Guidelines for Developing Automated Quality Control Procedures for Brain Magnetic Resonance Images Acquired in Multi-Centre Clinical Trials (2011)

Automated quality control (QC) procedures are critical for efficiently obtaining precise quantitative brain imaging-based metrics of in vivo brain pathology. This is especially important for multi-centre clinical trials of therapeutics for neurological diseases, in which brain imaging-based metrics may be used to quantify therapeutic efficacy. While there are many different types of brain imaging methods (e.g. computed tomography, magnetic resonance imaging, positron emission tomography, etc.) that have been used to quantify different aspects of in vivo pathology (e.g. presence of tumours, brain atrophy, hydrocephalus, abnormalities in blood vessels or the extravasation of blood, the depletion of receptors available for the binding of an injected substance, abnormal brain metabolism, etc.), this Chapter will focus on the automated QC procedures required to use magnetic resonance (MR) images (MRI) to yield imaging-based metrics of in vivo brain tissue pathology. Magnetic resonance imaging is a powerful non-invasive technology that can provide in vivo images sensitive to normal and pathological brain tissue. Important strengths of MR imaging include its superior grey-matter (GM)/ white-matter (WM) tissue contrast, sensitivity to WM pathology and clinical feasibility of relatively high-resolution whole-brain imaging. In conventional brain MRI, the signal intensities arise from the different relaxation characteristics of protons in water molecules present in different brain environments following radio-frequency (RF) excitation when the brain is in a magnetic field. MRI acquisition sequences vary the timing and duration of RF excitation pulses and magnetic field gradients, yielding different contrasts (termed MRI modalities) that can highlight different aspects of brain anatomy and pathology. This is illustrated in Fig. The T1w image most clearly differentiates brain GM, WM and www.intechopen.com Applications and Experiences of Quality Control 136 cerebrospinal fluid (CSF). This high tissue contrast is the reason why T1w is often the optimal input modality, or included with other input modalities, for image-processing algorithms that classify the image voxels (volume elements) as WM, GM and CSF, which can be a critical step that precedes the quantification of biologically important brain characteristics (e.g. the volumes of the entire brain, individual brain structures, GM, WM, and abnormal WM and GM). In addition to the high tissue contrast, T1w MRI also informs on brain pathology. It has been shown that WM hypointensities on T1w MRI of MS patients are associated histopathologically with severe tissue destruction By injecting a Gd contrast agent (Gd is paramagnetic in its trivalent state), the T1w modality can be further exploited to detect increased permeability of the " blood-brain barrier" (BBB), which under normal conditions restricts the transport of substances from the circulation into the brain, thus confining the Gd contrast agent to the blood vessels and resulting in a relatively bright intensity of blood vessels large enough to be resolved by T1w imaging. Under pathological conditions (e.g. stroke, trauma, tumour, inflammation), the permeability of the BBB may be transiently increased and the Gd contrast agent will enter the brain, resulting in a relatively bright intensity in the region of the pathology. For example, in Fig. The T2w image is more sensitive to different types of WM pathology (not only the severe tissue destruction) than the T1w, exhibiting abnormally hyperintense signal in regions with pathological abnormalities such as: tissue loss, injury, incomplete repair, inflammation and scarring. Important limitations of using T2w images to quantifying brain pathology is the lack of specificity of the hyperintensities (e.g. they may be oedema that may resolve quickly, they may be irreversible tissue destruction that may never repair and result in further degeneration), and the poor CSF/ abnormal WM contrast (e.g. abnormal WM that abuts the CSF-filled ventricles cannot be reliably quantified). The latter limitation is addressed by acquiring PDw and FLAIR images, in which abnormal WM is hyperintense and CSF is hypointense. Furthermore FLAIR imaging has been shown to be more sensitive to focal WM MS pathology compared to standard T2w imaging In clinical trials of therapies for neurological diseases (e.g. multiple sclerosis and Alzheimer's disease), various MRI modalities may be acquired within a single scanning session to quantify various aspects of brain pathology, and multiple scanning sessions may be performed on each patient throughout the trial to track the changes in MRI-derived brain pathology metrics from the baseline pre-treatment state (Fig. This multiple imaging modalities at multiple timepoints for many patients paradigm to yield a snapshot of the brain pathology at a certain timepoint or to yield the dynamics of progressing/ resolving pathology, relies upon the assumption that image intensity variations are biological. Within this assumption, MRI-derived brain pathology metrics may be calculated using an imageprocessing pipeline comprised of leading edge automated techniques including image intensity normalization, co-registration of different MRI modalities, registration to brain atlases, brain tissue classification, segmentation of brain structures and types of pathology (Fig. The success of these automated image-processing techniques may be significantly affected by spatial and/ or temporal variability in the MRI intensities resulting from methodological sources including scanner software/ hardware www.intechopen.com