MARS-MD: Rejection based image domain material decomposition

C. J. Bateman; D. Knight; B. Brandwacht; J. Mc Mahon; J. Healy; R. Panta; R. Aamir; K. Rajendran; M. Moghiseh; M. Ramyar; D. Rundle; J. Bennett; N. De Ruiter; D. Smithies; S. T. Bell; R. Doesburg; A. Chernoglazov; V. B.H. Mandalika; M. Walsh; M. Shamshad; M. Anjomrouz; A. Atharifard; L. Vanden Broeke; S. Bheesette; T. Kirkbride; N. G. Anderson; S. P. Gieseg; T. Woodfield; P. F. Renaud; A. P.H. Butler; P. H. Butler

doi:10.1088/1748-0221/13/05/P05020

MARS-MD: Rejection based image domain material decomposition

C. J. Bateman, D. Knight, B. Brandwacht, J. Mc Mahon, J. Healy, R. Panta, R. Aamir, K. Rajendran, M. Moghiseh, M. Ramyar, D. Rundle, J. Bennett, N. De Ruiter, D. Smithies, S. T. Bell, R. Doesburg, A. Chernoglazov, V. B.H. Mandalika, M. Walsh, M. ShamshadM. Anjomrouz, A. Atharifard, L. Vanden Broeke, S. Bheesette, T. Kirkbride, N. G. Anderson, S. P. Gieseg, T. Woodfield, P. F. Renaud, A. P.H. Butler, P. H. Butler

Radiology

Research output: Contribution to journal › Article › peer-review

Abstract

This paper outlines image domain material decomposition algorithms that have been routinely used in MARS spectral CT systems. These algorithms (known collectively as MARS-MD) are based on a pragmatic heuristic for solving the under-determined problem where there are more materials than energy bins. This heuristic contains three parts: (1) splitting the problem into a number of possible sub-problems, each containing fewer materials; (2) solving each sub-problem; and (3) applying rejection criteria to eliminate all but one sub-problem's solution. An advantage of this process is that different constraints can be applied to each sub-problem if necessary. In addition, the result of this process is that solutions will be sparse in the material domain, which reduces crossover of signal between material images. Two algorithms based on this process are presented: the Segmentation variant, which uses segmented material classes to define each sub-problem; and the Angular Rejection variant, which defines the rejection criteria using the angle between reconstructed attenuation vectors.

Original language	English (US)
Article number	P05020
Journal	Journal of Instrumentation
Volume	13
Issue number	5
DOIs	https://doi.org/10.1088/1748-0221/13/05/P05020
State	Published - May 16 2018

Keywords

Computerized Tomography (CT) and Computed Radiography (CR)
Data processing methods
Medical-image reconstruction methods and algorithms, computer-aided diagnosis

ASJC Scopus subject areas

Instrumentation
Mathematical Physics

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10.1088/1748-0221/13/05/P05020

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Bateman, C. J., Knight, D., Brandwacht, B., Mahon, J. M., Healy, J., Panta, R., Aamir, R., Rajendran, K., Moghiseh, M., Ramyar, M., Rundle, D., Bennett, J., De Ruiter, N., Smithies, D., Bell, S. T., Doesburg, R., Chernoglazov, A., Mandalika, V. B. H., Walsh, M., ... Butler, P. H. (2018). MARS-MD: Rejection based image domain material decomposition. Journal of Instrumentation, 13(5), Article P05020. https://doi.org/10.1088/1748-0221/13/05/P05020

Bateman, CJ, Knight, D, Brandwacht, B, Mahon, JM, Healy, J, Panta, R, Aamir, R, Rajendran, K, Moghiseh, M, Ramyar, M, Rundle, D, Bennett, J, De Ruiter, N, Smithies, D, Bell, ST, Doesburg, R, Chernoglazov, A, Mandalika, VBH, Walsh, M, Shamshad, M, Anjomrouz, M, Atharifard, A, Broeke, LV, Bheesette, S, Kirkbride, T, Anderson, NG, Gieseg, SP, Woodfield, T, Renaud, PF, Butler, APH & Butler, PH 2018, 'MARS-MD: Rejection based image domain material decomposition', Journal of Instrumentation, vol. 13, no. 5, P05020. https://doi.org/10.1088/1748-0221/13/05/P05020

@article{db050fb4abf44a78bf0d3b46405772cf,

title = "MARS-MD: Rejection based image domain material decomposition",

abstract = "This paper outlines image domain material decomposition algorithms that have been routinely used in MARS spectral CT systems. These algorithms (known collectively as MARS-MD) are based on a pragmatic heuristic for solving the under-determined problem where there are more materials than energy bins. This heuristic contains three parts: (1) splitting the problem into a number of possible sub-problems, each containing fewer materials; (2) solving each sub-problem; and (3) applying rejection criteria to eliminate all but one sub-problem's solution. An advantage of this process is that different constraints can be applied to each sub-problem if necessary. In addition, the result of this process is that solutions will be sparse in the material domain, which reduces crossover of signal between material images. Two algorithms based on this process are presented: the Segmentation variant, which uses segmented material classes to define each sub-problem; and the Angular Rejection variant, which defines the rejection criteria using the angle between reconstructed attenuation vectors.",

keywords = "Computerized Tomography (CT) and Computed Radiography (CR), Data processing methods, Medical-image reconstruction methods and algorithms, computer-aided diagnosis",

author = "Bateman, {C. J.} and D. Knight and B. Brandwacht and Mahon, {J. Mc} and J. Healy and R. Panta and R. Aamir and K. Rajendran and M. Moghiseh and M. Ramyar and D. Rundle and J. Bennett and {De Ruiter}, N. and D. Smithies and Bell, {S. T.} and R. Doesburg and A. Chernoglazov and Mandalika, {V. B.H.} and M. Walsh and M. Shamshad and M. Anjomrouz and A. Atharifard and Broeke, {L. Vanden} and S. Bheesette and T. Kirkbride and Anderson, {N. G.} and Gieseg, {S. P.} and T. Woodfield and Renaud, {P. F.} and Butler, {A. P.H.} and Butler, {P. H.}",

note = "Publisher Copyright: {\textcopyright} 2018 CERN.",

year = "2018",

month = may,

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doi = "10.1088/1748-0221/13/05/P05020",

language = "English (US)",

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T2 - Rejection based image domain material decomposition

AU - Bateman, C. J.

AU - Knight, D.

AU - Brandwacht, B.

AU - Mahon, J. Mc

AU - Healy, J.

AU - Panta, R.

AU - Aamir, R.

AU - Rajendran, K.

AU - Moghiseh, M.

AU - Ramyar, M.

AU - Rundle, D.

AU - Bennett, J.

AU - De Ruiter, N.

AU - Smithies, D.

AU - Bell, S. T.

AU - Doesburg, R.

AU - Chernoglazov, A.

AU - Mandalika, V. B.H.

AU - Walsh, M.

AU - Shamshad, M.

AU - Anjomrouz, M.

AU - Atharifard, A.

AU - Broeke, L. Vanden

AU - Bheesette, S.

AU - Kirkbride, T.

AU - Anderson, N. G.

AU - Gieseg, S. P.

AU - Woodfield, T.

AU - Renaud, P. F.

AU - Butler, A. P.H.

AU - Butler, P. H.

PY - 2018/5/16

Y1 - 2018/5/16

N2 - This paper outlines image domain material decomposition algorithms that have been routinely used in MARS spectral CT systems. These algorithms (known collectively as MARS-MD) are based on a pragmatic heuristic for solving the under-determined problem where there are more materials than energy bins. This heuristic contains three parts: (1) splitting the problem into a number of possible sub-problems, each containing fewer materials; (2) solving each sub-problem; and (3) applying rejection criteria to eliminate all but one sub-problem's solution. An advantage of this process is that different constraints can be applied to each sub-problem if necessary. In addition, the result of this process is that solutions will be sparse in the material domain, which reduces crossover of signal between material images. Two algorithms based on this process are presented: the Segmentation variant, which uses segmented material classes to define each sub-problem; and the Angular Rejection variant, which defines the rejection criteria using the angle between reconstructed attenuation vectors.

AB - This paper outlines image domain material decomposition algorithms that have been routinely used in MARS spectral CT systems. These algorithms (known collectively as MARS-MD) are based on a pragmatic heuristic for solving the under-determined problem where there are more materials than energy bins. This heuristic contains three parts: (1) splitting the problem into a number of possible sub-problems, each containing fewer materials; (2) solving each sub-problem; and (3) applying rejection criteria to eliminate all but one sub-problem's solution. An advantage of this process is that different constraints can be applied to each sub-problem if necessary. In addition, the result of this process is that solutions will be sparse in the material domain, which reduces crossover of signal between material images. Two algorithms based on this process are presented: the Segmentation variant, which uses segmented material classes to define each sub-problem; and the Angular Rejection variant, which defines the rejection criteria using the angle between reconstructed attenuation vectors.

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JO - Journal of Instrumentation

JF - Journal of Instrumentation

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ER -

MARS-MD: Rejection based image domain material decomposition

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Keywords

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