| 1. |
- Källman, Hans-Erik
(författare)
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Dose Management in Diagnostic Radiology - application of the DICOM imaging standard and a Monte Carlo dose engine for exposure surveillance
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Ionizing radiation is used in diagnostic radiology with a large contribution to the health of the patients. The regulations to limit the detrimental effects, e.g. cancer induction, are based on recommendations from the International Commission on Radiological Protection (ICRP). Epidemiological evidence for radiation induced cancer is expressed as a function of absorbed dose in the irradiated organs. The committee for Biological Effects of Ionizing Radiation has favored the use of Lifetime Attributable Risk, a risk estimator applicable to individuals exposed in medical applications. The imaging in radiology complies with a technical standard that potentiates the retrieval of exposure information that can be used in optimization of patient exposure. The information can also be used as input in organ dose calculations.The aims were to apply the benefits of the technical image standard to radiation safety management by automated collection and analysis of exposure data and to adapt a Therapy Planning System (TPS) for radiotherapy to calculate dose for a Computed Tomography (CT) machine.An automated workflow for extraction, communication and analysis of exposure data from the image files in the central image archive was defined and implemented at the institution (papers I-II). A source model for Monte Carlo simulation of the CT was developed taking into consideration the energy spectrum of the photons, the spiral movement of the X-ray beam, the beam shaping filter and the tube current modulation (paper III). The source model was used exploring the possibilities to utilize the tissue characterization methods and segmentation tools available in the TPS to devise a strategy to automate organ dose calculations for patients undergoing thorax examinations in a CT (paper IV).The exposure data workflow was finalized showing that the technical standard for images could supply a framework for automated assembly and analysis of the data, supporting the local implementation of optimization. The CT was modeled with regard to its irradiation characteristics with uncertainties in the dose calculations below 4%. Dose calculations with the tissue characterization methods available in the TPS deviated by less than 2% from measurements and a strategy for automation of organ dose calculations was devised that could facilitate individual risk estimates in CT.
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| 2. |
- Tilly, David, 1974-
(författare)
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Probabilistic treatment planning based on dose coverage : How to quantify and minimize the effects of geometric uncertainties in radiotherapy
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Traditionally, uncertainties are handled by expanding the irradiated volume to ensure target dose coverage to a certain probability. The uncertainties arise from e.g. the uncertainty in positioning of the patient at every fraction, organ motion and in defining the region of interests on the acquired images. The applied margins are inherently population based and do not exploit the geometry of the individual patient. Probabilistic planning on the other hand incorporates the uncertainties directly into the treatment optimization and therefore has more degrees of freedom to tailor the dose distribution to the individual patient. The aim of this thesis is to create a framework for probabilistic evaluation and optimization based on the concept of dose coverage probabilities. Several computational challenges for this purpose are addressed in this thesis.The accuracy of the fraction by fraction accumulated dose depends directly on the accuracy of the deformable image registration (DIR). Using the simulation framework, we could quantify the requirements on the DIR to 2 mm or less for a 3% uncertainty in the target dose coverage.Probabilistic planning is computationally intensive since many hundred treatments must be simulated for sufficient statistical accuracy in the calculated treatment outcome. A fast dose calculation algorithm was developed based on the perturbation of a pre-calculated dose distribution with the local ratio of the simulated treatment’s fluence and the fluence of the pre-calculated dose. A speedup factor of ~1000 compared to full dose calculation was achieved with near identical dose coverage probabilities for a prostate treatment.For some body sites, such as the cervix dataset in this work, organ motion must be included for realistic treatment simulation. A statistical shape model (SSM) based on principal component analysis (PCA) provided the samples of deformation. Seven eigenmodes from the PCA was sufficient to model the dosimetric impact of the interfraction deformation.A probabilistic optimization method was developed using constructs from risk management of stock portfolios that enabled the dose planner to request a target dose coverage probability. Probabilistic optimization was for the first time applied to dataset from cervical cancer patients where the SSM provided samples of deformation. The average dose coverage probability of all patients in the dataset was within 1% of the requested.
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| 3. |
- Andersson, Karin M., 1989-, et al.
(författare)
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Evaluation of two commercial CT metal artifact reduction algorithms for use in proton radiotherapy treatment planning in the head and neck area
- 2018
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Ingår i: Medical physics (Lancaster). - : Wiley-Blackwell Publishing Inc.. - 0094-2405 .- 2473-4209. ; 45:10, s. 4329-4344
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: To evaluate two commercial CT metal artifact reduction (MAR) algorithms for use in proton treatment planning in the head and neck (H&N) area.METHODS: An anthropomorphic head phantom with removable metallic implants (dental fillings or neck implant) was CT-scanned to evaluate the O-MAR (Philips) and the iMAR (Siemens) algorithms. Reference images were acquired without any metallic implants in place. Water equivalent thickness (WET) was calculated for different path directions and compared between image sets. Images were also evaluated for use in proton treatment planning for parotid, tonsil, tongue base, and neck node targets. The beams were arranged so as to not traverse any metal prior to the target, enabling evaluation of the impact on dose calculation accuracy from artifacts surrounding the metal volume. Plans were compared based on γ analysis (1 mm distance-to-agreement/1% difference in local dose) and dose volume histogram metrics for targets and organs at risk (OARs). Visual grading evaluation of 30 dental implant patient MAR images was performed by three radiation oncologists.RESULTS: In the dental fillings images, ΔWET along a low-density streak was reduced from -17.0 to -4.3 mm with O-MAR and from -16.1 mm to -2.3 mm with iMAR, while for other directions the deviations were increased or approximately unchanged when the MAR algorithms were used. For the neck implant images, ΔWET was generally reduced with MAR but residual deviations remained (of up to -2.3 mm with O-MAR and of up to -1.5 mm with iMAR). The γ analysis comparing proton dose distributions for uncorrected/MAR plans and corresponding reference plans showed passing rates >98% of the voxels for all phantom plans. However, substantial dose differences were seen in areas of most severe artifacts (γ passing rates of down to 89% for some cases). MAR reduced the deviations in some cases, but not for all plans. For a single patient case dosimetrically evaluated, minor dose differences were seen between the uncorrected and MAR plans (γ passing rate approximately 97%). The visual grading of patient images showed that MAR significantly improved image quality (P < 0.001).CONCLUSIONS: O-MAR and iMAR significantly improved image quality in terms of anatomical visualization for target and OAR delineation in dental implant patient images. WET calculations along several directions, all outside the metallic regions, showed that both uncorrected and MAR images contained metal artifacts which could potentially lead to unacceptable errors in proton treatment planning. ΔWET was reduced by MAR in some areas, while increased or unchanged deviations were seen for other path directions. The proton treatment plans created for the phantom images showed overall acceptable dose distributions differences when compared to the reference cases, both for the uncorrected and MAR images. However, substantial dose distribution differences in the areas of most severe artifacts were seen for some plans, which were reduced by MAR in some cases but not all. In conclusion, MAR could be beneficial to use for proton treatment planning; however, case-by-case evaluations of the metal artifact-degraded images are always recommended.
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| 4. |
- Grönlund, Eric, 1987-
(författare)
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Dose painting : Can radiotherapy be improved with image driven dose-responses derived from retrospective radiotherapy data?
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The main aim of curative radiotherapy for cancer is to prescribe and deliver doses that eradicate the tumor and spare the normal healthy tissues. Radiotherapy is commonly performed by delivering a homogeneous radiation dose to the tumor. However, concern have been raised that functional imaging methods such as magnetic resonance imaging (MRI) and positron emission tomography (PET) can provide a basis for prescribing heterogeneous doses - higher doses in malignant regions of the tumor and less dose where the tumor is less malignant. This form of radiotherapy is called “dose painting” and has the aim of utilizing the radiant energy as efficiently as possible to increase the tumor control probability (TCP) and to reduce the risk for unwanted side effects of the neighboring normal tissues.In this project we have studied how dose painting prescriptions could be derived through retrospectively analyzing pre-RT image data and post-RT outcomes for two different patient groups: one diagnosed with head and neck cancer with pre-RT fluorodeoxyglucose (18F-FDG) PET image data; and one patient group diagnosed with prostate cancer with pre-RT Gleason score data that were constructed to be mapped from apparent diffusion coefficient (ADC) data acquired from MRI. The resulting dose painting prescriptions for each of these diagnoses indicated that the TCP could be increased without increasing the average dose to the tumor volumes as compared to homogeneous dose treatments. These TCP increases were more noticeable when the tumors were larger and more heterogeneous than for smaller and more homogeneous tumors.We have also studied the potential to realize TCP increases with dose painting in comparison to homogeneous dose treatments by optimizing clinically deliverable dose painting plans for both diagnoses, i.e. head and neck cancer and prostate cancer. These plans were optimized with minimax optimization that aimed to maximize a robust TCP increase by considering uncertainties of the patient geometry. These plan optimizations indicated that the TCP compared to homogeneous dose treatments was increasing and robust regarding uncertainties of the patient geometry.
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