Field of the Invention
The present invention relates generally to the field of medical imaging. More particularly, embodiments of the invention relate to phase sensitive magnetic resonance imaging (MRI) using an extended phase correction algorithm, which among other things can process images with more flexible phases for successful water and fat imaging.
Description of Related Art
MRI has proven useful in the diagnosis of many diseases such as hepatic steatosis, cancer, multiple sclerosis, sports related injury, and bone marrow disorders. MRI provides unique imaging capabilities that are not attainable in any other imaging method. For example, MRI can provide detailed images of soft tissues, abnormal tissues such as tumors, and other structures that cannot be readily imaged using techniques like X-rays. Further, MRI operates without exposing patients to ionizing radiation experienced in X-rays. For these and other reasons, MRI is commonly utilized in medical and other fields.
In comparison to other imaging modalities, MRI is unique in that an MRI signal is represented by a complex number, rather than simply a scalar (such as X-ray attenuation in Computed Tomography). The image value for each image pixel, therefore, usually includes a magnitude and a phase. Although the phase of an image pixel may carry important information and may be used in many applications such as chemical shift imaging, thermal imaging, and blood flow quantification, it is usually discarded in a standard image reconstruction process. An underlying reason is that some unwanted background or error phase almost always accompanies the desired phase.
One application for phase correction of MR images includes inversion recovery imaging. Inversion recovery (IR) is generally used as a magnetization preparation technique in MRI. In IR imaging, a longitudinal magnetization along a main magnetic field is first rotated to the opposite direction using a 180 degree radiofrequency (RF) pulse. The inverted magnetization can be recovered by T1 relaxation during an inversion time (TI) between the inversion and the excitation RF pulse. One example application of IR imaging is for suppression of a given type of tissue with a characteristic T1, such as short-tau inversion recovery (STIR) for fat suppression or fluid-attenuated inversion recovery (FLAIR) for cerebral spinal fluid attenuation. Another example application of IR imaging is for increased tissue contrast from the doubling of the dynamic range of the longitudinal magnetization. The latter application could be useful for imaging of neonate brains, myocardium at delayed enhancement, or for evaluating pulmonary blood flow.
The potential for increased tissue contrast by IR imaging, however, is not always realized because conventional MR image reconstruction preserves only the magnitude of the MR signals and may actually lead to reduced or even reversed contrast in an IR image.
Phase-sensitive IR (PSIR) image reconstruction, in which unwanted background or error phase in an IR image is removed, is a technique that can restore the contrast loss or reversal resulting from conventional magnitude image reconstruction. One challenge in PSIR image reconstruction is a phase-correction process to separate the intrinsic signal phase in the complex image from the background or error phase, which is almost unavoidable in an MR image. Several approaches have been proposed for PSIR image reconstruction including calibration of the phase errors through acquisition of another image without IR or with IR at different TIs. However, these approaches reduce data acquisition efficiency. Further, spatial misregistration between the actual and calibration scans due to patient motion can also be problematic.
An alternative approach for PSIR image reconstruction is to determine the background or error phase from the IR image itself using various phase correction algorithms. In general, only the signal phase of a neighbor pixel for overall phase correction is used in many of these phase correction algorithms. As such, pixels with large phase variation, such as in regions of low signal-to-noise ratio (SNR) or along tissue boundaries may corrupt the phase correction process. In order to minimize the effect, an empirical threshold is usually selected to exclude regions of large phase uncertainty. The actual threshold value, however, can be critical. If the value selected is too small, phase correction cannot reach beyond the regions defined by the threshold value and may thus be terminated prematurely. Alternatively, if the value selected is too large, errors in phase correction may propagate and even corrupt the rest of the process. In a region growing-based approach, for example, the selection of the threshold value together with that of the initial seed and the path of the region growing, determines the quality and the scope of the phase correction. To allow phase correction to proceed beyond local phase fluctuations and to avoid potential corruption due to phase correction errors, an additional ad hoc treatment, such as a “bridge filter” is required. Another limitation of the phase correction algorithms is the global polarity of a PSIR image, which cannot be unambiguously determined from the phase correction process itself. Consequently, images from different component channels of a phased array coil cannot be readily combined and inconsistency in display may arise for different images of a multi-slice acquisition.
Another application where correction of phase errors may be important is the Dixon chemical shift imaging technique. In MRI, the signal-emitting protons may resonate at different Larmor frequencies because they have different local molecular environments or chemical shift. The two most distinct species found in the human body are water and fat, whose Larmor frequencies are separated by about 3.5 ppm (parts per million). In many clinical MRI applications, it is desirable to suppress signals from fat because they are usually very bright and obscure lesions. Presently, a commonly used method for fat suppression is chemical shift selective saturation (CHESS), which, despite its many advantages, is known to be intrinsically susceptible to both radiofrequency (RF) and magnetic field inhomogeneity. Another technique that is sometimes used for fat suppression is the short tau inversion recovery (STIR), which is based on the characteristically short T1 relaxation constant for fat, rather than on its Larmor frequency. The drawbacks of STIR include reduction in scan efficiency and signal-to-noise ratio as well as potential alteration to the image contrast.
In U.S. Pat. No. 7,227,359, which is incorporated herein by reference, the present inventor described, among other things, region growing based phase correction methods for phase sensitive MRI. Potential applications for such methods include a two-point Dixon method for water and fat imaging. In a typical two-point Dixon method, two acquired input images have water and fat relative phase angles of approximately 0° (in phase) and approximately 180° (opposed phase), respectively. This restriction of relative phase angles, in turn, imposes certain restrictions on corresponding echo times that are used for acquiring input images.
In the article Xiang, Magnetic Resonance in Medicine 56:572-584, 2006, which is incorporated herein by reference, Xiang proposed that it is possible to do water and fat imaging using two input images acquired at more flexible echo times (e.g., phases that are not substantially equal to 0° and 180°). In that article, Xiang discussed an iterative phase correction method and demonstrated water and fat imaging using an input image that is in phase and another input image that has a more flexible phase. Xiang called the iterative phase correction method RIPE, which stands for Regional Iterative Phasor Extraction. RIPE fundamentally relies on a global convergence of local statistical iterations of different phasor candidates in different regions of an image. Potential limitations of the approach include a requirement for prior image thresholding to successfully exclude low signal-to-noise regions. The RIPE approach may also run into difficulties when two input images are substantially in-phase and substantially 180° out-of-phase, or when regions of large artifacts (e.g., near metallic implants) are present to create an incorrect initial bias for the phasor iterations. Further, the fat signal is modeled as a single spectral resonance with no attenuation as a function of the echo time in Xiang's 2006 Magnetic Resonance in Medicine 56:572-584 implementation of the RIPE approach for two-point Dixon imaging.
More recently in Eggers et al, abstract, 2010 Annual Scientific Meeting of the International Society of Magnetic Resonance in Medicine, and Magnetic Resonance in Medicine 65(1):96-107, 2011, which are incorporated herein by reference, Eggers et al. reported that by using the RIPE method, two input images can both be relaxed to have flexible phases that are substantially different from in-phase and substantially different from 180° out-of-phase. Further, the fat signal model is extended to include up to 7 separate spectral resonance peaks, which are determined from measurements using magnetic resonance spectroscopy.
The increased flexibility associated with the use of images having echo times that are more flexible than being substantially in-phase and substantially 180° out-of-phase can reduce some restrictions of scan parameters and further improve the scan efficiency for techniques such as dual-echo acquisition. This flexibility, however, also adds additional variables and complexity to phase error calculations that were not considered in previous algorithms. For example, in the above-referenced publications both by Xiang and by Eggers et al, the important step of phase correction in postprocessing images with flexible echo times has—prior to embodiments of the present disclosure—been based on a statistical iterative process that is named RIPE by Xiang. This process involves empirical image thresholding to exclude low signal-to-noise regions. The process may also run into difficulties when two input images are substantially in-phase and substantially 180° out-of-phase, or when regions of large artifacts (e.g., near metallic implants) are present to create an incorrect initial bias for the phasor iterations. Furthermore, modeling of the fat signal by Eggers et al. is based on measurement using magnetic resonance spectroscopy that can only account for limited number of spectral peaks and cannot account for other confounding factors such as magnetic field strength, pulse sequence and scan parameters used, and potentially different relaxation times for the different spectral peaks. Embodiments of the present disclosure relate generally to alternative post-processing strategies for phase sensitive magnetic resonance imaging. When applied to two point Dixon imaging, certain embodiments of the present disclosure use a generalized signal model for fat and feature a particular type of sequenced region-growing scheme that accounts for additional complexities, without a need for image thresholding or a statistical iterative processing. Further, the disclosed sequenced region growing scheme may naturally encompass input images that are acquired substantially in-phase and substantially 180° out-of-phase, and is not affected by the presence of regions with large image artifacts. Using this post-processing strategy, successful water and fat separation can be accomplished with, for example, phantom and in vivo images by a 3D dual-echo acquisition with flexible echo times. Post-processing strategies of the present disclosure can also be directly applied to other useful applications such as, but not limited to, phase sensitive inversion recovery image, single point Dixon imaging, and single point silicone-specific imaging. In general, embodiments of this disclosure provide, in part, a new sequenced region growing algorithm that is able to correct background or error phase in acquired magnetic resonance image or images.
Referenced shortcomings of some existing or traditional approaches to phase sensitive MRI are not intended to be exhaustive, but rather are among many that tend to impair the effectiveness of previously known techniques concerning image reconstruction; however, those mentioned here are sufficient to demonstrate that the methodologies appearing in the art have not been satisfactory and that a need exists for techniques described and claimed in this disclosure.
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