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Treatment planning using MRI data: an analysis of the dose calculation accuracy for different treatment regions
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
2010 (English)In: Radiation Oncology, ISSN 1748-717X, Vol. 5, 62- p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Because of superior soft tissue contrast, the use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To keep the workflow simple and cost effective and to reduce patient dose, it is natural to strive for a treatment planning procedure based entirely on MRI. In the present study, we investigate the dose calculation accuracy for different treatment regions when using bulk density assignments on MRI data and compare it to treatment planning that uses CT data.

METHODS: MR and CT data were collected retrospectively for 40 patients with prostate, lung, head and neck, or brain cancers. Comparisons were made between calculations on CT data with and without inhomogeneity corrections and on MRI or CT data with bulk density assignments. The bulk densities were assigned using manual segmentation of tissue, bone, lung, and air cavities.

RESULTS: The deviations between calculations on CT data with inhomogeneity correction and on bulk density assigned MR data were small. The maximum difference in the number of monitor units required to reach the prescribed dose was 1.6%. This result also includes effects of possible geometrical distortions.

CONCLUSIONS: The dose calculation accuracy at the investigated treatment sites is not significantly compromised when using MRI data when adequate bulk density assignments are made. With respect to treatment planning, MRI can replace CT in all steps of the treatment workflow, reducing the radiation exposure to the patient, removing any systematic registration errors that may occur when combining MR and CT, and decreasing time and cost for the extra CT investigation.

Place, publisher, year, edition, pages
2010. Vol. 5, 62- p.
National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
radiofysik
Identifiers
URN: urn:nbn:se:umu:diva-35610DOI: 10.1186/1748-717X-5-62ISI: 000280269500002PubMedID: 20591179OAI: oai:DiVA.org:umu-35610DiVA: diva2:345613
Available from: 2010-08-26 Created: 2010-08-26 Last updated: 2013-05-15Bibliographically approved
In thesis
1. Integration of MRI into the radiotherapy workflow
Open this publication in new window or tab >>Integration of MRI into the radiotherapy workflow
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The modern day radiotherapy treatments are almost exclusively based on computed tomography (CT) images. The CT images are acquired using x-rays, and therefore reflect the radiation interaction properties of the material. This information is used to perform accurate dose calculation by the treatment planning system, and the data is also well suited for creating digitally reconstructed radiographs for comparing patient set up at the treatment machine where x-ray images are routinely acquired for this purpose.

The magnetic resonance (MR) scanner has many attractive features for radiotherapy purposes. The soft tissue contrast as compared to CT is far superior, and it is possible to vary the sequences in order to visualize different anatomical and physiological properties of an organ. Both of these properties may contribute to an increase in accuracy of radiotherapy treatment.

Using the MR images by themselves for treatment planning is, however, problematic. MR data reflects the magnetic properties of protons, and thus have no connection to the radiointeraction properties of the material. MRI also has inherent difficulty in imaging bone, which will appear in images as areas of no signal similar to air. This makes both dose calculation and patient positioning at the treatment machine troublesome.

There are several clinics that use MR images together with CT images to perform treatment planning. The images are registered to a common coordinate system, a process often described as image fusion. In these cases, the MR images are primarily used for target definition and the CT images are used for dose calculations. This method is now not ideal, however, since the image fusion may introduce systematic uncertainties into the treatment due to the fact that the tumor is often able to move relatively freely with respect to the patients’ bony anatomy and outer contour, especially when the image registration algorithms take the entire patient anatomy in the volume of interest into account.

The work presented in the thesis “Integration of MRI into the radiotherapy workflow” aim towards investigating the possibilities of workflows based entirely on MRI without using image registration, as well as workflows using image registration methods that are better suited for targets that can move with respect to surrounding bony anatomy, such as the prostate.

Abstract [sv]

Modern strålterapi av cancer baseras nästan helt på datortomografiska (CT) bilder. CT bilder tas med hjälp av röntgenfotoner, och återger därför hur det avbildade materialet växelverkar med strålning. Denna information används för att utföra noggranna dosberäkningar i ett dosplaneringssystem, och data från CT bilder lämpar sig också väl för att skapa digitalt rekonstruerade röntgenbilder vilka kan användas för att verifiera patientens position vid behandling.

Bildgivande magnetresonanstomografi (MRI) har många egenskaper som är intressanta för radioterapi. Mjukdelskontrasten i MR bilder är överlägsen CT, och det är möjligt att i stor utstäckning variera sekvensparametrar för att synliggöra olika anatomiska och funktionella attribut hos ett organ. Dessa bägge egenskaper kan bidra till ökad noggrannhet i strålbehandling av cancer.

Att använda enbart MR bilder som planeringsunderlag för radioterapi är dock problematiskt. MR data reflekterar magnetiska attribut hos protoner, och har därför ingen koppling till materialets egenskaper då det gäller strålningsväxelverkan. Dessutom är det komplicerat att avbilda ben med MR; ben uppträder som områden av signalförlust i bilderna, på samma sätt som luft gör. Detta gör det svårt att utföra noggranna dosberäkningar och positionera patienten vid behandling.

Många moderna kliniker använder redan idag MR tillsammans med CT under dosplanering. Bilderna registreras till ett gemensamt koordinatsystem i en process som kallas bildfusion. I dessa fall används MR bilderna primärt som underlag för utlinjering av tumör, eller target, och CT bilderna används som grund för dosberäkningar. Denna metod är dock inte ideal, då bildregistreringen kan införa systematiska geometriska fel i behandlingen. Detta på grund av att tumörer ofta är fria att röra sig relativt patientens skelett och yttre kontur, och många bildregistreringsalgoritmer tar hänsyn till hela bildvolymen.

Arbetet som presenteras i denna avhandling syftar till att undersöka möjligheterna med arbetsflöden som baseras helt på MR data utan bildregistrering, samt arbetsflöden som använder bildregistrerings-algoritmer som är bättre anpassade för tumörer som kan röra sig i förhållande till patientens övriga anatomi, som till exempel prostatacancer.

Place, publisher, year, edition, pages
Umeå: Umeå Universitet, 2013. 73 p.
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1573
Keyword
magnetic resonance imaging; radiotherapy; treatment planning; image registration; workflow
National Category
Medical Image Processing
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-68959 (URN)978-91-7459-622-9 (ISBN)978-91-7459-621-2 (ISBN)
Public defence
2013-05-24, E04, byggnad 6E, Norrlands universitetssjukhus, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2013-05-03 Created: 2013-05-02 Last updated: 2014-05-12Bibliographically approved

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Jonsson, Joakim HKarlsson, Magnus GKarlsson, MikaelNyholm, Tufve

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