Body MRI artifacts in clinical practice: a physicist\u27s and radiologist\u27s perspective.

The high information content of MRI exams brings with it unintended effects, which we call artifacts. The purpose of this review is to promote understanding of these artifacts, so they can be prevented or properly interpreted to optimize diagnostic effectiveness. We begin by addressing static magnetic field uniformity, which is essential for many techniques, such as fat saturation. Eddy currents, resulting from imperfect gradient pulses, are especially problematic for new techniques that depend on high performance gradient switching. Nonuniformity of the transmit radiofrequency system constitutes another source of artifacts, which are increasingly important as magnetic field strength increases. Defects in the receive portion of the radiofrequency system have become a more complex source of problems as the number of radiofrequency coils, and the sophistication of the analysis of their received signals, has increased. Unwanted signals and noise spikes have many causes, often manifesting as zipper or banding artifacts. These image alterations become particularly severe and complex when they are combined with aliasing effects. Aliasing is one of several phenomena addressed in our final section, on artifacts that derive from encoding the MR signals to produce images, also including those related to parallel imaging, chemical shift, motion, and image subtraction

"MASSIVE" Brain Dataset: Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation

PURPOSE: In this work, we present the MASSIVE (Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation) brain dataset of a single healthy subject, which is intended to facilitate diffusion MRI (dMRI) modeling and methodology development. METHODS: MRI data of one healthy subject (female, 25 years) were acquired on a clinical 3 Tesla system (Philips Achieva) with an eight-channel head coil. In total, the subject was scanned on 18 different occasions with a total acquisition time of 22.5 h. The dMRI data were acquired with an isotropic resolution of 2.5 mm(3) and distributed over five shells with b-values up to 4000 s/mm(2) and two Cartesian grids with b-values up to 9000 s/mm(2) . RESULTS: The final dataset consists of 8000 dMRI volumes, corresponding B0 field maps and noise maps for subsets of the dMRI scans, and ten three-dimensional FLAIR, T1 -, and T2 -weighted scans. The average signal-to-noise-ratio of the non-diffusion-weighted images was roughly 35. CONCLUSION: This unique set of in vivo MRI data will provide a robust framework to evaluate novel diffusion processing techniques and to reliably compare different approaches for diffusion modeling. The MASSIVE dataset is made publically available (both unprocessed and processed) on www.massive-data.org. Magn Reson Med, 2016

Intelligent Imaging of Perfusion Using Arterial Spin Labelling

Arterial spin labelling (ASL) is a powerful magnetic resonance imaging technique, which can be used to noninvasively measure perfusion in the brain and other organs of the body. Promising research results show how ASL might be used in stroke, tumours, dementia and paediatric medicine, in addition to many other areas. However, significant obstacles remain to prevent widespread use: ASL images have an inherently low signal to noise ratio, and are susceptible to corrupting artifacts from motion and other sources. The objective of the work in this thesis is to move towards an "intelligent imaging" paradigm: one in which the image acquisition, reconstruction and processing are mutually coupled, and tailored to the individual patient. This thesis explores how ASL images may be improved at several stages of the imaging pipeline. We review the relevant ASL literature, exploring details of ASL acquisitions, parameter inference and artifact post-processing. We subsequently present original work: we use the framework of Bayesian experimental design to generate optimised ASL acquisitions, we present original methods to improve parameter inference through anatomically-driven modelling of spatial correlation, and we describe a novel deep learning approach for simultaneous denoising and artifact filtering. Using a mixture of theoretical derivation, simulation results and imaging experiments, the work in this thesis presents several new approaches for ASL, and hopefully will shape future research and future ASL usage

Robust Magnetic Resonance Imaging of Short T2 Tissues

Tissues with short transverse relaxation times are defined as ‘short T2 tissues’, and short T2 tissues often appear dark on images generated by conventional magnetic resonance imaging techniques. Common short T2 tissues include tendons, meniscus, and cortical bone. Ultrashort Echo Time (UTE) pulse sequences can provide morphologic contrasts and quantitative maps for short T2 tissues by reducing time-of-echo to the system minimum (e.g., less than 100 us). Therefore, UTE sequences have become a powerful imaging tool for visualizing and quantifying short T2 tissues in many applications. In this work, we developed a new Flexible Ultra Short time Echo (FUSE) pulse sequence employing a total of thirteen acquisition features with adjustable parameters, including optimized radiofrequency pulses, trajectories, choice of two or three dimensions, and multiple long-T2 suppression techniques. Together with the FUSE sequence, an improved analytical density correction and an auto-deblurring algorithm were incorporated as part of a novel reconstruction pipeline for reducing imaging artifacts. Firstly, we evaluated the FUSE sequence using a phantom containing short T2 components. The results demonstrated that differing UTE acquisition methods, improving the density correction functions and improving the deblurring algorithm could reduce the various artifacts, improve the overall signal, and enhance short T2 contrast. Secondly, we applied the FUSE sequence in bovine stifle joints (similar to the human knee) for morphologic imaging and quantitative assessment. The results showed that it was feasible to use the FUSE sequence to create morphologic images that isolate signals from the various knee joint tissues and carry out comprehensive quantitative assessments, using the meniscus as a model, including the mappings of longitudinal relaxation (T1) times, quantitative magnetization transfer parameters, and effective transverse relaxation (T2*) times. Lastly, we utilized the FUSE sequence to image the human skull for evaluating its feasibility in synthetic computed tomography (CT) generation and radiation treatment planning. The results demonstrated that the radiation treatment plans created using the FUSE-based synthetic CT and traditional CT data were able to present comparable dose calculations with the dose difference of mean less than a percent. In summary, this thesis clearly demonstrated the need for the FUSE sequence and its potential for robustly imaging short T2 tissues in various applications

Dual-energy imaging in stroke : feasibility of dual-layer detector cone-beam computed tomography

Background: Dual-energy computed tomography (DECT) is increasingly available and used in the standard diagnostic setting of ischemic stroke patients. For stroke patients with suspected large vessel occlusion, cone-beam computed tomography (CBCT) in the interventional suite could be an alternative to CT to shorten door to thrombectomy time. This approach could potentially lead to an improved patient outcome. However, image quality in CBCT is typically limited by artifacts and poor differentiation between gray and white matter. A dual-layer detector CBCT (DL-CBCT) system could be used to separate photon energy spectra with the potential to increase visibility of clinically relevant features, and acquire additional information. Purpose: Paper I evaluated how a range of DECT virtual monoenergetic images (VMI) impact identification of early ischemic changes, compared to conventional polyenergetic CT images. Paper II characterized the performance of a novel DLCBCT system with regards to clinically relevant imaging features. Paper III & IV investigated if DL-CBCT VMIs are sufficient for stroke diagnosis in the interventional suite, compared to reference standard CT. Methods: Paper I was a retrospective single-center study including consecutive patients presenting with acute ischemic stroke caused by an occlusion of the intracranial internal carotid artery or proximal middle cerebral artery. Automated Alberta Stroke Program Early Computed Tomography Score (ASPECTS) results from conventional images and 40-120 keV VMI were generated and compared to reference standard CT ASPECTS. In paper II, a prototype dual-layer detector was fitted into a commercial interventional C-arm CBCT system to enable dual-energy acquisitions. Metrics for spatial resolution, noise and uniformity were gathered. Clinically relevant tissue and iodine substitutes were characterized in terms of effective atomic numbers and electron densities. Iodine quantification was performed and virtual non-contrast (VNC) images were evaluated. VMIs were reconstructed and used for CT number estimation and evaluation of contrast-to-noise ratios (CNR) in relevant tissue pairings. In paper III and IV, a prospective single-center study enrolled consecutive participants with ischemic or hemorrhagic stroke on CT. In paper III, hemorrhage detection accuracy, ASPECTS accuracy, subjective and objective image quality were evaluated on non-contrast DL-CBCT 75 keV VMI and compared to reference standard CT. In paper IV, intracranial arterial segment vessel visibility and artifacts were evaluated on intravenous DL-CBCT angiography (DL-CBCTA) 70 keV VMI and compared to CT angiography (CTA). In both paper III and IV, non-inferiority was determined by the exact binomial test with a one-sided lower performance boundary set to 80% (98.75% CI). Main results: In paper I, 24 patients were included. 70 keV VMI had the highest region-based ASPECTS accuracy (0.90), sensitivity (0.82) and negative predictive value (0.94), whereas 40 keV VMI had the lowest accuracy (0.77), sensitivity (0.34) and negative predictive value (0.80). In paper II, the prototype and commercial CBCT had a similar spatial resolution and noise using the same standard reconstruction. For all tissue substitutes, the mean accuracy in effective atomic number was 98.2% (SD 1.2%) and 100.3% (SD 0.9%) for electron density. Iodine quantification had a mean difference of -0.1 (SD 0.5) mg/ml compared to the true concentrations. For VNC images, iodine substitutes with blood averaged 43.2 HU, blood only 44.8 HU, iodine substitutes with water 2.6 HU. A noise-suppressed dataset showed a CNR peak at 40 keV VMI and low at 120 keV VMI. In the same dataset without noise suppression, peak CNR was seen at 70 keV VMI and a low at 120 keV VMI. CT numbers of various clinically relevant objects generally matched the calculated CT number in a wide range of VMIs. In paper III, 27 participants were included. One reader missed a small bleeding, however all hemorrhages were detected in the majority analysis (100% accuracy, CI lower boundary 86%, p=0.002). ASPECTS majority analysis had 90% accuracy (CI lower boundary 85%, p<0.001), sensitivity was 66% (individual readers 67%, 69% and 76%), specificity was 97% (97%, 96% and 89%). Subjective and objective image quality metrics were inferior to CT. In paper IV, 21 participants had matched image sets. After excluding examinations with scan issues, all readers considered DL-CBCTA non-inferior to CTA (CI boundary 93%, 84%, 80%, respectively), when assessing arteries relevant in candidates for intracranial thrombectomy. Artifacts were more prevalent compared to CTA. Conclusions: In paper I, automated 70 keV VMI ASPECTS had the highest diagnostic accuracy, sensitivity and negative predictive value overall. Different VMI energy levels impact the identification of early ischemic changes on DECT. In paper II, the DL-CBCT prototype system showed comparable technical metrics to a commercial CBCT system, while offering dual-energy capability. The dual-energy images indicated a consistent ability to separate and characterize clinically relevant tissues, blood and iodine. Thus, the DL-CBCT system could find utility in the diagnostic setting. In paper III, non-contrast DL-CBCT 75 keV VMI showed non-inferior hemorrhage detection and ASPECTS accuracy to CT. However, image quality was inferior compared to CT, and visualization of small subarachnoid hemorrhages after treatment remains a challenge. In the same stroke cohort, paper IV showed non-inferior vessel visibility for DL-CBCTA 70 keV VMI compared to CTA under certain conditions. Specifically, the prototype system had a long scan time and was not capable of bolus tracking which resulted in scan issues. After excluding participants with such issues, DL-CBCTA 70 keV VMI were found non-inferior to CTA. In summary, the findings of this thesis indicate that DL-CBCT may be sufficient for stroke assessment in the interventional suite with the potential to bypass CT in patients eligible for thrombectomy. However, issues related to the prototype system and the visualization of small hemorrhages highlight the need of further development

Blind source separation for clutter and noise suppression in ultrasound imaging:review for different applications

Blind source separation (BSS) refers to a number of signal processing techniques that decompose a signal into several 'source' signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review the existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound super-resolution, contrast quantification, and speckle tracking. For all applications, this was done in silico, in vitro, and in vivo. We found that the critical step in BSS filtering is the identification of components containing the desired signal and highlighted the value of a priori domain knowledge to define effective criteria for signal component selection