| 1. |
- Benedek, Hunor, et al.
(författare)
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The effect of prostate motion during hypofractionated radiotherapy can be reduced by using flattening filter free beams
- 2018
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Ingår i: Physics and imaging in radiation oncology. - : Elsevier BV. - 2405-6316. ; 6, s. 66-70
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Tidskriftsartikel (refereegranskat)abstract
- Background and purpose: Hypofractionated radiotherapy of prostate cancer reduces the overall treatment time but increases the per-fraction beam-on time due to the higher fraction doses. This increased fraction treatment time results in a larger uncertainty of the prostate position. The purpose of this study was to investigate the effect of prostate motion during flattening filter free (FFF) Volumetric Modulated Arc Therapy (VMAT) in ultrahypofractionation of prostate cancer radiotherapy with preserved plan quality compared to conventional flattened beams.Materials and methods: Nine prostate patients from the Scandinavian HYPO-RT-PC trial were re-planned using VMAT technique with both conventional and flattening filter free beams. Two fractionation schedules were used, one hypofractionated (42.7 Gy in 7 fractions), and one conventional (78.0 Gy in 39 fractions). Pre-treatment verification measurements were performed on all plans and the treatment time was recorded. Measurements with simulated prostate motion were performed for the plans with the longest treatment times. Results: All the 10FFF plans fulfilled the clinical gamma pass rate, 90% (3%, 2 mm), during all simulated prostate motion trajectories. The 10MV plans only fulfilled the clinical pass rate for three of the trajectories. The mean beam-on-time for the hypofractionated plans were reduced from 2.3 min to 1.0 min when using 10FFF compared to 10MV. No clinically relevant differences in dose distribution were identified when comparing the plans with different beam qualities. Conclusion: Flattening-filter free VMAT reduces treatment times, limiting the dosimetric effect of organ motion for ultrahypofractionated prostate cancer with preserved plan quality.
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| 2. |
- Brynolfsson, Patrik, et al.
(författare)
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Tensor-valued diffusion magnetic resonance imaging in a radiotherapy setting
- 2022
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Ingår i: Physics and imaging in radiation oncology. - : Elsevier BV. - 2405-6316. ; 24, s. 144-151
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Tidskriftsartikel (refereegranskat)abstract
- Background and purposeDiagnostic information about cell density variations and microscopic tissue anisotropy can be gained from tensor-valued diffusion magnetic resonance imaging (MRI). These properties of tissue microstructure have the potential to become novel imaging biomarkers for radiotherapy response. However, tensor-valued diffusion encoding is more demanding than conventional encoding, and its compatibility with MR scanners that are dedicated to radiotherapy has not been established. Thus, our aim was to investigate the feasibility of tensor-valued diffusion MRI with radiotherapy dedicated MR equipment.Material and methodsA tensor-valued diffusion protocol was implemented, and five healthy volunteers were scanned with different resolutions using conventional head coil and radiotherapy coil setup with fixation masks. Signal-to-noise-ratio (SNR) was evaluated to assess the risk of signal bias due to rectified noise floor. We also evaluated the repeatability and reproducibility of the microstructure parameters. One patient with brain metastasis was scanned to investigate the image quality and the transferability of the setup to diseased tissue.ResultsA resolution of 3×3×3 mm3 provided images with SNR>3 for 93% of the voxels using radiotherapy coil setup. The parameter maps and repeatability characteristics were comparable to those observed with a conventional head coil. The patient evaluation demonstrated successful parameter analysis also in tumor tissue, with SNR>3 for 93% of the voxels.ConclusionWe demonstrate that tensor-valued diffusion MRI is compatible with radiotherapy fixation masks and coil setup for investigations of microstructure parameters. The reported reproducibility may be used to plan future investigations of imaging biomarkers in brain cancer radiotherapy.
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| 3. |
- Gorgisyan, Jenny, et al.
(författare)
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Evalutation of two commercial deep learning OAR segmentation models for prostate cancer treatment
- 2022
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Konferensbidrag (refereegranskat)abstract
- Purpose or ObjectiveTo evaluate two commercial, CE labeled deep learning-based models for automatic organs at risk segmentation on planning CT images for prostate cancer radiotherapy. Model evaluation was focused on assessing both geometrical metrics and evaluating a potential time saving.Material and MethodsThe evaluated models consisted of RayStation 10B Deep Learning Segmentation (RaySearch Laboratories AB, Stockholm, Sweden) and MVision AI Segmentation Service (MVision, Helsinki, Finland) and were applied to CT images for a dataset of 54 male pelvis patients. The RaySearch model was re-trained with 44 clinic specific patients (Skåne University Hospital, Lund, Sweden) for the femoral head structures to adjust the model to our specific delineation guidelines. The model was evaluated on 10 patients from the same clinic. Dice similarity coefficient (DSC) and Hausdorff distance (95th percentile) was computed for model evaluation, using an in-house developed Python script. The average time for manual and AI model delineations was recorded.ResultsAverage DSC scores and Hausdorff distances for all patients and both models are presented in Figure 1 and Table 1, respectively. The femoral head segmentations in the re-trained RaySearch model had increased overlap with our clinical data, with a DSC (mean±1 STD) for the right femoral head of 0.55±0.06 (n=53) increasing to 0.91±0.02 (n=10) and mean Hausdorff (mm) decreasing from 55±7 (n=53) to 4±1 (n=10) (similar results for the left femoral head). The deviation in femoral head compared to the RaySearch and MVision original models occurred due to a difference in the femoral head segmentation guideline in the clinic specific data, see Figure 2. Time recording of manual delineation was 13 minutes compared to 0.5 minutes (RaySearch) and 1.4 minutes (MVision) for the AI models, manual correction not included.ConclusionBoth AI models demonstrate good segmentation performance for bladder and rectum. Clinic specific training data (or data that complies to the clinic specific delineation guideline) might be necessary to achieve segmentation results in accordance to the clinical specific standard for some anatomical structures, such as the femoral heads in our case. The time saving was around 90%, not including manual correction.
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| 4. |
- Lerner, Minna, et al.
(författare)
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Clinical validation of a commercially available deep learning software for synthetic CT generation for brain
- 2021
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Ingår i: Radiation Oncology. - : Springer Science and Business Media LLC. - 1748-717X. ; 16:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Most studies on synthetic computed tomography (sCT) generation for brain rely on in-house developed methods. They often focus on performance rather than clinical feasibility. Therefore, the aim of this work was to validate sCT images generated using a commercially available software, based on a convolutional neural network (CNN) algorithm, to enable MRI-only treatment planning for the brain in a clinical setting. Methods: This prospective study included 20 patients with brain malignancies of which 14 had areas of resected skull bone due to surgery. A Dixon magnetic resonance (MR) acquisition sequence for sCT generation was added to the clinical brain MR-protocol. The corresponding sCT images were provided by the software MRI Planner (Spectronic Medical AB, Sweden). sCT images were rigidly registered and resampled to CT for each patient. Treatment plans were optimized on CT and recalculated on sCT images for evaluation of dosimetric and geometric endpoints. Further analysis was also performed for the post-surgical cases. Clinical robustness in patient setup verification was assessed by rigidly registering cone beam CT (CBCT) to sCT and CT images, respectively. Results: All sCT images were successfully generated. Areas of bone resection due to surgery were accurately depicted. Mean absolute error of the sCT images within the body contour for all patients was 62.2 ± 4.1 HU. Average absorbed dose differences were below 0.2% for parameters evaluated for both targets and organs at risk. Mean pass rate of global gamma (1%/1 mm) for all patients was 100.0 ± 0.0% within PTV and 99.1 ± 0.6% for the full dose distribution. No clinically relevant deviations were found in the CBCT-sCT vs CBCT-CT image registrations. In addition, mean values of voxel-wise patient specific geometric distortion in the Dixon images for sCT generation were below 0.1 mm for soft tissue, and below 0.2 mm for air and bone. Conclusions: This work successfully validated a commercially available CNN-based software for sCT generation. Results were comparable for sCT and CT images in both dosimetric and geometric evaluation, for both patients with and without anatomical anomalies. Thus, MRI Planner is feasible to use for radiotherapy treatment planning of brain tumours.
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| 5. |
- Lerner, Minna, et al.
(författare)
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MRI-only based treatment with a commercial deep-learning generation method for synthetic CT of brain
- 2020
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Ingår i: ; , s. 47-47
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Konferensbidrag (refereegranskat)abstract
- ObjectivesTo show feasibility of synthetic computed tomography (sCT) images generated using a commerciallyavailable software, enabling MRI-only treatment planning for the brain in a clinical setting.Patients and Methods20 and 16 patients with brain malignancies, including post-surgical cases, were included for validationand treatment, respectively. Dixon MR images of the skull were exported to the MRI Planner software(Spectronic Medical AB), which utilizes convolutional neural network algorithms for sCT generation.In the validation study, sCT images were rigidly registered and resampled to CT geometry for eachpatient. Treatment plans were optimized on CT and retrospectively recalculated on sCT images forevaluation of dosimetric and geometric endpoints. Clinical robustness in patient setup verification wasassessed by rigidly registering cone beam CT (CBCT) to sCT and CT images, respectively.The treatment study was performed on sCT images, using CT solely for QA purposes.ResultsAll sCT images were successfully generated in the validation study. Mean absolute error of the sCTimages within the body contour for all patients was 62.2 ± 4.1 HU. Average absorbed dose differenceswere below 0.2%. Mean pass rate of global gamma (1%/1mm) for all patients was 100.0 ± 0.0 % withinPTV and 99.1 ± 0.6 % for the full dose distribution. No clinically relevant deviations were found in theCBCT-sCT vs CBCT-CT image registrations. Areas of bone resection due to surgery were accuratelydepicted in the sCT images. Finally, treatment success rate was 15/16. One patient was excluded due tosCT artifacts from a haemostatic substance injected during surgery.Conclusion15 patients have successfully received MRI-only RT for brain tumours using the validated commerciallyavailable sCT software. Validation showed comparable results between sCT and CT images for bothdosimetric and geometric endpoints
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| 6. |
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| 7. |
- Lerner, Minna, et al.
(författare)
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Prospective Clinical Feasibility Study for MRI-Only Brain Radiotherapy
- 2022
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Ingår i: Frontiers in Oncology. - : Frontiers Media SA. - 2234-943X. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Objectives: MRI-only radiotherapy (RT) provides a workflow to decrease the geometric uncertainty introduced by the image registration process between MRI and CT data and to streamline the RT planning. Despite the recent availability of validated synthetic CT (sCT) methods for the head region, there are no clinical implementations reported for brain tumors. Based on a preceding validation study of sCT, this study aims to investigate MRI-only brain RT through a prospective clinical feasibility study with endpoints for dosimetry and patient setup. Material and Methods: Twenty-one glioma patients were included. MRI Dixon images were used to generate sCT images using a CE-marked deep learning-based software. RT treatment plans were generated based on MRI delineated anatomical structures and sCT for absorbed dose calculations. CT scans were acquired but strictly used for sCT quality assurance (QA). Prospective QA was performed prior to MRI-only treatment approval, comparing sCT and CT image characteristics and calculated dose distributions. Additional retrospective analysis of patient positioning and dose distribution gamma evaluation was performed. Results: Twenty out of 21 patients were treated using the MRI-only workflow. A single patient was excluded due to an MRI artifact caused by a hemostatic substance injected near the target during surgery preceding radiotherapy. All other patients fulfilled the acceptance criteria. Dose deviations in target were within ±1% for all patients in the prospective analysis. Retrospective analysis yielded gamma pass rates (2%, 2 mm) above 99%. Patient positioning using CBCT images was within ± 1 mm for registrations with sCT compared to CT. Conclusion: We report a successful clinical study of MRI-only brain radiotherapy, conducted using both prospective and retrospective analysis. Synthetic CT images generated using the CE-marked deep learning-based software were clinically robust based on endpoints for dosimetry and patient positioning.
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