Investigating CT to CBCT image registration for head and neck proton therapy as a tool for daily dose recalculation.
OBJECTIVE
Intensity modulated proton therapy (IMPT) of head and neck (H&N) cancer patients may be improved by plan adaptation. The decision to adapt the treatment plan based on a dose recalculation on the current anatomy requires a diagnostic quality computed tomography (CT) scan of the patient. As gantry-mounted cone beam CT (CBCT) scanners are currently being offered by vendors, they may offer daily or weekly updates of patient anatomy. CBCT image quality may not be sufficient for accurate proton dose calculation and it is likely necessary to perform CBCT CT number correction. In this work, the authors investigated deformable image registration (DIR) of the planning CT (pCT) to the CBCT to generate a virtual CT (vCT) to be used for proton dose recalculation.


METHODS
Datasets of six H&N cancer patients undergoing photon intensity modulated radiation therapy were used in this study to validate the vCT approach. Each dataset contained a CBCT acquired within 3 days of a replanning CT (rpCT), in addition to a pCT. The pCT and rpCT were delineated by a physician. A Morphons algorithm was employed in this work to perform DIR of the pCT to CBCT following a rigid registration of the two images. The contours from the pCT were deformed using the vector field resulting from DIR to yield a contoured vCT. The DIR accuracy was evaluated with a scale invariant feature transform (SIFT) algorithm comparing automatically identified matching features between vCT and CBCT. The rpCT was used as reference for evaluation of the vCT. The vCT and rpCT CT numbers were converted to stopping power ratio and the water equivalent thickness (WET) was calculated. IMPT dose distributions from treatment plans optimized on the pCT were recalculated with a Monte Carlo algorithm on the rpCT and vCT for comparison in terms of gamma index, dose volume histogram (DVH) statistics as well as proton range. The DIR generated contours on the vCT were compared to physician-drawn contours on the rpCT.


RESULTS
The DIR accuracy was better than 1.4 mm according to the SIFT evaluation. The mean WET differences between vCT (pCT) and rpCT were below 1 mm (2.6 mm). The amount of voxels passing 3%/3 mm gamma criteria were above 95% for the vCT vs rpCT. When using the rpCT contour set to derive DVH statistics from dose distributions calculated on the rpCT and vCT the differences, expressed in terms of 30 fractions of 2 Gy, were within [-4, 2 Gy] for parotid glands (D(mean)), spinal cord (D(2%)), brainstem (D(2%)), and CTV (D(95%)). When using DIR generated contours for the vCT, those differences ranged within [-8, 11 Gy].


CONCLUSIONS
In this work, the authors generated CBCT based stopping power distributions using DIR of the pCT to a CBCT scan. DIR accuracy was below 1.4 mm as evaluated by the SIFT algorithm. Dose distributions calculated on the vCT agreed well to those calculated on the rpCT when using gamma index evaluation as well as DVH statistics based on the same contours. The use of DIR generated contours introduced variability in DVH statistics.
Department of Medical Physics, Ludwig-Maximilians-University, Munich D85748, Germany and Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany.
Guillaume
G
Landry
Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany.
Reinoud
R
Nijhuis
Department of Medical Physics, Ludwig-Maximilians-University, Munich D85748, Germany.
George
G
Dedes
Department of Medical Physics, Ludwig-Maximilians-University, Munich D85748, Germany.
Josefine
J
Handrack
Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany.
Christian
C
Thieke
ICTEAM, Université Catholique de Louvain, Louvain-La-Neuve B1348, Belgium.
Guillaume
G
Janssens
ICTEAM, Université Catholique de Louvain, Louvain-La-Neuve B1348, Belgium.
Jonathan
J
Orban de Xivry
Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany.
Michael
M
Reiner
Department of Radiation Oncology, Technische Universität München, Klinikum rechts der Isar, Munich D81675, Germany and Physik-Department, Technische Universität München, Garching D85748, Germany.
Florian
F
Kamp
Department of Radiation Oncology, Technische Universität München, Klinikum rechts der Isar, Munich D81675, Germany and Physik-Department, Technische Universität München, Garching D85748, Germany.
Jan J
JJ
Wilkens
Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milan 20133, Italy.
Chiara
C
Paganelli
Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milan 20133, Italy.
Marco
M
Riboldi
Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milan 20133, Italy.
Guido
G
Baroni
Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany.
Ute
U
Ganswindt
Department of Radiation Oncology, Ludwig-Maximilians-University, Munich D81377, Germany.
Claus
C
Belka
Department of Medical Physics, Ludwig-Maximilians-University, Munich D85748, Germany.
Katia
K
Parodi
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D054893:Cone-Beam Computed Tomography; D005260:Female; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D061766:Proton Therapy; D011829:Radiation Dosage; D011874:Radiometry; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated