Improving acquisition speed and efficiency of advanced arterial spin labelling MRI

Magnetic resonance imaging (MRI) is a notoriously slow imaging method compared with other imaging modalities, for example computed tomography. It can, however, produce contrasts and attain biophysical information that is unattainable by other methods and is thus a valuable clinical tool. In recent years, image acquisition times have been reduced through image reconstruction methods that require less data than traditional methods. In this thesis, these kinds of methods are used and extended upon for improved acquisition speed of an MRI method that is particularly slow due to requiring multiple encoded acquisitions to produce a single composite image: arterial spin labelling (ASL). In conventional ASL, two encodings are used; one with "labelled" blood and one without "labelling", such that subtracting one from the other gives an image of just the blood signal. Angiography can be used to visualise blood flow through the arteries, and perfusion imaging to assess oxygen and nutrient supply to the tissue. Some advanced ASL methods require even more encodings that can be decoded to reveal more information about the cerebral haemodynamics. An example of such an advanced ASL method is vessel-encoded ASL, which allows for generation of separate images of blood originating from different arteries. In this thesis, modern MRI sampling and reconstruction methods are optimised for vessel-encoded ASL and other ASL variants, with the aim of bringing these modalities towards clinically feasible scan times, which would eventually allow for more information-rich assessments of the cerebrovasculature and the perfusion state of the brain in, for example, patients suffering from stroke, dementia, and arteriovenous malformations. By careful joint consideration of the multi-dimensional data, the data acquisition, and the reconstruction, very high acceleration factors can be achieved. This is demonstrated, first for vessel-encoded ASL angiography in 2D and 3D, then in similar advanced ASL methods (time-encoded ASL angiography, and combined angiography and perfusion imaging). A new radial sampling scheme is also presented and assessed on ASL angiographic data, that could have impact on imaging methods beyond ASL, in particular other dynamic MRI modalities.</p

Highly accelerated vessel-selective arterial spin labelling angiography using sparsity and smoothness constraints

Purpose: To demonstrate that vessel-selectivity in dynamic arterial spin labeling angiography can be achieved without any scan time penalty or noticeable loss of image quality compared to conventional arterial spin labeling angiography. Methods: Simulations on a numerical phantom were used to assess whether the increased sparsity of vessel-encoded angiograms compared to non-vessel-encoded angiograms alone can improve reconstruction results in a compressed sensing framework. Further simulations were performed to study whether the difference in relative sparsity between non-selective and vessel-selective dynamic angiograms were sufficient to achieve similar image quality at matched scan times in the presence of noise. Finally, data were acquired from 5 healthy volunteers to validate the technique in vivo. All data, both simulated and in vivo, were sampled in 2D using a golden angle radial trajectory and reconstructed by enforcing image domain sparsity and temporal smoothness on the angiograms in a parallel imaging and compressed sensing framework. Results: Relative sparsity was established as a primary factor governing the reconstruction fidelity. Using the proposed reconstruction scheme, differences between vessel-selective and non-selective angiography were negligible compared to the dominant factor of total scan time in both simulations and in vivo experiments at acceleration factors up to R = 34. The reconstruction quality was not heavily dependent on hand-tuning the parameters of the reconstruction. Conclusion: The increase in relative sparsity of vessel-selective angiograms compared to nonselective angiograms can be leveraged to achieve higher acceleration without loss of image quality, resulting in the acquisition of vessel-selective information at no scan time cost

Highly accelerated vessel-selective arterial spin labelling angiography using sparsity and smoothness constraints

Purpose: To demonstrate that vessel-selectivity in dynamic arterial spin labeling angiography can be achieved without any scan time penalty or noticeable loss of image quality compared to conventional arterial spin labeling angiography. Methods: Simulations on a numerical phantom were used to assess whether the increased sparsity of vessel-encoded angiograms compared to non-vessel-encoded angiograms alone can improve reconstruction results in a compressed sensing framework. Further simulations were performed to study whether the difference in relative sparsity between non-selective and vessel-selective dynamic angiograms were sufficient to achieve similar image quality at matched scan times in the presence of noise. Finally, data were acquired from 5 healthy volunteers to validate the technique in vivo. All data, both simulated and in vivo, were sampled in 2D using a golden angle radial trajectory and reconstructed by enforcing image domain sparsity and temporal smoothness on the angiograms in a parallel imaging and compressed sensing framework. Results: Relative sparsity was established as a primary factor governing the reconstruction fidelity. Using the proposed reconstruction scheme, differences between vessel-selective and non-selective angiography were negligible compared to the dominant factor of total scan time in both simulations and in vivo experiments at acceleration factors up to R = 34. The reconstruction quality was not heavily dependent on hand-tuning the parameters of the reconstruction. Conclusion: The increase in relative sparsity of vessel-selective angiograms compared to nonselective angiograms can be leveraged to achieve higher acceleration without loss of image quality, resulting in the acquisition of vessel-selective information at no scan time cost