This paper describes an automatic algorithm to restore
the shape of three-dimensional (3D) left ventricle (LV) models created
from magnetic resonance imaging (MRI) data using a geometry-driven
optimization approach. Our basic premise is to restore the LV shape
such that the LV epicardial surface is smooth after the restoration. A
geometrical measure known as the Minimum Principle Curvature (κ2)
is used to assess the smoothness of the LV. This measure is used to
construct the objective function of a two-step optimization process.
The objective of the optimization is to achieve a smooth epicardial
shape by iterative in-plane translation of the MRI slices.
Quantitatively, this yields a minimum sum in terms of the magnitude
of κ
2, when κ2 is negative. A limited memory quasi-Newton algorithm,
L-BFGS-B, is used to solve the optimization problem. We tested our
algorithm on an in vitro theoretical LV model and 10 in vivo
patient-specific models which contain significant motion artifacts. The
results show that our method is able to automatically restore the shape
of LV models back to smoothness without altering the general shape of
the model. The magnitudes of in-plane translations are also consistent
with existing registration techniques and experimental findings.
[1] L. Zhong, Y. Su, L. Gobeawan, S. Sola, R.-S. Tan, J. L. Navia, D. N.
Ghista, T. Chua, J. Guccione, and G. S. Kassab, "Impact of surgical
ventricular restoration on ventricular shape, wall stress, and function in
heart failure patients," Am. J. Physiol. Heart Circ. Physiol., 300(5):
H1653-H1660, 2011.
[2] L. Zhong, Y. Su, S. Y. Yeo, R.-S. Tan, D. N. Ghista, and G. Kassab, "Left
ventricular regional wall curvedness and wall stress assessment in
patients with ischemic dilated cardiomyopathy," Am. J. Physiol. Heart
Circ. Physiol., 296(3): H573-H584, 2009.
[3] Q. Li, L. Zamorano, Z. Jiang, F. Vinas, and F. Diaz, "The application
accuracy of the frameless implantable marker system and analysis of
related affecting factors," in Lecture Notes in Computer Science 1496:
Medical Image Computing and Computer-Assisted Intervention,
MICCAI98, W. M. Wells, A. Colchester, and S. Delp, Eds., 1998, pp.
253-260.
[4] S. Eberl, I. Kanno, R. R. Fulton, A. Ryan, B. F. Hutton, and M. J. Fulham,
"Automated interstudy image registration technique for SPECT and
PET," J. Nucl. Med., vol. 37, no. 1, pp. 137-145, 1996.
[5] G. J. Klein and R. H. Huesman, "Four-dimensional processing of deformable
cardiac PET data," Med. Image Anal., vol. 6, pp. 29-46, 2002.
[6] T. Mäkelä, P. Clarysse, O. Sipilä, N. Pauna, Q. C. Pham, T. Katila, and
I.E. Magnin, "A review of cardiac image registration methods", IEEE
Transaction on Medical Imaging, 21, pp. 1011-1021, 2002.
[7] A. Elen, J. Hermans, J. Ganame, D. Loeckx, J. Bogaert, F. Maes, and P.
Suetens, "Automatic 3-D breath-hold related motion correction of
dynamic multislice MRI", IEEE Transaction on Medical Imaging, 29, pp.
868-878, 2010.
[8] C.-Y. Zhu, R. H. Byrd, P.-H. Lu, and J. Nocedal, "L-BFGS-B: Fortran
Subroutines for large-scale bound constrained optimization," in ACM
Transactions on Mathematical Software (TOMS) Vol. 23 Issue 4, Dec.
1997.
[9] Y. Su, L. Zhong, C.-W. Lim, D. Ghista, T. Chua, and R.-S. Tan, "A
geometrical approach for evaluating left ventricular remodeling in
myocardial infarct patients," Comput. Methods Programs Biomed., 2011,
in press. DOI:10.1016/j.cmpb.2011.03.008
[10] K. McLeish, D. L. G. Hill, D. Atkinson, J. M. Blackall, and R. Razavi, "A
study of the motion and deformation of the heart due to respiration," IEEE
Transaction on Medical Imaging, 21, pp. 1142-1150, 2002.
[11] R. P Woods, Handbook of Medical Imaging: Processing and Analysis.
New York: Academic, 2000, ch. Validation of registration accuracy, pp.
491-497.
[12] S. Pallotta, M. C. Gilardi, V. Bettinardi, G. Rizzo, C. Landoni, G. Striano,
R. Masi, and F. Fazio, "Application of a surface matching image
registration technique to the correlation of cardiac studies in positron
emission tomography by transmission images," Phys. Med. Biol., vol. 40,
pp. 1695-1708, 1995
[13] M. C. Gilardi, G. Rizzo, A. Savi, C. Landoni, V. Bettinardi, C. Rossetti,
G. Striano, and F. N. Fazio, "Correlation of SPECT and PET cardiac
images by a surface matching registration technique," Comput. Med.
Imag. Graph., vol. 22, pp. 391-398, Dec. 1998.
[14] T. J. M├ñkel├ñ, P. Clarysse, J. Lötjönen, O. Sipil├ñ, K. Lauerma, H.
H├ñnninen, E.-P Pyökkimies, J. Nenonen, J. Knuuti, T. Katila, and I. E.
Magnin, "A new method for the registration of cardiac PET and MR
images using deformable model based segmentation of the main thorax
structures," in Lecture Notes in Computer Science 2208: Medical Image
Computing and Computer-Assisted Intervention, MICCAI01, W. J.
Niessen and M. Viergever, Eds., 2001, pp. 557-564.
[15] T. L. Faber, R. W. McColl, R. M. Opperman, J. R. Corbett, and R. M.
Peshock, "Spatial and temporal registration of cardiac SPECT and MR
images: Methods and evaluation," Radiology, vol. 179, no. 3, pp.
857-861, 1991.
[16] S. Sinha, U. Sinha, J. Czernin, G. Porenta, and H. R. Schelbert, "Noninvasive
assessment of myocardial perfusion and metabolism: Feasibility of
registering gated MR and PET images," Amer. J. Roentgenol., vol. 36, pp.
301-307, 1995.
[17] S. Nekolla, T. Ibrahim, T. Balbach, and C. Klein, Understanding Cardiac
Imaging TechniquesÔÇöFrom Basic Pathology to Image Fusion,
Amsterdam, The Netherlands: IOS Press, 2001, vol. 322, ch.
Coregistration and fusion of cardiac magnetic resonance and positron
emission tomography studies, pp. 144-154.
[18] C. M. Gallippi and G. E. Trahey, "Automatic image registration for MR
and ultrasound cardiac images," in Lecture Notes in Computer Science
2082: Information Processing in Medical Imaging, IPMI01, M. F. Insana
and R. M. Leahy, Eds., 2001, pp. 141-147.
[19] L. M. Bidaut and J.-P. Vallee, "Automated registration of dynamic MR
images for the quantification of myocardial perfusion," J. Magn. Res.
Imag., vol. 13, pp. 648-655, 2001.
[20] S. L. Bacharach, M. A. Douglas, R. E. Carson, P. J. Kalkowski, N. M.
Freedman, P. Perrone, and R. O. Bonow, "Three-dimensional registration
of cardiac positron emission tomography attenution scans," Comput.
Vision, Graph. Image Process, vol. 34, no. 2, pp. 311-321, 1993.
[21] T. G. Turkington, T. R. DeGrado, M. W. Hanson, and R. E. Coleman,
"Alignment of dynamic cardiac PET images for correction of motion,"
IEEE Trans. Nucl. Sci., vol. 44, pp. 235-242, Apr. 1997.
[22] G. J. Klein and R. H. Huesman, "Four-dimensional processing of
deformable cardiac PET data," Med. Image Anal., vol. 6, pp. 29-46, 2002.
[23] C. K. Hoh, M. Dahlbom, G. Harris, Y. Choi, R. A. Hawkins, M. E. Philps,
and J. Maddahi, "Automated iterative three-dimensional registration of
positron emission tomography images," J. Nucl. Med., vol. 34, no. 11, pp.
2009-2018, 1993.
[24] D. Dey, P. J. Slomka, L. J. Hahn, and R. Kloiber, "Automatic
three-dimensional multimodality registration using radionuclide
transmission CT attenuation maps: A phantom study," J. Nucl. Med., vol.
40, no. 3, pp. 448-455, 1999.
[25] S. Eberl, I. Kanno, R. R. Fulton, A. Ryan, B. F. Hutton, and M. J. Fulham,
"Automated interstudy image registration technique for SPECT and
PET," J. Nucl. Med., vol. 37, no. 1, pp. 137-145, 1996.
[26] P. J. Slomka, A. H. Gilbert, J. Stephenson, and T. Cradduc, "Automated
alignment and sizing of myocardial stress and rest scans to
three-dimensional normal templates using an image registration
algorithm," J. Nucl. Med., vol. 36, pp. 1115-1122, 1995.
[1] L. Zhong, Y. Su, L. Gobeawan, S. Sola, R.-S. Tan, J. L. Navia, D. N.
Ghista, T. Chua, J. Guccione, and G. S. Kassab, "Impact of surgical
ventricular restoration on ventricular shape, wall stress, and function in
heart failure patients," Am. J. Physiol. Heart Circ. Physiol., 300(5):
H1653-H1660, 2011.
[2] L. Zhong, Y. Su, S. Y. Yeo, R.-S. Tan, D. N. Ghista, and G. Kassab, "Left
ventricular regional wall curvedness and wall stress assessment in
patients with ischemic dilated cardiomyopathy," Am. J. Physiol. Heart
Circ. Physiol., 296(3): H573-H584, 2009.
[3] Q. Li, L. Zamorano, Z. Jiang, F. Vinas, and F. Diaz, "The application
accuracy of the frameless implantable marker system and analysis of
related affecting factors," in Lecture Notes in Computer Science 1496:
Medical Image Computing and Computer-Assisted Intervention,
MICCAI98, W. M. Wells, A. Colchester, and S. Delp, Eds., 1998, pp.
253-260.
[4] S. Eberl, I. Kanno, R. R. Fulton, A. Ryan, B. F. Hutton, and M. J. Fulham,
"Automated interstudy image registration technique for SPECT and
PET," J. Nucl. Med., vol. 37, no. 1, pp. 137-145, 1996.
[5] G. J. Klein and R. H. Huesman, "Four-dimensional processing of deformable
cardiac PET data," Med. Image Anal., vol. 6, pp. 29-46, 2002.
[6] T. Mäkelä, P. Clarysse, O. Sipilä, N. Pauna, Q. C. Pham, T. Katila, and
I.E. Magnin, "A review of cardiac image registration methods", IEEE
Transaction on Medical Imaging, 21, pp. 1011-1021, 2002.
[7] A. Elen, J. Hermans, J. Ganame, D. Loeckx, J. Bogaert, F. Maes, and P.
Suetens, "Automatic 3-D breath-hold related motion correction of
dynamic multislice MRI", IEEE Transaction on Medical Imaging, 29, pp.
868-878, 2010.
[8] C.-Y. Zhu, R. H. Byrd, P.-H. Lu, and J. Nocedal, "L-BFGS-B: Fortran
Subroutines for large-scale bound constrained optimization," in ACM
Transactions on Mathematical Software (TOMS) Vol. 23 Issue 4, Dec.
1997.
[9] Y. Su, L. Zhong, C.-W. Lim, D. Ghista, T. Chua, and R.-S. Tan, "A
geometrical approach for evaluating left ventricular remodeling in
myocardial infarct patients," Comput. Methods Programs Biomed., 2011,
in press. DOI:10.1016/j.cmpb.2011.03.008
[10] K. McLeish, D. L. G. Hill, D. Atkinson, J. M. Blackall, and R. Razavi, "A
study of the motion and deformation of the heart due to respiration," IEEE
Transaction on Medical Imaging, 21, pp. 1142-1150, 2002.
[11] R. P Woods, Handbook of Medical Imaging: Processing and Analysis.
New York: Academic, 2000, ch. Validation of registration accuracy, pp.
491-497.
[12] S. Pallotta, M. C. Gilardi, V. Bettinardi, G. Rizzo, C. Landoni, G. Striano,
R. Masi, and F. Fazio, "Application of a surface matching image
registration technique to the correlation of cardiac studies in positron
emission tomography by transmission images," Phys. Med. Biol., vol. 40,
pp. 1695-1708, 1995
[13] M. C. Gilardi, G. Rizzo, A. Savi, C. Landoni, V. Bettinardi, C. Rossetti,
G. Striano, and F. N. Fazio, "Correlation of SPECT and PET cardiac
images by a surface matching registration technique," Comput. Med.
Imag. Graph., vol. 22, pp. 391-398, Dec. 1998.
[14] T. J. M├ñkel├ñ, P. Clarysse, J. Lötjönen, O. Sipil├ñ, K. Lauerma, H.
H├ñnninen, E.-P Pyökkimies, J. Nenonen, J. Knuuti, T. Katila, and I. E.
Magnin, "A new method for the registration of cardiac PET and MR
images using deformable model based segmentation of the main thorax
structures," in Lecture Notes in Computer Science 2208: Medical Image
Computing and Computer-Assisted Intervention, MICCAI01, W. J.
Niessen and M. Viergever, Eds., 2001, pp. 557-564.
[15] T. L. Faber, R. W. McColl, R. M. Opperman, J. R. Corbett, and R. M.
Peshock, "Spatial and temporal registration of cardiac SPECT and MR
images: Methods and evaluation," Radiology, vol. 179, no. 3, pp.
857-861, 1991.
[16] S. Sinha, U. Sinha, J. Czernin, G. Porenta, and H. R. Schelbert, "Noninvasive
assessment of myocardial perfusion and metabolism: Feasibility of
registering gated MR and PET images," Amer. J. Roentgenol., vol. 36, pp.
301-307, 1995.
[17] S. Nekolla, T. Ibrahim, T. Balbach, and C. Klein, Understanding Cardiac
Imaging TechniquesÔÇöFrom Basic Pathology to Image Fusion,
Amsterdam, The Netherlands: IOS Press, 2001, vol. 322, ch.
Coregistration and fusion of cardiac magnetic resonance and positron
emission tomography studies, pp. 144-154.
[18] C. M. Gallippi and G. E. Trahey, "Automatic image registration for MR
and ultrasound cardiac images," in Lecture Notes in Computer Science
2082: Information Processing in Medical Imaging, IPMI01, M. F. Insana
and R. M. Leahy, Eds., 2001, pp. 141-147.
[19] L. M. Bidaut and J.-P. Vallee, "Automated registration of dynamic MR
images for the quantification of myocardial perfusion," J. Magn. Res.
Imag., vol. 13, pp. 648-655, 2001.
[20] S. L. Bacharach, M. A. Douglas, R. E. Carson, P. J. Kalkowski, N. M.
Freedman, P. Perrone, and R. O. Bonow, "Three-dimensional registration
of cardiac positron emission tomography attenution scans," Comput.
Vision, Graph. Image Process, vol. 34, no. 2, pp. 311-321, 1993.
[21] T. G. Turkington, T. R. DeGrado, M. W. Hanson, and R. E. Coleman,
"Alignment of dynamic cardiac PET images for correction of motion,"
IEEE Trans. Nucl. Sci., vol. 44, pp. 235-242, Apr. 1997.
[22] G. J. Klein and R. H. Huesman, "Four-dimensional processing of
deformable cardiac PET data," Med. Image Anal., vol. 6, pp. 29-46, 2002.
[23] C. K. Hoh, M. Dahlbom, G. Harris, Y. Choi, R. A. Hawkins, M. E. Philps,
and J. Maddahi, "Automated iterative three-dimensional registration of
positron emission tomography images," J. Nucl. Med., vol. 34, no. 11, pp.
2009-2018, 1993.
[24] D. Dey, P. J. Slomka, L. J. Hahn, and R. Kloiber, "Automatic
three-dimensional multimodality registration using radionuclide
transmission CT attenuation maps: A phantom study," J. Nucl. Med., vol.
40, no. 3, pp. 448-455, 1999.
[25] S. Eberl, I. Kanno, R. R. Fulton, A. Ryan, B. F. Hutton, and M. J. Fulham,
"Automated interstudy image registration technique for SPECT and
PET," J. Nucl. Med., vol. 37, no. 1, pp. 137-145, 1996.
[26] P. J. Slomka, A. H. Gilbert, J. Stephenson, and T. Cradduc, "Automated
alignment and sizing of myocardial stress and rest scans to
three-dimensional normal templates using an image registration
algorithm," J. Nucl. Med., vol. 36, pp. 1115-1122, 1995.
@article{"International Journal of Medical, Medicine and Health Sciences:56753", author = "May-Ling Tan and Yi Su and Chi-Wan Lim and Liang Zhong and Ru-San Tan", title = "Shape Restoration of the Left Ventricle", abstract = "This paper describes an automatic algorithm to restore
the shape of three-dimensional (3D) left ventricle (LV) models created
from magnetic resonance imaging (MRI) data using a geometry-driven
optimization approach. Our basic premise is to restore the LV shape
such that the LV epicardial surface is smooth after the restoration. A
geometrical measure known as the Minimum Principle Curvature (κ2)
is used to assess the smoothness of the LV. This measure is used to
construct the objective function of a two-step optimization process.
The objective of the optimization is to achieve a smooth epicardial
shape by iterative in-plane translation of the MRI slices.
Quantitatively, this yields a minimum sum in terms of the magnitude
of κ
2, when κ2 is negative. A limited memory quasi-Newton algorithm,
L-BFGS-B, is used to solve the optimization problem. We tested our
algorithm on an in vitro theoretical LV model and 10 in vivo
patient-specific models which contain significant motion artifacts. The
results show that our method is able to automatically restore the shape
of LV models back to smoothness without altering the general shape of
the model. The magnitudes of in-plane translations are also consistent
with existing registration techniques and experimental findings.", keywords = "Magnetic Resonance Imaging, Left Ventricle, ShapeRestoration, Principle Curvature, Optimization", volume = "5", number = "11", pages = "592-6", }
