Rotational Motion Artifact Correction in Magnetic Resonance Imaging

The body motion of patients, during magnetic resonance (MR) imaging causes significant artifacts in the reconstructed image. Artifacts are manifested as a motion induced blur and ghost repetitions of the moving structures. which obscure vital anatomical and pathological detail. The techniques that have been proposed for suppressing motion artifacts fall into two major categories. Real-time techniques attempt to prevent the motion from corrupting the data by restricting the data acquisition times or motion of the patients, whereas the post-processing techniques use the information embedded in the corrupted data to restore the image. Most methods currently in widespread use belong to the real-time techniques, however with the advent of fast computing platforms and sophisticated signal processing algorithms, the emergence of post-processing techniques is clearly evident. The post-processing techniques usually demand an appropriate model of the motion. The restoration of the image requires that the motion parameters be determined in order to invert the data degradation process. Methods for the correction of translational motion have been studied extensively in the past. The subject of this thesis encompasses the rotational motion model and the effect of rotational motion on the collected MR data in the spatial frequency space (k-space), which is in general, more complicated than the translational model. Rotational motion artifacts are notably prevalent in MR images of head, brain and limbs. Post-processing techniques for the correction of rotational motion artifacts often involve interpolation and re-gridding of the acquired data in the k-space. These methods create significant data overlap and void regions. Therefore, in the past, proposed corrective techniques have been limited to suppression of artifacts caused by small angle rotations. This thesis presents a method of managing overlap regions, using weighted averaging of redundant data, in order to correct for large angle rotations. An iterative estimation technique for filling the data void regions has also been developed by the use of iterated application of projection operators onto constraint sets. These constraint sets are derived from the k-space data generated by the MR imager, and available a priori knowledge. It is shown that the iterative algorithm diverges at times from the required image, due to inconsistency among the constraint sets. It is also shown that this can be overcome by using soft. constraint sets and fuzzy projections. One of the constraints applied in the iterative algorithm is the finite support of the imaged object, marked by the outer boundary of the region of interest (ROI). However, object boundary extraction directly from the motion affected MR image can be difficult, specially if the motion function of the object is unknown. This thesis presents a new ROI extraction scheme based on entropy minimization in the image background. The object rotation function is usually unknown or unable to be measured with sufficient accuracy. The motion estimation algorithm proposed in this thesis is based on maximizing the similarity among the k-space data subjected to angular overlap. This method is different to the typically applied parameter estimation technique based on minimization of pixel energy outside the ROI, and has higher efficiency and ability to estimate rotational motion parameters in the midst of concurrent translational motion. The algorithms for ROI extraction, rotation estimation and data correction have been tested with both phantom images and spin echo MR images producing encouraging results

Doctor of Philosophy

dissertationEach year in the United States, a quarter million cases of stroke are caused directly by atherosclerotic disease of the cervical carotid artery. This represents a significant portion of health care costs that could be avoided if high-risk carotid artery lesions could be detected early on in disease progression. There is mounting evidence that Magnetic Resonance Imaging of the carotid artery can better classify subjects who would benefit from interventions. Turbo Spin Echo sequences are a class of Magnetic Resonance Imaging sequences that provide a variety of tissue contrasts. While high resolution Turbo Spin Echo images have demonstrated important details of carotid artery morphology, it is evident that pulsatile blood and wall motion related to the cardiac cycle are still significant sources of image degradation. In addition, patient motion artifacts due to the relatively long scan times of Turbo Spin Echo sequences result in an unacceptable fraction of noninterpretable studies. This dissertation presents work done to detect and correct for types of voluntary and physiological patient motion

The Role of 3 Tesla MRA in the Detection of Intracranial Aneurysms

Intracranial aneurysms constitute a common pathological entity, affecting approximately 1–8% of the general population. Their early detection is essential for their prompt treatment. Digital subtraction angiography is considered the imaging method of choice. However, other noninvasive methodologies such as CTA and MRA have been employed in the investigation of patients with suspected aneurysms. MRA is a noninvasive angiographic modality requiring no radiation exposure. However, its sensitivity and diagnostic accuracy were initially inadequate. Several MRA techniques have been developed for overcoming all these drawbacks and for improving its sensitivity. 3D TOF MRA and contrast-enhanced MRA are the most commonly employed techniques. The introduction of 3 T magnetic field further increased MRA's sensitivity, allowing detection of aneurysms smaller than 3 mm. The development of newer MRA techniques may provide valuable information regarding the flow characteristics of an aneurysm. Meticulous knowledge of MRA's limitations and pitfalls is of paramount importance for avoiding any erroneous interpretation of its findings

Techniques for Analysis and Motion Correction of Arterial Spin Labelling (ASL) Data from Dementia Group Studies

This investigation examines how Arterial Spin Labelling (ASL) Magnetic Resonance Imaging can be optimised to assist in the early diagnosis of diseases which cause dementia, by considering group study analysis and control of motion artefacts. ASL can produce quantitative cerebral blood flow maps noninvasively - without a radioactive or paramagnetic contrast agent being injected. ASL studies have already shown perfusion changes which correlate with the metabolic changes measured by Positron Emission Tomography in the early stages of dementia, before structural changes are evident. But the clinical use of ASL for dementia diagnosis is not yet widespread, due to a combination of a lack of protocol consistency, lack of accepted biomarkers, and sensitivity to motion artefacts. Applying ASL to improve early diagnosis of dementia may allow emerging treatments to be administered earlier, thus with greater effect. In this project, ASL data acquired from two separate patient cohorts ( (i) Young Onset Alzheimer’s Disease (YOAD) study, acquired at Queen Square; and (ii) Incidence and RISk of dementia (IRIS) study, acquired in Rotterdam) were analysed using a pipeline optimised for each acquisition protocol, with several statistical approaches considered including support-vector machine learning. Machine learning was also applied to improve the compatibility of the two studies, and to demonstrate a novel method to disentangle perfusion changes measured by ASL from grey matter atrophy. Also in this project, retrospective motion correction techniques for specific ASL sequences were developed, based on autofocusing and exploiting parallel imaging algorithms. These were tested using a specially developed simulation of the 3D GRASE ASL protocol, which is capable of modelling motion. The parallel imaging based approach was verified by performing a specifically designed MRI experiment involving deliberate motion, then applying the algorithm to demonstrably reduce motion artefacts retrospectively

Segmentation of patient motion from other MRI system instabilities

Patient motion causes the same image artifacts or ghosting patterns as system instabilities cause in Magnetic Resonance images (MRI). Misinterpreting a patient-motion- induced artifact as a system instability can cause unnecessary system downtime and expense while a field engineer searches for a non-existent system problem. Although the images may look the same, the original sampled data is different and can be used to determine the cause of the image artifacts. The process for segmentation is to detect the existing instabilities and bulk motion vectors within the sampled frequency-space complex data. Because the imaged object is real, the frequency-space sampled data is Hermitian and redundant information exists. The two halves of the data set are compared and statistics extracted. Vectors are created representing the instability types: magnitude, phase, and echo shift, in addition to frequency encoding and phase encoding bulk motion. Each element of the vector represents how the phase encoding view differed from the complex conjugate view. The vector is then used to correct the data to remove the instability or motion. The order used for segmentation is: magnitude, echo shift, frequency encoding bulk motion, phase, phase encoding bulk motion. Bulk motion in the frequency encoding direction can be segmented from all types of system instability. Bulk motion in the phase encoding direction can be segmented from magnitude and echo shift instabilities, but not from phase instabilities. Other types of motion like scaling and rotation are problematic since the quantized nature of the sampled data precludes their characterization. The methods developed for isolating bulk motion from system instabilities are demonstrated on synthetic data

Development of Methodologies for Diffusion-weighted Magnetic Resonance Imaging at High Field Strength

Diffusion-weighted imaging of small animals at high field strengths is a challenging prospect due to its extreme sensitivity to motion. Periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) was introduced at 9.4T as an imaging method that is robust to motion and distortion. Proton density (PD)-weighted and T2-weighted PROPELLER data were generally superior to that acquired with single-shot, Cartesian and echo planar imaging-based methods in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio and resistance to artifacts. Simulations and experiments revealed that PROPELLER image quality was dependent on the field strength and echo times specified. In particular, PD-weighted imaging at high field led to artifacts that reduced image contrast. In PROPELLER, data are acquired in progressively rotated blades in k-space and combined on a Cartesian grid. PROPELLER with echo truncation at low spatial frequencies (PETALS) was conceived as a postprocessing method that improved contrast by reducing the overlap of k-space data from different blades with different echo times. Where the addition of diffusion weighting gradients typically leads to catastrophic motion artifacts in multi-shot sequences, diffusion-weighted PROPELLER enabled the acquisition of high quality, motion-robust data. Applications in the healthy mouse brain and abdomen at 9.4T and in stroke patients at 3T are presented. PROPELLER increases the minimum scan time by approximately 50%. Consequently, methods were explored to reduce the acquisition time. Two k-space undersampling regimes were investigated by examining image fidelity as a function of degree of undersampling. Undersampling by acquiring fewer k-space blades was shown to be more robust to motion and artifacts than undersampling by expanding the distance between successive phase encoding steps. To improve the consistency of undersampled data, the non-uniform fast Fourier transform was employed. It was found that acceleration factors of up to two could be used with minimal visual impact on image fidelity. To reduce the number of scans required for isotropic diffusion weighting, the use of rotating diffusion gradients was investigated, exploiting the rotational symmetry of the PROPELLER acquisition. Fixing the diffusion weighting direction to the individual rotating blades yielded geometry and anisotropy-dependent diffusion measurements. However, alternating the orientations of diffusion weighting with successive blades led to more accurate measurements of the apparent diffusion coefficient while halving the overall acquisition time. Optimized strategies are proposed for the use of PROPELLER in rapid high resolution imaging at high field strength

VALIDATION, OPTIMIZATION, AND IMAGE PROCESSING OF SPIRAL CINE DENSE MAGNETIC RESONANCE IMAGING FOR THE QUANTIFICATION OF LEFT AND RIGHT VENTRICULAR MECHANICS

Recent evidence suggests that cardiac mechanics (e.g. cardiac strains) are better measures of heart function compared to common clinical metrics like ejection fraction. However, commonly-used parameters of cardiac mechanics remain limited to just a few measurements averaged over the whole left ventricle. We hypothesized that recent advances in cardiac magnetic resonance imaging (MRI) could be extended to provide measures of cardiac mechanics throughout the left and right ventricles (LV and RV, respectively). Displacement Encoding with Stimulated Echoes (DENSE) is a cardiac MRI technique that has been validated for measuring LV mechanics at a magnetic field strength of 1.5 T but not at higher field strengths such as 3.0 T. However, it is desirable to perform DENSE at 3.0 T, which would yield a better signal to noise ratio for imaging the thin RV wall. Results in Chapter 2 support the hypothesis that DENSE has similar accuracy at 1.5 and 3.0 T. Compared to standard, clinical cardiac MRI, DENSE requires more expertise to perform and is not as widely used. If accurate mechanics could be measured from standard MRI, the need for DENSE would be reduced. However, results from Chapter 3 support the hypothesis that measured cardiac mechanics from standard MRI do not agree with, and thus cannot be used in place of, measurements from DENSE. Imaging the thin RV wall with its complex contraction pattern requires both three-dimensional (3D) measures of myocardial motion and higher resolution imaging. Results from Chapter 4 support the hypothesis that a lower displacement-encoding frequency can be used to allow for easier processing of 3D DENSE images. Results from Chapter 5 support the hypothesis that images with higher resolution (decreased blurring) can be achieved by using more spiral interleaves during the DENSE image acquisition. Finally, processing DENSE images to yield measures of cardiac mechanics in the LV is relatively simple due to the LV’s mostly cylindrical geometry. Results from Chapter 6 support the hypothesis that a local coordinate system can be adapted to the geometry of the RV to quantify mechanics in an equivalent manner as the LV. In summary, cardiac mechanics can now be quantified throughout the left and right ventricles using DENSE cardiac MRI

Acquisition strategies for fat/water separated MRI

This thesis focuses on new ways to more efficiently acquire the signal for fat/water separated MRI, also known as Dixon methods. In paper I, the concept of dual bandwidths was introduced to improve SNR efficiency by removing dead times in a spin echo PROPELLER sequence. By correcting for the displacement of fat, we were able to improve the motion correction. This required additional considerations during reconstruction in order to avoid noise amplification, which was solved with a noise-whitening Tikhonov regularization. Paper II explores the combination of fat/water separation in k-space with partially acquired data, i.e. partial Fourier sampling. With reduced sampling coverage comes the ability of increased spatial resolution, which is often limited in fat/water imaging, particularly in gradient echo sequences. A modified POCS routine was also developed with real-valued estimates, exploiting Hermitian symmetry to improve the inverse problem conditioning in the fully sampled region. A single-TR dual-bandwidth RARE (fast/turbo spin echo) sequence without dead times was developed in Paper III, which uses partial Fourier sampling with late and early echoes to improve the chemical shift encoding. The proposed sequence can acquire images with 0.5 mm in-plane resolution without dead times, with image quality exceeding current state-of-the-art techniques. An automated selection of gradient waveforms based on Cramér-Rao bounds was implemented on the scanner. In Paper IV, the dual-bandwidth concept was generalized to continuous bandwidths. Instead of the conventional shift of a trapezoidal readout gradient, we describe a new method of encoding chemical shift by using asymmetric readout waveforms. Asymmetric readouts were implemented in a RARE sequence to completely remove dead times from multi-TR acquisitions, with typical scan time reductions of 25 %. The developed methods enable fat/water imaging with reduced scan times and increased spatial resolution, which has previously limited their use

A new discrete dipole kernel for quantitative susceptibility mapping

PURPOSE: Most approaches for quantitative susceptibility mapping (QSM) are based on a forward model approximation that employs a continuous Fourier transform operator to solve a differential equation system. Such formulation, however, is prone to high-frequency aliasing. The aim of this study was to reduce such errors using an alternative dipole kernel formulation based on the discrete Fourier transform and discrete operators. METHODS: The impact of such an approach on forward model calculation and susceptibility inversion was evaluated in contrast to the continuous formulation both with synthetic phantoms and in vivo MRI data. RESULTS: The discrete kernel demonstrated systematically better fits to analytic field solutions, and showed less over-oscillations and aliasing artifacts while preserving low- and medium-frequency responses relative to those obtained with the continuous kernel. In the context of QSM estimation, the use of the proposed discrete kernel resulted in error reduction and increased sharpness. CONCLUSION: This proof-of-concept study demonstrated that discretizing the dipole kernel is advantageous for QSM. The impact on small or narrow structures such as the venous vasculature might by particularly relevant to high-resolution QSM applications with ultra-high field MRI - a topic for future investigations. The proposed dipole kernel has a straightforward implementation to existing QSM routines