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Treatment planning of intracranial targets on MRI derived substitute CT data
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, Oncology.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
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2013 (English)In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 108, no 1, 118-122 p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: The use of magnetic resonance imaging (MRI) as a complement to computed tomography (CT) in the target definition procedure for radiotherapy is increasing. To eliminate systematic uncertainties due to image registration, a workflow based entirely on MRI may be preferable. In the present pilot study, we investigate dose calculation accuracy for automatically generated substitute CT (s-CT) images of the head based on MRI. We also produce digitally reconstructed radiographs (DRRs) from s-CT data to evaluate the feasibility of patient positioning based on MR images. METHODS AND MATERIALS: Five patients were included in the study. The dose calculation was performed on CT, s-CT, s-CT data without inhomogeneity correction and bulk density assigned MRI images. Evaluation of the results was performed using point dose and dose volume histogram (DVH) comparisons, and gamma index evaluation. RESULTS: The results demonstrate that the s-CT images improves the dose calculation accuracy compared to the method of non-inhomogeneity corrected dose calculations (mean improvement 2.0 percentage points) and that it performs almost identically to the method of bulk density assignment. The s-CT based DRRs appear to be adequate for patient positioning of intra-cranial targets, although further investigation is needed on this subject. CONCLUSIONS: The s-CT method is very fast and yields data that can be used for treatment planning without sacrificing accuracy.

Place, publisher, year, edition, pages
2013. Vol. 108, no 1, 118-122 p.
Keyword [en]
magnetic resonance, radiotherapy, treatment planning, dose calculation, substitute CT
National Category
Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging Cancer and Oncology
Research subject
radiofysik
Identifiers
URN: urn:nbn:se:umu:diva-68958DOI: 10.1016/j.radonc.2013.04.028ISI: 000324155900018PubMedID: 23830190OAI: oai:DiVA.org:umu-68958DiVA: diva2:619141
Note

Included in thesis in manuscript form.

Available from: 2013-05-02 Created: 2013-05-02 Last updated: 2017-12-06Bibliographically 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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