Tarraf Torfeh

Characterization of 3D geometric distortion of magnetic resonance imaging scanners commissioned for radiation therapy planning

OBJECTIVE To develop a method for the assessment and characterization of 3D geometric distortion as part of routine quality assurance for MRI scanners commissioned for Radiation Therapy planning. MATERIALS AND METHODS In this study, the in-plane and through-plane geometric distortions on a 1.5T GE MRI-SIM unit are characterized and the 2D and 3D correction algorithms provided by the vendor are evaluated. We used a phantom developed by GE Healthcare that covers a large field of view of 500mm, and consists of layers of foam embedded with a matrix of ellipsoidal markers. An in-house Java-based software module was developed to automatically assess the geometric distortion by calculating the center of each marker using the center of mass method, correcting of gross rotation errors and comparing the corrected positions with a CT gold standard data set. Spatial accuracy of typical pulse sequences used in RT planning was assessed (2D T1/T2 FSE, 3D CUBE, T1 SPGR) using the software. The accuracy of vendor specific geometric distortion correction (GDC) algorithms was quantified by measuring distortions before and after the application of the 2D and 3D correction algorithms. RESULTS Our algorithm was able to accurately calculate geometric distortion with sub-pixel precision. For all typical MR sequences used in Radiotherapy, the vendors GDC was able to substantially reduce the distortions. Our results showed also that the impact of the acquisition produced a maximum variation of 0.2mm over a radial distance of 200mm. It has been shown that while the 2D correction algorithm remarkably reduces the in-plane geometric distortion, 3D geometric distortion further reduced the geometric distortion by correcting both in-plane and through-plane distortions in all acquisitions. CONCLUSION The presented methods represent a valuable tool for routine quality assurance of MR applications that require stringent spatial accuracy assessment such as radiotherapy. The phantom used in this study provides three dimensional arrays of control points. These tools and the detailed results can be also used for developing new geometric distortion correction algorithms or improving the existing ones.

Development and validation of a novel large field of view phantom and a software module for the quality assurance of geometric distortion in magnetic resonance imaging

OBJECTIVE To develop and validate a large field of view phantom and quality assurance software tool for the assessment and characterization of geometric distortion in MRI scanners commissioned for radiation therapy planning. MATERIALS AND METHODS A purpose built phantom was developed consisting of 357 rods (6mm in diameter) of polymethyl-methacrylat separated by 20mm intervals, providing a three dimensional array of control points at known spatial locations covering a large field of view up to a diameter of 420mm. An in-house software module was developed to allow automatic geometric distortion assessment. This software module was validated against a virtual dataset of the phantom that reproduced the exact geometry of the physical phantom, but with known translational and rotational displacements and warping. For validation experiments, clinical MRI sequences were acquired with and without the application of a commercial 3D distortion correction algorithm (Gradwarp™). The software module was used to characterize and assess system-related geometric distortion in the sequences relative to a benchmark CT dataset, and the efficacy of the vendor geometric distortion correction algorithms (GDC) was also assessed. RESULTS Results issued from the validation of the software against virtual images demonstrate the algorithms ability to accurately calculate geometric distortion with sub-pixel precision by the extraction of rods and quantization of displacements. Geometric distortion was assessed for the typical sequences used in radiotherapy applications and over a clinically relevant 420mm field of view (FOV). As expected and towards the edges of the field of view (FOV), distortion increased with increasing FOV. For all assessed sequences, the vendor GDC was able to reduce the mean distortion to below 1mm over a field of view of 5, 10, 15 and 20cm radius respectively. CONCLUSION Results issued from the application of the developed phantoms and algorithms demonstrate a high level of precision. The results indicate that this platform represents an important, robust and objective tool to perform routine quality assurance of MR-guided therapeutic applications, where spatial accuracy is paramount.

Generation of synthetic CT using multi-scale and dual-contrast patches for brain MRI-only external beam radiotherapy

PURPOSE To create a synthetic CT (sCT) from conventional brain MRI using a patch-based method for MRI-only radiotherapy planning and verification. METHODS Conventional T1 and T2-weighted MRI and CT datasets from 13 patients who underwent brain radiotherapy were included in a retrospective study whereas 6 patients were tested prospectively. A new contribution to the Non-local Means Patch-Based Method (NMPBM) framework was done with the use of novel multi-scale and dual-contrast patches. Furthermore, the training dataset was improved by pre-selecting the closest database patients to the target patient for computation time/accuracy balance. sCT and derived DRRs were assessed visually and quantitatively. VMAT planning was performed on CT and sCT for hypothetical PTVs in homogeneous and heterogeneous regions. Dosimetric analysis was done by comparing Dose Volume Histogram (DVH) parameters of PTVs and organs at risk (OARs). Positional accuracy of MRI-only image-guided radiation therapy based on CBCT or kV images was evaluated. RESULTS The retrospective (respectively prospective) evaluation of the proposed Multi-scale and Dual-contrast Patch-Based Method (MDPBM) gave a mean absolute error MAE=99.69±11.07HU (98.95±8.35HU), and a Dice in bones DIbone=83±0.03 (0.82±0.03). Good agreement with conventional planning techniques was obtained; the highest percentage of DVH metric deviations was 0.43% (0.53%) for PTVs and 0.59% (0.75%) for OARs. The accuracy of sCT/CBCT or DRRsCT/kV images registration parameters was <2mm and <2°. Improvements with MDPBM, compared to NMPBM, were significant. CONCLUSION We presented a novel method for sCT generation from T1 and T2-weighted MRI potentially suitable for MRI-only external beam radiotherapy in brain sites.

Geometric accuracy of the MR imaging techniques in the presence of motion

Abstract Magnetic Resonance Imaging (MRI) is increasingly being used for improving tumor delineation and tumor tracking in the presence of respiratory motion. The purpose of this work is to design and build an MR compatible motion platform and to use it for evaluating the geometric accuracy of MR imaging techniques during respiratory motion. The motion platform presented in this work is composed of a mobile base made up of a flat plate and four wheels. The mobile base is attached from one end and through a rigid rod to a synchrony motion table by Accuray® placed at the end of the MRI table and from the other end to an elastic rod. The geometric accuracy was measured by placing a control point‐based phantom on top of the mobile base. In‐house software module was used to automatically assess the geometric distortion. The blurring artifact was also assessed by measuring the Full Width Half Maximum (FWHM) of each control point. Our results were assessed for 50, 100, and 150 mm radial distances, with a mean geometric distortion during the superior–inferior motion of 0.27, 0.41, and 0.55 mm, respectively. Adding the anterior–posterior motion, the mean geometric distortions increased to 0.4, 0.6, and 0.8 mm. Blurring was observed during motion causing an increase in the FWHM of ≈30%. The platform presented in this work provides a valuable tool for the assessment of the geometric accuracy and blurring artifact for MR during motion. Although the main objective was to test the spatial accuracy of an MR system during motion, the modular aspect of the presented platform enables the use of any commercially available phantom for a full quality control of the MR system during motion.

MO‐F‐CAMPUS‐J‐05: Toward MRI‐Only Radiotherapy: Novel Tissue Segmentation and Pseudo‐CT Generation Techniques Based On T1 MRI Sequences

Purpose: To develop and validate a 4 class tissue segmentation approach (air cavities, background, bone and soft-tissue) on T1 -weighted brain MRI and to create a pseudo-CT for MRI-only radiation therapy verification. Methods: Contrast-enhanced T1-weighted fast-spin-echo sequences (TR = 756ms, TE= 7.152ms), acquired on a 1.5T GE MRI-Simulator, are used.MRIs are firstly pre-processed to correct for non uniformity using the non parametric, non uniformity intensity normalization algorithm. Subsequently, a logarithmic inverse scaling log(1/image) is applied, prior to segmentation, to better differentiate bone and air from soft-tissues. Finally, the following method is enrolled to classify intensities into air cavities, background, bone and soft-tissue:Thresholded region growing with seed points in image corners is applied to get a mask of Air+Bone+Background. The background is, afterward, separated by the scan-line filling algorithm. The air mask is extracted by morphological opening followed by a post-processing based on knowledge about air regions geometry. The remaining rough bone pre-segmentation is refined by applying 3D geodesic active contours; bone segmentation evolves by the sum of internal forces from contour geometry and external force derived from image gradient magnitude.Pseudo-CT is obtained by assigning −1000HU to air and background voxels, performing linear mapping of soft-tissue MR intensities in [-400HU, 200HU] and inverse linear mapping of bone MR intensities in [200HU, 1000HU]. Results: Three brain patients having registered MRI and CT are used for validation. CT intensities classification into 4 classes is performed by thresholding. Dice and misclassification errors are quantified. Correct classifications for soft-tissue, bone, and air are respectively 89.67%, 77.8%, and 64.5%. Dice indices are acceptable for bone (0.74) and soft-tissue (0.91) but low for air regions (0.48). Pseudo-CT produces DRRs with acceptable clinical visual agreement to CT-based DRR. Conclusion: The proposed approach makes it possible to use T1-weighted MRI to generate accurate pseudo-CT from 4-class segmentation.

SU-E-I-59: Virtual Phantom: A First Step to a Comprehensive Automated Quality Control Program for Magnetic Resonance Image Guided Applications

Purpose: To design a virtual phantom for the calculation of quality metrics required for controlling MRI guided applications and to develop software tools to automatically perform the required controls. Methods: The virtual phantom is designed using 3D objects arranged in different dispositions in the space. Six cylinders of 6mm diameter and 500mm length, tilted 30 degrees with respect to the Y axis, are used for slice thickness and location. These cylinders are placed in coronal planes distanced by 60mm. Their width and position allow measuring the slice location and thickness. A 15*15*15 mm3 cube centered at the origin and rotated 3 degrees with respect to the Z axis is used for the calculation of the vertical and horizontal Modulation Transfer Function. Spheres of 10 mm diameter distanced by 20 mm covering a field of view of 500mm are used for estimating the in-plane and through-plane geometric distortion. Sets of cylinders with lengths ranging from 0.5mm to 2.0mm and diameters ranging from 4mm to 10mm are used for the low contrast. The Low Contrast test assesses the number of cylinders that can be detected.In-house Java based software is developed and validated. The software automatically corrects positioning errors before constructing ROIs and acquiring measurements with respect to each control. Results: The virtual phantom was used to produce a large set of 2D DICOM images. Results showed that the software robustly calculated positions and distances with sub-voxel accuracy. For a 500mm FOV and a 0.9mm pixel size, mean errors were in the order of 0.15mm. Conclusion: This virtual phantom is an important step for building a new physical phantom and for validating new medical image processing algorithms. These tools represent a first step toward designing a comprehensive phantom and software for a complete Quality Control program for MRI guided application.