MRI in the assessment and monitoring of multiple sclerosis: an update on best practice

Magnetic resonance imaging (MRI) has developed into the most important tool for the diagnosis and monitoring of multiple sclerosis (MS). Its high sensitivity for the evaluation of inflammatory and neurodegenerative processes in the brain and spinal cord has made it the most commonly used technique for the evaluation of patients with MS. Moreover, MRI has become a powerful tool for treatment monitoring, safety assessment as well as for the prognostication of disease progression. Clinically, the use of MRI has increased in the past couple decades as a result of improved technology and increased availability that now extends well beyond academic centers. Consequently, there are numerous studies supporting the role of MRI in the management of patients with MS. The aim of this review is to summarize the latest insights into the utility of MRI in MS

Comparison of two different methods of image analysis for the assessment of microglial activation in patients with multiple sclerosis using (R)-[N-methyl-carbon-11]PK11195.

Chronic active multiple sclerosis (MS) lesions have a rim of activated microglia/macrophages (m/M) leading to ongoing tissue damage, and thus represent a potential treatment target. Activation of this innate immune response in MS has been visualized and quantified using PET imaging with [11C]-(R)-PK11195 (PK). Accurate identification of m/M activation in chronic MS lesions requires the sensitivity to detect lower levels of activity within a small tissue volume. We assessed the ability of kinetic modeling of PK PET data to detect m/M activity in different central nervous system (CNS) tissue regions of varying sizes and in chronic MS lesions. Ten patients with MS underwent a single brain MRI and two PK PET scans 2 hours apart. Volume of interest (VOI) masks were generated for the white matter (WM), cortical gray matter (CGM), and thalamus (TH). The distribution volume (VT) was calculated with the Logan graphical method (LGM-VT) utilizing an image-derived input function (IDIF). The binding potential (BPND) was calculated with the reference Logan graphical method (RLGM) utilizing a supervised clustering algorithm (SuperPK) to determine the non-specific binding region. Masks of varying volume were created in the CNS to assess the impact of region size on the various metrics among high and low uptake regions. Chronic MS lesions were also evaluated and individual lesion masks were generated. The highest PK uptake occurred the TH and lowest within the WM, as demonstrated by the mean time activity curves. In the TH, both reference and IDIF based methods resulted in estimates that did not significantly depend on VOI size. However, in the WM, the test-retest reliability of BPND was significantly lower in the smallest VOI, compared to the estimates of LGM-VT. These observations were consistent for all chronic MS lesions examined. In this study, we demonstrate that BPND and LGM-VT are both reliable for quantifying m/M activation in regions of high uptake, however with blood input function LGM-VT is preferred to assess longitudinal m/M activation in regions of relatively low uptake, such as chronic MS lesions

Lesion Features on Magnetic Resonance Imaging Discriminate Multiple Sclerosis Patients

Background Magnetic resonance imaging (MRI) provides insight into various pathological processes in multiple sclerosis (MS) and may provide insight into patterns of damage among patients. Objective We sought to determine if MRI features have clinical discriminative power among a cohort of MS patients. Methods Ninety-six relapsing remitting and seven progressive MS patients underwent myelin water fraction (MWF) imaging and conventional MRI for cortical thickness and thalamic volume. Patients were clustered based on lesion level MRI features using an agglomerative hierarchical clustering algorithm based on principal component analysis (PCA). Results One hundred and three patients with 1689 MS lesions were analyzed. PCA on MRI features demonstrated that lesion MWF and volume distributions (characterized by 25th, 50th, and 75th percentiles) accounted for 87% of the total variability based on four principal components. The best hierarchical cluster confirmed two distinct patient clusters. The clustering features in order of importance were lesion median MWF, MWF 25th, MWF 75th, volume 75th percentiles, median individual lesion volume, total lesion volume, cortical thickness, and thalamic volume (all p value

Distribution volume differences with test-retest studies (n = 10).

<p>Illustrate correlation of values of LGM_V_T between test and retest.</p

Image-derived input function characteristics.

<p>(A) Time activity curves of the image-derived input function with test-retest studies were shown. There was no significant difference (p = 0.001). (B) It was shown area under the curves of IDIF with test-retest studies and was not shown any significant difference.</p

Subject characteristic.

<p>Subject characteristic.</p

Reference tissue curve characteristics.

<p>(A) Mean TAC curves for the reference curves between test-retest studies (n = 6). (B) Mean AUC of the reference curves between test and retest.</p

V_T and BP_ND values for all VOIs.

<p>V_T and BP_ND values for all VOIs.</p

Volume of interests—Volume information.

<p>Volume of interests—Volume information.</p