Publications
28 results found
Kreitner L, Paetzold JC, Rauch N, et al., 2024, Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations., IEEE Trans Med Imaging, Vol: PP
Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images.
Sutton J, Menten MJ, Riedl S, et al., 2023, Correction: Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration-the PINNACLE trial protocol, Eye, Vol: 37, Pages: 1-1, ISSN: 0950-222X
Sutton J, Menten MJ, Riedl S, et al., 2023, Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration-the PINNACLE trial protocol, Eye, Vol: 37, Pages: 1275-1283, ISSN: 0950-222X
AimsAge-related macular degeneration (AMD) is characterised by a progressive loss of central vision. Intermediate AMD is a risk factor for progression to advanced stages categorised as geographic atrophy (GA) and neovascular AMD. However, rates of progression to advanced stages vary between individuals. Recent advances in imaging and computing technologies have enabled deep phenotyping of intermediate AMD. The aim of this project is to utilise machine learning (ML) and advanced statistical modelling as an innovative approach to discover novel features and accurately quantify markers of pathological retinal ageing that can individualise progression to advanced AMD.MethodsThe PINNACLE study consists of both retrospective and prospective parts. In the retrospective part, more than 400,000 optical coherent tomography (OCT) images collected from four University Teaching Hospitals and the UK Biobank Population Study are being pooled, centrally stored and pre-processed. With this large dataset featuring eyes with AMD at various stages and healthy controls, we aim to identify imaging biomarkers for disease progression for intermediate AMD via supervised and unsupervised ML. The prospective study part will firstly characterise the progression of intermediate AMD in patients followed between one and three years; secondly, it will validate the utility of biomarkers identified in the retrospective cohort as predictors of progression towards late AMD. Patients aged 55–90 years old with intermediate AMD in at least one eye will be recruited across multiple sites in UK, Austria and Switzerland for visual function tests, multimodal retinal imaging and genotyping. Imaging will be repeated every four months to identify early focal signs of deterioration on spectral-domain optical coherence tomography (OCT) by human graders. A focal event triggers more frequent follow-up with visual function and imaging tests. The primary outcome is the sensitivity and specificity of the OCT ima
Menten MJ, Holland R, Leingang O, et al., 2023, Exploring healthy retinal aging with deep learning, Ophthalmology Science, Vol: 3, Pages: 1-10, ISSN: 2666-9145
PurposeTo study the individual course of retinal changes caused by healthy aging using deep learning.DesignRetrospective analysis of a large data set of retinal OCT images.ParticipantsA total of 85 709 adults between the age of 40 and 75 years of whom OCT images were acquired in the scope of the UK Biobank population study.MethodsWe created a counterfactual generative adversarial network (GAN), a type of neural network that learns from cross-sectional, retrospective data. It then synthesizes high-resolution counterfactual OCT images and longitudinal time series. These counterfactuals allow visualization and analysis of hypothetical scenarios in which certain characteristics of the imaged subject, such as age or sex, are altered, whereas other attributes, crucially the subject’s identity and image acquisition settings, remain fixed.Main Outcome MeasuresUsing our counterfactual GAN, we investigated subject-specific changes in the retinal layer structure as a function of age and sex. In particular, we measured changes in the retinal nerve fiber layer (RNFL), combined ganglion cell layer plus inner plexiform layer (GCIPL), inner nuclear layer to the inner boundary of the retinal pigment epithelium (INL-RPE), and retinal pigment epithelium (RPE).ResultsOur counterfactual GAN is able to smoothly visualize the individual course of retinal aging. Across all counterfactual images, the RNFL, GCIPL, INL-RPE, and RPE changed by −0.1 μm ± 0.1 μm, −0.5 μm ± 0.2 μm, −0.2 μm ± 0.1 μm, and 0.1 μm ± 0.1 μm, respectively, per decade of age. These results agree well with previous studies based on the same cohort from the UK Biobank population study. Beyond population-wide average measures, our counterfactual GAN allows us to explore whether the retinal layers of a given eye will increase in thickness, decrease in thickness, or stagnate as a subject ages.ConclusionThis study demonstrates how counterfactual GANs
Ezhov I, Scibilia K, Franitza K, et al., 2023, Learn-Morph-Infer: A new way of solving the inverse problem for brain tumor modeling, Medical Image Analysis, Vol: 83, Pages: 1-8, ISSN: 1361-8415
Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer, the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction–diffusion and reaction–advection–diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains.
Feiner LF, Menten MJ, Hammernik K, et al., 2023, Propagation and Attribution of Uncertainty in Medical Imaging Pipelines, Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, Publisher: Springer Nature Switzerland, Pages: 1-11, ISBN: 9783031443350
Ezhov I, Rosier M, Zimmer L, et al., 2022, A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling, NeurIPS 2022, Publisher: Proceedings of Machine Learning Research, Pages: 1-12
Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches aim to identify a set of free physical model parameters that results in a simulation best matching an empirical observation. When applied to brain tumor modeling, one of the instances of image-based model personalization in medical image computing, the overarching drawback of the methods is the time complexity of finding such a set. In a clinical setting with limited time between imaging and diagnosis or even intervention, this time complexity may prove critical. As the history of quantitative science is the history of compression (Schmidhuber and Fridman, 2018), we align in this paper with the historical tendency and propose a method compressing complex traditional strategies for solving an inverse problem into a simple database query task. We evaluated different ways of performing the database query task assessing the trade-off between accuracy and execution time. On the exemplary task of brain tumor growth modeling, we prove that the proposed method achieves one order speed-up compared to existing approaches for solving the inverse problem. The resulting compute time offers critical means for relying on more complex and, hence, realistic models, for integrating image preprocessing and inverse modeling even deeper, or for implementing the current model into a clinical workflow. The code is available at https://github.com/IvanEz/for-loop-tumor.
Menten MJ, Paetzold JC, Dima A, et al., 2022, Physiology-Based Simulation of the Retinal Vasculature Enables Annotation-Free Segmentation of OCT Angiographs, MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT VIII, Vol: 13438, Pages: 330-340, ISSN: 0302-9743
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Hassan ON, Menten MJ, Bogunovic H, et al., 2021, DEEP LEARNING PREDICTION OF AGE AND SEX FROM OPTICAL COHERENCE TOMOGRAPHY, 18th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 238-242, ISSN: 1945-7928
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Nill S, Hanson I, Costa F, et al., 2020, MR guided tumour tracking on a high field MR Linac: feasibility and first experimental results, Publisher: ELSEVIER IRELAND LTD, Pages: S867-S868, ISSN: 0167-8140
Eiben B, Bertholet J, Menten MJ, et al., 2020, Consistent and invertible deformation vector fields for a breathing anthropomorphic phantom: a post-processing framework for the XCAT phantom, PHYSICS IN MEDICINE AND BIOLOGY, Vol: 65, ISSN: 0031-9155
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Menten MJ, Mohajer JK, Nilawar R, et al., 2020, Automatic reconstruction of the delivered dose of the day using MR-linac treatment log files and online MR imaging, Radiotherapy and Oncology, Vol: 145, Pages: 88-94, ISSN: 0167-8140
Background and purposeAnatomical changes during external beam radiotherapy prevent the accurate delivery of the intended dose distribution. Resolving the delivered dose, which is currently unknown, is crucial to link radiotherapy doses to clinical outcomes and ultimately improve the standard of care.Material and methodsIn this study, we present a dose reconstruction workflow based on data routinely acquired during MR-guided radiotherapy. It employs 3D MR images, 2D cine MR images and treatment machine log files to calculate the delivered dose taking intrafractional motion into account. The developed pipeline was used to measure anatomical changes and assess their dosimetric impact in 89 prostate radiotherapy fractions delivered with a 1.5 T MR-linac at our institute.ResultsOver the course of radiation delivery, the CTV shifted 0.6 mm ± 2.1 mm posteriorly and 1.3 mm ± 1.5 mm inferiorly. When extrapolating the dose changes in each case to 20 fractions, the mean clinical target volume and clinical target volume dose-volume metrics decreased by 1.1 Gy ± 1.6 Gy and 0.1 Gy ± 0.2 Gy, respectively. Bladder did not change (0.0 Gy ± 1.2 Gy), while rectum decreased by 1.0 Gy ± 2.0 Gy. Although anatomical changes and their dosimetric impact were small in the majority of cases, large intrafractional motion caused the delivered dose to substantially deviate from the intended plan in some fractions.ConclusionsThe presented end-to-end workflow is able to reliably, non-invasively and automatically reconstruct the delivered prostate radiotherapy dose by processing MR-linac treatment log files and online MR images. In the future, we envision this workflow to be adapted to other cancer sites and ultimately to enter widespread clinical use.
Bertholet J, Knopf A, Eiben B, et al., 2019, Real-time intrafraction motion monitoring in external beam radiotherapy, Physics in Medicine and Biology, Vol: 64, Pages: 15TR01-15TR01, ISSN: 0031-9155
Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.
Costa F, Menten MJ, Doran S, et al., 2019, Dose verification of dynamic MLC-tracked radiotherapy using small PRESAGE (R) 3D dosimeters and a motion phantom, 10th International Conference on 3D Radiation Dosimetry (IC3DDose), Publisher: IOP PUBLISHING LTD, ISSN: 1742-6588
With the increasing complexity of radiotherapy treatments typical 1D and 2D quality assurance (QA) detectors may fail to detect out-of-plane dose discrepancies, in particular in the presence of motion. In this work, small samples of the PRESAGE® 3D radiochromic dosimeter were used in combination with a motion phantom to measure real-time multileaf collimator (MLC)-tracked radiotherapy treatments. A different sample of PRESAGE® was irradiated for each of three different irradiation scenarios: (1) static: static sample, without tracking (2) motion: moving sample, without tracking and (3) tracking: moving sample, with tracking. Our in-house software DynaTrack dynamically moves the linac's MLC leafs based on the target position. The doses delivered to the samples were reconstructed based on the recorded positions of the MLC and phantom during the beam delivery. PRESAGE® samples were imaged with an in-house optical-CT scanner. Comparison between simulated and measured 3D dose showed good agreement for all three irradiation scenarios (static: 99.2%; motion: 99.7%; tracking: 99.3% with a 3%, 2 mm and a 10% threshold local gamma criterion), failing only at the edges of the PRESAGE® samples (~ 6 mm). Given that the dose distributions deposited using the DynaTrack system have been independently verified, this experiment demonstrates the ability of PRESAGE to measure 3D doses correctly in a tracking context. We conclude that this methodology could be used in the future to validate the delivery of dynamic MLC-tracked radiotherapy.
Menten MJ, Fast MF, Wetscherek A, et al., 2018, The impact of 2D cine MR imaging parameters on automated tumor and organ localization for MR-guided real-time adaptive radiotherapy, PHYSICS IN MEDICINE AND BIOLOGY, Vol: 63, Pages: 1-16, ISSN: 0031-9155
2D cine MR imaging may be utilized to monitor rapidly moving tumors and organs-at-risk forreal-time adaptive radiotherapy. This study systematically investigates the impact of geometricimaging parameters on the ability of 2D cine MR imaging to guide template-matching-drivenautocontouring of lung tumors and abdominal organs.Abdominal 4D MR images were acquired of six healthy volunteers and thoracic 4D MR imageswere obtained of eight lung cancer patients. At each breathing phase of the images, the left kidneyand gallbladder or lung tumor, respectively, were outlined as volumes of interest. These imagesand contours were used to create artificial 2D cine MR images, while simultaneously serving as 3Dground truth. We explored the impact of five different imaging parameters (pixel size, slice thickness,imaging plane orientation, number and relative alignment of images as well as strategies to createtraining images). For each possible combination of imaging parameters, we generated artificial 2Dcine MR images as training and test images. A template-matching algorithm used the training imagesto determine the tumor or organ position in the test images. Subsequently, a 3D base contour wasshifted to the determined position and compared to the ground truth via centroid distance and Dicesimilarity coefficient.The median centroid distance between adapted and ground truth contours was 1.56mm forthe kidney, 3.81mm for the gallbladder and 1.03mm for the lung tumor (median Dice similaritycoefficient: 0.95, 0.72 and 0.93). We observed that a decrease in image resolution led to a modestdecrease in localization accuracy, especially for the small gallbladder. However, for all volumes ofinterest localization accuracy varied substantially more between subjects than due to the differentimaging parameters.Automated tumor and organ localization using 2D cine MR imaging and template-matchingbased autocontouring is robust against variation of geometric imaging parameters. Future workand optimiza
Kieselmann JP, Kamerling CP, Burgos N, et al., 2018, Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region., Phys Med Biol, Vol: 63
Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow. We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers. To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences. The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously publishe
Kieselmann JP, Kamerling CP, Burgos N, et al., 2018, Geometric and dosimetric evaluations of atlas-based segmentation methods of MR images in the head and neck region, Physics in Medicine and Biology, Vol: 63, ISSN: 0031-9155
Owing to its excellent soft-tissue contrast, magnetic resonance (MR) imaging has found an increased application in radiation therapy (RT). By harnessing these properties for treatment planning, automated segmentation methods can alleviate the manual workload burden to the clinical workflow.We investigated atlas-based segmentation methods of organs at risk (OARs) in the head and neck (H&N) region using one approach that selected the most similar atlas from a library of segmented images and two multi-atlas approaches. The latter were based on weighted majority voting and an iterative atlas-fusion approach called STEPS. We built the atlas library from pre-treatment T1-weighted MR images of 12 patients with manual contours of the parotids, spinal cord and mandible, delineated by a clinician. Following a leave-one-out cross-validation strategy, we measured the geometric accuracy by calculating Dice similarity coefficients (DSC), standard and 95% Hausdorff distances (HD and HD95), and the mean surface distance (MSD), whereby the manual contours served as the gold standard. To benchmark the algorithm, we determined the inter-observer variability (IOV) between three observers.To investigate the dosimetric effect of segmentation inaccuracies, we implemented an auto-planning strategy within the treatment planning system Monaco (Elekta AB, Stockholm, Sweden). For each set of auto-segmented OARs, we generated a plan for a 9-beam step and shoot intensity modulated RT treatment, designed according to our institution's clinical H&N protocol. Superimposing the dose distributions on the gold standard OARs, we calculated dose differences to OARs caused by delineation differences between auto-segmented and gold standard OARs. We investigated the correlations between geometric and dosimetric differences.The mean DSC was larger than 0.8 and the mean MSD smaller than 2 mm for the multi-atlas approaches, resulting in a geometric accuracy comparable to previously published r
Eiben B, Tran EH, Menten MJ, et al., 2018, Statistical motion mask and sliding registration, Biomedical Image Registration, Editors: Klein, Staring, Durrleman, Sommer, Publisher: Springer International Publishing, Pages: 13-23, ISBN: 9783319922577
Accurate registration of images depicting respiratory motion, e.g. 4DCT or 4DMR, can be challenging due to sliding motion that occurs between the chest wall and organs within the pleural sac (lungs, mediastinum, liver). In this paper we propose a methodology that (1) segments one of the images to be registered (the source or floating/moving image) into two distinct regions by fitting a statistical motion mask, and (2) registers the image with a modified B-spline registration algorithm that can account for sliding motion between the regions. This registration requires the segmentation of the regions in the source image domain as a signed distance map. Two underlying transformations allow the regions to deform independently, while a constraint term based on the transformed distance maps penalises gaps and overlaps between the regions. Although implemented in a B-spline algorithm, the required modifications are not specific to the transformation type and thus can be applied to parametric and non-parametric frameworks alike. The registration accuracy is evaluated using the landmark registration error on the basis of the publicly available DIR-Lab dataset. The overall average landmark error after registration is 1.21 mm and the average gap and overlap volumes are 26.4 cm
Menten MJ, Wetscherek A, Fast MF, 2017, MRI-guided lung SBRT: Present and future developments, Physica Medica-European Journal of Medical Physics, Vol: 44, Pages: 139-149, ISSN: 1120-1797
Stereotactic body radiotherapy (SBRT) is rapidly becoming an alternative to surgery for the treatment of early-stage non-small cell lung cancer patients. Lung SBRT is administered in a hypo-fractionated, conformal manner, delivering high doses to the target. To avoid normal-tissue toxicity, it is crucial to limit the exposure of nearby healthy organs-at-risk (OAR).Current image-guided radiotherapy strategies for lung SBRT are mostly based on X-ray imaging modalities. Although still in its infancy, magnetic resonance imaging (MRI) guidance for lung SBRT is not exposure-limited and MRI promises to improve crucial soft-tissue contrast. Looking beyond anatomical imaging, functional MRI is expected to inform treatment decisions and adaptations in the future.This review summarises and discusses how MRI could be advantageous to the different links of the radiotherapy treatment chain for lung SBRT: diagnosis and staging, tumour and OAR delineation, treatment planning, and inter- or intrafractional motion management. Special emphasis is placed on a new generation of hybrid MRI treatment devices and their potential for real-time adaptive radiotherapy.
Fast MF, Eiben B, Menten MJ, et al., 2017, Tumour auto-contouring on 2d cine MRI for locally advanced lung cancer: A comparative study, Radiotherapy and Oncology, Vol: 125, Pages: 485-491, ISSN: 0167-8140
Background and purposeRadiotherapy guidance based on magnetic resonance imaging (MRI) is currently becoming a clinical reality. Fast 2d cine MRI sequences are expected to increase the precision of radiation delivery by facilitating tumour delineation during treatment. This study compares four auto-contouring algorithms for the task of delineating the primary tumour in six locally advanced (LA) lung cancer patients.Material and methodsTwenty-two cine MRI sequences were acquired using either a balanced steady-state free precession or a spoiled gradient echo imaging technique. Contours derived by the auto-contouring algorithms were compared against manual reference contours. A selection of eight image data sets was also used to assess the inter-observer delineation uncertainty.ResultsAlgorithmically derived contours agreed well with the manual reference contours (median Dice similarity index: ). Multi-template matching and deformable image registration performed significantly better than feature-driven registration and the pulse-coupled neural network (PCNN). Neither MRI sequence nor image orientation was a conclusive predictor for algorithmic performance. Motion significantly degraded the performance of the PCNN. The inter-observer variability was of the same order of magnitude as the algorithmic performance.ConclusionAuto-contouring of tumours on cine MRI is feasible in LA lung cancer patients. Despite large variations in implementation complexity, the different algorithms all have relatively similar performance.
Bainbridge HE, Menten MJ, Fast MF, et al., 2017, Treating locally advanced lung cancer with a 1.5 T MR-Linac - Effects of the magnetic field and irradiation geometry on conventionally fractionated and isotoxic dose-escalated radiotherapy, Radiotherapy and Oncology, Vol: 125, Pages: 280-285, ISSN: 0167-8140
PurposeThis study investigates the feasibility and potential benefits of radiotherapy with a 1.5 T MR-Linac for locally advanced non-small cell lung cancer (LA NSCLC) patients.Material and methodsTen patients with LA NSCLC were retrospectively re-planned six times: three treatment plans were created according to a protocol for conventionally fractionated radiotherapy and three treatment plans following guidelines for isotoxic target dose escalation. In each case, two plans were designed for the MR-Linac, either with standard (∼7 mm) or reduced (∼3 mm) planning target volume (PTV) margins, while one conventional linac plan was created with standard margins. Treatment plan quality was evaluated using dose–volume metrics or by quantifying dose escalation potential.ResultsAll generated treatment plans fulfilled their respective planning constraints. For conventionally fractionated treatments, MR-Linac plans with standard margins had slightly increased skin dose when compared to conventional linac plans. Using reduced margins alleviated this issue and decreased exposure of several other organs-at-risk (OAR). Reduced margins also enabled increased isotoxic target dose escalation.ConclusionIt is feasible to generate treatment plans for LA NSCLC patients on a 1.5 T MR-Linac. Margin reduction, facilitated by an envisioned MRI-guided workflow, enables increased OAR sparing and isotoxic target dose escalation for the respective treatment approaches.
Kamerling CP, Fast MF, Ziegenhein P, et al., 2017, Online dose reconstruction for tracked volumetric arc therapy: Real-time implementation and offline quality assurance for prostate SBRT, Medical Physics, Vol: 44, Pages: 5997-6007, ISSN: 0094-2405
PurposeFirstly, this study provides a real‐time implementation of online dose reconstruction for tracked volumetric arc therapy (VMAT). Secondly, this study describes a novel offline quality assurance tool, based on commercial dose calculation algorithms.MethodsOnline dose reconstruction for VMAT is a computationally challenging task in terms of computer memory usage and calculation speed. To potentially reduce the amount of memory used, we analyzed the impact of beam angle sampling for dose calculation on the accuracy of the dose distribution. To establish the performance of the method, we planned two single‐arc VMAT prostate stereotactic body radiation therapy cases for delivery with dynamic MLC tracking. For quality assurance of our online dose reconstruction method we have also developed a stand‐alone offline dose reconstruction tool, which utilizes the RayStation treatment planning system to calculate dose.ResultsFor the online reconstructed dose distributions of the tracked deliveries, we could establish strong resemblance for 72 and 36 beam co‐planar equidistant beam samples with less than 1.2% deviation for the assessed dose‐volume indicators (clinical target volume D98 and D2, and rectum D2). We could achieve average runtimes of 28–31 ms per reported MLC aperture for both dose computation and accumulation, meeting our real‐time requirement. To cross‐validate the offline tool, we have compared the planned dose to the offline reconstructed dose for static deliveries and found excellent agreement (3%/3 mm global gamma passing rates of 99.8%–100%).ConclusionBeing able to reconstruct dose during delivery enables online quality assurance and online replanning strategies for VMAT. The offline quality assurance tool provides the means to validate novel online dose reconstruction applications using a commercial dose calculation engine.
Kamerling CP, Fast MF, Ziegenhein P, et al., 2016, Real-time 4D dose reconstruction for tracked dynamic MLC deliveries for lung SBRT, Medical Physics, Vol: 43, Pages: 6072-6081, ISSN: 0094-2405
PurposeThis study provides a proof of concept for real‐time 4D dose reconstruction for lung stereotactic body radiation therapy (SBRT) with multileaf collimator (MLC) tracking and assesses the impact of tumor tracking on the size of target margins.MethodsThe authors have implemented real‐time 4D dose reconstruction by connecting their tracking and delivery software to an Agility MLC at an Elekta Synergy linac and to their in‐house treatment planning software (TPS). Actual MLC apertures and (simulated) target positions are reported to the TPS every 40 ms. The dose is calculated in real‐time from 4DCT data directly after each reported aperture by utilization of precalculated dose‐influence data based on a Monte Carlo algorithm. The dose is accumulated onto the peak‐exhale (reference) phase using energy‐mass transfer mapping. To investigate the impact of a potentially reducible safety margin, the authors have created and delivered treatment plans designed for a conventional internal target volume (ITV) + 5 mm, a midventilation approach, and three tracking scenarios for four lung SBRT patients. For the tracking plans, a moving target volume (MTV) was established by delineating the gross target volume (GTV) on every 4DCT phase. These were rigidly aligned to the reference phase, resulting in a unified maximum GTV to which a 1, 3, or 5 mm isotropic margin was added. All scenarios were planned for 9‐beam step‐and‐shoot IMRT to meet the criteria of RTOG 1021 (3 × 18 Gy). The GTV 3D center‐of‐volume shift varied from 6 to 14 mm.ResultsReal‐time dose reconstruction at 25 Hz could be realized on a single workstation due to the highly efficient implementation of dose calculation and dose accumulation. Decreased PTV margins resulted in inadequate target coverage during untracked deliveries for patients with substantial tumor motion. MLC tracking could ensure the GTV target dose for these patients. Organ‐at‐risk (OAR) doses were consistently reduced by decreased PTV margins.
Menten MJ, Fast MF, Nill S, et al., 2016, Lung stereotactic body radiotherapy with an MR-linac - Quantifying the impact of the magnetic field and real-time tumor tracking, Radiotherapy and Oncology, Vol: 119, Pages: 461-466, ISSN: 0167-8140
Background and purposeThere are concerns that radiotherapy doses delivered in a magnetic field might be distorted due to the Lorentz force deflecting secondary electrons. This study investigates this effect on lung stereotactic body radiotherapy (SBRT) treatments, conducted either with or without multileaf collimator (MLC) tumor tracking.Material and methodsLung SBRT treatments with an MR-linac were simulated for nine patients. Two different treatment techniques were compared: conventional, non-tracked deliveries and deliveries with real-time MLC tumor tracking, each conducted either with or without a 1.5 T magnetic field.ResultsSlight dose distortions at air-tissue-interfaces were observed in the presence of the magnetic field. Most prominently, the dose to 2% of the skin increased by 1.4 Gy on average. Regardless of the presence of the magnetic field, MLC tracking was able to spare healthy tissue, for example by decreasing the mean lung dose by 0.3 Gy on average, while maintaining the target dose.ConclusionsAccounting for the magnetic field during treatment plan optimization allowed for design and delivery of clinically acceptable lung SBRT treatments with an MR-linac. Furthermore, the ability of MLC tumor tracking to decrease dose exposure of healthy tissue, was not inhibited by the magnetic field.
Fast MF, Kamerling CP, Ziegenhein P, et al., 2016, Assessment of MLC tracking performance during hypofractionated prostate radiotherapy using real-time dose reconstruction, PHYSICS IN MEDICINE AND BIOLOGY, Vol: 61, Pages: 1546-1562, ISSN: 0031-9155
By adapting to the actual patient anatomy during treatment, tracked multi-leaf collimator (MLC) treatment deliveries offer an opportunity for margin reduction and healthy tissue sparing. This is assumed to be especially relevant for hypofractionated protocols in which intrafractional motion does not easily average out. In order to confidently deliver tracked treatments with potentially reduced margins, it is necessary to monitor not only the patient anatomy but also the actually delivered dose during irradiation. In this study, we present a novel real-time online dose reconstruction tool which calculates actually delivered dose based on pre-calculated dose influence data in less than 10 ms at a rate of 25 Hz. Using this tool we investigate the impact of clinical target volume (CTV) to planning target volume (PTV) margins on CTV coverage and organ-at-risk dose. On our research linear accelerator, a set of four different CTV-to-PTV margins were tested for three patient cases subject to four different motion conditions. Based on this data, we can conclude that tracking eliminates dose cold spots which can occur in the CTV during conventional deliveries even for the smallest CTV-to-PTV margin of 1 mm. Changes of organ-at-risk dose do occur frequently during MLC tracking and are not negligible in some cases. Intrafractional dose reconstruction is expected to become an important element in any attempt of re-planning the treatment plan during the delivery based on the observed anatomy of the day.
Menten MJ, Fast MF, Nill S, et al., 2015, Using dual-energy x-ray imaging to enhance automated lung tumor tracking during real-time adaptive radiotherapy, MEDICAL PHYSICS, Vol: 42, Pages: 6987-6998, ISSN: 0094-2405
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- Citations: 23
Fast MF, Nill S, Menten MJ, et al., 2015, Dosimetric consequences of dynamic MLC tracking with an FFF beam for off-axis targets, Publisher: ELSEVIER IRELAND LTD, Pages: S484-S484, ISSN: 0167-8140
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- Citations: 1
Menten MJ, Guckenberger M, Herrmann C, et al., 2012, Comparison of a multileaf collimator tracking system and a robotic treatment couch tracking system for organ motion compensation during radiotherapy, MEDICAL PHYSICS, Vol: 39, Pages: 7032-7041, ISSN: 0094-2405
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- Citations: 24
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