Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis

Focal lesions and brain atrophy are the most extensively studied aspects of multiple sclerosis (MS), but the image acquisition and analysis techniques used can be further improved, especially those for studying within-patient changes of lesion load and atrophy longitudinally. Improved accuracy and sensitivity will reduce the numbers of patients required to detect a given treatment effect in a trial, and ultimately, will allow reliable characterization of individual patients for personalized treatment. Based on open issues in the field of MS research, and the current state of the art in magnetic resonance image analysis methods for assessing brain lesion load and atrophy, this paper makes recommendations to improve these measures for longitudinal studies of MS. Briefly, they are (1) images should be acquired using 3D pulse sequences, with near-isotropic spatial resolution and multiple image contrasts to allow more comprehensive analyses of lesion load and atrophy, across timepoints. Image artifacts need special attention given their effects on image analysis results. (2) Automated image segmentation methods integrating the assessment of lesion load and atrophy are desirable. (3) A standard dataset with benchmark results should be set up to facilitate development, calibration, and objective evaluation of image analysis methods for MS

Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy

Introduction Despite the recognized importance of atrophy in multiple sclerosis (MS), methods for its quantification have been mostly restricted to the research domain. Recently, a CE labelled and FDA approved MS-specific atrophy quantification method, MSmetrix, has become commercially available. Here we perform a validation of MSmetrix against established methods in simulated and in vivo MRI data. Methods Whole-brain and gray matter (GM) volume were measured with the cross-sectional pipeline of MSmetrix and compared to the outcomes of FreeSurfer (cross-sectional pipeline), SIENAX and SPM. For this comparison we investigated 20 simulated brain images, as well as in vivo data from 100 MS patients and 20 matched healthy controls. In fifty of the MS patients a second time point was available. In this subgroup, we additionally analyzed the whole-brain and GM volume change using the longitudinal pipeline of MSmetrix and compared the results with those of FreeSurfer (longitudinal pipeline) and SIENA. Results In the simulated data, SIENAX displayed the smallest average deviation compared with the reference whole-brain volume (+ 19.56 ± 10.34 mL), followed by MSmetrix (− 38.15 ± 17.77 mL), SPM (− 42.99 ± 17.12 mL) and FreeSurfer (− 78.51 ± 12.68 mL). A similar pattern was seen in vivo. Among the cross-sectional methods, Deming regression analyses revealed proportional errors particularly in MSmetrix and SPM. The mean difference percentage brain volume change (PBVC) was lowest between longitudinal MSmetrix and SIENA (+ 0.16 ± 0.91%). A strong proportional error was present between longitudinal percentage gray matter volume change (PGVC) measures of MSmetrix and FreeSurfer (slope = 2.48). All longitudinal methods were sensitive to the MRI hardware upgrade that occurred during the time of the study. Conclusion MSmetrix, FreeSurfer, FSL and SPM show differences in atrophy measurements, even at the whole-brain level, that are large compared to typical atrophy rates observed in MS. Especially striking are the proportional errors between methods. Cross-sectional MSmetrix behaved similarly to SPM, both in terms of mean volume difference as well as proportional error. Longitudinal MSmetrix behaved most similar to SIENA. Our results indicate that brain volume measurement and normalization from T1-weighted images remains an unsolved problem that requires much more attention

Determining the partial volumes of brain tissue from spectroscopy voxels

MS tai magneettispektroskopia on kuvantamistekniikka, jolla saadaan tietoa kudosten metaboli ittikonsentraatioista tietystä tilavuudesta. Näistä konsentraatioarvoista voidaan tehdä potilaan diagnostiikkaan vaikuttavia johtopäätöksiä. Tekniikkaa voidaan käyttää myös aivokuvantamisessa. Aivot koostuvat pääasiassa kolmesta kudoksesta, jotka ovat selkäydinneste, valkoinen ja harmaa aine. MS:n kannalta on tärkeä tietää näiden aivokudosten suhteelliset tilavuusosuudet, koska tekni ikan resoluutio on huomattavasti pienempi kuin perinteisen magneettikuvantamisen. Jokaisella kudostyypillä on erilainen metaboliittiprofiili. Saadakseen merkityksellistä dataa, on otettava huomioon kudosten suhteelliset määrät. Tämä tieto voidaan saada segmentoimalla magneettiku vauksella muodostetut kuvat. Tässä työssä luotiin ohjelma, joka pystyy määrittämään suhteelliset tilavuusosuudet kuvauslait teen tuottamasta .rda-tiedostosta ja Nifti-tiedostosta. Nifti-tiedosto on magneettikuvauksella muo dostettu kolmiulotteinen kuvatiedosto ja .rda-tiedosto on valmistajan raakadataformaatti spek troskopiadatalle, jossa on tarvittava informaatio valitusta tilavuudesta. Ohjelma on toteutettu käyttäen Python ja JavaScript ohjelmointikieliä sekä FSL:n eri toimintoja. FSL on kattava kirjasto analyysityökaluja, joka on tarkoitettu aivojen kuvantamisdatan käsittelyyn. Ohjelman toimintavaiheet ovat: valitun tilavuuden koordinaattien määrittäminen FSL:n vokseli koordinaateissa, maskin luominen oikeaan orientaatioon ja sijaintiin, aivokudoksen erottaminen käyttäen FSL:n siihen tarkoitettua työkalua (BET), kuvan segmentointi käyttäen FSL:n segmen tointi työkalua (FAST), maskin rekisteröinti segmentoituihin kuviin ja suhteellisten tilavuuksien laskeminen. Ohjelmaa testattiin Meilahden Kolmiosairaalassa tätä työtä varten kuvattuun dataan. Dataan ku ului viisi eri spektroskopiatilavuutta eri osista aivoja ja T1-painoitteinen aivokuva. Ohjelmalle an nettiin tarvittavat tiedot .rda-tiedoston muodossa ja T1-painoitteinen aivokuva Nifti-tiedoston muo dossa. Ohjelma määritti näiden tiedostojen perusteella suhteelliset tilavuusosuudet jokaisesta vii destä tilavuudesta. Aivojen eri alueiden suhteellisissa tilavuusosuuksissa on yksilöllisiä vaihteluita, joten absoluuttista vertailuarvoa ei voi määrittää. Tulokset vastasivat valittujen tilavuuksien mah dollisia suhteellisia tilavuusosuuksia. Kehitettyä ohjelmaa voidaan käyttää tutkimuksissa, jotka tarvitsevat aivokudosten suhteelliset tilavuusosuudet spektroskopiatilavuudesta. Kliinisen käytön mahdollisuus vaatisi ohjelmalta DICOM-standardi tukea.MRS or magnetic resonance spectroscopy is an imagining technique which can be used to gain information about the metabolite concentration within a certain volume of interest. This can be used for example in brain imagining. The brain consists of three main types of tissue: cerebrospinal fluid, white and gray matter. It is important to know the different volume fractions of these tissues as the resolution in MRS is significantly lower than that of magnetic resonance imagining (MRI). The tissues all have different metabolite profiles and in order to get meaningful data the volume fractions need to be taken into account. This information can be gained from the segmentation of an image formed by using MRI. In this work a software tool was created to find these volume fractions with the input of a .rda file that is created by the scanner and Nifti file. The Nifti file is the image formed by using MRI and the .rda file is the manufacturers raw data format for spectroscopy data which has the relevant information about the volumes of interest. The software tool was created using Python and JavaScript programming languages and different functions of FSL. FSL is a comprehensive library of analysis tools used in brain imaging data processing. The steps for the software tool are: determining the coordinates of the volume of interest in FSL voxel coordinates, creating a mask in the correct orientation and location, removing non-brain tissue from the image using FSL’s tool tailored for that purpose (BET), segmenting the image using FSL’s segmenting tool (FAST), registering the mask on the segmented images and calculating the volume fractions. The software tool was tested on imaging data that was obtained at Meilahti Kolmiosairaala for the purpose of the testing. The testing data set included five different spectroscopy volumes from different parts of the brain and a T1 weighted image. The software tool was given the relevant information about the volume of interest in the form of a .rda file and the T1 weighted image in the form of a Nifti file. The software tool then determined the different volume fractions from all of the five volumes of interest. There is variation on the volume fraction of different brain areas within different brains and it is not possible to have an absolute reference value. The results of the test corresponded to the possible volume fractions that can be expected from the volumes in question

Urgent challenges in quantification and interpretation of brain grey matter atrophy in individual MS patients using MRI

Atrophy of the brain grey matter (GM) is an accepted and important feature of multiple sclerosis (MS). However, its accurate measurement is hampered by various technical, pathological and physiological factors. As a consequence, it is challenging to investigate the role of GM atrophy in the disease process as well as the effect of treatments that aim to reduce neurodegeneration. In this paper we discuss the most important challenges currently hampering the measurement and interpretation of GM atrophy in MS. The focus is on measurements that are obtained in individual patients rather than on group analysis methods, because of their importance in clinical trials and ultimately in clinical care. We discuss the sources and possible solutions of the current challenges, and provide recommendations to achieve reliable measurement and interpretation of brain GM atrophy in MS

Automated brain segmentation methods for clinical quality MRI and CT images

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder associated with brain tissue loss. Accurate estimation of this loss is critical for the diagnosis, prognosis, and tracking the progression of AD. Structural magnetic resonance imaging (sMRI) and X-ray computed tomography (CT) are widely used imaging modalities that help to in vivo map brain tissue distributions. As manual image segmentations are tedious and time-consuming, automated segmentation methods are increasingly applied to head MRI and head CT images to estimate brain tissue volumes. However, existing automated methods can be applied only to images that have high spatial resolution and their accuracy on heterogeneous low-quality clinical images has not been tested. Further, automated brain tissue segmentation methods for CT are not available, although CT is more widely acquired than MRI in the clinical setting. For these reasons, large clinical imaging archives are unusable for research studies. In this work, we identify and develop automated tissue segmentation and brain volumetry methods that can be applied to clinical quality MRI and CT images. In the first project, we surveyed the current MRI methods and validated the accuracy of these methods when applied to clinical quality images. We then developed CTSeg, a tissue segmentation method for CT images, by adopting the MRI technique that exhibited the highest reliability. CTSeg is an atlas-based statistical modeling method that relies on hand-curated features and cannot be applied to images of subjects with different diseases and age groups. Advanced deep learning-based segmentation methods use hierarchical representations and learn complex features in a data-driven manner. In our final project, we develop a fully automated deep learning segmentation method that uses contextual information to segment clinical quality head CT images. The application of this method on an AD dataset revealed larger differences between brain volumes of AD and control subjects. This dissertation demonstrates the potential of applying automated methods to large clinical imaging archives to answer research questions in a variety of studies