MRI-guided prostate radiation therapy planning: Investigation of dosimetric accuracy of MRI-based dose planning

Lambert, Jonathan; Greer, Peter B.; Menk, F. W.; Patterson, Jackie; Parker, J. Wm.; Dahl, K; Gupta, Saurabh; Capp, Anne; Wratten, Chris; Tang, Colin; Kumar, Mahesh; Dowling, Jason; Hauville, Sarah; Hughes, Cynthia T.; Fisher, Kristen; Lau, Peter; Denham, James W.; Salvado, Olivier

doi:10.1016/j.radonc.2011.01.012

Cited by 133 publications

(133 citation statements)

References 16 publications

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“…This difference is larger than what has been found in the present study. However, Lambert et al (2011) did not use coil support during MRI. In this way, the skin surface of the patient may be compressed, possibly leading to the larger observed differences.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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

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Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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“…Multiple methods for creating sCT for prostate RTP has been presented. [6][7][8][9][10] The dosimetric accuracy in high-quality sCTs for prostate cancer RTP is sufficient. 6,7,[11][12][13] The sCTs can also be used as reference images for image-guided RT.…”

Section: Introductionmentioning

confidence: 99%

Registration free automatic identification of gold fiducial markers in MRI target delineation images for prostate radiotherapy

et al. 2017

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Purpose: The superior soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) has urged the integration of MRI and elimination of CT in radiotherapy treatment (RT) for prostate. An intraprostatic gold fiducial marker (GFM) appears hyperintense on CT. On T2-weighted (T2w) MRI target delineation images, the GFM appear as a small signal void similar to calcifications and post biopsy fibrosis. It can therefore be difficult to identify the markers without CT. Detectability of GFMs can be improved using additional MR images, which are manually registered to target delineation images. This task requires manual labor, and is associated with interoperator differences and image registration errors. The aim of this work was to develop and evaluate an automatic method for identification of GFMs directly in the target delineation images without the need for image registration. Methods: T2w images, intended for target delineation, and multiecho gradient echo (MEGRE) images intended for GFM identification, were acquired for prostate cancer patients. Signal voids in the target delineation images were identified as GFM candidates. The GFM appeared as round, symmetric, signal void with increasing area for increasing echo time in the MEGRE images. These image features were exploited for automatic identification of GFMs in a MATLAB model using a patient training dataset (n = 20). The model was validated on an independent patient dataset (n = 40). The distances between the identified GFM in the target delineation images and the GFM in CT images were measured. A human observatory study was conducted to validate the use of MEGRE images. Results: The sensitivity, specificity, and accuracy of the automatic method and the observatory study was 84%, 74%, 81% and 98%, 94%, 97%, respectively. The mean absolute difference in the GFM distances for the automatic method and observatory study was 1.28 AE 1.25 mm and 1.14 AE 1.06 mm, respectively. Conclusions: Multiecho gradient echo images were shown to be a feasible and reliable way to perform GFM identification. For clinical practice, visual inspection of the results from the automatic method is needed at the current stage.

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“…Bulk density assignment approaches, aim to assign homogeneous densities to different volumes of interest (or tissue classes such as air, soft tissues, bones) defined on a target MRI [5], [9], [10]. Usually, densities are obtained from a CT set which have been manually or automatically segmented.…”

Section: Introductionmentioning

confidence: 99%

Pseudo-CT generation by conditional inference random forest for MRI-based radiotherapy treatment planning

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Nunes

Saint‐Jalmes

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Dose calculation from MRI is a topical issue. New treatment systems combining a linear accelerator with a MRI have been recently being developed. MRI has good soft tissue contrast without ionizing radiation exposure. However, unlike CT, MRI does not provide electron density information necessary for dose calculation. We propose in this paper a machine learning method to simulate a CT from a target MRI and co-registered CT-MRI training set. Ten prostate MR and CT images have been considered. Firstly, a reference image was randomly selected in the training set. A common space has been built thanks to affine registrations between the training set and the reference image. Multiscale image descriptors such as spatial information, gradients and texture features were extracted from MRI patches at different levels of a Gaussian pyramid and used as voxel-wise characteristics in the learning scheme. A Conditional Inference Random Forest (CIRF) modelled the relation between MRI descriptors and CT patches. For validation, test images were spatially normalized and the same descriptors were computed to generate a new pCT. Leave-one out experiments were performed. We obtained a MAE = 45.79 (pCT vs CT). Dose volume histograms inside PTV and organs at risk are in close agreement. The D98% was 0.45 % (inside PTV) and the 3D gamma pass rate (1mm, 1%) was 99,2%. Our method has better results than direct bulk assignment. And the results suggest that the method may be used for dose calculations in an MR based planning system.

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MRI-guided prostate radiation therapy planning: Investigation of dosimetric accuracy of MRI-based dose planning

Cited by 133 publications

References 16 publications

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

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

Registration free automatic identification of gold fiducial markers in MRI target delineation images for prostate radiotherapy

Pseudo-CT generation by conditional inference random forest for MRI-based radiotherapy treatment planning

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