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1. Magic DIAMOND: Multi-Fascicle Diffusion Compartment Imaging with Tensor Distribution Modeling and Tensor-Valued Diffusion Encoding

Author

Reymbaut, A., Caron, A. Valcourt, Gilbert, G., Szczepankiewicz, F., Nilsson, M., Warfield, S. K., Descoteaux, M., and Scherrer, B.

Subjects
Physics - Medical Physics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distribution of diffusion tensors, thereby resolving crossing fascicles while accounting for their respective heterogeneity. Alternatively, tensor-valued diffusion encoding defines new acquisition schemes tagging specific features of the intra-voxel diffusion tensor distribution directly from the outcome of the measurement. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this "Magic DIAMOND" model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We show that our estimated metrics can be mapped along tracks robustly across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts., Comment: 16 pages, 15 figures

Published
2020
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2. Biomechanical modeling and computer simulation of the brain during neurosurgery

Author

Miller, K., Joldes, G. R., Bourantas, G., Warfield, S. K., Hyde, D. E., Kikinis, R., and Wittek, A.

Subjects
Computer Science - Computational Engineering, Finance, and Science
Abstract

Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and it's collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach, and describe the brain deformation in mechanical terms (such as displacements, strains and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the Total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.

Published
2019

3. Brain charts for the human lifespan

Author

Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V., Beyer, F., Blangero, J., Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, A., Costantino, M., Courchesne, E., Crivello, F., Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, S., Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, G., Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galan-Garcia, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, D. C., Goodyer, I. M., Grant, P. E., Groenewold, N. A., Gunning, F. M., Gur, R. E., Gur, R. C., Hammill, C. F., Hansson, O., Hedden, T., Heinz, A., Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, A. J., Holt, R., Huang, H., Im, K., Ipser, J., Jack, Jr, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, D. T., Kahn, R. S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, S., Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, M. V., Lv, J., Lynch, C., Mallard, T. T., Marcelis, M., Markello, R. D., Mathias, S. R., Mazoyer, B., McGuire, P., Meaney, M. J., Mechelli, A., Medic, N., Misic, B., Morgan, S. E., Mothersill, D., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, R., Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, C., Park, M. M., Paus, T., Pausova, Z., Paz-Linares, D., Pichet Binette, A., Pierce, K., Qian, X., Qiu, J., Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero-Garcia, R., Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, T. D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, G., Schöll, M., Sharp, D., Shinohara, R. T., Skoog, I., Smyser, C. D., Sperling, R. A., Stein, D. J., Stolicyn, A., Suckling, J., Sullivan, G., Taki, Y., Thyreau, B., Toro, R., Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, S. L., van Amelsvoort, T., Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, A., Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, E., Westwater, M. L., Whalley, H. C., Witte, A. V., Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, A., Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.

Published
2022
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4. Publisher Correction: Brain charts for the human lifespan

Author

Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V., Beyer, F., Blangero, J., Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, A., Costantino, M., Courchesne, E., Crivello, F., Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, S., Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, G., Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galan-Garcia, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, D. C., Goodyer, I. M., Grant, P. E., Groenewold, N. A., Gunning, F. M., Gur, R. E., Gur, R. C., Hammill, C. F., Hansson, O., Hedden, T., Heinz, A., Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, A. J., Holt, R., Huang, H., Im, K., Ipser, J., Jack, Jr, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, D. T., Kahn, R. S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, S., Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, M. V., Lv, J., Lynch, C., Mallard, T. T., Marcelis, M., Markello, R. D., Mathias, S. R., Mazoyer, B., McGuire, P., Meaney, M. J., Mechelli, A., Medic, N., Misic, B., Morgan, S. E., Mothersill, D., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, R., Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, C., Park, M. M., Paus, T., Pausova, Z., Paz-Linares, D., Pichet Binette, A., Pierce, K., Qian, X., Qiu, J., Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero-Garcia, R., Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, T. D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, G., Schöll, M., Sharp, D., Shinohara, R. T., Skoog, I., Smyser, C. D., Sperling, R. A., Stein, D. J., Stolicyn, A., Suckling, J., Sullivan, G., Taki, Y., Thyreau, B., Toro, R., Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, S. L., van Amelsvoort, T., Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, A., Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, E., Westwater, M. L., Whalley, H. C., Witte, A. V., Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, A., Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.

Published
2022
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5. Quantitative Body DW-MRI Biomarkers Uncertainty Estimation Using Unscented Wild-Bootstrap

Author

Freiman, M., Voss, S. D., Mulkern, R. V., Perez-Rossello, J. M., Warfield, S. K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Fichtinger, Gabor, editor, Martel, Anne, editor, and Peters, Terry, editor

Published
2011
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6. Non-rigid Registration of Pre-procedural MR Images with Intra-procedural Unenhanced CT Images for Improved Targeting of Tumors During Liver Radiofrequency Ablations

Author

Archip, N., Tatli, S., Morrison, P., Jolesz, F., Warfield, S. K., Silverman, S., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ayache, Nicholas, editor, Ourselin, Sébastien, editor, and Maeder, Anthony, editor

Published
2007
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7. Geostatistical Medical Image Registration

Author

Ruiz-Alzola, J., Suarez, E., Alberola-Lopez, C., Warfield, S. K., Westin, C. -F., Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Ellis, Randy E., editor, and Peters, Terry M., editor

Published
2003
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10. A Novel Nonrigid Registration Algorithm and Applications

Author

Rexilius, J., Warfield, S. K., Guttmann, C. R. G., Wei, X., Benson, R., Wolfson, L., Shenton, M., Handels, H., Kikinis, R., Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Niessen, Wiro J., editor, and Viergever, Max A., editor

Published
2001
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11. Brain charts for the human lifespan

Author

British Academy, Autism Research Trust, National Institute of Mental Health (US), UK Research and Innovation, Medical Research Council (UK), National Institute for Health and Care Research (US), Wellcome Trust, University of Cambridge, Cambridge Biomedical Research Centre, Bethlehem, Richard A. I., Seidlitz, J., White, S. R., Vogel, Jacob W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-González, A., Astle, D. E., Auyeung, Bonnie, Ayub, Muhammad, Bae, J., Ball, G, Baron-Cohen, Simon, Beare, R., Bedford, S. A., Benegal, V., Beyer, Frauke, Blangero, John, Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, Aiden, Costantino, M., Courchesne, E., Crivello, Fabrice, Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, Sylvane, Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, Gary, Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galán-García, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, David C., Goodyer, I. M., Grant, P. E., Groenewold, Nynke A., Gunning, F. M., Gur, Rachel E., Gur, Ruben C., Hammill, C. F., Hansson, Oskar, Hedden, T., Heinz, Andreas, Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, Avram J., Holt, Rosemary, Huang, Hailiang, Im, K., Ipser, J., Jack, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, David T. W., Kahn, René S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, Silke, Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, Michael, V., Lv, J., Lynch, Christopher D., Mallard, T. T., Marcelis, Machteld, Markello, R. D., Mathias, Samuel R., Mazoyer, Bernard, McGuire, Philip, Meaney, M. J., Mechelli, Andrea, Medic, N., Misic, B., Morgan, S. E., Mothersill, David O., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, Rik, Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, Christos, Park, M. M., Paus, Tomas, Pausova, Zdenka, Paz-Linares, D., Pichler, Alexander, Pierce, K., Qian, X., Qiu, Jing, Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero García, Rafael, Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, Theodore D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, Gunter, Schöll, Michael, Sharp, D., Shinohara, R. T., Skoog, Ingmar, Smyser, C. D., Sperling, R. A., Stein, Dan J., Stolicyn, A., Suckling, John, Sullivan, G., Taki, Y., Thyreau, B., Toro, Raquel del, Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, Sofie L., van Amelsvoort, Therese, Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, Arno, Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, Eric, Westwater, M. L., Whalley, Heather, Witte, A. Veronica, Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, Anna, Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., Alexander-Bloch, A. F., British Academy, Autism Research Trust, National Institute of Mental Health (US), UK Research and Innovation, Medical Research Council (UK), National Institute for Health and Care Research (US), Wellcome Trust, University of Cambridge, Cambridge Biomedical Research Centre, Bethlehem, Richard A. I., Seidlitz, J., White, S. R., Vogel, Jacob W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-González, A., Astle, D. E., Auyeung, Bonnie, Ayub, Muhammad, Bae, J., Ball, G, Baron-Cohen, Simon, Beare, R., Bedford, S. A., Benegal, V., Beyer, Frauke, Blangero, John, Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, Aiden, Costantino, M., Courchesne, E., Crivello, Fabrice, Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, Sylvane, Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, Gary, Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galán-García, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, David C., Goodyer, I. M., Grant, P. E., Groenewold, Nynke A., Gunning, F. M., Gur, Rachel E., Gur, Ruben C., Hammill, C. F., Hansson, Oskar, Hedden, T., Heinz, Andreas, Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, Avram J., Holt, Rosemary, Huang, Hailiang, Im, K., Ipser, J., Jack, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, David T. W., Kahn, René S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, Silke, Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, Michael, V., Lv, J., Lynch, Christopher D., Mallard, T. T., Marcelis, Machteld, Markello, R. D., Mathias, Samuel R., Mazoyer, Bernard, McGuire, Philip, Meaney, M. J., Mechelli, Andrea, Medic, N., Misic, B., Morgan, S. E., Mothersill, David O., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, Rik, Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, Christos, Park, M. M., Paus, Tomas, Pausova, Zdenka, Paz-Linares, D., Pichler, Alexander, Pierce, K., Qian, X., Qiu, Jing, Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero García, Rafael, Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, Theodore D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, Gunter, Schöll, Michael, Sharp, D., Shinohara, R. T., Skoog, Ingmar, Smyser, C. D., Sperling, R. A., Stein, Dan J., Stolicyn, A., Suckling, John, Sullivan, G., Taki, Y., Thyreau, B., Toro, Raquel del, Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, Sofie L., van Amelsvoort, Therese, Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, Arno, Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, Eric, Westwater, M. L., Whalley, Heather, Witte, A. Veronica, Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, Anna, Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.

Abstract

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

Published
2022

28. Primary cortical folding in the human newborn: an early marker of later functional development

Author

Dubois, J., Benders, M., Borradori-Tolsa, C., Cachia, A., Lazeyras, F., Ha-Vinh Leuchter, R., Sizonenko, S. V., Warfield, S. K., Mangin, J. F., Hüppi, P. S., Dubois, J., Benders, M., Borradori-Tolsa, C., Cachia, A., Lazeyras, F., Ha-Vinh Leuchter, R., Sizonenko, S. V., Warfield, S. K., Mangin, J. F., and Hüppi, P. S.

Abstract

In the human brain, the morphology of cortical gyri and sulci is complex and variable among individuals, and it may reflect pathological functioning with specific abnormalities observed in certain developmental and neuropsychiatric disorders. Since cortical folding occurs early during brain development, these structural abnormalities might be present long before the appearance of functional symptoms. So far, the precise mechanisms responsible for such alteration in the convolution pattern during intra-uterine or post-natal development are still poorly understood. Here we compared anatomical and functional brain development in vivo among 45 premature newborns who experienced different intra-uterine environments: 22 normal singletons, 12 twins and 11 newborns with intrauterine growth restriction (IUGR). Using magnetic resonance imaging (MRI) and dedicated post-processing tools, we investigated early disturbances in cortical formation at birth, over the developmental period critical for the emergence of convolutions (26-36 weeks of gestational age), and defined early ‘endophenotypes' of sulcal development. We demonstrated that twins have a delayed but harmonious maturation, with reduced surface and sulcation index compared to singletons, whereas the gyrification of IUGR newborns is discordant to the normal developmental trajectory, with a more pronounced reduction of surface in relation to the sulcation index compared to normal newborns. Furthermore, we showed that these structural measurements of the brain at birth are predictors of infants' outcome at term equivalent age, for MRI-based cerebral volumes and neurobehavioural development evaluated with the assessment of preterm infant's behaviour (APIB)

Published
2017

29. Optimal MAP Parameters Estimation in STAPLE - Learning from Performance Parameters versus Image Similarity Information

Author

Gorthi, S., Akhondi-Asl, A., Jean-Philippe Thiran, and Warfield, S. K.

Abstract

In many medical imaging applications merging segmentations obtained from multiple reference images (i.e. templates) has become a standard practice for improving the accuracy as well as reliability. Simultaneous Truth And Performance Level Estimation (STAPLE) is a widely used fusion algorithm that simultaneously estimates both performance parameters for each template and the output segmentation; a more accurate estimation of performance parameters consequently results in more accurate output segmentations. In this paper we propose a new approach for learning prior knowledge about the performance parameters of each template and for incorporating it into the Maximum a Posteriori (MAP) formulation of the STAPLE so that more accurate output segmentations can be obtained. More specifically we propose a new approach to learn for each structure to be segmented the relationships between the performance parameters (viz. sensitivity and specificity) and the intensity similarities; we also propose a methodology for transferring this prior knowledge about the performance parameters into the STAPLE algorithm through optimal setting of the MAP parameters. The proposed approach is evaluated for the segmentation of structures in the brain MR images. These experiments have clearly demonstrated the advantages of incorporating such prior knowledge.

Published
2014

32. Unsupervised and adaptive segmentation of multispectral 3D magnetic resonance images of human brain : a generic approach

Author

Pachai, C, Zhu, Y M, Guttmann, C, Kikinis, R, Jolesz, F A, Gimenez, G, Froment, J C, Confavreux, C, Warfield, S K, Centre de Recherche et d'Application en Traitement de l'Image et du Signal (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Unité de Recherche en Biologie et Epidémiologie Parasitaires, Institut de Médecine Tropicale du Service de Santé des Armées-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Images et Modèles, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, imagerie_volumique, categ_mixte, ComputingMilieux_MISCELLANEOUS, sclerose_en_plaque
Abstract

International audience

Published
2001

33. Automatic quantification of brain atrophy in MS using millimetric T1-weighted MRI

Author

Pachai, C, Warfield, S K, Zhu, Y M, Guttmann, C, Froment, J C, Confavreux, C, Centre de Recherche et d'Application en Traitement de l'Image et du Signal (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Images et Modèles, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, imagerie_volumique, categ_mixte, ComputingMilieux_MISCELLANEOUS, sclerose_en_plaque
Abstract

International audience

Published
2000

34. Delayed Cortical Development in Fetuses with Complex Congenital Heart Disease

Author

Clouchoux, C., primary, du Plessis, A. J., additional, Bouyssi-Kobar, M., additional, Tworetzky, W., additional, McElhinney, D. B., additional, Brown, D. W., additional, Gholipour, A., additional, Kudelski, D., additional, Warfield, S. K., additional, McCarter, R. J., additional, Robertson, R. L., additional, Evans, A. C., additional, Newburger, J. W., additional, and Limperopoulos, C., additional

Published
2012
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43. Registration of 3-D intraoperative MR images of the brain using a finite-element biomechanical model.

Author

UCL - FSA/ELEC - Département d'électricité, Ferrant, M., Nabavi, A, Macq, Benoît, Jolesz, F A, Kikinis, R., Warfield, S K, UCL - FSA/ELEC - Département d'électricité, Ferrant, M., Nabavi, A, Macq, Benoît, Jolesz, F A, Kikinis, R., and Warfield, S K

Abstract

We present a new algorithm for the nonrigid registration of three-dimensional magnetic resonance (MR) intraoperative image sequences showing brain shift. The algorithm tracks key surfaces of objects (cortical surface and the lateral ventricles) in the image sequence using a deformable surface matching algorithm. The volumetric deformation field of the objects is then inferred from the displacements at the boundary surfaces using a linear elastic biomechanical finite-element model. Two experiments on synthetic image sequences are presented, as well as an initial experiment on intraoperative MR images showing brain shift. The results of the registration algorithm show a good correlation of the internal brain structures after deformation, and a good capability of measuring surface as well as subsurface shift. We measured distances between landmarks in the deformed initial image and the corresponding landmarks in the target scan. Cortical surface shifts of up to 10 mm and subsurface shifts of up to 6 mm were recovered with an accuracy of 1 mm or less and 3 mm or less respectively.

Published
2001

49. Segmentation of Meningiomas and Low Grade Gliomas in MRI.

Author

Taylor, Chris, Colchester, Alain, Kaus, M. R., Warfield, S. K., Nabavi, A., Chatzidakis, E., Black, P. M., Jolesz, F. A., and Kikinis, R.

Abstract

Computer assisted surgical planning and image guided technology have become increasingly used in neurosurgery. We have developed a system based on ATmC (Adaptive Template moderated Classification) for the automated segmentation of 3D MRI brain data sets of patients with brain tumors (meningiomas and low grade gliomas) into the skin, the brain, the ventricles and the tumor. In a validation study of 13 patients with brain tumors, the segmentation results of the automated method are compared to manual segmentations carried out by 4 independent trained human observers. It is shown that the automated method segments brain and tumor with accuracy comparable to the manual method and with improved reproducibility. [ABSTRACT FROM AUTHOR]

Published
1999
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50. Improved Watershed Transform for Medical Image Segmentation Using Prior Information.

Author

Grau, V., Mewes, A. U. J., Kikinis, R., Warfield, S. K., and Alcañiz, M.

Subjects
DIAGNOSTIC imaging, IMAGE analysis, NOISE, IMAGING systems, PROBABILITY theory, ALGORITHMS
Abstract

The watershed transform has interesting properties that make it useful for many different image segmentation applications: it is simple and intuitive, can be parallelized, and always produces a complete division of the image. However, when applied to medical image analysis, it has important drawbacks (overseg-mentation, sensitivity to noise, poor detection of thin or low signal to noise ratio structures). We present an improvement to the watershed transform that enables the introduction of prior information in its calculation. We propose to introduce this information via the use of a previous probability calculation. Furthermore, we introduce a method to combine the watershed transform and atlas registration, through the use of markers. We have applied our new algorithm to two challenging applications: knee cartilage and gray matter/white matter segmentation in MR images. Numerical validation of the results is provided, demonstrating the strength of the algorithm for medical image segmentation. [ABSTRACT FROM AUTHOR]

Published
2004
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