What is the difference between t1 and t2 lesions

It has strong underpinnings in physics which must be understood before any real sense of "how it works" is gained. What follows is a very abbreviated or "broad strokes" description of the process. A magnetic resonance system the actual machine consists of the following components: 1 A large magnet to generate the magnetic field. T1 with Contrast T1-weighting causes the nerve connections of white matter to appear white, and the congregations of neurons of gray matter to appear gray, while CSF appears dark.

Obtaining the same sort of information for T1 lesions would be of similar value when evaluating prognosis and treatment efficacy in patients with MS.

The objective of this study was to establish clinical determinants of T1 lesions in subgroups of patients with MS. The hypothesis is that T1 lesions are more frequent in late or more aggressive disease, reflecting an exhaustion of repair mechanisms that results in axonal loss.

One hundred thirty-eight patients with clinically definite MS 14 were recruited from the outpatient clinic of the Department of Neurology, University Hospital "Vrije Universiteit,"Amsterdam, the Netherlands. Informed consent was obtained from each patient after the nature of the procedures had been explained. Clinical evaluation consisted of assessment of the duration of the disease ie, the interval between the date of first symptoms and the date of examination [measured in years] , patient's age at first symptoms, disease course, 15 and the Expanded Disability Status Scale EDSS score.

Brain MRI was performed at 1. Twenty-one axial slices with an in-plane resolution of 1 mm, slice thickness of 5 mm, and an interslice gap of 0. T1 lesions were defined as regions with a signal intensity similar to or reduced to the signal intensity of gray matter and corresponding to a hyperintense region on T2-weighted MRI. Hyperintense—T2 lesions were defined as sharply demarcated regions of high signal intensity compared with surrounding brain tissue. A single observer, using home-developed, semiautomated local thresholding software, quantified the areas of previously identified brain lesions.

T1 and T2 lesion volumes in cubic centimeters were calculated by adding the area of all lesions multiplied by interslice distance. The median and interquartile range were used to describe clinical and MRI characteristics for subgroups of patients. In patients with PP MS the first symptoms usually occur at an older age.

In our study only 3 patients experienced their first symptoms when they were aged 25 years or younger; patients with PP MS, therefore, were classified with those who were 39 years or younger at first symptoms and with those who were older than 40 years at first symptoms to provide a homogeneous division.

Multiple linear regression analysis was performed to determine the relationship between T1 and T2 lesion volume and the clinical characteristics in the subgroup of patients. A forward stepwise procedure was used to find out which clinical characteristics were most influential probability of F to enter,. Independent variables included were age at first symptoms before or after the age of 25 years, EDSS score high or low disability scores , sex, and disease duration.

Age at first symptoms before or after the age of 40 years was used as an independent variable in patients with PP MS, and the cutoff level for EDSS scores was 5. A Spearman rank correlation coefficient r s was used to determine the associations between MRI parameters and clinical characteristics. Since a large variance in T1 lesion volume is present between the subgroups of patients with MS, only subgroup analysis was performed to evaluate the clinical characteristics of T1 lesions.

A division has been made according to sex, level of disability, and age at first symptoms. No difference was shown for T2 lesion volume. In contrast, a trend toward a higher T1 lesion volume was found 3. T2 lesion volume was higher for patients with an earlier onset of disease A trend toward a significant difference in T1 lesion volume 0. No significant difference was present for T2 lesion volume 2. In the subgroups with RR MS, no meaningful model could be found. Our findings suggest that a proportion of the periventricular T2 lesion volume may arise from mechanisms other than those associated with early breakdown of the blood-brain barrier leading to T1 Gd enhancement.

Abstract It is generally believed that most T2-weighted T2 lesions in the central white matter of patients with multiple sclerosis begin with a variable period of T1-weighted T1 gadolinium Gd enhancement and that T1 Gd-enhancing and T2 lesions represent stages of a single pathological process. Publication types Research Support, Non-U. In this study, we showed that i T1 hypointense lesions are more easily detectable using an FSPGR sequence than an SE sequence; ii Almost all of the T2 hyperintense lesions were easily detectable on FSPGR, but only less than half of the lesions were easily detected using a SE sequence; iii Interestingly, lesions not standing out on SE sequences are the one which are clinically more important; and iv the very same lesions are correlated with brain atrophy.

Our results are interesting in the view of earlier investigations suggesting a stronger correlation between clinical variables and SE T1 hypointense lesions than T2 hyperintense lesions. Truyen and colleagues showed that hypointense lesion load on the SE images correlated with EDSS, but T2 hyperintense lesion load did not.

Enzinger presented univariate correlation between the black-hole ratio on SE personal communication at baseline and MS severity score 10 years later, but the association did not hold if multiple regression model was used The baseline black-hole ratio was a significant predictor of conversion to secondary-progressive multiple sclerosis SPMS , but when baseline clinical variables were inserted into the models no significant predictive value remained among the investigated MRI parameters Similarly, in Giorgio's study, both the volume and count of baseline T1w and T2w lesions correlated with the EDSS scores, but the combination of baseline count and volume of T1w lesions predicted more precisely the year EDSS worsening 8.

Moreover, most of the studies describing correlation between clinical parameters and black-hole lesion load were carried out on SPMS patients 26 , We showed that the volume and number of T1 hypointense lesions in Cluster 2 significantly correlated with the normalized brain volumes of the patients.

There are only a few publications examining the relationship between BHs and cerebral atrophy. Sailer et al. Paolillo et al. Both studies suggested that the link between the volume increase of T1 hypointense lesions and cerebral atrophy is that local tissue damages and axonal loss might lead to increased neuronal packing density as well as contraction scarring caused by gliosis.

Our results are concordant with these former studies in that there was no correlation between Cluster 1 lesion number or volume with the clinical parameters. These are the lesions that were well differentiated on SE sequence from surrounding white matter, most comparable to traditional black-holes detected on SE images. The importance of our findings is emphasized by Tam's studies 16 , The consecutive study in a larger SPMS group showed the opposite, best correlation between MSFC when the brightest part of the hypointense lesions were also considered most inclusive lesion masks These results were strengthened by incorporating T1 relaxation times in the analysis 3.

While these studies only included SE images personal communication , the importance of intensity variation of T1 hypointense lesions is clear. Along those lines, Adusumilli used a weighted lesion burden in which intensity values closer to CSF contribute most to the T1 hypointense lesion burden Their measure shows good correlation with clinical and cognitive functions of the patients.

The importance of the intensity variation in the BHs is also supported by the histopathological studies. The degree of hypointensity correlated strongly with the axonal density in the lesions but not with the degree of demyelination or the number of reactive astrocytes Fisher found that the contrast ratio of the T1 and MTR images lesion intensity compared to the mean NAWM intensity highly correlated with the axon count 9.

Importantly, chronic inactive lesions had the lowest contrast ratio darkest lesions as compared to active and chronic active lesions. The question arises: Why did Cluster 2, with the relatively lighter lesions, show correlation with clinical measures, if the most severe tissue destruction is at the darkest part of the hypointense lesions? According to Fisher's results, the most hypointense part of the lesions are the inactive chronic lesions 9.

One might speculate that these lesions are the oldest; hence, the most time was available for compensatory mechanisms and neural plasticity. Here we used an automatic clustering approach to identify different types of lesions based on median intensity values on commonly used sequences. There were already studies to classify lesions, but some of those used images not included in the standard clinical routine e. Our work is not without limitations. The different spatial resolution of the two sequences might introduce a bias, but using these sequences we were investigating a real-life problem.

Investigating various patient populations RRMS and SPMS and including patients with contrast-enhancing active lesion could further elaborate our findings. The clinical relevance of the intensity variation of the hypointense lesion is critical. Further studies are warranted to find a standardization approach and cutoff values for the FSPGR images that could result in reducing the number of measurements e. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Gray matter atrophy to explain subclinical oculomotor deficit in multiple sclerosis. Front Neurol. Barkhof F.