Morphometric and clinical comparison of MRI-based synthetic CT to conventional CT of the hip in children

BACKGROUND

MRI-based synthetic CT (sCT) generates CT-like images from MRI data.

OBJECTIVE

To evaluate equivalence, inter- and intraobserver reliability, and image quality of sCT compared to conventional (cCT) for assessing hip morphology and maturity in pediatric patients.

MATERIALS AND METHODS

We prospectively enrolled patients <21 years old with cCT and 3T MRI of the hips/pelvis. A dual-echo gradient-echo sequence was used to generate sCT via a commercially available post-processing software (BoneMRI v1.5 research version, MRIguidance BV, Utrecht, NL). Two pediatric musculoskeletal radiologists measured seven morphologic hip parameters. 3D surface distances between cCT and sCT were computed. Physeal status was established at seven locations with cCT as reference standard. Images were qualitatively scored on a 5-point Likert scale regarding diagnostic quality, signal-to-noise ratio, clarity of bony margin, corticomedullary differentiation, and presence and severity of artifacts. Quantitative evaluation of Hounsfield units (HU) was performed in bone, muscle, and fat tissue. Inter- and intraobserver reliability were measured by intraclass correlation coefficients. The cCT-to-sCT intermodal agreement was assessed via Bland-Altman analysis. The equivalence between modalities was tested using paired two one-sided tests. The quality parameter scores of each imaging modality were compared via Wilcoxon signed-rank test. For tissue-specific HU measurements, mean absolute error and mean percentage error values were calculated using the cCT as the reference standard.

RESULTS

Thirty-eight hips in 19 patients were included (16.6 ± 3 years, range 9.9-20.9; male = 5). cCT- and sCT-based morphologic measurements demonstrated good to excellent inter- and intraobserver correlation (0.77

CONCLUSION

sCT is equivalent to cCT for the assessment of hip morphology, physeal status, and radiodensity assessment in pediatric patients.

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Key Words

• Hip imaging
• Hip morphology
• Hounsfield units
• MRI based
• Quality
• Synthetic CT

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MESH Headings

• Humans
• Male
• Child
• Female
• Magnetic Resonance Imaging/methods
• Prospective Studies
• Reproducibility of Results
• Adolescent
• Tomography, X-Ray Computed/methods
• Hip Joint/diagnostic imaging
• Child, Preschool
• Imaging, Three-Dimensional/methods

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Affiliation(s)

Jade Iwasaka-Neder Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA.
M Alejandra Bedoya Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA
James Connors Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA
Simon Warfield Computational Radiology Laboratory, Boston Children's Hospital, 401 Park Drive, Boston, MA, 02215, USA
Sarah D Bixby Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA, 02115, USA

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Detection of structural lesions of the sacroiliac joints in patients with spondyloarthritis: A comparison of T1-weighted 3D spoiled gradient echo MRI and MRI-based synthetic CT versus T1-weighted turbo spin echo MRI

OBJECTIVES

To investigate the detection of erosion, sclerosis and ankylosis using 1 mm 3D T1-weighted spoiled gradient echo (T1w-GRE) MRI and 1 mm MRI-based synthetic CT (sCT), compared with conventional 4 mm T1w-TSE.

MATERIALS AND METHODS

Prospective, cross-sectional study. Semi-coronal 4 mm T1w-TSE and axial T1w-GRE with 1.6 mm slice thickness and 0.8 mm spacing between overlapping slices were performed. The T1w-GRE images were processed into sCT images using a commercial deep learning algorithm, BoneMRI. Both were reconstructed into 1 mm semi-coronal images. T1w-TSE, T1w-GRE and sCT images were assessed independently by 3 expert and 4 non-expert readers for erosion, sclerosis and ankylosis. Cohen's kappa for inter-reader agreement, exact McNemar test for lesion frequencies and Wilcoxon signed-rank test for confidence in lesion detection were used.

RESULTS

Nineteen patients with axial spondyloarthritis were evaluated. T1w-GRE increased inter-reader agreement for detecting erosion (kappa 0.42 vs 0.21 in non-experts), increased detection of erosion (57 vs 43 of 152 joint quadrants) and sclerosis (26 vs 17 of 152 joint quadrants) among experts, and increased reader confidence for scoring erosion and sclerosis. sCT increased inter-reader agreement for detecting sclerosis (kappa 0.69 vs 0.37 in experts) and ankylosis (0.71 vs 0.52 in non-experts), increased detection of sclerosis (34 vs 17 of 152 joint quadrants) and ankylosis (20 vs 13 of 76 joint halves) among experts, and increased reader confidence for scoring erosion, sclerosis and ankylosis.

CONCLUSION

T1w-GRE and sCT increase sensitivity and reader confidence for the detection of erosion, sclerosis and ankylosis, compared with T1w-TSE.

CLINICAL RELEVANCE STATEMENT

These methods improve the detection of sacroiliac joint structural lesions and might be a useful addition to SIJ MRI protocols both in routine clinical care and as structural outcome measures in clinical trials.

Advances in Imaging (Intraop Cone-Beam Computed Tomography, Synthetic Computed Tomography, Bone Scan, Low-Dose Protocols)

Spine surgery has seen a rapid advance in the refinement and development of 3-dimensional and nuclear imaging modalities in recent years. Cone-beam CT has proven to be a valuable tool for improving the accuracy of pedicle screw placement. The use of synthetic CT and low-dose CT have also emerged as modalities which allow for little to no radiation while streamlining imaging workflows. Bone scans also serve to provide functional information about bone metabolism in both the preoperative and postoperative monitoring phases.

SC-GAN: Structure-completion generative adversarial network for synthetic CT generation from MR images with truncated anatomy

Creating synthetic CT (sCT) from magnetic resonance (MR) images enables MR-based treatment planning in radiation therapy. However, the MR images used for MR-guided adaptive planning are often truncated in the boundary regions due to the limited field of view and the need for sequence optimization. Consequently, the sCT generated from these truncated MR images lacks complete anatomic information, leading to dose calculation error for MR-based adaptive planning. We propose a novel structure-completion generative adversarial network (SC-GAN) to generate sCT with full anatomic details from the truncated MR images. To enable anatomy compensation, we expand input channels of the CT generator by including a body mask and introduce a truncation loss between sCT and real CT. The body mask for each patient was automatically created from the simulation CT scans and transformed to daily MR images by rigid registration as another input for our SC-GAN in addition to the MR images. The truncation loss was constructed by implementing either an auto-segmentor or an edge detector to penalize the difference in body outlines between sCT and real CT. The experimental results show that our SC-GAN achieved much improved accuracy of sCT generation in both truncated and untruncated regions compared to the original cycleGAN and conditional GAN methods.

"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy

BACKGROUND

Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process. The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data.

METHODS

A total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery.

DISCUSSION

Compared to CT, MRI offers a superior soft tissue contrast without additional radiation dose to the patients. However, up to now, even though the dosimetrical equivalence of CT and sCT has been shown in several retrospective studies, MRI-only workflows have still not been widely adopted. The present study aims to determine feasibility and safety of deep learning-based MRI-only radiotherapy in a holistic manner incorporating the whole radiotherapy workflow.

TRIAL REGISTRATION

NCT06106997.

CT synthesis from MR images using frequency attention conditional generative adversarial network

Magnetic resonance (MR) image-guided radiotherapy is widely used in the treatment planning of malignant tumors, and MR-only radiotherapy, a representative of this technique, requires synthetic computed tomography (sCT) images for effective radiotherapy planning. Convolutional neural networks (CNN) have shown remarkable performance in generating sCT images. However, CNN-based models tend to synthesize more low-frequency components and the pixel-wise loss function usually used to optimize the model can result in blurred images. To address these problems, a frequency attention conditional generative adversarial network (FACGAN) is proposed in this paper. Specifically, a frequency cycle generative model (FCGM) is designed to enhance the inter-mapping between MR and CT and extract more rich tissue structure information. Additionally, a residual frequency channel attention (RFCA) module is proposed and incorporated into the generator to enhance its ability in perceiving the high-frequency image features. Finally, high-frequency loss (HFL) and cycle consistency high-frequency loss (CHFL) are added to the objective function to optimize the model training. The effectiveness of the proposed model is validated on pelvic and brain datasets and compared with state-of-the-art deep learning models. The results show that FACGAN produces higher-quality sCT images while retaining clearer and richer high-frequency texture information.

Evaluating AI-generated CBCT-based synthetic CT images for target delineation in palliative treatments of pelvic bone metastasis at conventional C-arm linacs

PURPOSE

One-table treatments with treatment imaging, preparation and delivery occurring at one treatment couch, could increase patients' comfort and throughput for palliative treatments. On regular C-arm linacs, however, cone-beam CT (CBCT) imaging quality is currently insufficient. Therefore, our goal was to assess the suitability of AI-generated CBCT based synthetic CT (sCT) images for target delineation and treatment planning for palliative radiotherapy.

MATERIALS AND METHODS

CBCTs and planning CT-scans of 22 female patients with pelvic bone metastasis were included. For each CBCT, a corresponding sCT image was generated by a deep learning model in ADMIRE 3.38.0. Radiation oncologists delineated 23 target volumes (TV) on the sCTs (TVsCT) and scored their delineation confidence. The delineations were transferred to planning CTs and manually adjusted if needed to yield gold standard target volumes (TVclin). TVsCT were geometrically compared to TVclin using Dice coefficient (DC) and Hausdorff Distance (HD). The dosimetric impact of TVsCT inaccuracies was evaluated for VMAT plans with different PTV margins.

RESULTS

Radiation oncologists scored the sCT quality as sufficient for 13/23 TVsCT (median: DC = 0.9, HD = 11 mm) and insufficient for 10/23 TVsCT (median: DC = 0.7, HD = 34 mm). For the sufficient category, remaining inaccuracies could be compensated by +1 to +4 mm additional margin to achieve coverage of V95% > 95% and V95% > 98%, respectively in 12/13 TVsCT.

CONCLUSION

The evaluated sCT quality allowed for accurate delineation for most targets. sCTs with insufficient quality could be identified accurately upfront. A moderate PTV margin expansion could address remaining delineation inaccuracies. Therefore, these findings support further exploration of CBCT based one-table treatments on C-arm linacs.

A study of Bayesian deep network uncertainty and its application to synthetic CT generation for MR-only radiotherapy treatment planning

BACKGROUND

The use of synthetic computed tomography (CT) for radiotherapy treatment planning has received considerable attention because of the absence of ionizing radiation and close spatial correspondence to source magnetic resonance (MR) images, which have excellent tissue contrast. However, in an MR-only environment, little effort has been made to examine the quality of synthetic CT images without using the original CT images.

PURPOSE

To estimate synthetic CT quality without referring to original CT images, this study established the relationship between synthetic CT uncertainty and Bayesian uncertainty, and proposed a new Bayesian deep network for generating synthetic CT images and estimating synthetic CT uncertainty for MR-only radiotherapy treatment planning.

METHODS AND MATERIALS

A novel deep Bayesian network was formulated using probabilistic network weights. Two mathematical expressions were proposed to quantify the Bayesian uncertainty of the network and synthetic CT uncertainty, which was closely related to the mean absolute error (MAE) in Hounsfield Unit (HU) of synthetic CT. These uncertainties were examined to demonstrate the accuracy of representing the synthetic CT uncertainty using a Bayesian counterpart. We developed a hybrid Bayesian architecture and a new data normalization scheme, enabling the Bayesian network to generate both accurate synthetic CT and reliable uncertainty information when probabilistic weights were applied. The proposed method was evaluated in 59 patients (13/12/32/2 for training/validation/testing/uncertainty visualization) diagnosed with prostate cancer, who underwent same-day pelvic CT- and MR-acquisitions. To assess the relationship between Bayesian and synthetic CT uncertainties, linear and non-linear correlation coefficients were calculated on per-voxel, per-tissue, and per-patient bases. For accessing the accuracy of the CT number and dosimetric accuracy, the proposed method was compared with a commercially available atlas-based method (MRCAT) and a U-Net conditional-generative adversarial network (UcGAN).

RESULTS

The proposed model exhibited 44.33 MAE, outperforming UcGAN 52.51 and MRCAT 54.87. The gamma rate (2%/2 mm dose difference/distance to agreement) of the proposed model was 98.68%, comparable to that of UcGAN (98.60%) and MRCAT (98.56%). The per-patient and per-tissue linear correlation coefficients between the Bayesian and synthetic CT uncertainties ranged from 0.53 to 0.83, implying a moderate to strong linear correlation. Per-voxel correlation coefficients varied from -0.13 to 0.67 depending on the regions-of-interest evaluated, indicating tissue-dependent correlation. The R2 value for estimating MAE solely using Bayesian uncertainty was 0.98, suggesting that the uncertainty of the proposed model was an ideal candidate for predicting synthetic CT error, without referring to the original CT.

CONCLUSION

This study established a relationship between the Bayesian model uncertainty and synthetic CT uncertainty. A novel Bayesian deep network was proposed to generate a synthetic CT and estimate its uncertainty. Various metrics were used to thoroughly examine the relationship between the uncertainties of the proposed Bayesian model and the generated synthetic CT. Compared with existing approaches, the proposed model showed comparable CT number and dosimetric accuracies. The experiments showed that the proposed Bayesian model was capable of producing accurate synthetic CT, and was an effective indicator of the uncertainty and error associated with synthetic CT in MR-only workflows.

Evaluating a radiotherapy deep learning synthetic CT algorithm for PET-MR attenuation correction in the pelvis

BACKGROUND

Positron emission tomography-magnetic resonance (PET-MR) attenuation correction is challenging because the MR signal does not represent tissue density and conventional MR sequences cannot image bone. A novel zero echo time (ZTE) MR sequence has been previously developed which generates signal from cortical bone with images acquired in 65 s. This has been combined with a deep learning model to generate a synthetic computed tomography (sCT) for MR-only radiotherapy. This study aimed to evaluate this algorithm for PET-MR attenuation correction in the pelvis.

METHODS

Ten patients being treated with ano-rectal radiotherapy received a [Formula: see text]F-FDG-PET-MR in the radiotherapy position. Attenuation maps were generated from ZTE-based sCT (sCTAC) and the standard vendor-supplied MRAC. The radiotherapy planning CT scan was rigidly registered and cropped to generate a gold standard attenuation map (CTAC). PET images were reconstructed using each attenuation map and compared for standard uptake value (SUV) measurement, automatic thresholded gross tumour volume (GTV) delineation and GTV metabolic parameter measurement. The last was assessed for clinical equivalence to CTAC using two one-sided paired t tests with a significance level corrected for multiple testing of [Formula: see text]. Equivalence margins of [Formula: see text] were used.

RESULTS

Mean whole-image SUV differences were -0.02% (sCTAC) compared to -3.0% (MRAC), with larger differences in the bone regions (-0.5% to -16.3%). There was no difference in thresholded GTVs, with Dice similarity coefficients [Formula: see text]. However, there were larger differences in GTV metabolic parameters. Mean differences to CTAC in [Formula: see text] were [Formula: see text] (± standard error, sCTAC) and [Formula: see text] (MRAC), and [Formula: see text] (sCTAC) and [Formula: see text] (MRAC) in [Formula: see text]. The sCTAC was statistically equivalent to CTAC within a [Formula: see text] equivalence margin for [Formula: see text] and [Formula: see text] ([Formula: see text] and [Formula: see text]), whereas the MRAC was not ([Formula: see text] and [Formula: see text]).

CONCLUSION

Attenuation correction using this radiotherapy ZTE-based sCT algorithm was substantially more accurate than current MRAC methods with only a 40 s increase in MR acquisition time. This did not impact tumour delineation but did significantly improve the accuracy of whole-image and tumour SUV measurements, which were clinically equivalent to CTAC. This suggests PET images reconstructed with sCTAC would enable accurate quantitative PET images to be acquired on a PET-MR scanner.

Evaluation of a deep learning magnetic resonance imaging reconstruction method for synthetic computed tomography generation in prostate radiotherapy

Background and Purpose

In magnetic resonance imaging (MRI) only radiotherapy computed tomography (CT) is excluded. The method relies entirely on synthetic CT images generated from MRI. This study evaluates the compatibility of a commercial synthetic CT (sCT) with an accelerated commercial deep learning reconstruction (DLR) in MRI-only prostate radiotherapy.

Materials and Methods

For a group of 24 patients (cohort 1) the effects of DLR were studied in isolation. MRI data were reconstructed conventionally and with DLR from identical k-space data, and sCTs were generated for both reconstructions. The sCT quality, Hounsfield Unit (HU) and dosimetric impact were investigated. In another group of 15 patients (cohort 2) effects on sCT generation using accelerated MRI acquisition (40 % time reduction) reconstructed with DLR were investigated.

Results

sCT images from both cohorts, generated from DLR MRI data, were of clinically expected image quality. The mean dose differences for targets and organs at risks in cohort 1 were <0.06 Gy, corresponding to a 0.1 % prescribed dose difference. Similar dose differences were observed in cohort 2. Gamma pass rates for cohort 1 were 100 % for criteria 3 %/3mm, 2 %/2mm and 1 %/1mm for all dose levels. Mean error and mean absolute error inside the body, between sCTs, averaged over all cohort 1 subjects, were -1.1 ± 0.6 [-2.4 0.2] and 2.9 ± 0.4 [2.3 3.9] HU, respectively.

Conclusions

DLR was suitable for sCT generation with clinically negligible differences in HU and calculated dose compared to the conventional MRI reconstruction method. For sCT generation DLR enables scan time reduction, without compromised sCT quality.

Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma

BACKGROUND

Although magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis studies based on deep learning have significantly progressed, the similarity between synthetic CT (sCT) and real CT (rCT) has only been evaluated in image quality metrics (IQMs). To evaluate the similarity between synthetic CT (sCT) and real CT (rCT) comprehensively, we comprehensively evaluated IQMs and radiomic features for the first time.

METHODS

This study enrolled 127 patients with nasopharyngeal carcinoma who underwent CT and MRI scans. Supervised-learning (Unet) and unsupervised-learning (CycleGAN) methods were applied to build MRI-to-CT synthesis models. The regions of interest (ROIs) included nasopharynx gross tumor volume (GTVnx), brainstem, parotid glands, and temporal lobes. The peak signal-to-noise ratio (PSNR), mean absolute error (MAE), root mean square error (RMSE), and structural similarity (SSIM) were used to evaluate image quality. Additionally, 837 radiomic features were extracted for each ROI, and the correlation was evaluated using the concordance correlation coefficient (CCC).

RESULTS

The MAE, RMSE, SSIM, and PSNR of the body were 91.99, 187.12, 0.97, and 51.15 for Unet and 108.30, 211.63, 0.96, and 49.84 for CycleGAN. For the metrics, Unet was superior to CycleGAN (P < 0.05). For the radiomic features, the percentage of four levels (i.e., excellent, good, moderate, and poor, respectively) were as follows: GTVnx, 8.5%, 14.6%, 26.5%, and 50.4% for Unet and 12.3%, 25%, 38.4%, and 24.4% for CycleGAN; other ROIs, 5.44% ± 3.27%, 5.56% ± 2.92%, 21.38% ± 6.91%, and 67.58% ± 8.96% for Unet and 5.16% ± 1.69%, 3.5% ± 1.52%, 12.68% ± 7.51%, and 78.62% ± 8.57% for CycleGAN.

CONCLUSIONS

Unet-sCT was superior to CycleGAN-sCT for the IQMs. However, neither exhibited absolute superiority in radiomic features, and both were far less similar to rCT. Therefore, further work is required to improve the radiomic similarity for MRI-to-CT synthesis.

TRIAL REGISTRATION

This study was a retrospective study, so it was free from registration.

Generating synthetic images from cone beam computed tomography using self-attention residual UNet for head and neck radiotherapy

Background and purpose

Accurate CT numbers in Cone Beam CT (CBCT) are crucial for precise dose calculations in adaptive radiotherapy (ART). This study aimed to generate synthetic CT (sCT) from CBCT using deep learning (DL) models in head and neck (HN) radiotherapy.

Materials and methods

A novel DL model, the 'self-attention-residual-UNet' (ResUNet), was developed for accurate sCT generation. ResUNet incorporates a self-attention mechanism in its long skip connections to enhance information transfer between the encoder and decoder. Data from 93 HN patients, each with planning CT (pCT) and first-day CBCT images were used. Model performance was evaluated using two DL approaches (non-adversarial and adversarial training) and two model types (2D axial only vs. 2.5D axial, sagittal, and coronal). ResUNet was compared with the traditional UNet through image quality assessment (Mean Absolute Error (MAE), Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM)) and dose calculation accuracy evaluation (DVH deviation and gamma evaluation (1 %/1mm)).

Results

Image similarity evaluation results for the 2.5D-ResUNet and 2.5D-UNet models were: MAE: 46±7 HU vs. 51±9 HU, PSNR: 66.6±2.0 dB vs. 65.8±1.8 dB, and SSIM: 0.81±0.04 vs. 0.79±0.05. There were no significant differences in dose calculation accuracy between DL models. Both models demonstrated DVH deviation below 0.5 % and a gamma-pass-rate (1 %/1mm) exceeding 97 %.

Conclusions

ResUNet enhanced CT number accuracy and image quality of sCT and outperformed UNet in sCT generation from CBCT. This method holds promise for generating precise sCT for HN ART.

Comparison of four synthetic CT generators for brain and prostate MR-only workflow in radiotherapy

BACKGROUND

The interest in MR-only workflows is growing with the introduction of artificial intelligence in the synthetic CT generators converting MR images into CT images. The aim of this study was to evaluate several commercially available sCT generators for two anatomical localizations.

METHODS

Four sCT generators were evaluated: one based on the bulk density method and three based on deep learning methods. The comparison was performed on large patient cohorts (brain: 42 patients and pelvis: 52 patients). It included geometric accuracy with the evaluation of Hounsfield Units (HU) mean error (ME) for several structures like the body, bones and soft tissues. Dose evaluation included metrics like the Dmean ME for bone structures (skull or femoral heads), PTV and soft tissues (brain or bladder or rectum). A 1%/1 mm gamma analysis was also performed.

RESULTS

HU ME in the body were similar to those reported in the literature. Dmean ME were smaller than 2% for all structures. Mean gamma pass rate down to 78% were observed for the bulk density method in the brain. Performances of the bulk density generator were generally worse than the artificial intelligence generators for the brain but similar for the pelvis. None of the generators performed best in all the metrics studied.

CONCLUSIONS

All four generators can be used in clinical practice to implement a MR-only workflow but the bulk density method clearly performed worst in the brain.

Evaluation of the Ethos synthetic computed tomography for bolus-covered surfaces

PURPOSE

Ethos allows online adaption of radiotherapy treatment plans. Dose is calculated on synthetic computed tomographies (sCT), CT-like images generated by deforming planning CTs (pCT) onto daily cone beam CTs (CBCT) acquired during treatment sessions. Errors in sCT density distribution may lead to dose calculation errors. sCT correctness was investigated for bolus-covered surfaces.

METHODS

pCTs were recorded of a slab phantom covered with bolus of different thicknesses and with air gaps introduced by spacer rings of variable diameters and heights. Treatment plans were irradiated following the adaptive workflow with different bolus configurations present in the pCT and CBCT. sCT densities were compared to those of the pCT for the same air gap size. Additionally, the neck region of an anthropomorphic phantom was imaged using a plane standard bolus versus an individual bolus adapted to the phantom's outer contour.

RESULTS

Varying bolus thickness by 5 mm between pCT and CBCT was reproduced in the sCT within 2 mm accuracy. Different air gaps in pCT and CBCT resulted in highly variable bolus thickness in the sCT with a typical error of 5 mm or more. In extreme cases, air gaps were filled with bolus material density in the sCT or the phantom was unrealistically deformed near changed bolus geometries. Changes in bolus thickness and deformation also occurred in the anthropomorphic phantom.

CONCLUSION

sCTs must be critically examined and included in plan-specific quality assurance. The use of tight-fitting air gap-free bolus should be preferred to increase the similarity between sCT and CBCT.

Clinical evaluation of synthetic computed tomography methods in adaptive proton therapy of lung cancer patients

Background and purpose

Efficient workflows for adaptive proton therapy are of high importance. This study evaluated the possibility to replace repeat-CTs (reCTs) with synthetic CTs (sCTs), created based on cone-beam CTs (CBCTs), for flagging the need of plan adaptations in intensity-modulated proton therapy (IMPT) treatment of lung cancer patients.

Materials and methods

Forty-two IMPT patients were retrospectively included. For each patient, one CBCT and a same-day reCT were included. Two commercial sCT methods were applied; one based on CBCT number correction (Cor-sCT), and one based on deformable image registration (DIR-sCT). The clinical reCT workflow (deformable contour propagation and robust dose re-computation) was performed on the reCT as well as the two sCTs. The deformed target contours on the reCT/sCTs were checked by radiation oncologists and edited if needed. A dose-volume-histogram triggered plan adaptation method was compared between the reCT and the sCTs; patients needing a plan adaptation on the reCT but not on the sCT were denoted false negatives. As secondary evaluation, dose-volume-histogram comparison and gamma analysis (2%/2mm) were performed between the reCT and sCTs.

Results

There were five false negatives, two for Cor-sCT and three for DIR-sCT. However, three of these were only minor, and one was caused by tumour position differences between the reCT and CBCT and not by sCT quality issues. An average gamma pass rate of 93% was obtained for both sCT methods.

Conclusion

Both sCT methods were judged to be of clinical quality and valuable for reducing the amount of reCT acquisitions.

Evaluating synthetic computed tomography images for adaptive radiotherapy decision making in head and neck cancer

Background and purpose

Adaptive radiotherapy (ART) decision-making benefits from dosimetric information to supplement image inspection when assessing the significance of anatomical changes. This study evaluated a dosimetry-based clinical decision workflow for ART utilizing deformable registration of the original planning computed tomography (CT) image to the daily Cone Beam CT (CBCT) to replace the need for a replan CT for dose estimation.

Materials and methods

We used 12 retrospective Head & Neck patient cases having a ground truth - a replan CT (rCT) in response to anatomical changes apparent in the daily CBCT - to evaluate the accuracy of dosimetric assessment conducted on synthetic CTs (sCT) generated by deforming the original planning CT Hounsfield Units to the daily CBCT anatomy.The original plan was applied to the sCT and dosimetric accuracy of the sCT was assessed by analyzing plan objectives for targets and organs-at-risk compared to calculations on the ground-truth rCT. Three commercial DIR algorithms were compared.

Results

For the best-performing algorithms, the majority of dose metrics calculated on the sCTs differed by less than 4 Gy (5.7% of 70 Gy prescription dose). An uncertainty of ±2.5 Gy (3.6% of 70 Gy prescription) is recommended as a conservative tolerance when evaluating dose metrics on sCTs for head and neck.

Conclusions

Synthetic CTs present a valuable addition to the adaptive radiotherapy workflow, and synthetic CT dose estimates can be effectively used in addition to the current practice of visually inspecting the overlay of the planning CT and CBCT to assess the significance of anatomical change.

Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors

OBJECTIVE

In the current MR-Linac online adaptive workflow, air regions on the MR images need to be manually delineated for abdominal targets, and then overridden by air density for dose calculation. Auto-delineation of these regions is desirable for speed purposes, but poses a challenge, since unlike CT, they don't occupy all dark regions on the image. The purpose of this study is to develop an automated method to segment the air regions on MRI-guided adaptive radiation therapy (MRgART) of abdominal tumors.

APPROACH

A modified ResUNet3D deep learning (DL)-based auto air delineation model was trained using 102 patients' MR images. The MR images were acquired by a dedicated in-house sequence named "Air-Scan", which is designed to generate air regions that are especially dark and accentuated. The air volumes generated by the newly developed DL model were compared with the manual air contours using geometric similarity (Dice Similarity Coefficient, DSC), and dosimetric equivalence using Gamma index and dose-volume parameters.

MAIN RESULTS

The average DSC agreement between the DL generated and manual air contours is 99% ± 1%. The gamma index between the dose calculations with overriding the DL vs manual air volumes with density of 0.01 is 97% ± 2% for a local gamma calculation with a tolerance of 2% and 2mm. The dosimetric parameters from planning target volume - PTV and organs at risk - OARs were all within 1% between when DL vs manual contours were overridden by air density. The model runs in less than five seconds on a PC with 28 Core processor and NVIDIA Quadro® P2000 GPU.

SIGNIFICANCE

A DL based automated segmentation method was developed to generate air volumes on specialized abdominal MR images and generate results that are practically equivalent to the manual contouring of air volumes.

A body mass index-based method for "MR-only" abdominal MR-guided adaptive radiotherapy

PURPOSE

Dose calculation for MR-guided radiotherapy (MRgRT) at the 0.35 T MR-Linac is currently based on deformation of planning CTs (defCT) acquired for each patient. We present a simple and robust bulk density overwrite synthetic CT (sCT) method for abdominal treatments in order to streamline clinical workflows.

METHOD

Fifty-six abdominal patient treatment plans were retrospectively evaluated. All patients had been treated at the MR-Linac using MR datasets for treatment planning and plan adaption and defCT for dose calculation. Bulk density CTs (4M-sCT) were generated from MR images with four material compartments (bone, lung, air, soft tissue). The relative electron densities (RED) for bone and lung were extracted from contoured CT structure average REDs. For soft tissue, a correlation between BMI and RED was evaluated. Dose was recalculated on 4M-sCT and compared to dose distributions on defCTs assessing dose differences in the PTV and organs at risk (OAR).

RESULTS

Mean RED of bone was 1.17 ± 0.02, mean RED of lung 0.17 ± 0.05. The correlation between BMI and RED for soft tissue was statistically significant (p < 0.01). PTV dose differences between 4M-sCT and defCT were Dmean: -0.4 ± 1.0%, D1%: -0.3 ± 1.1% and D95%: -0.5 ± 1.0%. OARs showed D2%: -0.3 ± 1.9% and Dmean: -0.1 ± 1.4% differences. Local 3D gamma index pass rates (2%/2mm) between dose calculated using 4M-sCT and defCT were 96.8 ± 2.6% (range 89.9-99.6%).

CONCLUSION

The presented method for sCT generation enables precise dose calculation for MR-only abdominal MRgRT.

Clinical implementation of magnetic resonance imaging simulation for radiation oncology planning: 5 year experience

PURPOSE

Integrating magnetic resonance (MR) into radiotherapy planning has several advantages. This report details the clinical implementation of an MR simulation (MR-planning) program for external beam radiotherapy (EBRT) in one of North America's largest radiotherapy programs.

METHODS AND MATERIALS

An MR radiotherapy planning program was developed and implemented at Sunnybrook Health Sciences Center in 2016 with two dedicated wide-bore MR platforms (1.5 and 3.0 Tesla). Planning MR was sequentially implemented every 3 months for separate treatment sites, including the central nervous system (CNS), gynecologic (GYN), head and neck (HN), genitourinary (GU), gastrointestinal (GI), breast, and brachial plexus. Essential protocols and processes were detailed in this report, including clinical workflow, optimized MR-image acquisition protocols, MR-adapted patient setup, strategies to overcome risks and challenges, and an MR-planning quality assurance program. This study retrospectively reviewed simulation site data for all MR-planning sessions performed for EBRT over the past 5 years.

RESULTS

From July 2016 to December 2021, 8798 MR-planning sessions were carried out, which corresponds to 25% of all computer tomography (CT) simulations (CT-planning) performed during the same period at our institution. There was a progressive rise from 80 MR-planning sessions in 2016 to 1126 in 2017, 1492 in 2018, 1824 in 2019, 2040 in 2020, and 2236 in 2021. As a result, the relative number of planning MR/CT increased from 3% of all planning sessions in 2016 to 36% in 2021. The most common site of MR-planning was CNS (49%), HN (13%), GYN (12%), GU (12%), and others (8%).

CONCLUSION

Detailed clinical processes and protocols of our MR-planning program were presented, which have been improved over more than 5 years of robust experience. Strategies to overcome risks and challenges in the implementation process are highlighted. Our work provides details that can be used by institutions interested in implementing an MR-planning program.

Synthetic computed tomography generation for abdominal adaptive radiotherapy using low-field magnetic resonance imaging

Background and Purpose

Magnetic Resonance guided Radiotherapy (MRgRT) still needs the acquisition of Computed Tomography (CT) images and co-registration between CT and Magnetic Resonance Imaging (MRI). The generation of synthetic CT (sCT) images from the MR data can overcome this limitation. In this study we aim to propose a Deep Learning (DL) based approach for sCT image generation for abdominal Radiotherapy using low field MR images.

Materials and methods

CT and MR images were collected from 76 patients treated on abdominal sites. U-Net and conditional Generative Adversarial Network (cGAN) architectures were used to generate sCT images. Additionally, sCT images composed of only six bulk densities were generated with the aim of having a Simplified sCT.Radiotherapy plans calculated using the generated images were compared to the original plan in terms of gamma pass rate and Dose Volume Histogram (DVH) parameters.

Results

sCT images were generated in 2 s and 2.5 s with U-Net and cGAN architectures respectively.Gamma pass rates for 2%/2mm and 3%/3mm criteria were 91% and 95% respectively. Dose differences within 1% for DVH parameters on the target volume and organs at risk were obtained.

Conclusion

U-Net and cGAN architectures are able to generate abdominal sCT images fast and accurately from low field MRI.

Investigation of a water equivalent depth method for dosimetric accuracy evaluation of synthetic CT

PURPOSE

To provide a metric that reflects the dosimetric utility of the synthetic CT (sCT) and can be rapidly determined.

METHODS

Retrospective CT and atlas-based sCT of 62 (53 IMRT and 9 VMAT) prostate cancer patients were used. For image similarity measurements, the sCT and reference CT (rCT) were aligned using clinical registration parameters. Conventional image similarity metrics including the mean absolute error (MAE) and mean error (ME) were calculated. The water equivalent depth (WED) was automatically determined for each patient on the rCT and sCT as the distance from the skin surface to the treatment plan isocentre at 36 equidistant gantry angles, and the mean WED difference (ΔWED¯) between the two scans was calculated. Doses were calculated on each scan pair for the clinical plan in the treatment planning system. The image similarity measurements and ΔWED¯ were then compared to the isocentre dose difference (ΔD_iso) between the two scans.

RESULTS

While no particular relationship to dose was observed for the other image similarity metrics, the ME results showed a linear trend against ΔD_iso with R² = 0.6, and the 95 % prediction interval for ΔD_iso between -1.2 and 1 %. The ΔWED¯ results showed an improved linear trend (R² = 0.8) with a narrower 95 % prediction interval from -0.8 % to 0.8 %.

CONCLUSION

ΔWED¯ highly correlates with ΔD_iso for the reference and synthetic CT scans. This is easy to calculate automatically and does not require time-consuming dose calculations. Therefore, it can facilitate the process of developing and evaluating new sCT generation algorithms.

A deep learning approach to generate synthetic CT in low field MR-guided radiotherapy for lung cases

INTRODUCTION

This study aims to apply a conditional Generative Adversarial Network (cGAN) to generate synthetic Computed Tomography (sCT) from 0.35 Tesla Magnetic Resonance (MR) images of the thorax.

METHODS

Sixty patients treated for lung lesions were enrolled and divided into training (32), validation (8), internal (10,T_A) and external (10,T_B) test set. Image accuracy of generated sCT was evaluated computing the mean absolute (MAE) and mean error (ME) with respect the original CT. Three treatment plans were calculated for each patient considering MRI as reference image: original CT, sCT (pure sCT) and sCT with GTV density override (hybrid sCT) were used as Electron Density (ED) map. Dose accuracy was evaluated comparing treatment plans in terms of gamma analysis and Dose Volume Histogram (DVH) parameters.

RESULTS

No significant difference was observed between the test sets for image and dose accuracy parameters. Considering the whole test cohort, a MAE of 54.9 ± 10.5 HU and a ME of 4.4 ± 7.4 HU was obtained. Mean gamma passing rates for 2%/2mm, and 3%/3mm tolerance criteria were 95.5 ± 5.9% and 98.2 ± 4.1% for pure sCT, 96.1 ± 5.1% and 98.5 ± 3.9% for hybrid sCT: the difference between the two approaches was significant (p = 0.01). As regards DVH analysis, differences in target parameters estimation were found to be within 5% using hybrid approach and 20% using pure sCT.

CONCLUSION

The DL algorithm here presented can generate sCT images in the thorax with good image and dose accuracy, especially when the hybrid approach is used. The algorithm does not suffer from inter-scanner variability, making feasible the implementation of MR-only workflows for palliative treatments.

Possibilities and challenges when using synthetic computed tomography in an adaptive carbon-ion treatment workflow

BACKGROUND AND PURPOSE

Anatomical surveillance during ion-beam therapy is the basis for an effective tumor treatment and optimal organ at risk (OAR) sparing. Synthetic computed tomography (sCT) based on magnetic resonance imaging (MRI) can replace the X-ray based planning CT (X-rayCT) in photon radiotherapy and improve the workflow efficiency without additional imaging dose. The extension to carbon-ion radiotherapy is highly challenging; complex patient positioning, unique anatomical situations, distinct horizontal and vertical beam incidence directions, and limited training data are only few problems. This study gives insight into the possibilities and challenges of using sCTs in carbon-ion therapy.

MATERIALS AND METHODS

For head and neck patients immobilised with thermoplastic masks 30 clinically applied actively scanned carbon-ion treatment plans on 15 CTs comprising 60 beams were analyzed. Those treatment plans were re-calculated on MRI based sCTs which were created employing a 3D U-Net. Dose differences and carbon-ion spot displacements between sCT and X-rayCT were evaluated on a patient specific basis.

RESULTS

Spot displacement analysis showed a peak displacement by 0.2 cm caused by the immobilisation mask not measurable with the MRI. 95.7% of all spot displacements were located within 1 cm. For the clinical target volume (CTV) the median D_50% agreed within -0.2% (-1.3 to 1.4%), while the median D_0.01cc differed up to 4.2% (-1.3 to 25.3%) comparing the dose distribution on the X-rayCT and the sCT. OAR deviations depended strongly on the position and the dose gradient. For three patients no deterioration of the OAR parameters was observed. Other patients showed large deteriorations, e.g. for one patient D_2% of the chiasm differed by 28.1%.

CONCLUSION

The usage of sCTs opens several new questions, concluding that we are not ready yet for an MR-only workflow in carbon-ion therapy, as envisaged in photon therapy. Although omitting the X-rayCT seems unfavourable in the case of carbon-ion therapy, an sCT could be advantageous for monitoring, re-planning, and adaptation.

GAN for synthesizing CT from T2-weighted MRI data towards MR-guided radiation treatment

OBJECTIVE

In medical domain, cross-modality image synthesis suffers from multiple issues , such as context-misalignment, image distortion, image blurriness, and loss of details. The fundamental objective behind this study is to address these issues in estimating synthetic Computed tomography (sCT) scans from T2-weighted Magnetic Resonance Imaging (MRI) scans to achieve MRI-guided Radiation Treatment (RT).

MATERIALS AND METHODS

We proposed a conditional generative adversarial network (cGAN) with multiple residual blocks to estimate sCT from T2-weighted MRI scans using 367 paired brain MR-CT images dataset. Few state-of-the-art deep learning models were implemented to generate sCT including Pix2Pix model, U-Net model, autoencoder model and their results were compared, respectively.

RESULTS

Results with paired MR-CT image dataset demonstrate that the proposed model with nine residual blocks in generator architecture results in the smallest mean absolute error (MAE) value of [Formula: see text], and mean squared error (MSE) value of [Formula: see text], and produces the largest Pearson correlation coefficient (PCC) value of [Formula: see text], SSIM value of [Formula: see text] and peak signal-to-noise ratio (PSNR) value of [Formula: see text], respectively. We qualitatively evaluated our result by visual comparisons of generated sCT to original CT of respective MRI input.

DISCUSSION

The quantitative and qualitative comparison of this work demonstrates that deep learning-based cGAN model can be used to estimate sCT scan from a reference T2 weighted MRI scan. The overall accuracy of our proposed model outperforms different state-of-the-art deep learning-based models.

Validation of an MRI-only planning workflow for definitive pelvic radiotherapy

Purpose

Previous work on Magnetic Resonance Imaging (MRI) only planning has been applied to limited treatment regions with a focus on male anatomy. This research aimed to validate the use of a hybrid multi-atlas synthetic computed tomography (sCT) generation technique from a MRI, using a female and male atlas, for MRI only radiation therapy treatment planning of rectum, anal canal, cervix and endometrial malignancies.

Patients and methods

Forty patients receiving radiation treatment for a range of pelvic malignancies, were separated into male (n = 20) and female (n = 20) cohorts for the creation of gender specific atlases. A multi-atlas local weighted voting method was used to generate a sCT from a T1-weighted VIBE DIXON MRI sequence. The original treatment plans were copied from the CT scan to the corresponding sCT for dosimetric validation.

Results

The median percentage dose difference between the treatment plan on the CT and sCT at the ICRU reference point for the male cohort was − 0.4% (IQR of 0 to − 0.6), and − 0.3% (IQR of 0 to − 0.6) for the female cohort. The mean gamma agreement for both cohorts was > 99% for criteria of 3%/2 mm and 2%/2 mm. With dose criteria of 1%/1 mm, the pass rate was higher for the male cohort at 96.3% than the female cohort at 93.4%. MRI to sCT anatomical agreement for bone and body delineated contours was assessed, with a resulting Dice score of 0.91 ± 0.2 (mean ± 1 SD) and 0.97 ± 0.0 for the male cohort respectively; and 0.96 ± 0.0 and 0.98 ± 0.0 for the female cohort respectively. The mean absolute error in Hounsfield units (HUs) within the entire body for the male and female cohorts was 59.1 HU ± 7.2 HU and 53.3 HU ± 8.9 HU respectively.

Conclusions

A multi-atlas based method for sCT generation can be applied to a standard T1-weighted MRI sequence for male and female pelvic patients. The implications of this study support MRI only planning being applied more broadly for both male and female pelvic sites.

Trial registration This trial was registered in the Australian New Zealand Clinical Trials Registry (ANZCTR) (www.anzctr.org.au) on 04/10/2017. Trial identifier ACTRN12617001406392.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-022-02023-4.

Synthetic CT for the planning of MR-HIFU treatment of bone metastases in pelvic and femoral bones: a feasibility study

Objectives

Visualization of the bone distribution is an important prerequisite for MRI-guided high-intensity focused ultrasound (MRI-HIFU) treatment planning of bone metastases. In this context, we evaluated MRI-based synthetic CT (sCT) imaging for the visualization of cortical bone.

Methods

MR and CT images of nine patients with pelvic and femoral metastases were retrospectively analyzed in this study. The metastatic lesions were osteolytic, osteoblastic or mixed. sCT were generated from pre-treatment or treatment MR images using a UNet-like neural network. sCT was qualitatively and quantitatively compared to CT in the bone (pelvis or femur) containing the metastasis and in a region of interest placed on the metastasis itself, through mean absolute difference (MAD), mean difference (MD), Dice similarity coefficient (DSC), and root mean square surface distance (RMSD).

Results

The dataset consisted of 3 osteolytic, 4 osteoblastic and 2 mixed metastases. For most patients, the general morphology of the bone was well represented in the sCT images and osteolytic, osteoblastic and mixed lesions could be discriminated. Despite an average timespan between MR and CT acquisitions of 61 days, in bone, the average (± standard deviation) MAD was 116 ± 26 HU, MD − 14 ± 66 HU, DSC 0.85 ± 0.05, and RMSD 2.05 ± 0.48 mm and, in the lesion, MAD was 132 ± 62 HU, MD − 31 ± 106 HU, DSC 0.75 ± 0.2, and RMSD 2.73 ± 2.28 mm.

Conclusions

Synthetic CT images adequately depicted the cancellous and cortical bone distribution in the different lesion types, which shows its potential for MRI-HIFU treatment planning.

Key Points

• Synthetic computed tomography was able to depict bone distribution in metastatic lesions.

• Synthetic computed tomography images intrinsically aligned with treatment MR images may have the potential to facilitate MR-HIFU treatment planning of bone metastases, by combining visualization of soft tissues and cancellous and cortical bone.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08568-y.

An MRI sequence independent convolutional neural network for synthetic head CT generation in proton therapy

A magnetic resonance imaging (MRI) sequence independent deep learning technique was developed and validated to generate synthetic computed tomography (sCT) scans for MR guided proton therapy. 47 meningioma patients previously undergoing proton therapy based on pencil beam scanning were divided into training (33), validation (6), and test (8) cohorts. T₁, T₂, and contrast enhanced T₁ (T1CM) MRI sequences were used in combination with the planning CT (pCT) data to train a 3D U-Net architecture with ResNet-Blocks. A hyperparameter search was performed including two loss functions, two group sizes of normalisation, and depth of the network. Training outcome was compared between models trained for each individual MRI sequence and for all sequences combined. The performance was evaluated based on a metric and dosimetric analysis as well as spot difference maps. Furthermore, the influence of immobilisation masks that are not visible on MRIs was investigated. Based on the hyperparameter search, the final model was trained with fixed features per group for the group normalisation, six down-convolution steps, an input size of 128×192×192, and feature loss. For the test dataset for body/bone the mean absolute error (MAE) values were on average 79.8/216.3Houndsfield unit (HU) when trained using T1 images, 71.1/186.1HU for T2, and 82.9/236.4HU for T1CM. The structural similarity metric (SSIM) ranged from 0.95 to 0.98 for all sequences. The investigated dose parameters of the target structures agreed within 1% between original proton treatment plans and plans recalculated on sCTs. The spot difference maps had peaks at ±0.2cm and for 98% of all spots the difference was less than 1cm. A novel MRI sequence independent sCT generator was developed, which suggests that the training phase of neural networks can be disengaged from specific MRI acquisition protocols. In contrast to previous studies, the patient cohort consisted exclusively of actual proton therapy patients (i.e. "real-world data").

Optimisation and validation of an integrated magnetic resonance imaging-only radiotherapy planning solution

Background and purpose

Magnetic resonance imaging (MRI)-only treatment planning is gaining in popularity in radiation oncology, with various methods available to generate a synthetic computed tomography (sCT) for this purpose. The aim of this study was to validate a sCT generation software for MRI-only radiotherapy planning of male and female pelvic cancers. The secondary aim of this study was to improve dose agreement by applying a derived relative electron and mass density (RED) curve to the sCT.

Method and materials

Computed tomography (CT) and MRI scans of forty patients with pelvic neoplasms were used in the study. Treatment plans were copied from the CT scan to the sCT scan for dose comparison. Dose difference at reference point, 3D gamma comparison and dose volume histogram analysis was used to validate the dose impact of the sCT. The RED values were optimised to improve dose agreement by using a linear plot.

Results

The average percentage dose difference at isocentre was 1.2% and the mean 3D gamma comparison with a criteria of 1%/1 mm was 84.0% ± 9.7%. The results indicate an inherent systematic difference in the dosimetry of the sCT plans, deriving from the tissue densities. With the adapted RED_mod table, the average percentage dose difference was reduced to −0.1% and the mean 3D gamma analysis improved to 92.9% ± 5.7% at 1%/1 mm.

Conclusions

CT generation software is a viable solution for MRI-only radiotherapy planning. The option makes it relatively easy for departments to implement a MRI-only planning workflow for cancers of male and female pelvic anatomy.

Ultrashort Echo-Time MR Imaging of the Pediatric Head and Neck

Bone MR imaging techniques use extremely rapid echo times to maximize detection of short-T2 tissues with low water concentrations. The major approaches used in clinical practice are ultrashort echo-time and zero echo-time. Synthetic CT generation is feasible using atlas-based, voxel-based, and deep learning approaches. Major clinical applications in the pediatric head and neck include evaluation for craniosynostosis, sinonasal and jaw imaging, trauma, interventional planning, and postoperative follow-up. In this article, we review the technical background and practical usefulness of bone MR imaging with key imaging examples.

Synthetic digital reconstructed radiographs for MR-only robotic stereotactic radiation therapy: A proof of concept

PURPOSE

To create synthetic CTs and digital reconstructed radiographs (DRRs) from MR images that allow for fiducial visualization and accurate dose calculation for MR-only radiosurgery.

METHODS

We developed a machine learning model to create synthetic CTs from pelvic MRs for prostate treatments. This model has been previously proven to generate synthetic CTs with accuracy on par or better than alternate methods, such as atlas-based registration. Our dataset consisted of 11 paired CT and conventional MR (T2) images used for previous CyberKnife (Accuray, Inc) radiotherapy treatments. The MR images were pre-processed to mimic the appearance of fiducial-enhancing images. Two models were trained for each parameter case, using a sub-set of the available image pairs, with the remaining images set aside for testing and validation of the model to identify the optimal patch size and number of image pairs used for training. Four models were then trained using the identified parameters and used to generate synthetic CTs, which in turn were used to generate DRRs at angles 45° and 315°, as would be used for a CyberKnife treatment. The synthetic CTs and DRRs were compared visually and using the mean squared error and peak signal-to-noise ratio against the ground-truth images to evaluate their similarity.

RESULTS

The synthetic CTs, as well as the DRRs generated from them, gave similar visualization of the fiducial markers in the prostate as the true counterparts. There was no significant difference found for the fiducial localization for the CTs and DRRs. Across the 8 DRRs analyzed, the mean MSE between the normalized true and synthetic DRRs was 0.66 ± 0.42% and the mean PSNR for this region was 22.9 ± 3.7 dB. For the full CTs, the mean MAE was 72.9 ± 88.1 HU and the mean PSNR was 31.2 ± 2.2 dB.

CONCLUSIONS

Our machine learning-based method provides a proof of concept of a way to generate synthetic CTs and DRRs for accurate dose calculation and fiducial localization for use in radiation treatment of the prostate.

A practical methodology to improve the dosimetric accuracy of MR-based radiotherapy simulation for brain tumors

PURPOSE

To investigate the dosimetric accuracy of synthetic computed tomography (sCT) images generated by a clinically-ready voxel-based MRI simulation package, and to develop a simple and feasible method to improve the accuracy.

METHODS

20 patients with brain tumor were selected to undergo CT and MRI simulation. sCT images were generated by a clinical MRI simulation package. The discrepancy between planning CT and sCT in CT number and body contour were evaluated. To resolve the discrepancies, an sCT specific CT-relative electron density (RED) calibration curve was used, and a layer of pseudo-skin was created on the sCT. The dosimetric impact of these discrepancies, and the improvement brought about by the modifications, were evaluated by a planning study. Volumetric modulated arc therapy (VMAT) treatment plans for each patient were created and optimized on the planning CT, which were then transferred to the original sCT and the modified-sCT for dose re-calculation. Dosimetric comparisons and gamma analysis between the calculated doses in different images were performed.

RESULTS

The average gamma passing rate with 1%/1 mm criteria was only 70.8% for the comparison of dose distribution between planning CT and original sCT. The mean dose difference between the planning CT and the original sCT were -1.2% for PTV D₉₅ and -1.7% for PTV D_max, while the mean dose difference was within 0.7 Gy for all relevant OARs. After applying the modifications on the sCT, the average gamma passing rate was increased to 92.2%. Mean dose difference in PTV D₉₅ and D_max were reduced to -0.1% and -0.3% respectively. The mean dose difference was within 0.2 Gy for all OAR structures and no statistically significant difference were found.

CONCLUSIONS

The modified-sCT demonstrated improved dosimetric agreement with the planning CT. These results indicated the overall dosimetric accuracy and practicality of this improved MR-based treatment planning method.

Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy

OBJECTIVE

To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy.

METHODS

The CBCT/planning CT images of 170 patients undergoing thoracic radiotherapy were used for training and testing. The CBCT images were scanned under a fast protocol with 50% less clinical projection frames compared with standard chest M20 protocol. Training with aligned paired images was performed using conditional adversarial networks (so-called pix2pix), and training with unpaired images was carried out with cycle-consistent adversarial networks (cycleGAN) and AGGAN, through which sCT images were generated. The image quality and Hounsfield unit (HU) value of the sCT images generated by the three neural networks were compared. The treatment plan was designed on CT and copied to sCT images to calculated dose distribution.

RESULTS

The image quality of sCT images by all the three methods are significantly improved compared with original CBCT images. The AGGAN achieves the best image quality in the testing patients with the smallest mean absolute error (MAE, 43.5 ± 6.69), largest structural similarity (SSIM, 93.7 ± 3.88) and peak signal-to-noise ratio (PSNR, 29.5 ± 2.36). The sCT images generated by all the three methods showed superior dose calculation accuracy with higher gamma passing rates compared with original CBCT image. The AGGAN offered the highest gamma passing rates (91.4 ± 3.26) under the strictest criteria of 1 mm/1% compared with other methods. In the phantom study, the sCT images generated by AGGAN demonstrated the best image quality and the highest dose calculation accuracy.

CONCLUSIONS

High-quality sCT images were generated from low-dose thoracic CBCT images by using the proposed AGGAN through unpaired CBCT and CT images. The dose distribution could be calculated accurately based on sCT images in radiotherapy.

Accurate Estimation of Total Intracranial Volume in MRI using a Multi-tasked Image-to-Image Translation Network

Total intracranial volume (TIV) is the volume enclosed inside the cranium, inclusive of the meninges and the brain. TIV is extensively used to correct variations in inter-subject head size for the evaluation of neurodegenerative diseases. In this work, we present an automatic method to generate a TIV mask from MR images while synthesizing a CT image to be used in subsequent analysis. In addition, we propose an alternative way to obtain ground truth TIV masks using a semi-manual approach, which results in significant time savings. We train a conditional generative adversarial network (cGAN) using 2D MR slices to realize our tasks. The quantitative evaluation showed that the model was able to synthesize CT and generate TIV masks that closely approximate the reference images. This study also provides a comparison of the described method against skull stripping tools that output a mask enclosing the cranial volume, using MRI scan. In particular, highlighting the deficiencies in using such tools to approximate the volume using MRI scan.

Clinical experience and cost evaluation of magnetic resonance imaging -only workflow in radiation therapy planning of prostate cancer

•

Using MRI-only for prostate cancer radiation therapy planning (RTP) can reduce costs.

•

An MRI-only workflow is particularly suitable for medium-sized and large departments.

•

Omitting CT in the RTP workflow saves scanner, staff, and patient time.

Background and purpose

In radiation therapy (RT), significant improvements have been made recently particularly in the practices of planning imaging. This study aimed to conduct a cost evaluation between magnetic resonance imaging (MRI) -only and combined computed tomography (CT) and MRI workflows.

Materials and methods

The time-driven activity-based costing (TDABC) model was used to conduct a cost evaluation between the two workflows in those steps, where cost differences were expected. Costs were divided into capital costs and operational costs. The former consisted of fixed, one-time expenses, e.g. the purchase of a scanner, whereas the latter were partially based on the amount of activity consumed i.e. time required for image acquisition, image registration and structure contouring.

Results

In a review over a period of 10 years for 300 annual prostate cancer patients, the total cost of the workflow steps included in the study for an individual patient applying the MRI-only workflow was 903 € (100%), comprised of 537 € (59%) capital costs and 366 € (41%) operational costs. The corresponding total cost for an individual patient applying the CT + MRI workflow was 922 € (100%), comprised of 197 € (21%) capital costs and 726 € (79%) operational costs. In 10 years for 3000 patients, a total saving of 58,544 € (2%) was achieved with the MRI-only workflow compared with the dual imaging workflow.

Conclusions

MRI-only workflow is a feasible and economic way to perform clinical RT for localized prostate cancer, in particular for medium- and large-sized departments treating a sufficient number of patients.

A role for magnetic susceptibility in synthetic computed tomography

PURPOSE

Radiotherapy treatment planning based on magnetic resonance imaging (MRI) benefits from increased soft-tissue contrast and functional imaging. MRI-only planning is attractive but limited by the lack of electron density information required for dose calculation, and the difficulty to differentiate air and bone. MRI can map magnetic susceptibility to separate bone from air. A method is introduced to produce synthetic CT (sCT) through automatic voxel-wise assignment of CT numbers from an MRI dataset processed that includes magnetic susceptibility mapping.

METHODS

Volumetric multi-echo gradient echo datasets were acquired in the heads of five healthy volunteers and fourteen patients with cancer using a 3 T MRI system. An algorithm for CT synthesis was designed using the volunteer data, based on fuzzy c-means clustering and adaptive thresholding of the MR data (magnitude, fat, water, and magnetic susceptibility). Susceptibility mapping was performed using a modified version of the iterative phase replacement algorithm. On patient data, the algorithm was assessed by direct comparison to X-ray computed tomography (CT) scans.

RESULTS

The skull, spine, teeth, and major sinuses were clearly distinguished in all sCT, from healthy volunteers and patients. The mean absolute CT number error between X-ray CT and sCT in patients ranged from 78 and 134 HU.

CONCLUSION

Susceptibility mapping using MRI can differentiate air and bone for CT synthesis. The proposed method is automated, fast, and based on a commercially available MRI pulse sequence. The method avoids registration errors and does not rely on a priori information, making it suitable for nonstandard anatomy.

Cone beam computed tomography based image guidance and quality assessment of prostate cancer for magnetic resonance imaging-only radiotherapy in the pelvis

•

MRI-only IGRT accuracy is ≤2 mm as compared to CT but significant differences were observed.

•

MRI-only CBCT-based IGRT seems feasible but caution is advised.

•

The median absolute error (MeAE) for independent verification on the sCT quality is proposed.

•

A MeAE around 0.1 in mass density could call for sCT quality inspection.

Background and purpose

Radiotherapy (RT) based on magentic resonance imaging (MRI) only is currently used clinically in the pelvis. A synthetic computed tomography (sCT) is needed for dose planning. Here, we investigate the accuracy of cone beam CT (CBCT) based MRI-only image guided RT (IGRT) and sCT image quality.

Materials and methods

CT, MRI and CBCT scans of ten prostate cancer patients were included. The MRI was converted to a sCT using a multi-atlas approach. The sCT, CT and MR images were auto-matched with the CBCT on the bony anatomy. Paired sCT-CT and sCT-CBCT data were created. CT numbers were converted to relative electron (RED) and mass densities (DES) using a standard calibration curve for the CT and sCT. For the CBCT RED/DES conversion, a phantom and paired CT-CBCT population based calibration curve was used. For the latter, the CBCT numbers were averaged in 100 HU bins and the known RED/DES of the CT were assigned. The paired sCT-CT and sCT-CBCT data were averaged in bins of 10 HU or 0.01 RED/DES. The median absolute error (MeAE) between the sCT-CT and sCT-CBCT bins was calculated. Wilcoxon rank-sum tests were carried out for the IGRT and MeAE study.

Results

The mean sCT or MR IGRT difference from CT was ≤ 2 mm but significant differences were observed. A CBCT HU or phantom-based RED/DES MeAE did not estimate the sCT quality similar to a CT based MeAE but the CBCT population-based RED/DES MeAE did.

Conclusions

MRI-only CBCT-based IGRT seems feasible but caution is advised. A MeAE around 0.1 DES could call for sCT quality inspection.

Clinical validation of a commercially available deep learning software for synthetic CT generation for brain

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.

Accuracy of an MR-only workflow for prostate radiotherapy using semi-automatically burned-in fiducial markers

BACKGROUND

The benefit of MR-only workflow compared to current CT-based workflow for prostate radiotherapy is reduction of systematic errors in the radiotherapy chain by 2-3 mm. Nowadays, MRI is used for target delineation while CT is needed for position verification. In MR-only workflows, MRI based synthetic CT (sCT) replaces CT. Intraprostatic fiducial markers (FMs) are used as a surrogate for the position of the prostate improving targeting. However, FMs are not visible on sCT. Therefore, a semi-automatic method for burning-in FMs on sCT was developed. Accuracy of MR-only workflow using semi-automatically burned-in FMs was assessed and compared to CT/MR workflow.

METHODS

Thirty-one prostate cancer patients receiving radiotherapy, underwent an additional MR sequence (mDIXON) to create an sCT for MR-only workflow simulation. Three sources of accuracy in the CT/MR- and MR-only workflow were investigated. To compare image registrations for target delineation, the inter-observer error (IOE) of FM-based CT-to-MR image registrations and soft-tissue-based MR-to-MR image registrations were determined on twenty patients. Secondly, the inter-observer variation of the resulting FM positions was determined on twenty patients. Thirdly, on 26 patients CBCTs were retrospectively registered on sCT with burned-in FMs and compared to CT-CBCT registrations.

RESULTS

Image registration for target delineation shows a three times smaller IOE for MR-only workflow compared to CT/MR workflow. All observers agreed in correctly identifying all FMs for 18 out of 20 patients (90%). The IOE in CC direction of the center of mass (COM) position of the markers was within the CT slice thickness (2.5 mm), the IOE in AP and RL direction were below 1.0 mm and 1.5 mm, respectively. Registrations for IGRT position verification in MR-only workflow compared to CT/MR workflow were equivalent in RL-, CC- and AP-direction, except for a significant difference for random error in rotation.

CONCLUSIONS

MR-only workflow using sCT with burned-in FMs is an improvement compared to the current CT/MR workflow, with a three times smaller inter observer error in CT-MR registration and comparable CBCT registration results between CT and sCT reference scans. Trial registry Medical Research Involving Human Subjects Act (WMO) does apply to this study and was approved by the Medical Ethics review Committee of the Academic Medical Center. Registration number: NL65414.018.18. Date of registration: 21-08-2018.

Synthetic computed tomography data allows for accurate absorbed dose calculations in a magnetic resonance imaging only workflow for head and neck radiotherapy

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The geometry of the synthetic CT is comparable to the CT in the H&N region.

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Synthetic CT in the H&N region provides similar absorbed dose calculation as the CT.

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Absorbed dose calculations in the dental region could benefit from using synthetic CT.

Background and purpose

Few studies on magnetic resonance imaging (MRI) only head and neck radiation treatment planning exist, and none using a generally available software. The aim of this study was to evaluate the accuracy of absorbed dose for head and neck synthetic computed tomography data (sCT) generated by a commercial convolutional neural network-based algorithm.

Materials and methods

For 44 head and neck cancer patients, sCT were generated and the geometry was validated against computed tomography data (CT). The clinical CT based treatment plan was transferred to the sCT and recalculated without re-optimization, and differences in relative absorbed dose were determined for dose-volume-histogram (DVH) parameters and the 3D volume.

Results

For overall body, the results of the geometric validation were (Mean ± 1sd): Mean error −5 ± 10 HU, mean absolute error 67 ± 14 HU, Dice similarity coefficient 0.98 ± 0.05, and Hausdorff distance difference 4.2 ± 1.7 mm. Water equivalent depth difference for region Th1-C7, mid mandible and mid nose were −0.3 ± 3.4, 1.1 ± 2.0 and 0.7 ± 3.8 mm respectively. The maximum mean deviation in absorbed dose for all DVH parameters was 0.30% (0.12 Gy). The absorbed doses were considered equivalent (p-value < 0.001) and the mean 3D gamma passing rate was 99.4 (range: 95.7–99.9%).

Conclusions

The convolutional neural network-based algorithm generates sCT which allows for accurate absorbed dose calculations for MRI-only head and neck radiation treatment planning. The sCT allows for statistically equivalent absorbed dose calculations compared to CT based radiotherapy.

Performance of deep learning synthetic CTs for MR-only brain radiation therapy

PURPOSE

To evaluate the dosimetric and image-guided radiation therapy (IGRT) performance of a novel generative adversarial network (GAN) generated synthetic CT (synCT) in the brain and compare its performance for clinical use including conventional brain radiotherapy, cranial stereotactic radiosurgery (SRS), planar, and volumetric IGRT.

METHODS AND MATERIALS

SynCT images for 12 brain cancer patients (6 SRS, 6 conventional) were generated from T1-weighted postgadolinium magnetic resonance (MR) images by applying a GAN model with a residual network (ResNet) generator and a convolutional neural network (CNN) with 5 convolutional layers as the discriminator that classified input images as real or synthetic. Following rigid registration, clinical structures and treatment plans derived from simulation CT (simCT) images were transferred to synCTs. Dose was recalculated for 15 simCT/synCT plan pairs using fixed monitor units. Two-dimensional (2D) gamma analysis (2%/2 mm, 1%/1 mm) was performed to compare dose distributions at isocenter. Dose-volume histogram (DVH) metrics (D95% , D99% , D0.2cc, and D0.035cc ) were assessed for the targets and organ at risks (OARs). IGRT performance was evaluated via volumetric registration between cone beam CT (CBCT) to synCT/simCT and planar registration between KV images to synCT/simCT digital reconstructed radiographs (DRRs).

RESULTS

Average gamma passing rates at 1%/1mm and 2%/2mm were 99.0 ± 1.5% and 99.9 ± 0.2%, respectively. Excellent agreement in DVH metrics was observed (mean difference ≤0.10 ± 0.04 Gy for targets, 0.13 ± 0.04 Gy for OARs). The population averaged mean difference in CBCT-synCT registrations were <0.2 mm and 0.1 degree different from simCT-based registrations. The mean difference between kV-synCT DRR and kV-simCT DRR registrations was <0.5 mm with no statistically significant differences observed (P > 0.05). An outlier with a large resection cavity exhibited the worst-case scenario.

CONCLUSION

Brain GAN synCTs demonstrated excellent performance for dosimetric and IGRT endpoints, offering potential use in high precision brain cancer therapy.

Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy

PURPOSE

Image-guided radiation therapy could benefit from implementing adaptive radiation therapy (ART) techniques. A cycle-generative adversarial network (cycle-GAN)-based cone-beam computed tomography (CBCT)-to-synthetic CT (sCT) conversion algorithm was evaluated regarding image quality, image segmentation and dosimetric accuracy for head and neck (H&N), thoracic and pelvic body regions.

METHODS

Using a cycle-GAN, three body site-specific models were priorly trained with independent paired CT and CBCT datasets of a kV imaging system (XVI, Elekta). sCT were generated based on first-fraction CBCT for 15 patients of each body region. Mean errors (ME) and mean absolute errors (MAE) were analyzed for the sCT. On the sCT, manually delineated structures were compared to deformed structures from the planning CT (pCT) and evaluated with standard segmentation metrics. Treatment plans were recalculated on sCT. A comparison of clinically relevant dose-volume parameters (D₉₈, D₅₀ and D₂ of the target volume) and 3D-gamma (3%/3mm) analysis were performed.

RESULTS

The mean ME and MAE were 1.4, 29.6, 5.4 Hounsfield units (HU) and 77.2, 94.2, 41.8 HU for H&N, thoracic and pelvic region, respectively. Dice similarity coefficients varied between 66.7 ± 8.3% (seminal vesicles) and 94.9 ± 2.0% (lungs). Maximum mean surface distances were 6.3 mm (heart), followed by 3.5 mm (brainstem). The mean dosimetric differences of the target volumes did not exceed 1.7%. Mean 3D gamma pass rates greater than 97.8% were achieved in all cases.

CONCLUSIONS

The presented method generates sCT images with a quality close to pCT and yielded clinically acceptable dosimetric deviations. Thus, an important prerequisite towards clinical implementation of CBCT-based ART is fulfilled.

Analysis of dose using CBCT and synthetic CT during head and neck radiotherapy: A single centre feasibility study

OBJECTIVES

The study aimed to assess the suitability of deformable image registration (DIR) software to generate synthetic CT (sCT) scans for dose verification during radiotherapy to the head and neck. Planning and synthetic CT dose volume histograms were compared to evaluate dosimetric changes during the treatment course.

METHODS

Eligible patients had locally advanced (stage III, IVa and IVb) oropharyngeal cancer treated with primary radiotherapy. Weekly CBCT images were acquired post treatment at fractions 1, 6, 11, 16, 21 and 26 over a 30 fraction treatment course. Each CBCT was deformed with the planning CT to generate a sCT which was used to calculate the dose at that point in the treatment. A repeat planning CT2 was acquired at fraction 16 and deformed with the fraction 16 CBCT to compare differences between the calculations mid-treatment.

RESULTS

20 patients were evaluated generating 138 synthetic CT sets. The single fraction mean dose to PTV_HR between the synthetic and planning CT did not vary, although dose to 95% of PTV_HR was smaller at week 6 compared to planning (difference 2.0%, 95% CI (0.8 to 3.1), p = 0.0). There was no statistically significant difference in PRV_brainstem or PRV_spinal cord maximum dose, although greater variation using the sCT calculations was reported. The mean dose to structures based on the fraction 16 sCT and CT2 scans were similar.

CONCLUSIONS

Synthetic CT provides comparable dose calculations to those of a repeat planning CT; however the limitations of DIR must be understood before it is applied within the clinical setting.

MR-PROTECT: Clinical feasibility of a prostate MRI-only radiotherapy treatment workflow and investigation of acceptance criteria

BACKGROUND

Retrospective studies on MRI-only radiotherapy have been presented. Widespread clinical implementations of MRI-only workflows are however limited by the absence of guidelines. The MR-PROTECT trial presents an MRI-only radiotherapy workflow for prostate cancer using a new single sequence strategy. The workflow incorporated the commercial synthetic CT (sCT) generation software MriPlanner™ (Spectronic Medical, Helsingborg, Sweden). Feasibility of the workflow and limits for acceptance criteria were investigated for the suggested workflow with the aim to facilitate future clinical implementations.

METHODS

An MRI-only workflow including imaging, post imaging tasks, treatment plan creation, quality assurance and treatment delivery was created with questionnaires. All tasks were performed in a single MR-sequence geometry, eliminating image registrations. Prospective CT-quality assurance (QA) was performed prior treatment comparing the PTV mean dose between sCT and CT dose-distributions. Retrospective analysis of the MRI-only gold fiducial marker (GFM) identification, DVH- analysis, gamma evaluation and patient set-up verification using GFMs and cone beam CT were performed.

RESULTS

An MRI-only treatment was delivered to 39 out of 40 patients. The excluded patient was too large for the predefined imaging field-of-view. All tasks could successfully be performed for the treated patients. There was a maximum deviation of 1.2% in PTV mean dose was seen in the prospective CT-QA. Retrospective analysis showed a maximum deviation below 2% in the DVH-analysis after correction for rectal gas and gamma pass-rates above 98%. MRI-only patient set-up deviation was below 2 mm for all but one investigated case and a maximum of 2.2 mm deviation in the GFM-identification compared to CT.

CONCLUSIONS

The MR-PROTECT trial shows the feasibility of an MRI-only prostate radiotherapy workflow. A major advantage with the presented workflow is the incorporation of a sCT-generation method with multi-vendor capability. The presented single sequence approach are easily adapted by other clinics and the general implementation procedure can be replicated. The dose deviation and the gamma pass-rate acceptance criteria earlier suggested was achievable, and these limits can thereby be confirmed. GFM-identification acceptance criteria are depending on the choice of identification method and slice thickness. Patient positioning strategies needs further investigations to establish acceptance criteria.

Dosimetric evaluation of MR-derived synthetic-CTs for MR-only proton treatment planning

PURPOSE

To evaluate proton dose calculation accuracy of optimized pencil beam scanning (PBS) plans on MR-derived synthetic-CTs for prostate patients.

MATERIAL AND METHODS

Ten patient datasets with both a CT and an MRI were planned with opposed lateral proton beams optimized to single field uniform dose under an IRB-approved study. The proton plans were created on CT datasets generated by a commercial synthetic CT-based software called MRCAT (MR for Calculating ATtenuation) routinely used in our clinic for photon-based MR-only planning. A standard prescription of 79.2 Gy (RBE) and 68.4 Gy (RBE) was used for intact prostate and prostate bed cases, respectively. Proton plans were first generated and optimized using the MRCAT synthetic-CT (syn-CT), and then recalculated on the planning CT rigidly aligned with the syn-CT (aligned-CT) and a deformed planning CT (deformed-CT), which was deformed to match outer contour between syn-CT and aligned-CT. The same beam arrangement, total MUs, MUs/spot, spot positions were used to recalculate dose on deformed-CT and aligned-CT without renormalization. DVH analysis was performed on aligned-CT, deformed-CT, and syn-CT to compare D_98%, V_100%, V_95% for PTV, PTVeval, and GTV as well as V_70Gy, V_50Gy for OARs.

RESULTS

The relative percentage dose difference between syn-CT and deformed-CT, were (0.17 ± 0.33 %) for PTVeval D_98% and (0.07 ± 0.1 %) for CTV D_98%. Rectum V_70Gy, V_50Gy, and Bladder V_70Gy were (2.76 ± 4.01 %), (11.6 ± 11.2 %), and (3.41 ± 2.86 %), respectively for the syn-CT, and (3.23 ± 3.63 %), (11.3 ± 8.18 %), and (3.29 ± 2.76 %), respectively for the deformed-CT, and (1.37 ± 1.84 %), (8.48 ± 6.67 %), and (4.91 ± 3.65 %), respectively for aligned-CT.

CONCLUSION

Dosimetric analysis shows that MR-only proton planning is feasible using syn-CT based on current clinical margins that account for a range uncertainty.

On the accuracy of bulk synthetic CT for MR-guided online adaptive radiotherapy

PURPOSE

MR-guided radiotherapy (MRgRT) relies on the daily assignment of a relative electron density (RED) map to allow the fraction specific dose calculation. One approach to assign the RED map consists of segmenting the daily magnetic resonance image into five different density levels and assigning a RED bulk value to each level to generate a synthetic CT (sCT). The aim of this study is to evaluate the dose calculation accuracy of this approach for applications in MRgRT.

METHODS

A planning CT (pCT) was acquired for 26 patients with abdominal and pelvic lesions and segmented in five levels similar to an online approach: air, lung, fat, soft tissue and bone. For each patient, the median RED value was calculated for fat, soft tissue and bone. Two sCTs were generated assigning different bulk values to the segmented levels on pCT: The sCT_ICRU uses the RED values recommended by ICRU46, and the sCT_tailor uses the median patient-specific RED values. The same treatment plan was calculated on two the sCTs and the pCT. The dose calculation accuracy was investigated in terms of gamma analysis and dose volume histogram parameters.

RESULTS

Good agreement was found between dose calculated on sCTs and pCT (gamma passing rate 1%/1 mm equal to 91.2% ± 6.9% for sCT_ICRU and 93.7% ± 5.3% b or sCT_tailor). The mean difference in estimating V95 (PTV) was equal to 0.2% using sCT_tailor and 1.2% using sCT_ICRU, respect to pCT values CONCLUSIONS: The bulk sCT guarantees a high level of dose calculation accuracy also in presence of magnetic field, making this approach suitable to MRgRT. This accuracy can be improved by using patient-specific RED values.

Clinical workflow for MR-only simulation and planning in prostate

Purpose

To describe the details and experience of implementing a MR-only workflow in the clinic for simulation and planning of prostate cancer patients.

Methods

Forty-eight prostate cancer patients from June 2016 - Dec 2016 receiving external beam radiotherapy were scheduled to undergo MR-only simulation. MR images were acquired for contouring (T2w axial, coronal, sagittal), synthetic-CT generation (3D FFE-based) and fiducial identification (3D bFFE-based). The total acquisition time was 25 min. Syn-CT was generated at the console using commercial software called MRCAT. As part of acceptance testing of the MRCAT package, external laser positioning system QA (< 2 mm) and geometric fidelity QA (< 2 mm within 50 cm LR and 30 cm AP) were performed and baseline values were set. Our current combined CT + MR simulation process was modified to accommodate a MRCAT-based MR-only simulation workflow. An automated step-by-step process using a MIM™ workflow was created for contouring on the MR images. Patient setup for treatment was achieved by matching the MRCAT DRRs with the orthogonal KV radiographs based on either fiducial ROIs or bones. 3-D CBCTs were acquired and compared with the MR/syn-CT to assess the rectum and bladder filling compared to simulation conditions.

Results

Forty-two patients successfully underwent MR-only simulation and met all of our institutional dosimetric objectives that were developed based on a CT + MR-based workflow. The remaining six patients either had a hip prosthesis or their large body size fell outside of the geometric fidelity QA criteria and thus they were not candidates for MR-only simulation. A total time saving of ~15 min was achieved with MR-based simulation as compared to CT + MR-based simulation. An automated and organized MIM workflow made contouring on MR much easier, quicker and more accurate compared with combined CT + MR images because the temporal variations in normal structure was minimal. 2D and 3D treatment setup localization based on bones/fiducials using a MRCAT reference image was successfully achieved for all cases.

Conclusions

MR-only simulation and planning with equivalent or superior target delineation, planning and treatment setup localization accuracy is feasible in a clinical setting. Future work will focus on implementing a robust 3D isotropic acquisition for contouring.

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-017-0854-4) contains supplementary material, which is available to authorized users.

Dosimetric evaluation of synthetic CT for magnetic resonance-only based radiotherapy planning of lung cancer

Background

Interest in MR-only treatment planning for radiation therapy is growing rapidly with the emergence of integrated MRI/linear accelerator technology. The purpose of this study was to evaluate the feasibility of using synthetic CT images generated from conventional Dixon-based MRI scans for radiation treatment planning of lung cancer.

Methods

Eleven patients who underwent whole-body PET/MR imaging following a PET/CT exam were randomly selected from an ongoing prospective IRB-approved study. Attenuation maps derived from the Dixon MR Images and atlas-based method was used to create CT data (synCT). Treatment planning for radiation treatment of lung cancer was optimized on the synCT and subsequently copied to the registered CT (planCT) for dose calculation. Planning target volumes (PTVs) with three sizes and four different locations in the lung were planned for irradiation. The dose-volume metrics comparison and 3D gamma analysis were performed to assess agreement between the synCT and CT calculated dose distributions.

Results

Mean differences between PTV doses on synCT and CT across all the plans were −0.1% ± 0.4%, 0.1% ± 0.5%, and 0.4% ± 0.5% for D95, D98 and D100, respectively. Difference in dose between the two datasets for organs at risk (OARs) had average differences of −0.14 ± 0.07 Gy, 0.0% ± 0.1%, and −0.1% ± 0.2% for maximum spinal cord, lung V20, and heart V40 respectively. In patient groups based on tumor size and location, no significant differences were observed in the PTV and OARs dose-volume metrics (p > 0.05), except for the maximum spinal-cord dose when the target volumes were located at the lung apex (p = 0.001). Gamma analysis revealed a pass rate of 99.3% ± 1.1% for 2%/2 mm (dose difference/distance to agreement) acceptance criteria in every plan.

Conclusions

The synCT generated from Dixon-based MRI allows for dose calculation of comparable accuracy to the standard CT for lung cancer treatment planning. The dosimetric agreement between synCT and CT calculated doses warrants further development of a MR-only workflow for radiotherapy of lung cancer.

Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning

BACKGROUND AND PURPOSE

Computed tomography (CT) imaging is the current gold standard for radiotherapy treatment planning (RTP). The establishment of a magnetic resonance imaging (MRI) only RTP workflow requires the generation of a synthetic CT (sCT) for dose calculation. This study evaluates the feasibility of using a multi-atlas sCT synthesis approach (sCTa) for head and neck and prostate patients.

MATERIAL AND METHODS

The multi-atlas method was based on pairs of non-rigidly aligned MR and CT images. The sCTa was obtained by registering the MRI atlases to the patient's MRI and by fusing the mapped atlases according to morphological similarity to the patient. For comparison, a bulk density assignment approach (sCTbda) was also evaluated. The sCTbda was obtained by assigning density values to MRI tissue classes (air, bone and soft-tissue). After evaluating the synthesis accuracy of the sCTs (mean absolute error), sCT-based delineations were geometrically compared to the CT-based delineations. Clinical plans were re-calculated on both sCTs and a dose-volume histogram and a gamma analysis was performed using the CT dose as ground truth.

RESULTS

Results showed that both sCTs were suitable to perform clinical dose calculations with mean dose differences less than 1% for both the planning target volume and the organs at risk. However, only the sCTa provided an accurate and automatic delineation of bone.

CONCLUSIONS

Combining MR delineations with our multi-atlas CT synthesis method could enable MRI-only treatment planning and thus improve the dosimetric and geometric accuracy of the treatment, and reduce the number of imaging procedures.