Estimation of geometrically undistorted B₀inhomogeneity maps

Abstract

Geometric accuracy of MRI is one of the main concerns for its use as a sole image modality in precision radiation therapy (RT) planning. In a state-of-art scanner, system level geometric distortions are within acceptable levels for precision RT. However, subject-induced B0 inhomogeneity may vary substantially, especially in air-tissue interfaces. Recent studies have shown distortion levels of more than 2 mm near the sinus and ear canal are possible due to subject-induced field inhomogeneity. These distortions can be corrected with the use of accurate B0 inhomogeneity field maps. Most existing methods estimate these field maps from dual gradient-echo (GRE) images acquired at two different echo-times under the assumption that the GRE images are practically undistorted. However distortion that may exist in the GRE images can result in estimated field maps that are distorted in both geometry and intensity, leading to inaccurate correction of clinical images. This work proposes a method for estimating undistorted field maps from GRE acquisitions using an iterative joint estimation technique. The proposed method yields geometrically corrected GRE images and undistorted field maps that can also be used for the correction of images acquired by other sequences. The proposed method is validated through simulation, phantom experiments and applied to patient data. Our simulation results show that our method reduces the root-mean-squared error of the estimated field map from the ground truth by ten-fold compared to the distorted field map. Both the geometric distortion and the intensity corruption (artifact) in the images caused by the B0 field inhomogeneity are corrected almost completely. Our phantom experiment showed improvement in the geometric correction of approximately 1mm at an air-water interface using the undistorted field map compared to using a distorted field map. The proposed method for undistorted field map estimation can lead to improved geometric distortion correction at air-tissue interfaces, especially in low readout-bandwidth acquisitions, thus making them suitable for clinical use in precision RT without increasing the treatment planning margin.

Stereotactic radiosurgery planning of vestibular schwannomas: IsMRIat 3 Tesla geometrically accurate?

“…repeated the field mapping procedure, and their error assessment thus includes also differences associated with small changes in patient position and orientation in relation to the main magnetic field; they found stable results, suitable for in vivo monitoring of field inhomogeneity . The field maps are also geometrically distorted by the susceptibility‐related field inhomogeneity being mapped, as described by Matakos et al . In our work, the same bandwidth and spatial resolution were employed in field mapping and in SRS planning scans, and thus displacements reaching 0.5 mm are also expected in the field maps.…”

Quantifying the Effect of 3T Magnetic Resonance Imaging Residual System Distortions and Patient-Induced Susceptibility Distortions on Radiation Therapy Treatment Planning for Prostate Cancer

“…Because our results show that patient-induced susceptibility effects were larger, utilizing correction algorithms to reduce these distortions in an MR-only workflow could be useful. Suggested methods such as B 0 mapping (27)(28)(29) and reversed read-out gradient polarity (13) are currently to our knowledge not provided for routine clinical use from the major vendors and were not modeled in the present study. Intraprostatic fiducial markers introduce distortions in MR images due to the different magnetic properties between the markers and soft tissues.…”

Multiatlas approach with local registration goodness weighting for MRI-based electron density mapping of head and neck anatomy

“…Inhomogeneity of the B 0 and B 1 magnetic fields may lead to geometrical distortion and signal loss depending on the amount of field inhomogeneity and pulse sequence characteristics . While geometrical distortion is often handled by measuring and correcting the local field inhomogeneity, image analysis techniques may be used to address signal loss.…”

“…Inhomogeneity of the B 0 and B 1 magnetic fields may lead to geometrical distortion and signal loss depending on the amount of field inhomogeneity and pulse sequence characteristics. [23][24][25][26] While geometrical distortion is often handled by measuring and correcting the local field inhomogeneity, 24,26 image analysis techniques 27 may be used to address signal loss. In this work, local clustering properties of the image intensities were extracted using a model of intensity inhomogeneity in the neighborhood of each pixel 28 to estimate the regional signal loss due to bias fields inhomogeneity.…”