A Method for Assessing Ground-Truth Accuracy of the 5DCT Technique

Dou, T; Thomas, D; O’Connell, D; Lamb, James; Lee, Percy; Low, Daniel A.

doi:10.1016/j.ijrobp.2015.07.2272

Cited by 18 publications

(18 citation statements)

References 33 publications

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“…Motion modeling was performed using a 5D linear motion model, originally developed in the context of computed tomography imaging . The MRI‐based 5D motion modeling process was described in detail our recent publication and is briefly summarized as follows.…”

Section: Methodsmentioning

confidence: 99%

“…Motion modeling was performed using a 5D linear motion model, originally developed in the context of computed tomography imaging. [18][19][20][21] The MRI-based 5D motion modeling process was described in detail our recent publication 17 and is briefly summarized as follows. A respiratory pneumatic bellows (Lafayette Instrument, Lafayette, IN, USA) signal was recorded simultaneously during imaging studies on an external computer using LabVIEW (Austin, TX).…”

Section: A1 Motion Modeling: 5d Modelmentioning

confidence: 99%

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Multislice motion modeling forMRI‐guided radiotherapy gating

et al. 2019

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Purpose On‐board magnetic resonance imaging (MRI) greatly enhances real‐time target tracking capability during radiotherapy treatments. However, multislice and volumetric MRI techniques are frame rate limited and introduce unacceptable latency between the target moving out of position and the beam being turned off. We present a technique to estimate continuous volumetric tissue motion using motion models built from a repeated acquisition of a stack of MR slices. Applications including multislice target visualization and out‐of‐slice motion estimation during MRI‐guided radiotherapy are demonstrated. Methods Eight healthy volunteer studies were performed using a 0.35 T MRI‐guided radiotherapy system. Images were acquired at three frames per second in an interleaved fashion across ten adjacent sagittal slice positions covering 4.5 cm using a balanced steady‐state–free precession sequence. A previously published five‐dimensional (5D) linear motion model used for MRI‐guided radiotherapy gating was extended to include multiple slices. This model utilizes an external respiratory bellows signal recorded during imaging to simultaneously estimate motion across all imaged slices. For comparison to an image‐based approach, the manifold learning technique local linear embedding (LLE) was used to derive a respiratory surrogate for motion modeling. Manifolds for every slice were aligned during LLE in a group‐wise fashion, enabling motion estimation outside the current imaged slice using a motion model, a process which we denote as mSGA. Additionally, a method is developed to evaluate out‐of‐slice motion estimates. The multislice motion model was evaluated in a single slice with each newly acquired image using a leave‐one‐out approach. Model‐generated gating decision accuracy and beam‐on positive predictive value (PPV) are reported along with the median and 95th percentile distance between model and ground truth target centroids. Results The average model gating decision accuracy and PPV across all volunteer studies was 93.7% and 92.8% using the 5D model, and 96.8% and 96.1% using the mSGA model, respectively. The median and 95th percentile distance between model and ground truth target centroids was 0.91 and 2.90 mm, respectively, using the 5D model and 0.58 and 1.49 mm using the mSGA model, averaged over all eight subjects. The mSGA motion model provided a statistically significant improvement across all evaluation metrics compared to the external surrogate‐based 5D model. Conclusion The proposed techniques for out‐of‐slice target motion estimation demonstrated accuracy likely sufficient for clinical use. Results indicate the mSGA model may provide higher accuracy, however, the external surrogate‐based model allows for unbiased in vivo accuracy evaluation.

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Section: Methodsmentioning

confidence: 99%

Section: A1 Motion Modeling: 5d Modelmentioning

confidence: 99%

Multislice motion modeling forMRI‐guided radiotherapy gating

et al. 2019

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“…Recently it has been shown that prospective 4D CT reconstruction is superior to retrospective reconstruction resulting in less artefacts. Dou et al have proposed a promising implementation that was recently applied clinically [Dou 2015]. In order to capture motion variations and drift effects the value of 4DMR has been pointed out [von Siebenthal 2007.…”

Section: Imaging Motionmentioning

confidence: 99%

Required transition from research to clinical application: Report on the 4D treatment planning workshops 2014 and 2015

et al. 2016

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Since 2009, a 4D treatment planning workshop has taken place annually, gathering researchers working on the treatment of moving targets, mainly with scanned ion beams. Topics discussed during the workshops range from problems of time resolved imaging, the challenges of motion modelling, the implementation of 4D capabilities for treatment planning, up to different aspects related to 4D dosimetry and treatment verification. This report gives an overview on topics discussed at the 4D workshops in 2014 and 2015. It summarizes recent findings, developments and challenges in the field and discusses the relevant literature of the recent years. The report is structured in three parts pointing out developments in the context of understanding moving geometries, of treating moving targets and of 4D quality assurance (QA) and 4D dosimetry. The community represented at the 4D workshops agrees that research in the context of treating moving targets with scanned ion beams faces a crucial phase of clinical translation. In the coming years it will be important to define standards for motion monitoring, to establish 4D treatment planning guidelines and to develop 4D QA tools. These basic requirements for the clinical application of scanned ion beams to moving targets could e.g. be determined by a dedicated ESTRO task group. Besides reviewing recent research results and pointing out urgent needs when treating moving targets with scanned ion beams, the report also gives an outlook on the upcoming 4D workshop organized at the University Medical Center Groningen (UMCG) in the Netherlands at the end of 2016.

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“…The type of motion model used, and its ability to correctly represent anatomical motion, is another critical component. Previous studies on the 5D model have reported uncertainties of ~2 mm . It is important to acknowledge that the eight‐patient study in this work used 5DCT data, which incorporates the 5D model into reconstruction.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies on the 5D model have reported uncertainties of~2 mm. 49 It is important to acknowledge that the eight-patient study in this work used 5DCT data, which incorporates the 5D model into reconstruction. Since MC-SART also used the 5D model, errors in the 5D model itself may not be realistically reflected the corresponding CBCT reconstructions.…”

Section: Discussionmentioning

confidence: 99%

Reconstruction of a high‐quality volumetric image and a respiratory motion model from patient CBCT projections

et al. 2019

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Purpose To develop and evaluate a method of reconstructing a patient‐ and treatment day‐ specific volumetric image and motion model from free‐breathing cone‐beam projections and respiratory surrogate measurements. This Motion‐Compensated Simultaneous Algebraic Reconstruction Technique (MC‐SART) generates and uses a motion model derived directly from the cone‐beam projections, without requiring prior motion measurements from 4DCT, and can compensate for both inter‐ and intrabin deformations. The motion model can be used to generate images at arbitrary breathing points, which can be used for estimating volumetric images during treatment delivery. Methods The MC‐SART was formulated using simultaneous image reconstruction and motion model estimation. For image reconstruction, projections were first binned according to external surrogate measurements. Projections in each bin were used to reconstruct a set of volumetric images using MC‐SART. The motion model was estimated based on deformable image registration between the reconstructed bins, and least squares fitting to model parameters. The model was used to compensate for motion in both projection and backprojection operations in the subsequent image reconstruction iterations. These updated images were then used to update the motion model, and the two steps were alternated between. The final output is a volumetric reference image and a motion model that can be used to generate images at any other time point from surrogate measurements. Results A retrospective patient dataset consisting of eight lung cancer patients was used to evaluate the method. The absolute intensity differences in the lung regions compared to ground truth were 50.8 ± 43.9 HU in peak exhale phases (reference) and 80.8 ± 74.0 in peak inhale phases (generated). The 50th percentile of voxel registration error of all voxels in the lung regions with >5 mm amplitude was 1.3 mm. The MC‐SART was also applied to measured patient cone‐beam projections acquired with a linac‐mounted CBCT system. Results from this patient data demonstrate the feasibility of MC‐SART and showed qualitative image quality improvements compared to other state‐of‐the‐art algorithms. Conclusion We have developed a simultaneous image reconstruction and motion model estimation method that uses Cone‐beam computed tomography (CBCT) projections and respiratory surrogate measurements to reconstruct a high‐quality reference image and motion model of a patient in treatment position. The method provided superior performance in both HU accuracy and positional accuracy compared to other existing methods. The resultant reference image and motion model can be combined with respiratory surrogate measurements to generate volumetric images representing patient anatomy at arbitrary time points.

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A Method for Assessing Ground-Truth Accuracy of the 5DCT Technique

Cited by 18 publications

References 33 publications

Multislice motion modeling forMRI‐guided radiotherapy gating

Multislice motion modeling forMRI‐guided radiotherapy gating

Required transition from research to clinical application: Report on the 4D treatment planning workshops 2014 and 2015

Reconstruction of a high‐quality volumetric image and a respiratory motion model from patient CBCT projections

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