Personalised estimation of the arterial input function for improved pharmacokinetic modelling of colorectal cancer using dceMRI

Benjamin Irving; Lydia Tanner; Monica Enescu; Manav Bhushan; Esme J. Hill; Jamie Franklin; Ewan M. Anderson; Ricky A. Sharma; Julia A. Schnabel; Michael Brady

doi:10.1007/978-3-642-41083-3_15

Personalised estimation of the arterial input function for improved pharmacokinetic modelling of colorectal cancer using dceMRI

Benjamin Irving
, Lydia Tanner
, Monica Enescu
, Manav Bhushan
, Esme J. Hill
, Jamie Franklin
, Ewan M. Anderson
, Ricky A. Sharma
, Julia A. Schnabel
, Michael Brady

University of Oxford
Churchill Hospital

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

dceMRI is becoming a key modality for tumour characterisation and monitoring of response to therapy, because of the ability to identify the underlying tumour physiology. Pharmacokinetic (PK) models relate the contrast enhancement seen in dceMRI to physiological parameters but require accurate measurement of the AIF, the time-dependant contrast concentration in blood plasma. In this study, a novel method is introduced that overcomes the challenges of direct AIF measurement, by automatically estimating the AIF from the tumour tissue. This approach was evaluated on synthetic data (10% noise) and achieved a relative error in K^trans and k_ep of 11.8±3.5% and 25.7±4.7 %, respectively, compared to 41 ±15 % and 60 ±32 % using a population model. The method improved the fit of the PK model to clinical colorectal cancer cases, was stable for independent regions in the tumour, and showed improved localisation of the PK parameters. This demonstrates that personalised AIF estimation can lead to more accurate PK modelling.

Original language	English
Title of host publication	Abdominal Imaging
Subtitle of host publication	Computation and Clinical Applications - 5th International Workshop, Held in Conjunction with MICCAI 2013, Proceedings
Pages	126-135
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-41083-3_15
State	Published - 2013
Externally published	Yes
Event	5th International Workshop on Abdominal Imaging: Computation and Clinical Applications, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - Nagoya, Japan Duration: 22 Sep 2013 → 22 Sep 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8198 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Workshop on Abdominal Imaging: Computation and Clinical Applications, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013
Country/Territory	Japan
City	Nagoya
Period	22/09/13 → 22/09/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

arterial input function
dceMRI
pharmacokinetic modelling

Access to Document

10.1007/978-3-642-41083-3_15

Cite this

Irving, B., Tanner, L., Enescu, M., Bhushan, M., Hill, E. J., Franklin, J., Anderson, E. M., Sharma, R. A., Schnabel, J. A., & Brady, M. (2013). Personalised estimation of the arterial input function for improved pharmacokinetic modelling of colorectal cancer using dceMRI. In Abdominal Imaging: Computation and Clinical Applications - 5th International Workshop, Held in Conjunction with MICCAI 2013, Proceedings (pp. 126-135). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8198 LNCS). https://doi.org/10.1007/978-3-642-41083-3_15

Irving, Benjamin ; Tanner, Lydia ; Enescu, Monica et al. / Personalised estimation of the arterial input function for improved pharmacokinetic modelling of colorectal cancer using dceMRI. Abdominal Imaging: Computation and Clinical Applications - 5th International Workshop, Held in Conjunction with MICCAI 2013, Proceedings. 2013. pp. 126-135 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f41c7ac9973b431c8f15fae2f6aebbf6,

title = "Personalised estimation of the arterial input function for improved pharmacokinetic modelling of colorectal cancer using dceMRI",

abstract = "dceMRI is becoming a key modality for tumour characterisation and monitoring of response to therapy, because of the ability to identify the underlying tumour physiology. Pharmacokinetic (PK) models relate the contrast enhancement seen in dceMRI to physiological parameters but require accurate measurement of the AIF, the time-dependant contrast concentration in blood plasma. In this study, a novel method is introduced that overcomes the challenges of direct AIF measurement, by automatically estimating the AIF from the tumour tissue. This approach was evaluated on synthetic data (10\% noise) and achieved a relative error in Ktrans and kep of 11.8±3.5\% and 25.7±4.7 \%, respectively, compared to 41 ±15 \% and 60 ±32 \% using a population model. The method improved the fit of the PK model to clinical colorectal cancer cases, was stable for independent regions in the tumour, and showed improved localisation of the PK parameters. This demonstrates that personalised AIF estimation can lead to more accurate PK modelling.",

keywords = "arterial input function, dceMRI, pharmacokinetic modelling",

author = "Benjamin Irving and Lydia Tanner and Monica Enescu and Manav Bhushan and Hill, \{Esme J.\} and Jamie Franklin and Anderson, \{Ewan M.\} and Sharma, \{Ricky A.\} and Schnabel, \{Julia A.\} and Michael Brady",

year = "2013",

doi = "10.1007/978-3-642-41083-3\_15",

language = "English",

isbn = "9783642410826",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "126--135",

booktitle = "Abdominal Imaging",

note = "5th International Workshop on Abdominal Imaging: Computation and Clinical Applications, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 ; Conference date: 22-09-2013 Through 22-09-2013",

}

Irving, B, Tanner, L, Enescu, M, Bhushan, M, Hill, EJ, Franklin, J, Anderson, EM, Sharma, RA, Schnabel, JA & Brady, M 2013, Personalised estimation of the arterial input function for improved pharmacokinetic modelling of colorectal cancer using dceMRI. in Abdominal Imaging: Computation and Clinical Applications - 5th International Workshop, Held in Conjunction with MICCAI 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8198 LNCS, pp. 126-135, 5th International Workshop on Abdominal Imaging: Computation and Clinical Applications, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013, Nagoya, Japan, 22/09/13. https://doi.org/10.1007/978-3-642-41083-3_15

Personalised estimation of the arterial input function for improved pharmacokinetic modelling of colorectal cancer using dceMRI. / Irving, Benjamin; Tanner, Lydia; Enescu, Monica et al.
Abdominal Imaging: Computation and Clinical Applications - 5th International Workshop, Held in Conjunction with MICCAI 2013, Proceedings. 2013. p. 126-135 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8198 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Personalised estimation of the arterial input function for improved pharmacokinetic modelling of colorectal cancer using dceMRI

AU - Irving, Benjamin

AU - Tanner, Lydia

AU - Enescu, Monica

AU - Bhushan, Manav

AU - Hill, Esme J.

AU - Franklin, Jamie

AU - Anderson, Ewan M.

AU - Sharma, Ricky A.

AU - Schnabel, Julia A.

AU - Brady, Michael

PY - 2013

Y1 - 2013

N2 - dceMRI is becoming a key modality for tumour characterisation and monitoring of response to therapy, because of the ability to identify the underlying tumour physiology. Pharmacokinetic (PK) models relate the contrast enhancement seen in dceMRI to physiological parameters but require accurate measurement of the AIF, the time-dependant contrast concentration in blood plasma. In this study, a novel method is introduced that overcomes the challenges of direct AIF measurement, by automatically estimating the AIF from the tumour tissue. This approach was evaluated on synthetic data (10% noise) and achieved a relative error in Ktrans and kep of 11.8±3.5% and 25.7±4.7 %, respectively, compared to 41 ±15 % and 60 ±32 % using a population model. The method improved the fit of the PK model to clinical colorectal cancer cases, was stable for independent regions in the tumour, and showed improved localisation of the PK parameters. This demonstrates that personalised AIF estimation can lead to more accurate PK modelling.

AB - dceMRI is becoming a key modality for tumour characterisation and monitoring of response to therapy, because of the ability to identify the underlying tumour physiology. Pharmacokinetic (PK) models relate the contrast enhancement seen in dceMRI to physiological parameters but require accurate measurement of the AIF, the time-dependant contrast concentration in blood plasma. In this study, a novel method is introduced that overcomes the challenges of direct AIF measurement, by automatically estimating the AIF from the tumour tissue. This approach was evaluated on synthetic data (10% noise) and achieved a relative error in Ktrans and kep of 11.8±3.5% and 25.7±4.7 %, respectively, compared to 41 ±15 % and 60 ±32 % using a population model. The method improved the fit of the PK model to clinical colorectal cancer cases, was stable for independent regions in the tumour, and showed improved localisation of the PK parameters. This demonstrates that personalised AIF estimation can lead to more accurate PK modelling.

KW - arterial input function

KW - dceMRI

KW - pharmacokinetic modelling

UR - https://www.scopus.com/pages/publications/84886570291

U2 - 10.1007/978-3-642-41083-3_15

DO - 10.1007/978-3-642-41083-3_15

M3 - Conference contribution

AN - SCOPUS:84886570291

SN - 9783642410826

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 126

EP - 135

BT - Abdominal Imaging

T2 - 5th International Workshop on Abdominal Imaging: Computation and Clinical Applications, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013

Y2 - 22 September 2013 through 22 September 2013

ER -

Irving B, Tanner L, Enescu M, Bhushan M, Hill EJ, Franklin J et al. Personalised estimation of the arterial input function for improved pharmacokinetic modelling of colorectal cancer using dceMRI. In Abdominal Imaging: Computation and Clinical Applications - 5th International Workshop, Held in Conjunction with MICCAI 2013, Proceedings. 2013. p. 126-135. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-41083-3_15

Personalised estimation of the arterial input function for improved pharmacokinetic modelling of colorectal cancer using dceMRI

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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