| 1. |
- Lynggaard Riis, Hans, et al.
(författare)
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Dosimetric validation of the couch and coil model for high-field MR-linac treatment planning
- 2023
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Ingår i: Zeitschrift für Medizinische Physik. - : Elsevier. - 0939-3889 .- 1876-4436. ; 33:4, s. 567-577
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The precision of the dose delivery in radiation therapy with high-field MR-linacs is challenging due to the sub-stantial variation in the beam attenuation of the patient positioning system (PPS) (the couch and coils) as a function of the gantry angle. This work aimed to compare the attenuation of two PPSs located at two different MR-linac sites through measurements and calculations in the treatment planning system (TPS).Methods: Attenuation measurements were performed at every 1 degrees gantry angle at the two sites with a cylindrical water phantom with a Farmer chamber inserted along the rotational axis of the phantom. The phantom was positioned with the chamber reference point (CRP) at the MR-linac isocentre. A compensation strategy was applied to minimise sinusoidal measurement errors due to, e.g. air cavity or setup. A series of tests were performed to assess the sensitivity to measurement uncertainties. The dose to a model of the cylindrical water phantom with the PPS added was calculated in the TPS (Monaco v5.4 as well as in a development version Dev of an upcoming release), for the same gantry angles as for the measurements. The TPS PPS model dependency of the dose calculation voxelisation resolution was also investigated.Results: A comparison of the measured attenuation of the two PPSs yielded differences of less than 0.5% for most gantry angles. The maximum deviation between the attenuation measurements for the two different PPSs exceeded +/- 1% at two specific gantry angles 115 degrees and 245 degrees, where the beam traverses the most complex PPS structures. The attenuation increases from 0% to 25% in 15 degrees intervals around these angles. The measured and calculated attenuation, as calculated in v5.4, was generally within 1-2% with a systematic overestimation of the attenuation for gantry angles around 180 degrees, as well as a maximum error of 4-5% for a few discrete angles in 10 degrees gantry angle intervals around the complex PPS structures. The PPS modelling was improved compared to v5.4 in Dev, especially around 180 degrees, and the results of those calculations were within +/- 1%, but with a similar 4% maximum deviation for the most complex PPS structures.Conclusions: Generally, the two tested PPS structures exhibit very similar attenuation as a function of the gantry angle, including the angles with a steep change in attenuation. Both TPS versions, v5.4 and Dev delivered clinically acceptable accuracy of the calculated dose, as the differences in the measurements were overall better than +/- 2%. Additionally, Dev improved the accuracy of the dose calculation to +/- 1% for gantry angles around 180 degrees.
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| 2. |
- Tilly, David, 1974-, et al.
(författare)
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Dose mapping sensitivity to deformable registration uncertainties in fractionated radiotherapy – applied to prostate proton treatments
- 2013
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Ingår i: BMC Medical Physics. - : Springer Science and Business Media LLC. - 1756-6649. ; 13:2
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundCalculation of accumulated dose in fractionated radiotherapy based on spatial mapping of the dose points generally requires deformable image registration (DIR). The accuracy of the accumulated dose thus depends heavily on the DIR quality. This motivates investigations of how the registration uncertainty influences dose planning objectives and treatment outcome predictions.A framework was developed where the dose mapping can be associated with a variable known uncertainty to simulate the DIR uncertainties in a clinical workflow. The framework enabled us to study the dependence of dose planning metrics, and the predicted treatment outcome, on the DIR uncertainty. The additional planning margin needed to compensate for the dose mapping uncertainties can also be determined. We applied the simulation framework to a hypofractionated proton treatment of the prostate using two different scanning beam spot sizes to also study the dose mapping sensitivity to penumbra widths.ResultsThe planning parameter most sensitive to the DIR uncertainty was found to be the targetD95. We found that the registration mean absolute error needs to be ≤0.20 cm to obtain an uncertainty better than 3% of the calculated D95 for intermediate sized penumbras. Use of larger margins in constructing PTV from CTV relaxed the registration uncertainty requirements to the cost of increased dose burdens to the surrounding organs at risk.ConclusionsThe DIR uncertainty requirements should be considered in an adaptive radiotherapy workflow since this uncertainty can have significant impact on the accumulated dose. The simulation framework enabled quantification of the accuracy requirement for DIR algorithms to provide satisfactory clinical accuracy in the accumulated dose.
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| 3. |
- Bernchou, Uffe, et al.
(författare)
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End-to-end validation of the geometric dose delivery performance of MR linac adaptive radiotherapy
- 2021
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Ingår i: Physics in Medicine and Biology. - : Institute of Physics Publishing (IOPP). - 0031-9155 .- 1361-6560. ; 66:4
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Tidskriftsartikel (refereegranskat)abstract
- The clinical introduction of hybrid magnetic resonance (MR) guided radiotherapy (RT) delivery systems has led to the need to validate the end-to-end dose delivery performance on such machines. In the current study, an MR visible phantom was developed and used to test the spatial deviation between planned and delivered dose at two 1.5 T MR linear accelerator (MR linac) systems, including pre-treatment imaging, dose planning, online imaging, image registration, plan adaptation, and dose delivery. The phantom consisted of 3D printed plastic and MR visible silicone rubber. It was designed to minimise air gaps close to the radiochromic film used as a dosimeter. Furthermore, the phantom was designed to allow submillimetre, reproducible positioning of the film in the phantom. At both MR linac systems, 54 complete adaptive, MR guided RT workflow sessions were performed. To test the dose delivery performance of the MR linac systems in various adaptive RT (ART) scenarios, the sessions comprised a range of systematic positional shifts of the phantom and imaging or plan adaptation conditions. In each workflow session, the positional translation between the film and the adaptive planned dose was determined. The results showed that the accuracy of the MR linac systems was between 0.1 and 0.9 mm depending on direction. The highest mean deviance observed was in the posterior-anterior direction, and the direction of the error was consistent between centres. The precision of the systems was related to whether the workflow utilized the internal image registration algorithm of the MR linac. Workflows using the internal registration algorithm led to a worse precision (0.2-0.7 mm) compared to workflows where the algorithm was decoupled (0.2 mm). In summary, the spatial deviation between planned and delivered dose of MR-guided ART at the two MR linac systems was well below 1 mm and thus acceptable for clinical use.
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| 4. |
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| 5. |
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| 6. |
- Fransson, Samuel, et al.
(författare)
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Intrafractional motion models based on principal components in Magnetic Resonance guided prostate radiotherapy
- 2021
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Ingår i: Physics and Imaging in Radiation Oncology. - : Elsevier. - 2405-6316. ; 20, s. 17-22
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Tidskriftsartikel (refereegranskat)abstract
- Background and purpose: Devices that combine an MR-scanner with a Linac for radiotherapy, referred to as MR-Linac systems, introduce the possibility to acquire high resolution images prior and during treatment. Hence, there is a possibility to acquire individualised learning sets for motion models for each fraction and the construction of intrafractional motion models. We investigated the feasibility for a principal component analysis (PCA) based, intrafractional motion model of the male pelvic region.Materials and methods: 4D-scans of nine healthy male volunteers were utilized, FOV covering the entire pelvic region including prostate, bladder and rectum with manual segmentation of each organ at each time frame. Deformable image registration with an optical flow algorithm was performed for each subject with the first time frame as reference. PCA was performed on a subset of the resulting displacement vector fields to construct individualised motion models evaluated on the remaining fields.Results: The registration algorithm produced accurate registration result, in general DICE overlap >0.95 across all time frames. Cumulative variance of the eigen values from the PCA showed that 50% or more of the motion is explained in the first component for all subjects. However, the size and direction for the components differed between subjects. Adding more than two components did not improve the accuracy significantly and the model was able to explain motion down to about 1 mm.onclusions: An individualised intrafractional male pelvic motion model is feasible. Geometric accuracy was about 1 mm based on 1-2 principal components.
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| 7. |
- Fransson, Samuel, 1991-
(författare)
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Machine Learning in Magnetic Resonance-Guided Adaptive Radiotherapy
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In radiotherapy, treatments are frequently distributed over multiple weeks, and the radiation dose delivered across several sessions. A significant hurdle in this approach is the anatomical changes that occur between the planning stage and subsequent treatment sessions, leading to uncertainties in the treatment. The MR-Linac system, which combines a linear accelerator with an MRI scanner, addresses this issue by allowing for daily adjustments to the treatment plan based on the patient's current anatomy. However, the process for making these adjustments, involving image fusion, re-contouring, and plan re-optimization, can be quite elaborate and time-consuming. This project aimed to identify opportunities within the daily treatment routine where machine learning and deep learning could streamline the process, thereby enhancing efficiency, with a focus on prostate cancer treatments due to their frequent occurrence at our facility. We leveraged deep learning to train patient-specific models for segmenting anatomical structures in daily MRI scans, matching the accuracy of existing deformable image registration techniques. Furthermore, we extended this concept to segmenting structures and predicting radiation dose distributions, offering a swift assessment of potential dose distribution before engaging in the more complex manual workflow. This could aid in selecting the most suitable adaptation method more quickly. Additionally, we developed motion models for intrafractional motion and for segmenting images at lower resolutions to facilitate a target tracking process. Throughout this project, we showed how machine learning and deep learning techniques could contribute to optimizing the daily MR-Linac workflow.
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| 8. |
- Fransson, Samuel, et al.
(författare)
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Patient specific deep learning based segmentation for magnetic resonance guided prostate radiotherapy
- 2022
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Ingår i: Physics and Imaging in Radiation Oncology. - : Elsevier. - 2405-6316. ; 23, s. 38-42
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Tidskriftsartikel (refereegranskat)abstract
- Background and Purpose: Treatments on combined Magnetic Resonance (MR) scanners and Linear Accelerators (Linacs) for radiotherapy, called MR-Linacs, often require daily contouring. Currently, deformable image registration (DIR) algorithms propagate contours from reference scans, however large shape and size changes can be troublesome. Artificial neural network (ANN) based contouring may alleviate this issue, however generally requires large datasets for training. Mitigating the problem of scarcity of data, we propose patient specific networks trained on a single dataset for each patient, for contouring onto the following datasets in an adaptive MR-Linacworkflow. Materials and Methods: MR-scans from 17 prostate patients treated on an MR-Linac with contours of Clinical Target Volume (CTV), bladder and rectum were utilized. U-net shaped models were trained based on the image from the first fraction of each patient, and subsequently applied onto the following treatment images. Results were compared with manual contours in terms of the Dice coefficient and Added Path Length (APL). As benchmark, contours propagated through the clinical DIR algorithm were similarly evaluated. Results: In Dice coefficient the ANN output was 0.92 +/- 0.03, 0.93 +/- 0.07 and 0.84 +/- 0.10 while for DIR 0.95 +/- 0.03, 0.93 +/- 0.08, 0.88 +/- 0.06 for CTV, bladder and rectum respectively. Similarly, APL where 3109 +/- 1642, 7250 +/- 4234 and 5041 +/- 2666 for ANN and 1835 +/- 1621, 7236 +/- 4287 and 4170 +/- 2920 voxels for DIR. Conclusions: Patient specific ANN models trained on images from the first fraction of a prostate MR-Linac treatment showed similar accuracy when applied to the subsequent fraction images as a clinically implemented DIR method.
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| 9. |
- Freden, Emil, et al.
(författare)
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Adaptive dose painting for prostate cancer
- 2022
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Ingår i: Frontiers in Oncology. - : Frontiers Media S.A.. - 2234-943X. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Dose painting (DP) is a radiation therapy (RT) strategy for patients with heterogeneous tumors delivering higher dose to radiation resistant regions and less to sensitive ones, thus aiming to maximize tumor control with limited side effects. The success of DP treatments is influenced by the spatial accuracy in dose delivery. Adaptive RT (ART) workflows can reduce the overall geometric dose delivery uncertainty. The purpose of this study is to dosimetrically compare ART and non-adaptive conventional RT workflows for delivery of DP prescriptions in the treatment of prostate cancer (PCa).Materials and methods: We performed a planning and treatment simulation study of four study arms. Adaptive and conventional workflows were tested in combination with DP and Homogeneous dose. We used image data from 5 PCa patients that had been treated on the Elekta Unity MR linac; the patients had been imaged in treatment position before each treatment fraction (7 in total). The local radiation sensitivity from apparent diffusion coefficient maps of 15 high-risk PCa patients was modelled in a previous study. these maps were used as input for optimization of DP plans aiming for maximization of tumor control probability (TCP) under rectum dose constraints. A range of prostate doses were planned for the homogeneous arms. Adaptive plans were replanned based on the anatomy-of-the-day, whereas conventional plans were planned using a pre-treatment image and subsequently recalculated on the anatomy-of-the-day. The dose from 7 fractions was accumulated using dose mapping. The endpoints studied were the TCP and dose-volume histogram metrics for organs at risk.Results: Accumulated DP doses (adaptive and conventional) resulted in high TCP, between 96-99%. The largest difference between adaptive and conventional DP was 2.6 percentage points (in favor of adaptive DP). An analysis of the dose per fraction revealed substantial target misses for one patient in the conventional workflow that-if systematic-could jeopardize the TCP. Compared to homogeneous prescriptions with equal mean prostate dose, DP resulted in slightly higher TCP.Conclusion: Compared to homogeneous dose, DP maintains or marginally increases the TCP. Adaptive DP workflows could avoid target misses compared to conventional workflows.
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| 10. |
- Riis, Hans Lynggaard, et al.
(författare)
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The Quality Assurance of a 1.5 T MR-Linac
- 2024
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Ingår i: Seminars in radiation oncology. - : Elsevier. - 1053-4296 .- 1532-9461. ; 34:1, s. 120-128
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Forskningsöversikt (refereegranskat)abstract
- The recent introduction of a commercial 1.5 T MR-linac system has considerably improved the image quality of the patient acquired in the treatment unit as well as enabling online new technology requires new methodology that allows for the high field MR in a linac environment. The presence of the magnetic field requires special attention to the phantoms, detectors, and tools to perform QA. Due to the design of the system, the integrated megavoltage imager (MVI) is essential for radiation beam calibrations and QA. Additionally, the alignment between the MR image system and the radiation isocenter must be checked. The MR-linac system has vendor-supplied phantoms for calibration and QA tests. However, users have developed their own routine QA systems to independently check that the machine is performing as required, as to ensure we are able to deliver the intended dose with sufficient certainty. The aim of this work is therefore to review the MR-linac specific QA procedures reported in the literature.Semin Radiat Oncol 34:120-128
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| 11. |
- Tilly, David, 1974-, et al.
(författare)
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Dose coverage calculation using a statistical shape model : applied to cervical cancer radiotherapy
- 2017
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Ingår i: Physics in Medicine and Biology. - : IOP Publishing. - 0031-9155 .- 1361-6560. ; 62:10, s. 4140-4159
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Tidskriftsartikel (refereegranskat)abstract
- A comprehensive methodology for treatment simulation and evaluation of dose coverage probabilities is presented where a population based statistical shape model (SSM) provide samples of fraction specific patient geometry deformations.The learning data consists of vector fields from deformable image registration of repeated imaging giving intra-patient deformations which are mapped to an average patient serving as a common frame of reference. The SSM is created by extracting the most dominating eigenmodes through principal component analysis of the deformations from all patients. The sampling of a deformation is thus reduced to sampling weights for enough of the most dominating eigenmodes that describe the deformations.For the cervical cancer patient datasets in this work, we found seven eigenmodes to be sufficient to capture 90% of the variance in the deformations of the, and only three eigenmodes for stability in the simulated dose coverage probabilities. The normality assumption of the eigenmode weights was tested and found relevant for the 20 most dominating eigenmodes except for the first. Individualization of the SSM is demonstrated to be improved using two deformation samples from a new patient. The probabilistic evaluation provided additional information about the trade-offs compared to the conventional single dataset treatment planning.
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| 12. |
- Tilly, David, 1974-, et al.
(författare)
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Fast dose algorithm for generation of dose coverage probability for robustness analysis of fractionated radiotherapy
- 2015
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Ingår i: Physics in Medicine and Biology. - : IOP Publishing. - 0031-9155 .- 1361-6560. ; 60:14, s. 5439-5454
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Tidskriftsartikel (refereegranskat)abstract
- A fast algorithm is constructed to facilitate dose calculation for a large number of randomly sampled treatment scenarios, each representing a possible realisation of a full treatment with geometric, fraction specific displacements for an arbitrary number of fractions. The algorithm is applied to construct a dose volume coverage probability map (DVCM) based on dose calculated for several hundred treatment scenarios to enable the probabilistic evaluation of a treatment plan.For each treatment scenario, the algorithm calculates the total dose by perturbing a pre-calculated dose, separately for the primary and scatter dose components, for the nominal conditions. The ratio of the scenario specific accumulated fluence, and the average fluence for an infinite number of fractions is used to perturb the pre-calculated dose. Irregularities in the accumulated fluence may cause numerical instabilities in the ratio, which is mitigated by regularisation through convolution with a dose pencil kernel.Compared to full dose calculations the algorithm demonstrates a speedup factor of ~1000. The comparisons to full calculations show a 99% gamma index (2%/2 mm) pass rate for a single highly modulated beam in a virtual water phantom subject to setup errors during five fractions. The gamma comparison shows a 100% pass rate in a moving tumour irradiated by a single beam in a lung-like virtual phantom. DVCM iso-probability lines computed with the fast algorithm, and with full dose calculation for each of the fractions, for a hypo-fractionated prostate case treated with rotational arc therapy treatment were almost indistinguishable.
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| 13. |
- Tilly, David, 1974-, et al.
(författare)
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Probabilistic optimization of dose coverage in radiotherapy
- 2019
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Ingår i: Physics and Imaging in Radiation Oncology. - : ELSEVIER. - 2405-6316. ; 10, s. 1-6
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Tidskriftsartikel (refereegranskat)abstract
- Background and purpose: Probabilistic optimization is an alternative to margins for handling geometrical uncertainties in treatment planning of radiotherapy where uncertainties are explicitly incorporated in the optimization. We present a novel probabilistic method based on the same statistical measures as those behind conventional margin based planning. Material and methods: Percentile Dosage (PD) was defined as the dose coverage that a treatment plan meet or exceed to a given probability. For optimization, we used the convex measure Expected Percentile Dosage (EPD) defined as the average dose coverage below a given PD. An iterative method gradually adjusted the constraint tolerance associated with the EPD until the desired target PD was met. It was applied to planning of cervical cancer patients focusing on systematic uncertainty caused by organ deformation. The resulting plans were compared to margin based plans using target and organ at risk PDs. Results: The EPD tolerance converged in less than ten iterations to produce a PD within 0.1 Gy of the requested. The PD was on average within 0.5% of the requested PD when validated versus independent scenarios. The rectum volume, extracted from the PDs, receiving 90% of the intended target dose was decreased with 16% for the same target PD in comparison to margin based plans. Conclusions: The proposed probabilistic optimization method enabled prescription of a dose volume histogram metric to a chosen confidence. The probabilistic plans showed improved target dose homogeneity and decreased rectum dose for the same target dose coverage compared to margin based plans.
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| 14. |
- Tilly, David, 1974-, et al.
(författare)
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Probabilistic optimization of the dose coverage – applied to radiotherapy treatment planning of cervical cancer
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Probabilistic (or robust) optimization is an alternative to margins for handling geometrical uncertainties in treatment planning of radiotherapy where the uncertainties are explicitly incorporated in the optimization through sampling of treatment scenarios. We present a probabilistic method based on statistical measures close to those behind conventional margin based planning. The dose planner requests a dose coverage to a specified probability, which the algorithm then attempts to fulfil.We define the Percentile UnderDosage (PUD) as a measure of the target minimum dose coverage probability, i.e. the dose coverage that a treatment plan meet or exceed to a given probability. Margin based planning commonly use the implicit probabilistic treatment criteria that the 90th PUD is at least 95% of the intended dose. For optimization we use the Expected Percentile UnderDosage (EPUD) defined as the average dose coverage below a given PUD. The EPUD is, in contrast to PUD, a convex measure and hence standard optimization techniques can be used to find the optimal treatment plan. We propose an iterative method where a treatment optimization is performed at each iteration and the EPUD tolerance is adjusted gradually until a desired PUD is met.We demonstrate our proposed probabilistic planning method for cervical cancer patients. The uncertainty caused by organ deformation is explicitly included in the probabilistic optimization where a statistical shape model is used to sample scenarios with different deformations. For all patients in this work, the iterative process of finding the EPUD tolerance converged in less than 10 iterations to within 0.1Gy of the requested PUD even though a conservative update scheme was used. The resulting estimated PUD was validated based on 1000 simulated scenarios not part of the optimization yielding an agreement within 1.2% of the requested PUD.
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| 15. |
- 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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