Generation of synthetic CT data using patient specific daily MR image data and image registration

Kraus, Kim Melanie; Jäkel, Oliver; Niebuhr, Nina I.; Pfaffenberger, A.

doi:10.1088/1361-6560/aa5200

Cited by 41 publications

(39 citation statements)

References 31 publications

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“…These observed results compare favorably with those reported in the literature. Kraus et al used an atlas‐related method to achieve MAE values ranging from 29.9 to 66.6 HU in regions of bone, bladder, and soft tissue within the pelvic region. Other atlas‐based techniques report values ranging from 40 to 200 HU for sites including the pelvis and cranium .…”

Section: Discussionmentioning

confidence: 99%

“…Compared to x‐ray computed tomography (CT), MRI offers superior soft tissue contrast without exposing the patient to ionizing radiation . Taking full advantage of these attractive properties with MRI‐based therapy delivery systems allows for numerous applications including motion tracking and management as well as online adaptive therapy . The increasing use of MRI for tissue and target delineation makes an MRI‐only RT workflow, one in which MRI is the sole imaging modality employed for treatment planning and dose calculations, a favorable option for MR image‐guided RT (MR‐IGRT) applications.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR‐only breast radiotherapy

et al. 2019

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Purpose The superior soft‐tissue contrast achieved using magnetic resonance imaging (MRI) compared to x‐ray computed tomography (CT) has led to the popularization of MRI‐guided radiation therapy (MR‐IGRT), especially in recent years with the advent of first and second generation MRI‐based therapy delivery systems for MR‐IGRT. The expanding use of these systems is driving interest in MRI‐only RT workflows in which MRI is the sole imaging modality used for treatment planning and dose calculations. To enable such a workflow, synthetic CT (sCT) data must be generated based on a patient’s MRI data so that dose calculations may be performed using the electron density information derived from CT images. In this study, we propose a novel deep spatial pyramid convolutional framework for the MRI‐to‐CT image‐to‐image translation task and compare its performance to the well established U‐Net architecture in a generative adversarial network (GAN) framework. Methods Our proposed framework utilizes atrous convolution in a method named atrous spatial pyramid pooling (ASPP) to significantly reduce the total number of parameters required to describe the model while effectively capturing rich, multi‐scale structural information in a manner that is not possible in the conventional framework. The proposed framework consists of a generative model composed of stacked encoders and decoders separated by the ASPP module, where atrous convolution is applied at increasing rates in parallel to encode large‐scale features. The performance of the proposed method is compared to that of the conventional GAN framework in terms of the time required to train the model and the image quality of the generated sCT as measured by the root mean square error (RMSE), structural similarity index (SSIM), and peak signal‐to‐noise ratio (PSNR) depending on the size of the training data set. Dose calculations based on sCT data generated using the proposed architecture are also compared to clinical plans to evaluate the dosimetric accuracy of the method. Results Significant reductions in training time and improvements in image quality are observed at every training data set size when the proposed framework is adopted instead of the conventional framework. Over 1042 test images, values of 17.7 ± 4.3 HU, 0.9995 ± 0.0003, and 71.7 ± 2.3 are observed for the RMSE, SSIM, and PSNR metrics, respectively. Dose distributions calculated based on sCT data generated using the proposed framework demonstrate passing rates equal to or greater than 98% using the 3D gamma index with a 2%/2 mm criterion. Conclusions The deep spatial pyramid convolutional framework proposed here demonstrates improved performance compared to the conventional GAN framework that has been applied to the image‐to‐image translation task of sCT generation. Adopting the method is a first step toward an MRI‐only RT workflow that enables widespread clinical applications for MR‐IGRT including online adaptive therapy.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR‐only breast radiotherapy

et al. 2019

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“…Implementation of real‐time monitoring of tumor position, which is currently achieved mostly by fluoroscopic imaging of fiducial surrogates, with an on‐board MR system will be a significant advancement for particle therapy. Recent work has additionally investigated the possibility of MR only particle therapy dose calculation by creating pseudo‐CT images from MR images …”

Section: Beyond Radiological Image Guidancementioning

confidence: 99%

Current state and future applications of radiological image guidance for particle therapy

Landry

Hua

2018

Medical Physics

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In this review paper, we first give a short overview of radiological image guidance in photon radiotherapy, placing emphasis on the fact that linac based radiotherapy has outpaced particle therapy in the adoption of volumetric image guidance. While cone beam computed tomography (CBCT) has been an established technique in linac treatment rooms for almost two decades, the widespread adoption of volumetric image guidance in particle therapy, whether by means of CBCT or in‐room CT imaging, is recent. This lag may be attributable to the bespoke nature and lower number of particle therapy installations, as well as the differences in geometry between those installations and linac treatment rooms. In addition, for particle therapy the so called shift invariance of the dose distribution rarely applies. An overview of the different volumetric image guidance solutions found at modern particle therapy facilities is provided, covering gantry, nozzle, C‐arm, and couch‐mounted CBCT as well different in‐room CT configurations. A summary of the use of in‐room volumetric imaging data beyond anatomy‐based positioning is also presented as well as the necessary corrections to CBCT images for accurate water equivalent thickness calculation. Finally, the use of non‐ionizing imaging modalities is discussed.

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“…The estimation method utilized a synthetic CT in order to match DRRs to the on‐board projections in the data fidelity constraint. Different studies have been to done to investigate various ways of generating synthetic CT images, including use of bulk densities, voxel‐based intensity conversions, and deformable registration‐based methods . Each approach has its advantages and limitations.…”

Section: Discussionmentioning

confidence: 99%

“…In the discrepancy region, mean HU and noise values from a nearby region in the deformed CT are used to fill in realistic heterogeneous HU values. The internal textures of the liver including the tumor region were generated in the deformed CT directly based on the deformable registration, similar as the approach in other previous studies . The sCT prior is then used to optimize the deformation field map using the MMFD algorithm.…”

Section: Methodsmentioning

confidence: 99%

A Novel method to generate on‐board 4D MRI using prior 4D MRI and on‐board kV projections from a conventional LINAC for target localization in liver SBRT

et al. 2018

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Purpose: On-board MRI can provide superb soft tissue contrast for improving liver SBRT localization. However, the availability of on-board MRI in clinics is extremely limited. On the contrary, on-board kV imaging systems are widely available on radiotherapy machines, but its capability to localize tumors in soft tissue is limited due to its poor soft tissue contrast. This study aims to explore the feasibility of using an on-board kV imaging system and patient prior knowledge to generate on-board four-dimensional (4D)-MRI for target localization in liver SBRT. Methods: Prior 4D MRI volumes were separated into end of expiration (EOE) phase (MRI prior ) and all other phases. MRI prior was used to generate a synthetic CT at EOE phase (sCT prior ). On-board 4D MRI at each respiratory phase was considered a deformation of MRI prior . The deformation field map (DFM) was estimated by matching DRRs of the deformed sCT prior to on-board kV projections using a motion modeling and free-form deformation optimization algorithm. The on-board 4D MRI method was evaluated using both XCAT simulation and real patient data. The accuracy of the estimated on-board 4D MRI was quantitatively evaluated using Volume Percent Difference (VPD), Volume Dice Coefficient (VDC), and Center of Mass Shift (COMS). Effects of scan angle and number of projections were also evaluated. Results: In the XCAT study, VPD/VDC/COMS among all XCAT scenarios were 10.16 AE 1.31%/ 0.95 AE 0.01/0.88 AE 0.15 mm using orthogonal-view 30°scan angles with 102 projections. The onboard 4D MRI method was robust against the various scan angles and projection numbers evaluated. In the patient study, estimated on-board 4D MRI was generated successfully when compared to the "reference on-board 4D MRI" for the liver patient case. Conclusions: A method was developed to generate on-board 4D MRI using prior 4D MRI and onboard limited kV projections. Preliminary results demonstrated the potential for MRI-based image guidance for liver SBRT using only a kV imaging system on a conventional LINAC.

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Generation of synthetic CT data using patient specific daily MR image data and image registration

Cited by 41 publications

References 31 publications

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR‐only breast radiotherapy

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR‐only breast radiotherapy

Current state and future applications of radiological image guidance for particle therapy

A Novel method to generate on‐board 4D MRI using prior 4D MRI and on‐board kV projections from a conventional LINAC for target localization in liver SBRT

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