Publications by authors named "Matteo Maspero"

19 Publications

  • Page 1 of 1

Influence of eye movement on lens dose and optic nerve target coverage during craniospinal irradiation.

Clin Transl Radiat Oncol 2021 Nov 29;31:28-33. Epub 2021 Aug 29.

Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, the Netherlands.

Purpose: Optic nerves are part of the craniospinal irradiation (CSI) target volume. Modern radiotherapy techniques achieve highly conformal target doses while avoiding organs-at-risk such as the lens. The magnitude of eye movement and its influence on CSI target- and avoidance volumes are unclear. We aimed to evaluate the movement-range of lenses and optic nerves and its influence on dose distribution of several planning techniques.

Methods: Ten volunteers underwent MRI scans in various gaze directions (neutral, left, right, cranial, caudal). Lenses, orbital optic nerves, optic discs and CSI target volumes were delineated. 36-Gy cranial irradiation plans were constructed on synthetic CT images in neutral gaze, with Volumetric Modulated Arc Therapy, pencil-beam scanning proton therapy, and 3D-conventional photons. Movement-amplitudes of lenses and optic discs were analyzed, and influence of gaze direction on lens and orbital optic nerve dose distribution.

Results: Mean eye structures' shift from neutral position was greatest in caudal gaze; -5.8±1.2 mm (±SD) for lenses and 7.0±2.0 mm for optic discs. In 3D-conventional plans, caudal gaze decreased Mean Lens Dose (MLD). In VMAT and proton plans, eye movements mainly increased MLD and diminished D98 orbital optic nerve (D98) coverage; mean MLD increased up to 5.5 Gy [total ΔMLD range -8.1 to 10.0 Gy], and mean D98 decreased up to 3.3 Gy [total ΔD98 range -13.6 to 1.2 Gy]. VMAT plans optimized for optic disc Internal Target Volume and lens Planning organ-at-Risk Volume resulted in higher MLD over gaze directions. D98 became ≥95% of prescribed dose over 95/100 evaluated gaze directions, while all-gaze bilateral D98 significantly changed in 1 of 10 volunteers.

Conclusion: With modern CSI techniques, eye movements result in higher lens doses and a mean detriment for orbital optic nerve dose coverage of <10% of prescribed dose.
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http://dx.doi.org/10.1016/j.ctro.2021.08.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8427085PMC
November 2021

Deep learning based synthetic-CT generation in radiotherapy and PET: A review.

Med Phys 2021 Aug 18. Epub 2021 Aug 18.

Division of Biomedical Physics in Radiation Oncology, DKFZ German Cancer Research Center, Heidelberg, Germany.

Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.
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http://dx.doi.org/10.1002/mp.15150DOI Listing
August 2021

Optimal Conditions for Diapause Survival of , an Egg Parasitoid for Biological Control of .

Insects 2021 Jun 9;12(6). Epub 2021 Jun 9.

USDA-ARS European Biological Control Laboratory, 34980 Montferrier-sur-Lez, France.

is a specialist egg parasitoid of the citrus longhorned beetle , a high-risk invasive pest of hardwood trees. The parasitoid overwinters as diapausing mature larvae within the host egg and emerges in early summer in synchrony with the egg-laying peak of . This study investigated the parasitoid's diapause survival in parasitized host eggs that either remained in potted trees under semi-natural conditions in southern France or were removed from the wood and held at four different humidities (44, 75, 85-93 and 100% RH) at 11 °C or four different temperature regimes (2, 5, 10 and 12.5 °C) at 100% RH in the laboratory. The temperature regimes reflect overwintering temperatures across the parasitoid's geographical distribution in its native range. Results show that the parasitoid resumed its development to the adult stage at normal rearing conditions (22 °C, 100% RH, 14L:10D) after 6- or 7-months cold chilling at both the semi-natural and laboratory conditions. It had a low survival rate (36.7%) on potted plants due to desiccation or tree wound defense response. No parasitoids survived at 44% RH, but survival rate increased with humidity, reaching the highest (93.7%) at 100% RH. Survival rate also increased from 21.0% at 2 °C to 82.8% at 12.5 °C. Post-diapause developmental time decreased with increased humidity or temperature. There was no difference in the lifetime fecundity of emerged females from 2 and 12.5 °C. These results suggest that 100% RH and 12.5 °C are the most suitable diapause conditions for laboratory rearing of this parasitoid.
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http://dx.doi.org/10.3390/insects12060535DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8226561PMC
June 2021

A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer.

Phys Imaging Radiat Oncol 2020 Apr 25;14:24-31. Epub 2020 May 25.

Department of radiotherapy, division of imaging & oncology, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA Utrecht, The Netherlands.

Adaptive radiotherapy based on cone-beam computed tomography (CBCT) requires high CT number accuracy to ensure accurate dose calculations. Recently, deep learning has been proposed for fast CBCT artefact corrections on single anatomical sites. This study investigated the feasibility of applying a single convolutional network to facilitate dose calculation based on CBCT for head-and-neck, lung and breast cancer patients. Ninety-nine patients diagnosed with head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based position verification were included in this study. The CBCTs were registered to planning CT according to clinical procedures. Three cycle-consistent generative adversarial networks (cycle-GANs) were trained in an unpaired manner on 15 patients per anatomical site generating synthetic-CTs (sCTs). Another network was trained with all the anatomical sites together. Performances of all four networks were compared and evaluated for image similarity against rescan CT (rCT). Clinical plans were recalculated on rCT and sCT and analysed through voxel-based dose differences and -analysis. A sCT was generated in 10 s. Image similarity was comparable between models trained on different anatomical sites and a single model for all sites. Mean dose differences were obtained in high-dose regions. Mean gamma (3%, 3 mm) pass-rates were achieved for all sites. Cycle-GAN reduced CBCT artefacts and increased similarity to CT, enabling sCT-based dose calculations. A single network achieved CBCT-based dose calculation generating synthetic CT for head-and-neck, lung, and breast cancer patients with similar performance to a network specifically trained for each anatomical site.
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http://dx.doi.org/10.1016/j.phro.2020.04.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807541PMC
April 2020

Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy.

Radiother Oncol 2020 12 23;153:197-204. Epub 2020 Sep 23.

Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, , The Netherlands.

Background And Purpose: To enable accurate magnetic resonance imaging (MRI)-based dose calculations, synthetic computed tomography (sCT) images need to be generated. We aim at assessing the feasibility of dose calculations from MRI acquired with a heterogeneous set of imaging protocol for paediatric patients affected by brain tumours.

Materials And Methods: Sixty paediatric patients undergoing brain radiotherapy were included. MR imaging protocols varied among patients, and data heterogeneity was maintained in train/validation/test sets. Three 2D conditional generative adversarial networks (cGANs) were trained to generate sCT from T1-weighted MRI, considering the three orthogonal planes and its combination (multi-plane sCT). For each patient, median and standard deviation (σ) of the three views were calculated, obtaining a combined sCT and a proxy for uncertainty map, respectively. The sCTs were evaluated against the planning CT in terms of image similarity and accuracy for photon and proton dose calculations.

Results: A mean absolute error of 61 ± 14 HU (mean±1σ) was obtained in the intersection of the body contours between CT and sCT. The combined multi-plane sCTs performed better than sCTs from any single plane. Uncertainty maps highlighted that multi-plane sCTs differed at the body contours and air cavities. A dose difference of -0.1 ± 0.3% and 0.1 ± 0.4% was obtained on the D > 90% of the prescribed dose and mean γ pass-rate of 99.5 ± 0.8% and 99.2 ± 1.1% for photon and proton planning, respectively.

Conclusion: Accurate MR-based dose calculation using a combination of three orthogonal planes for sCT generation is feasible for paediatric brain cancer patients, even when training on a heterogeneous dataset.
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http://dx.doi.org/10.1016/j.radonc.2020.09.029DOI Listing
December 2020

Deep learning-based image reconstruction and motion estimation from undersampled radial k-space for real-time MRI-guided radiotherapy.

Phys Med Biol 2020 08 7;65(15):155015. Epub 2020 Aug 7.

Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands. Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.

To enable magnetic resonance imaging (MRI)-guided radiotherapy with real-time adaptation, motion must be quickly estimated with low latency. The motion estimate is used to adapt the radiation beam to the current anatomy, yielding a more conformal dose distribution. As the MR acquisition is the largest component of latency, deep learning (DL) may reduce the total latency by enabling much higher undersampling factors compared to conventional reconstruction and motion estimation methods. The benefit of DL on image reconstruction and motion estimation was investigated for obtaining accurate deformation vector fields (DVFs) with high temporal resolution and minimal latency. 2D cine MRI acquired at 1.5 T from 135 abdominal cancer patients were retrospectively included in this study. Undersampled radial golden angle acquisitions were retrospectively simulated. DVFs were computed using different combinations of conventional- and DL-based methods for image reconstruction and motion estimation, allowing a comparison of four approaches to achieve real-time motion estimation. The four approaches were evaluated based on the end-point-error and root-mean-square error compared to a ground-truth optical flow estimate on fully-sampled images, the structural similarity (SSIM) after registration and time necessary to acquire k-space, reconstruct an image and estimate motion. The lowest DVF error and highest SSIM were obtained using conventional methods up to [Formula: see text]. For undersampling factors [Formula: see text], the lowest DVF error and highest SSIM were obtained using conventional image reconstruction and DL-based motion estimation. We have found that, with this combination, accurate DVFs can be obtained up to [Formula: see text] with an average root-mean-square error up to 1 millimeter and an SSIM greater than 0.8 after registration, taking 60 milliseconds. High-quality 2D DVFs from highly undersampled k-space can be obtained with a high temporal resolution with conventional image reconstruction and a deep learning-based motion estimation approach for real-time adaptive MRI-guided radiotherapy.
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http://dx.doi.org/10.1088/1361-6560/ab9358DOI Listing
August 2020

Clinical implementation of MRI-based organs-at-risk auto-segmentation with convolutional networks for prostate radiotherapy.

Radiat Oncol 2020 May 11;15(1):104. Epub 2020 May 11.

Department of Radiotherapy, Division of Imaging & Oncology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, The Netherlands.

Background: Structure delineation is a necessary, yet time-consuming manual procedure in radiotherapy. Recently, convolutional neural networks have been proposed to speed-up and automatise this procedure, obtaining promising results. With the advent of magnetic resonance imaging (MRI)-guided radiotherapy, MR-based segmentation is becoming increasingly relevant. However, the majority of the studies investigated automatic contouring based on computed tomography (CT).

Purpose: In this study, we investigate the feasibility of clinical use of deep learning-based automatic OARs delineation on MRI.

Materials And Methods: We included 150 patients diagnosed with prostate cancer who underwent MR-only radiotherapy. A three-dimensional (3D) T1-weighted dual spoiled gradient-recalled echo sequence was acquired with 3T MRI for the generation of the synthetic-CT. The first 48 patients were included in a feasibility study training two 3D convolutional networks called DeepMedic and dense V-net (dV-net) to segment bladder, rectum and femurs. A research version of an atlas-based software was considered for comparison. Dice similarity coefficient, 95% Hausdorff distances (HD), and mean distances were calculated against clinical delineations. For eight patients, an expert RTT scored the quality of the contouring for all the three methods. A choice among the three approaches was made, and the chosen approach was retrained on 97 patients and implemented for automatic use in the clinical workflow. For the successive 53 patients, Dice, HD and mean distances were calculated against the clinically used delineations.

Results: DeepMedic, dV-net and the atlas-based software generated contours in 60 s, 4 s and 10-15 min, respectively. Performances were higher for both the networks compared to the atlas-based software. The qualitative analysis demonstrated that delineation from DeepMedic required fewer adaptations, followed by dV-net and the atlas-based software. DeepMedic was clinically implemented. After retraining DeepMedic and testing on the successive patients, the performances slightly improved.

Conclusion: High conformality for OARs delineation was achieved with two in-house trained networks, obtaining a significant speed-up of the delineation procedure. Comparison of different approaches has been performed leading to the succesful adoption of one of the neural networks, DeepMedic, in the clinical workflow. DeepMedic maintained in a clinical setting the accuracy obtained in the feasibility study.
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http://dx.doi.org/10.1186/s13014-020-01528-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7216473PMC
May 2020

CBCT correction using a cycle-consistent generative adversarial network and unpaired training to enable photon and proton dose calculation.

Phys Med Biol 2019 11 15;64(22):225004. Epub 2019 Nov 15.

Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany. Department of Radiotherapy, Center for Image Sciences, Universitair Medisch Centrum Utrecht, Utrecht, the Netherlands. Department of Medical Physics, Fakultät für Physik, Ludwig-Maximilians-Universität München (LMU Munich), Garching, Germany. Author to whom correspondence should be addressed.

In presence of inter-fractional anatomical changes, clinical benefits are anticipated from image-guided adaptive radiotherapy. Nowadays, cone-beam CT (CBCT) imaging is mostly utilized during pre-treatment imaging for position verification. Due to various artifacts, image quality is typically not sufficient for photon or proton dose calculation, thus demanding accurate CBCT correction, as potentially provided by deep learning techniques. This work aimed at investigating the feasibility of utilizing a cycle-consistent generative adversarial network (cycleGAN) for prostate CBCT correction using unpaired training. Thirty-three patients were included. The network was trained to translate uncorrected, original CBCT images (CBCT) into planning CT equivalent images (CBCT). HU accuracy was determined by comparison to a previously validated CBCT correction technique (CBCT). Dosimetric accuracy was inferred for volumetric-modulated arc photon therapy (VMAT) and opposing single-field uniform dose (OSFUD) proton plans, optimized on CBCT and recalculated on CBCT. Single-sided SFUD proton plans were utilized to assess proton range accuracy. The mean HU error of CBCT with respect to CBCT decreased from 24 HU for CBCT to  -6 HU. Dose calculation accuracy was high for VMAT, with average pass-rates of 100%/89% for a 2%/1% dose difference criterion. For proton OSFUD plans, the average pass-rate for a 2% dose difference criterion was 80%. Using a (2%, 2 mm) gamma criterion, the pass-rate was 96%. 93% of all analyzed SFUD profiles had a range agreement better than 3 mm. CBCT correction time was reduced from 6-10 min for CBCT to 10 s for CBCT. Our study demonstrated the feasibility of utilizing a cycleGAN for CBCT correction, achieving high dose calculation accuracy for VMAT. For proton therapy, further improvements may be required. Due to unpaired training, the approach does not rely on anatomically consistent training data or potentially inaccurate deformable image registration. The substantial speed-up for CBCT correction renders the method particularly interesting for adaptive radiotherapy.
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http://dx.doi.org/10.1088/1361-6560/ab4d8cDOI Listing
November 2019

Deep learning-based MR-to-CT synthesis: The influence of varying gradient echo-based MR images as input channels.

Magn Reson Med 2020 04 8;83(4):1429-1441. Epub 2019 Oct 8.

Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands.

Purpose: To study the influence of gradient echo-based contrasts as input channels to a 3D patch-based neural network trained for synthetic CT (sCT) generation in canine and human populations.

Methods: Magnetic resonance images and CT scans of human and canine pelvic regions were acquired and paired using nonrigid registration. Magnitude MR images and Dixon reconstructed water, fat, in-phase and opposed-phase images were obtained from a single T -weighted multi-echo gradient-echo acquisition. From this set, 6 input configurations were defined, each containing 1 to 4 MR images regarded as input channels. For each configuration, a UNet-derived deep learning model was trained for synthetic CT generation. Reconstructed Hounsfield unit maps were evaluated with peak SNR, mean absolute error, and mean error. Dice similarity coefficient and surface distance maps assessed the geometric fidelity of bones. Repeatability was estimated by replicating the training up to 10 times.

Results: Seventeen canines and 23 human subjects were included in the study. Performance and repeatability of single-channel models were dependent on the TE-related water-fat interference with variations of up to 17% in mean absolute error, and variations of up to 28% specifically in bones. Repeatability, Dice similarity coefficient, and mean absolute error were statistically significantly better in multichannel models with mean absolute error ranging from 33 to 40 Hounsfield units in humans and from 35 to 47 Hounsfield units in canines.

Conclusion: Significant differences in performance and robustness of deep learning models for synthetic CT generation were observed depending on the input. In-phase images outperformed opposed-phase images, and Dixon reconstructed multichannel inputs outperformed single-channel inputs.
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http://dx.doi.org/10.1002/mrm.28008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6972695PMC
April 2020

Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch-based three-dimensional convolutional neural network.

Med Phys 2019 Sep 9;46(9):4095-4104. Epub 2019 Jul 9.

Centre for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.

Purpose: To develop and evaluate a patch-based convolutional neural network (CNN) to generate synthetic computed tomography (sCT) images for magnetic resonance (MR)-only workflow for radiotherapy of head and neck tumors. A patch-based deep learning method was chosen to improve robustness to abnormal anatomies caused by large tumors, surgical excisions, or dental artifacts. In this study, we evaluate whether the generated sCT images enable accurate MR-based dose calculations in the head and neck region.

Methods: We conducted a retrospective study on 34 patients with head and neck cancer who underwent both CT and MR imaging for radiotherapy treatment planning. To generate the sCTs, a large field-of-view T2-weighted Turbo Spin Echo MR sequence was used from the clinical protocol for multiple types of head and neck tumors. To align images as well as possible on a voxel-wise level, CT scans were nonrigidly registered to the MR (CT ). The CNN was based on a U-net architecture and consisted of 14 layers with 3 × 3 × 3 filters. Patches of 48 × 48 × 48 were randomly extracted and fed into the training. sCTs were created for all patients using threefold cross validation. For each patient, the clinical CT-based treatment plan was recalculated on sCT using Monaco TPS (Elekta). We evaluated mean absolute error (MAE) and mean error (ME) within the body contours and dice scores in air and bone mask. Also, dose differences and gamma pass rates between CT- and sCT-based plans inside the body contours were calculated.

Results: sCT generation took 4 min per patient. The MAE over the patient population of the sCT within the intersection of body contours was 75 ± 9 Hounsfield Units (HU) (±1 SD), and the ME was 9 ± 11 HU. Dice scores of the air and bone masks (CT vs sCT) were 0.79 ± 0.08 and 0.70 ± 0.07, respectively. Dosimetric analysis showed mean deviations of -0.03% ± 0.05% for dose within the body contours and -0.07% ± 0.22% inside the >90% dose volume. Dental artifacts obscuring the CT could be circumvented in the sCT by the CNN-based approach in combination with Turbo Spin Echo (TSE) magnetic resonance imaging (MRI) sequence that typically is less prone to susceptibility artifacts.

Conclusions: The presented CNN generated sCTs from conventional MR images without adding scan time to the acquisition. Dosimetric evaluation suggests that dose calculations performed on the sCTs are accurate, and can therefore be used for MR-only radiotherapy treatment planning of the head and neck.
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http://dx.doi.org/10.1002/mp.13663DOI Listing
September 2019

Feasibility of magnetic resonance imaging-only rectum radiotherapy with a commercial synthetic computed tomography generation solution.

Phys Imaging Radiat Oncol 2018 Jul 2;7:58-64. Epub 2018 Oct 2.

Department of Radiotherapy, Universitair Medisch Centrum Utrecht, The Netherlands.

Background And Purpose: Synthetic computed tomography (sCT) images enable magnetic resonance (MR)-based dose calculations. This work investigated whether a commercially available sCT generation solution was suitable for accurate dose calculations and position verification on patients with rectal cancer.

Material And Methods: For twenty rectal cancer patients computed tomography (CT) images were rigidly registered to sCT images. Clinical volumetric modulated arc therapy plans were recalculated on registered CT and sCT images. Dose deviations were determined through gamma and voxelwise analysis. The impact on position verification was investigated by identifying differences in translations and rotation between cone-beam CT (CBCT) to CT and CBCT to sCT registrations.

Results: Across twenty patients, within a threshold of 90% of the prescription dose, a gamma analysis (2%, 2 mm) mean pass rate of 95.2 ± 4.0% (±1 ) and mean dose deviation of -0.3 ± 0.2% of prescription dose were obtained. The mean difference of translations and rotations over ten patients (76 CBCTs) was <1 mm and <0.5° in all directions. In the sole posterior-anterior direction a mean systematic shift of 0.7 ± 0.6 mm was found.

Conclusions: Accurate MR-based dose calculations using a commercial sCT generation method were clinically feasible for treatment of rectal cancer patients. The accuracy of position verification was clinically acceptable. However, before clinical implementation future investigations will be performed to determine the origin of the systematic shift.
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http://dx.doi.org/10.1016/j.phro.2018.09.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7807733PMC
July 2018

Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy.

Phys Med Biol 2018 09 10;63(18):185001. Epub 2018 Sep 10.

Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands. Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands. Image Science Institute, University Medical Center Utrecht, Utrecht, Netherlands. The authors equally contributed.

To enable magnetic resonance (MR)-only radiotherapy and facilitate modelling of radiation attenuation in humans, synthetic CT (sCT) images need to be generated. Considering the application of MR-guided radiotherapy and online adaptive replanning, sCT generation should occur within minutes. This work aims at assessing whether an existing deep learning network can rapidly generate sCT images for accurate MR-based dose calculations in the entire pelvis. A study was conducted on data of 91 patients with prostate (59), rectal (18) and cervical (14) cancer who underwent external beam radiotherapy acquiring both CT and MRI for patients' simulation. Dixon reconstructed water, fat and in-phase images obtained from a conventional dual gradient-recalled echo sequence were used to generate sCT images. A conditional generative adversarial network (cGAN) was trained in a paired fashion on 2D transverse slices of 32 prostate cancer patients. The trained network was tested on the remaining patients to generate sCT images. For 30 patients in the test set, dose recalculations of the clinical plan were performed on sCT images. Dose distributions were evaluated comparing voxel-based dose differences, gamma and dose-volume histogram (DVH) analysis. The sCT generation required 5.6 s and 21 s for a single patient volume on a GPU and CPU, respectively. On average, sCT images resulted in a higher dose to the target of maximum 0.3%. The average gamma pass rates using the 3%, 3 mm and 2%, 2 mm criteria were above 97 and 91%, respectively, for all volumes of interests considered. All DVH points calculated on sCT differed less than  ±2.5% from the corresponding points on CT. Results suggest that accurate MR-based dose calculation using sCT images generated with a cGAN trained on prostate cancer patients is feasible for the entire pelvis. The sCT generation was sufficiently fast for integration in an MR-guided radiotherapy workflow.
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http://dx.doi.org/10.1088/1361-6560/aada6dDOI Listing
September 2018

MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network.

Int J Radiat Oncol Biol Phys 2018 11 4;102(4):801-812. Epub 2018 Jun 4.

Department of Radiation Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.

Purpose: This work aims to facilitate a fast magnetic resonance (MR)-only workflow for radiation therapy of intracranial tumors. Here, we evaluate whether synthetic computed tomography (sCT) images generated with a dilated convolutional neural network (CNN) enable accurate MR-based dose calculations in the brain.

Methods And Materials: We conducted a retrospective study of 52 patients with brain tumors who underwent both computed tomography (CT) and MR imaging for radiation therapy treatment planning. To generate the sCTs, a T1-weighted gradient echo MR sequence was selected from the clinical protocol for multiple types of brain tumors. sCTs were created for all 52 patients with a dilated CNN using 2-fold cross validation; in each fold, 26 patients were used for training and the remaining 26 patients were used for evaluation. For each patient, the clinical CT-based treatment plan was recalculated on sCT. We calculated dose differences and gamma pass rates between CT- and sCT-based plans inside body and planning target volume. Geometric fidelity of the sCT and differences in beam depth and equivalent path length were assessed between both treatment plans.

Results: sCT generation took 1 minute per patient. Over the patient population, the mean absolute error of the sCT within the intersection of body contours was 67 ± 11 HU (±1 standard deviation [SD], range: 51-117 HU), and the mean error was 13 ± 9 HU (±1 SD, range: -2 to 38 HU). Dosimetric analysis showed mean deviations of 0.00% ± 0.02% (±1 SD, range: -0.05 to 0.03) for dose within the body contours and -0.13% ± 0.39% (±1 SD, range: -1.43 to 0.80) inside the planning target volume. Mean γ was 98.8% ± 2.2% for doses >50% of the prescribed dose.

Conclusions: The presented dilated CNN generated sCTs from conventional MR images without adding scan time to the acquisition. Dosimetric evaluation suggests that dose calculations performed on the sCTs are accurate and can therefore be used for MR-only intracranial radiation therapy treatment planning.
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http://dx.doi.org/10.1016/j.ijrobp.2018.05.058DOI Listing
November 2018

Evaluation of gold fiducial marker manual localisation for magnetic resonance-only prostate radiotherapy.

Radiat Oncol 2018 Jun 5;13(1):105. Epub 2018 Jun 5.

Universitair Medisch Centrum Utrecht, Heidelberglaan 100, Utrecht, 3508 GA, The Netherlands.

Background: The use of intraprostatic gold fiducial markers (FMs) ensures highly accurate and precise image-guided radiation therapy for patients diagnosed with prostate cancer thanks to the ease of localising FMs on photon-based imaging, like Computed Tomography (CT) images. Recently, Magnetic Resonance (MR)-only radiotherapy has been proposed to simplify the workflow and reduce possible systematic uncertainties. A critical, determining factor in the accuracy of such an MR-only simulation will be accurate FM localisation using solely MR images.

Purpose: The aim of this study is to evaluate the performances of manual MR-based FM localisation within a clinical environment.

Methods: We designed a study in which 5 clinically involved radiation therapy technicians (RTTs) independently localised the gold FMs implanted in 16 prostate cancer patients in two scenarios: employing a single MR sequence or a combination of sequences. Inter-observer precision and accuracy were assessed for the two scenarios for localisation in terms of 95% limit of agreement on single FMs (LoA)/ centre of mass (LoA ) and inter-marker distances (IDs), respectively.

Results: The number of precisely located FMs (LoA <2 mm) increased from 38/48 to 45/48 FMs when localisation was performed using multiple sequences instead of single one. When performing localisation on multiple sequences, imprecise localisation of the FMs (3/48 FMs) occurred for 1/3 implanted FMs in three different patients. In terms of precision, we obtained LoA within 0.25 mm in all directions over the precisely located FMs. In terms of accuracy, IDs difference of manual MR-based localisation versus CT-based localisation was on average (±1 STD) 0.6 ±0.6 mm.

Conclusions: For both the investigated scenarios, the results indicate that when FM classification was correct, the precision and accuracy are high and comparable to CT-based FM localisation. We found that use of multiple sequences led to better localisation performances compared with the use of single sequence. However, we observed that, due to the presence of calcification and motion, the risk of mislocated patient positioning is still too high to allow the sole use of manual FM localisation. Finally, strategies to possibly overcome the current challenges were proposed.
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http://dx.doi.org/10.1186/s13014-018-1029-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5989467PMC
June 2018

Feasibility of MR-only proton dose calculations for prostate cancer radiotherapy using a commercial pseudo-CT generation method.

Phys Med Biol 2017 Nov 21;62(24):9159-9176. Epub 2017 Nov 21.

Center for Image Sciences, Universitair Medisch Centrum Utrecht, Utrecht, Netherlands.

A magnetic resonance (MR)-only radiotherapy workflow can reduce cost, radiation exposure and uncertainties introduced by CT-MRI registration. A crucial prerequisite is generating the so called pseudo-CT (pCT) images for accurate dose calculation and planning. Many pCT generation methods have been proposed in the scope of photon radiotherapy. This work aims at verifying for the first time whether a commercially available photon-oriented pCT generation method can be employed for accurate intensity-modulated proton therapy (IMPT) dose calculation. A retrospective study was conducted on ten prostate cancer patients. For pCT generation from MR images, a commercial solution for creating bulk-assigned pCTs, called MR for Attenuation Correction (MRCAT), was employed. The assigned pseudo-Hounsfield Unit (HU) values were adapted to yield an increased agreement to the reference CT in terms of proton range. Internal air cavities were copied from the CT to minimise inter-scan differences. CT- and MRCAT-based dose calculations for opposing beam IMPT plans were compared by gamma analysis and evaluation of clinically relevant target and organ at risk dose volume histogram (DVH) parameters. The proton range in beam's eye view (BEV) was compared using single field uniform dose (SFUD) plans. On average, a [Formula: see text] mm) gamma pass rate of 98.4% was obtained using a [Formula: see text] dose threshold after adaptation of the pseudo-HU values. Mean differences between CT- and MRCAT-based dose in the DVH parameters were below 1 Gy ([Formula: see text]). The median proton range difference was [Formula: see text] mm, with on average 96% of all BEV dose profiles showing a range agreement better than 3 mm. Results suggest that accurate MR-based proton dose calculation using an automatic commercial bulk-assignment pCT generation method, originally designed for photon radiotherapy, is feasible following adaptation of the assigned pseudo-HU values.
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http://dx.doi.org/10.1088/1361-6560/aa9677DOI Listing
November 2017

Evaluation of an automatic MR-based gold fiducial marker localisation method for MR-only prostate radiotherapy.

Phys Med Biol 2017 Oct 3;62(20):7981-8002. Epub 2017 Oct 3.

Department of Radiotherapy, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3508 GA Utrecht, Netherlands. Image Science Institute, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3508 GA Utrecht, Netherlands. Center for Image Sciences, Universitair Medisch Centrum Utrecht, Heidelberglaan 100, 3508 GA Utrecht, Netherlands.

An MR-only radiotherapy planning (RTP) workflow would reduce the cost, radiation exposure and uncertainties introduced by CT-MRI registrations. In the case of prostate treatment, one of the remaining challenges currently holding back the implementation of an RTP workflow is the MR-based localisation of intraprostatic gold fiducial markers (FMs), which is crucial for accurate patient positioning. Currently, MR-based FM localisation is clinically performed manually. This is sub-optimal, as manual interaction increases the workload. Attempts to perform automatic FM detection often rely on being able to detect signal voids induced by the FMs in magnitude images. However, signal voids may not always be sufficiently specific, hampering accurate and robust automatic FM localisation. Here, we present an approach that aims at automatic MR-based FM localisation. This method is based on template matching using a library of simulated complex-valued templates, and exploiting the behaviour of the complex MR signal in the vicinity of the FM. Clinical evaluation was performed on seventeen prostate cancer patients undergoing external beam radiotherapy treatment. Automatic MR-based FM localisation was compared to manual MR-based and semi-automatic CT-based localisation (the current gold standard) in terms of detection rate and the spatial accuracy and precision of localisation. The proposed method correctly detected all three FMs in 15/17 patients. The spatial accuracy (mean) and precision (STD) were 0.9 mm and 0.5 mm respectively, which is below the voxel size of [Formula: see text] mm and comparable to MR-based manual localisation. FM localisation failed (3/51 FMs) in the presence of bleeding or calcifications in the direct vicinity of the FM. The method was found to be spatially accurate and precise, which is essential for clinical use. To overcome any missed detection, we envision the use of the proposed method along with verification by an observer. This will result in a semi-automatic workflow facilitating the introduction of an MR-only workflow.
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http://dx.doi.org/10.1088/1361-6560/aa875fDOI Listing
October 2017

The feasibility of semi-automatically generated red bone marrow segmentations based on MR-only for patients with gynecologic cancer.

Radiother Oncol 2017 04 23;123(1):164-168. Epub 2017 Feb 23.

Department of Radiotherapy, Center for Image Sciences, University Medical Center Utrecht, The Netherlands.

Purpose: For patients with cervical cancer the delivery of chemotherapy with radiotherapy improves survival compared with radiotherapy alone. However, high rates of acute hematologic toxicity occur when combining both therapies due to the damage of the red bone marrow (RBM). This study aimed to reduce the radiation damage to the RBM. A tool has been developed for semi-automatic delineation of the red bone marrow based on MR-only. This delineation can be included into the treatment planning process to reduce the volume of RBM irradiated in patients receiving pelvic radiation therapy.

Methods: 13 patients with cervical cancer were enrolled. All the patients underwent MR, CT and FDG-PET imaging. A tool for RBM determination from water and fat MR images was developed. Our MR-based RBM tool was optimized and validated with the FDG-PET scans of the patients.

Results: Our tool identified RBM regions in the pelvic area. The mean total volume of these regions was 34% of the pelvic bone marrow. The corresponding SUV values based on the FDG-PET scans were above the reported threshold of active/red bone marrow.

Conclusion: This study shows that delineations of the RBM for the radiotherapy with RBM sparing can be generated semi-automatically using MR scans only.
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http://dx.doi.org/10.1016/j.radonc.2017.01.020DOI Listing
April 2017

Quantification of confounding factors in MRI-based dose calculations as applied to prostate IMRT.

Phys Med Biol 2017 02 11;62(3):948-965. Epub 2017 Jan 11.

Universitair Medisch Centrum Utrecht, Center for Image Sciences, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.

Magnetic resonance (MR)-only radiotherapy treatment planning requires pseudo-CT (pCT) images to enable MR-based dose calculations. To verify the accuracy of MR-based dose calculations, institutions interested in introducing MR-only planning will have to compare pCT-based and computer tomography (CT)-based dose calculations. However, interpreting such comparison studies may be challenging, since potential differences arise from a range of confounding factors which are not necessarily specific to MR-only planning. Therefore, the aim of this study is to identify and quantify the contribution of factors confounding dosimetric accuracy estimation in comparison studies between CT and pCT. The following factors were distinguished: set-up and positioning differences between imaging sessions, MR-related geometric inaccuracy, pCT generation, use of specific calibration curves to convert pCT into electron density information, and registration errors. The study comprised fourteen prostate cancer patients who underwent CT/MRI-based treatment planning. To enable pCT generation, a commercial solution (MRCAT, Philips Healthcare, Vantaa, Finland) was adopted. IMRT plans were calculated on CT (gold standard) and pCTs. Dose difference maps in a high dose region (CTV) and in the body volume were evaluated, and the contribution to dose errors of possible confounding factors was individually quantified. We found that the largest confounding factor leading to dose difference was the use of different calibration curves to convert pCT and CT into electron density (0.7%). The second largest factor was the pCT generation which resulted in pCT stratified into a fixed number of tissue classes (0.16%). Inter-scan differences due to patient repositioning, MR-related geometric inaccuracy, and registration errors did not significantly contribute to dose differences (0.01%). The proposed approach successfully identified and quantified the factors confounding accurate MRI-based dose calculation in the prostate. This study will be valuable for institutions interested in introducing MR-only dose planning in their clinical practice.
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http://dx.doi.org/10.1088/1361-6560/aa4fe7DOI Listing
February 2017

Prospects for the use of biological control agents against Anoplophora in Europe.

Pest Manag Sci 2015 Jan 14;71(1):7-14. Epub 2014 Oct 14.

The Food and Environment Research Agency, Sand Hutton, York, UK.

This review summarises the literature on the biological control of Anoplophora spp. (Coleoptera: Cerambycidae) and discusses its potential for use in Europe. Entomopathogenic fungi: Beauveria brongniartii Petch (Hypocreales: Cordycipitaceae) has already been developed into a commercial product in Japan, and fungal infection results in high mortality rates. Parasitic nematodes: Steinernema feltiae Filipjev (Rhabditida: Steinernematidae) and Steinernema carpocapsae Weiser have potential for use as biopesticides as an alternative to chemical treatments. Parasitoids: a parasitoid of Anoplophora chinensis Forster, Aprostocetus anoplophorae Delvare (Hymenoptera: Eulophidae), was discovered in Italy in 2002 and has been shown to be capable of parasitising up to 72% of A. chinensis eggs; some native European parasitoid species (e.g. Spathius erythrocephalus) also have potential to be used as biological control agents. Predators: two woodpecker (Piciformis: Picidae) species that are native to Europe, Dendrocopos major Beicki and Picus canus Gmelin, have been shown to be effective at controlling Anoplophora glabripennis Motschulsky in Chinese forests. The removal and destruction of infested and potentially infested trees is the main eradication strategy for Anoplophora spp. in Europe, but biological control agents could be used in the future to complement other management strategies, especially in locations where eradication is no longer possible.
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http://dx.doi.org/10.1002/ps.3907DOI Listing
January 2015
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