadelphia, PA 19104, USA
Neuroimage 23:648-53. 2004..In addition to the reduction of local volume, overall enlargement of ventricles and overall volume reduction of both white matter and gray matter are also found in XXY males... - Estimating topology preserving and smooth displacement fieldsBilge Karacali
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
IEEE Trans Med Imaging 23:868-80. 2004..They also show that the proposed smoothing method can render morphometric analysis methods that are based on displacement field of shape transformations more robust to noise without removing important morphologic characteristics... - Using the fast marching method to extract curves with given global propertiesXiaodong Tao
Johns Hopkins University, Baltimore, MD 21218, USA
Med Image Comput Comput Assist Interv 8:870-7. 2005..We show some results on both a simulated image and a highly convoluted human brain cortical surface... - Deformable registration of brain tumor images via a statistical model of tumor-induced deformationAshraf Mohamed
CISST NSF Engineering Research Center, Department of Computer Science, Johns Hopkins University, USA
Med Image Comput Comput Assist Interv 8:263-70. 2005..Results for a real and a simulated tumor case indicate significant reduction in the registration error due to the presented approach as compared to the direct use of deformable image registration... - Morphometric analysis of cortical sulci using parametric ribbons: a study of the central sulcusChristos Davatzikos
Center for Biomedical Image Computing, Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA
J Comput Assist Tomogr 26:298-307. 2002..Position asymmetries were also found, which might be explained by a relative larger parietal association cortex in men but not in women... - Are brain volumes based on magnetic resonance imaging mediators of the associations of cumulative lead dose with cognitive function?Brian Caffo
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
Am J Epidemiol 167:429-37. 2008..The approach to evaluating volumetric mediation may have general applicability in epidemiologic neuroimaging settings... - Longitudinal magnetic resonance imaging studies of older adults: a shrinking brainSusan M Resnick
Laboratory of Personality and Cognition, National Institute on Aging, Baltimore, Maryland 21224 6825, USA
J Neurosci 23:3295-301. 2003.... - Using a statistical shape model to extract sulcal curves on the outer cortex of the human brainXiaodong Tao
Department of Elecltrical and Computer Engineering and the Center for Biomedical Image Computing, School of Medicine, Johns Hopkins University, Baltimore, MD 21218 USA
IEEE Trans Med Imaging 21:513-24. 2002..The method is tested against and shown to be as accurate as manually defined segmentations... - Voxel-based morphometric analysis using shape transformationsChristos Davatzikos
Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
Int Rev Neurobiol 66:125-46. 2005 - Imaging cortical association tracts in the human brain using diffusion-tensor-based axonal trackingSusumu Mori
Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
Magn Reson Med 47:215-23. 2002..As a first illustration of this technical capability, a reduction in brain connectivity in a patient with a childhood neurodegenerative disease (X-linked adrenoleukodystrophy) was demonstrated... - Computer-assisted imaging to assess brain structure in healthy and diseased brainsJohn Ashburner
The Wellcome Department of Imaging Neuroscience, Institute of Neurology, London, UK
Lancet Neurol 2:79-88. 2003.... - A framework for callosal fiber distribution analysisDongrong Xu
Center for Biomedical Image Computing, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA
Neuroimage 17:1131-43. 2002..The proposed approach combines methodologies for fiber tracking and spatial normalization and is applied on diffusion tensor images and standard magnetic resonance images... - Three-dimensional sonography with needle tracking: role in diagnosis and treatment of prostate cancerFeimo Shen
Eigen LLC, 13366 Grass Valley Ave, Grass Valley, CA 95945 USA
J Ultrasound Med 27:895-905. 2008..Because prostatic carcinomas are nonuniformly distributed within the prostate gland, it is crucial to accurately guide the needles toward clinically important locations within the 3D volume for both diagnosis and treatment... - Spatial normalization of spine MR images for statistical correlation of lesions with clinical symptomsChristos Davatzikos
Center for Biomedical Image Computing, Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N Caroline St, JHOC3220, Baltimore, MD 21287, USA
Radiology 224:919-26. 2002..Very good agreement with manual segmentations was observed. The main application of this method is in lesion-deficit analysis for determining associations between structural damage and clinical symptoms... - Puberty-related influences on brain developmentJay N Giedd
Child Psychiatry Branch, National Institute of Mental Health, Building 10, Room 4C110, 10 Center Drive, MSC 1367, Bethesda, MD 20892, United States
Mol Cell Endocrinol 254:154-62. 2006..Subjects with XXY have gray matter reductions in the insula, temporal gyri, amygdala, hippocampus, and cingulate-areas consistent with the language-based learning difficulties common in this group...