Abstract
Arterial Spin Labelling (ASL) imaging derives a perfusion image by tracing the accumulation of magnetically labeled blood water in the brain. As the image generated has an intrinsically low signal to noise ratio (SNR), multiple measurements are routinely acquired and averaged, at a penalty of increased scan duration and opportunity for motion artefact. However, this strategy alone might be ineffective in clinical settings where the time available for acquisition is limited and patient motion are increased. This study investigates the use of an Independent Component Analysis (ICA) approach for denoising ASL data, and its potential for automation.72 ASL datasets (pseudo-continuous ASL; 5 different post-labeling delays: 400, 800, 1200, 1600, 2000 m s; total volumes = 60) were collected from thirty consecutive acute stroke patients. The effects of ICA-based denoising (manual and automated) where compared to two different denoising approaches, aCompCor, a Principal Component-based method, and Enhancement of Automated Blood Flow Estimates (ENABLE), an algorithm based on the removal of corrupted volumes. Multiple metrics were used to assess the changes in the quality of the data following denoising, including changes in cerebral blood flow (CBF) and arterial transit time (ATT), SNR, and repeatability. Additionally, the relationship between SNR and number of repetitions acquired was estimated before and after denoising the data.The use of an ICA-based denoising approach resulted in significantly higher mean CBF and ATT values (p < 0.001), lower CBF and ATT variance (p < 0.001), increased SNR (p < 0.001), and improved repeatability (p < 0.05) when compared to the raw data. The performance of manual and automated ICA-based denoising was comparable. These results went beyond the effects of aCompCor or ENABLE. Following ICA-based denoising, the SNR was higher using only 50% of the ASL-dataset collected than when using the whole raw data.The results show that ICA can be used to separate signal from noise in ASL data, improving the quality of the data collected. In fact, this study suggests that the acquisition time could be reduced by 50% without penalty to data quality, something that merits further study. Independent component classification and regression can be carried out either manually, following simple criteria, or automatically.
Highlights
The measurement of cerebral perfusion is an indispensable tool in clinical practice across a broad range of acute and chronic pathologies, such as stroke and dementia (Grade et al, 2015; Albers et al, 2018; Wolters et al, 2017)
The use of multiple post-labeling delays (PLD) in the acquisition allows the estimation of arterial transit time (ATT) values, which may improve the accuracy of the quantification of cerebral blood flow (CBF) (Wang et al, 2013; Okell et al, 2013), but may provide relevant risk stratification
This study investigates the use of Independent Component Analysis (ICA)-based denoising on clinical arterial spin labeling (ASL) data acquired in acute ischemic stroke patients
Summary
The measurement of cerebral perfusion is an indispensable tool in clinical practice across a broad range of acute and chronic pathologies, such as stroke and dementia (Grade et al, 2015; Albers et al, 2018; Wolters et al, 2017). Existing approaches include applying filters or removing image volumes deemed to be corrupted (Shirzadi et al, 2018; Tan et al, 2009) The utility of ICA to improve SNR has been explored in diffusion-weighted imaging (Arfanakis et al, 2002), and dynamic susceptibility contrast-MRI (Calamante et al, 2004). It has shown promising preliminary results when applied to pre-clinical ASL data (Wells et al, 2010). Its performance is compared to two other denoising strategies: aCompCor (Behzadi et al, 2007), a Principal Component-based method; and, Enhancement of Automated Blood Flow Estimates (ENABLE) (Shirzadi et al, 2018), an algorithm based on the removal of corrupted volumes
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