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Background: Dose painting by numbers (DPBN) is a method to deliver an inhomogeneous tumor dose voxel-by-voxel with a prescription based on biological medical images. However, planning of DPBN is not supported by commercial treatment planning systems (TPS) today. Here, a straightforward method for DPBN with a standard TPS is presented., Material and Methods: DPBN tumor dose prescription maps were generated from (18)F-FDG-PET images applying a linear relationship between image voxel value and dose. An inverted DPBN prescription map was created and imported into a standard TPS where it was defined as a mock pre-treated dose. Using inverse optimization for the summed dose, a planned DPBN dose distribution was created. The procedure was tested in standard TPS for three different tumor cases; cervix, lung and head and neck. The treatment plans were compared to the prescribed DPBN dose distribution by three-dimensional (3D) gamma analysis and quality factors (QFs). Delivery of the DPBN plans was assessed with portal dosimetry (PD)., Results: Maximum tumor doses of 149%, 140% and 151% relative to the minimum tumor dose were prescribed for the cervix, lung and head and neck case, respectively. DPBN distributions were well achieved within the tumor whilst normal tissue doses were within constraints. Generally, high gamma pass rates (> 89% at 2%/2 mm) and low QFs (< 2.6%) were found. PD showed that all DPBN plans could be successfully delivered., Conclusions: The presented methodology enables the use of currently available TPSs for DPBN planning and delivery and may therefore pave the way for clinical implementation.

Introduction: Our primary aim was to make a phantom for PET that could mimic a highly irregular tumour and provide true tumour contours. The secondary aim was to use the phantom to assess the accuracy of different methods for delineation of tumour volume from the PET images., Material and Methods: An empty mould was produced on the basis of a contrast enhanced computed tomography (CT) study of a patient with a squamous cell carcinoma in the head and neck region. The mould was filled with a homogeneous fast-settling gel that contained both (18)F for positron emission tomography (PET) and an iodine contrast agent. This phantom (mould and gel) was scanned on a PET/CT scanner. A series of reference tumour contours were obtained from the CT images in the PET/CT. Tumour delineation based on the PET images was achieved manually, by isoSUV thresholding, and by a recently developed three-dimensional (3D) Difference of Gaussians algorithm (DoG). Average distances between the PET-derived and reference contours were assessed by a 3D distance transform., Results: The manual, thresholding and DoG delineation methods resulted in volumes that were 146%, 86% and 100% of the reference volume, respectively, and average distance deviations from the reference surface were 1.57 mm, 1.48 mm and 1.0, mm, respectively., Discussion: Manual drawing as well as isoSUV determination of tumour contours in geometrically irregular tumours may be unreliable. The DoG method may contribute to more correct delineation of the tumour. Although the present phantom had a homogeneous distribution of activity, it may also provide useful knowledge in the case of inhomogeneous activity distributions., Conclusion: The geometric irregular tumour phantom with its inherent reference contours was an important tool for testing of different delineation methods and for teaching delineation.

Cone beam CT (CBCT) imaging has become an integral part of radiation therapy, with images typically used for offline or online patient setup corrections based on bony anatomy co-registration. Ideally, the co-registration should be based on tumor localization. However, soft tissue contrast in CBCT images may be limited. In the present work, contrast enhanced CBCT (CECBCT) images were used for tumor visualization and treatment adaptation. Material and methods. A spontaneous canine maxillary tumor was subjected to repeated cone beam CT imaging during fractionated radiotherapy (10 fractions in total). At five of the treatment fractions, CECBCT images, employing an iodinated contrast agent, were acquired, as well as pre-contrast CBCT images. The tumor was clearly visible in post-contrast minus pre-contrast subtraction images, and these contrast images were used to delineate gross tumor volumes. IMRT dose plans were subsequently generated. Four different strategies were explored: 1) fully adapted planning based on each CECBCT image series, 2) planning based on images acquired at the first treatment fraction and patient repositioning following bony anatomy co-registration, 3) as for 2), but with patient repositioning based on co-registering contrast images, and 4) a strategy with no patient repositioning or treatment adaptation. The equivalent uniform dose (EUD) and tumor control probability (TCP) calculations to estimate treatment outcome for each strategy. Results. Similar translation vectors were found when bony anatomy and contrast enhancement co-registration were compared. Strategy 1 gave EUDs closest to the prescription dose and the highest TCP. Strategies 2 and 3 gave EUDs and TCPs close to that of strategy 1, with strategy 3 being slightly better than strategy 2. Even greater benefits from strategies 1 and 3 are expected with increasing tumor movement or deformation during treatment. The non-adaptive strategy 4 was clearly inferior to all three adaptive strategies. Conclusion. CECBCT may prove useful for adaptive radiotherapy.

Purpose: To present a methodology to estimate optimal treatment margins for radiotherapy of prostate cancer based on interfraction imaging., Materials and Methods: Cone beam CT images of a prostate cancer patient undergoing fractionated radiotherapy were acquired at all treatment sessions. The clinical target volume (CTV) and organs at risk (OARs; bladder and rectum) were delineated in the images. Random sampling from the CTV-OAR library was performed in order to simulate fractionated radiotherapy including intra- and interpatient variability in setup and organ motion/deformation. For each simulated patient, four treatment fields defined by multileaf collimators were automatically generated around the planning CTV. The treatment margin (the distance from the CTV to the field border) was varied between 2.5 and 20 mm. Resulting dose distributions were calculated by a convolution method. Doses to OARs were reconstructed by polynomial warping, while the CTV was assumed to be a rigid body. The equivalent uniform dose (EUD), the tumor control probability (TCP) and the normal tissue complication probability (NTCP) were used to estimate the clinical effect. Patient repositioning strategies at treatment were compared., Results: The simulations produced population based EUD histograms for the CTV and the OARs. The number of patients receiving an optimal target EUD increased with increasing margins, but at the cost of an increasing number receiving a high EUD to the OARs. Calculations of the probability of complication-free tumor control and subsequent analysis gave an optimal treatment margin of about 10mm for the simulated population, if no correction strategy was undertaken., Conclusions: The current work illustrates the principle of optimal treatment margins based on interfraction imaging. Clinically applicable margins may be obtained if a large patient image database is available.

Background: The advantage of MRI-based radiotherapy planning is the superior soft tissue differentiation. However, for accurate patient dose calculations, a conversion of the MR images into Hounsfield CT maps is necessary. The aim of the present study was to investigate the dose accuracy that can be achieved with segmented MR-images derived from the planning CT images by assigning fixed densities to different classes of tissues., Methods: Treatment plans for ten prostate cancer patients were selected. A collapsed cone algorithm was used to calculate patient dose distributions. The dose calculations were based on four different image sets: (1) the original CT-series (DD(DP)), (2) a simulated MR series with all tissue set to a homogenous water equivalent material of density 1.02 g/cm(3) (DD(W)), (3) a simulated MR series with soft tissue set to a water equivalent material with density 1.02 g/cm(3) and the bone set to a density of 1.3 g/cm(3) (DD(W+B1.3)), and (4) a simulated MR series identical to (3) but with a bone density equal to 2.1 g/cm(3) (DD(W+B2.1)). The dose distributions were compared by analysing dose difference histograms as well as through a visual display of spatial dose deviations., Results: The population based minimum, mean and maximum dose difference between the DD(DP) and DD(W) in the target volume was -2.8, -1.0 and 0.6%, respectively. Corresponding differences between DD(DP) and DD(W+B1.3) were -1.6, 0.2 and 1.5%, respectively, and between DD(DP) and DD(W+B2.1) -4.3, 4.2 and 9.7%, respectively. For the rectum, the differences between CT(DP) and the other image sets were in the range of -19.5 to 8.8%. For the bladder, the differences were in the range of -9.6 to 7.0%., Conclusions: A systematic study using segmented MR images was undertaken. To achieve an acceptable accuracy in the CTV dose, the MR images should be segmented into bone and water equivalent tissue. Still, significant dose deviation for the organs at risk may be present. As tissue segmentation in real MR images is introduced, segmentation errors and errors that stem from geometrical non-linearities may further reduce the accuracy.