Characterizing the microstructural basis of “unidentified bright objects” in neurofibromatosis type 1:A combined in vivo multicomponent T2 relaxation and multi-shell diffusion MRI analysis

AbstractIntroductionThe histopathological basis of “unidentified bright objects” (UBOs) (hyperintense regions seen on T2-weighted magnetic resonance (MR) brain scans in neurofibromatosis-1 (NF1)) remains unclear. New in vivo MRI-based techniques (multi-exponential T2 relaxation (MET2) and diffusion MR imaging (dMRI)) provide measures relating to microstructural change. We combined these methods and present previously unreported data on in vivo UBO microstructure in NF1.Methods3-Tesla dMRI data were acquired on 17 NF1 patients, covering 30 white matter UBOs. Diffusion tensor, kurtosis and neurite orientation and dispersion density imaging parameters were calculated within UBO sites and in contralateral normal appearing white matter (cNAWM). Analysis of MET2 parameters was performed on 24 UBO–cNAWM pairs.ResultsNo significant alterations in the myelin water fraction and intra- and extracellular (IE) water fraction were found. Mean T2 time of IE water was significantly higher in UBOs. UBOs furthermore showed increased axial, radial and mean diffusivity, and decreased fractional anisotropy, mean kurtosis and neurite density index compared to cNAWM. Neurite orientation dispersion and isotropic fluid fraction were unaltered.ConclusionOur results suggest that demyelination and axonal degeneration are unlikely to be present in UBOs, which appear to be mainly caused by a shift towards a higher T2-value of the intra- and extracellular water pool. This may arise from altered microstructural compartmentalization, and an increase in ‘extracellular-like’, intracellular water, possibly due to intramyelinic edema. These findings confirm the added value of combining dMRI and MET2 to characterize the microstructural basis of T2 hyperintensities in vivo

Developing advanced mathematical models for detecting abnormalities in 2D/3D medical structures.

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. The main advantage of the proposed tracking method over traditional texture-based methods is its ability to track the movement and rotation of the LV wall based on tracking the geometric features of the inner, mid-, and outer walls of the LV. This overcomes noise sources that come from scanner and heart motion. To identify the abnormalities in the CC from brain MRI, the CCs are aligned using a rigid registration model and are segmented using a shape-appearance model. Then, they are mapped to a simple unified space for analysis. This work introduces a novel cylindrical mapping model, which is conformal (i.e., one to one transformation and bijective), that enables accurate 3D shape analysis of the CC in the cylindrical domain. The framework can detect abnormalities in all divisions of the CC (i.e., splenium, rostrum, genu and body). In addition, it offers a whole 3D analysis of the CC abnormalities instead of only area-based analysis as done by previous groups. The initial classification results based on the centerline length and CC thickness suggest that the proposed CC shape analysis is a promising supplement to the current techniques for diagnosing dyslexia. The proposed techniques in this dissertation have been successfully tested on complex synthetic and MR images and can be used to advantage in many of today’s clinical applications of computer-assisted medical diagnostics and intervention

On the track of the brain's microstructure : myelin water imaging using quantitative MRI

Conventional quantitative magnetic resonance imaging (MRI), for example monoexponential determination of the relaxation times T1 and T2, is sensitive to the various pathologies of myelinated tissue in the brain. However, it gives relatively unspecific information about the underlying nature of the disease. A parameter that directly correlates with the integrity of the myelin sheath is the so-called myelin water fraction (MWF). Based on multi-component analysis of non-invasive quantitative MRI measurements, mapping of the MWF becomes feasible and proved to be useful for studying demyelination and remyelination processes in the course of multiple sclerosis (MS) and other myelin related pathologies. Common myelin water imaging techniques often suffer from a lack of volume coverage due to their 2D acquisition schemes. This thesis focuses on the development of new myelin water mapping procedures, especially on fast 3D MRI measurements that provide whole brain coverage. In chapter 2, an MWF mapping technique based on balanced steady-state free precession (bSSFP) sequences is introduced. An extended bSSFP signal equation, which is based on a two-pool water model describing brain tissue, is derived to determine typical multi-compartment parameters, including the MWF, of healthy subjects. Possible influences of magnetization transfer effects, infinite radiofrequency pulses and B0/B1 inhomogeneities are discussed extensively. Chapter 3 introduces a 3D acquisition scheme based on multi-gradient-echo (mGRE) pulse sequences that is applied for sampling multi-component T2* decays in the human brain of healthy volunteers and MS patients. Quantitative myelin water maps are generated based on analysis of T2* spectra. Chapter 4 discusses possible adaptations and modifications of the proposed procedure from chapter 3 when moving to higher main magnetic field strengths. The effects of B0 inhomogeneities on the data sets and possible correction methods are additionally covered in this part of the thesis. Finally, the crucial role of accurate B1 and B0 imaging and the influences on myelin water imaging are revisited in chapter 5. A solution to simultaneous mapping of B1 and B0 is presented that might help to overcome systematic error sources in MWF mapping in the future

Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI

Age-related microstructural differences have been detected using diffusion tensor imaging (DTI). Although DTI is sensitive to the effects of aging, it is not specific to any underlying biological mechanism, including demyelination. Combining multiexponential T2 relaxation (MET2) and multishell diffusion MRI (dMRI) techniques may elucidate such processes. Multishell dMRI and MET2 data were acquired from 59 healthy participants aged 17-70 years. Whole-brain and regional age-associated correlations of measures related to multiple dMRI models (DTI, diffusion kurtosis imaging [DKI], neurite orientation dispersion and density imaging [NODDI]) and myelin-sensitive MET2 metrics were assessed. DTI and NODDI revealed widespread increases in isotropic diffusivity with increasing age. In frontal white matter, fractional anisotropy linearly decreased with age, paralleled by increased "neurite" dispersion and no difference in myelin water fraction. DKI measures and neurite density correlated well with myelin water fraction and intracellular and extracellular water fraction. DTI estimates remain among the most sensitive markers for age-related alterations in white matter. NODDI, DKI, and MET2 indicate that the initial decrease in frontal fractional anisotropy may be due to increased axonal dispersion rather than demyelination

Kaasasündinud N-glükosüülimise haigused Eestis

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKaasasündinud glükosüülimise haigused (KGH) moodustavad kiirelt areneva ainevahetushaiguste grupi ning on põhjustatud valkude ja lipiididega seotud glükaanide häirunud sünteesist. Erinevad valkude N-glükosüülimise haigused on enim diagnoositavad KGH-d ja PMM2-CDG on kõige sagedasem N-glükosüülimise haigus. KGH sümptomid on mittespetsiifilised ja multisüsteemsed. Valikmeetod KGH skriinimiseks on seerumi transferriini isoelektriline fokuseerimine (IEF). Käesoleva uuringu eesmärk oli juurutada Eestis KGH diagnostikaks transferriini IEF ja hinnata kolme aasta jooksul N-glükosüülimise haiguste esinemist meie patsientide hulgas. Kuuel patsiendil 1230-st esines KGH skriiningul positiivne tulemus, mis leidis molekulaarse kinnituse. Esmalt näitasime, et kõige sagedasem KGH Eestis on PMM2-CDG, mida diagnoositi neljal patsiendil kahest perekonnast. Ühe pere lastel väljendub haigus kerge neuroloogilise vormina, kuid normaalse kognitiivse arenguga, mida PMM2-CDG patsientide hulgas esineb harva. Eesti PMM2-CDG patsientidel oli kõige sagedasem variant PMM2 geenis p.Val131Met. Teiseks, esitasime tulemused PMM2-CDG eeldatava sageduse kohta, kasutades Tartu Ülikooli Eesti Geenivaramu andmeid. Leidsime viis erinevat PMM2 heterosügootset mutatsiooni. Kõige sagedasem geenivariant on p.Arg141His kandlussagedusega 1/224. p.Val131Met kandlussagedus on 1/449. Eeldatav PMM2-CDG sagedus Eestis on 1/77,000. Kolmandaks, kirjeldasime patsienti KGH alatüübiga SLC35A2-CDG ning võrdlesime tema fenotüüpi ja genotüüpi 14 rahvusvahelise patsiendi kliiniliste andmetega. Patsientidele on iseloomulik mittespetsiifiline neuroloogiline haigus üldise arengu hilistumise, lihashüpotoonia, krampide ning epileptilise entsefalopaatiaga, düsmorfsed tunnused ja lühike kasv. Lisaks võib transferriini IEF olla vale-negatiivne. Neljandaks, kirjeldasime multisüsteemsete kliiniliste sümptomitega ning uue, seni kirjeldamata KGH alatüübiga patsienti, kellel on KGH alatüübi põhjuseks tõenäoliselt haiguspõhjuslik homosügootne muutus STX5 geenis. Käesolev uuring näitas, et Eesti patsientide puhul on transferriini IEF on tulemuslik meetod KGH diagnostikas. Skriiningu rakendamine võimaldas lisada uusi kliinilisi ja epidemioloogilisi andmeid erinevate teadaolevate ning uue KGH alatüübi kohta.Congenital disorders of glycosylation (CDG) are an expanding group of inherited metabolic diseases caused by impaired synthesis and attachment of glycans on proteins and lipids. Disorders affecting the N-glycosylation pathway form the most common CDG subgroup, and the most common N-glycosylation disorder is PMM2-CDG. The symptoms of different CDG are often non-specific and multisystem. Serum transferrin isoelectric focusing (Tf IEF) is a routine method to screen CDG. The aim of this study was to implement Tf IEF in Estonian clinical practice and to study the presence of N-glycosylation defects among Estonian patients in a three-year screening period. Altogether, positive CDG screening with subsequent molecular confirmation was detected in six patients among 1230 subjects screened. First, the most frequent CDG in Estonia is PMM2-CDG as we diagnosed this disorder in four patients from two families. In one family, the siblings show a mild neurological phenotype with normal-borderline cognitive development, which has previously been seldom described. Among PMM2-CDG patients, the most common variant in PMM2 gene is p.Val131Met. Second, we reported the expected frequency of PMM2-CDG based on the Estonian population data. In this cohort, we identified five different heterozygous variants in PMM2 gene. The most frequent variant is p.Arg141His with carrier frequency 1/224. The carrier frequency for p.Val131Met based on the Estonian population data is 1/449. The expected frequency of PMM2-CDG is 1/77,000. Third, we described a patient with SLC35A2-CDG and compared his phenotype-genotype with 14 international SLC35A2-CDG patients. This type of CDG presents as a non-specific neurological syndrome with global developmental delay, hypotonia, seizures and epileptic encephalopathy, together with dysmorphic features and short stature. In addition, Tf IEF can show a normal profile. Fourth, we presented a patient with multisystem clinical CDG features and a novel type II CDG likely caused by homozygous variant in STX5. In conclusion, Tf IEF proved to be an effective method to detect CDG among Estonian patients. Our results led to many findings, which have helped to add new clinical and epidemiological data about different known types of CDG, but also to expand the group of CDG by the discovery of a new type of CDG

Kaasasündinud N-glükosüülimise haigused Eestis

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKaasasündinud glükosüülimise haigused (KGH) moodustavad kiirelt areneva ainevahetushaiguste grupi ning on põhjustatud valkude ja lipiididega seotud glükaanide häirunud sünteesist. Erinevad valkude N-glükosüülimise haigused on enim diagnoositavad KGH-d ja PMM2-CDG on kõige sagedasem N-glükosüülimise haigus. KGH sümptomid on mittespetsiifilised ja multisüsteemsed. Valikmeetod KGH skriinimiseks on seerumi transferriini isoelektriline fokuseerimine (IEF). Käesoleva uuringu eesmärk oli juurutada Eestis KGH diagnostikaks transferriini IEF ja hinnata kolme aasta jooksul N-glükosüülimise haiguste esinemist meie patsientide hulgas. Kuuel patsiendil 1230-st esines KGH skriiningul positiivne tulemus, mis leidis molekulaarse kinnituse. Esmalt näitasime, et kõige sagedasem KGH Eestis on PMM2-CDG, mida diagnoositi neljal patsiendil kahest perekonnast. Ühe pere lastel väljendub haigus kerge neuroloogilise vormina, kuid normaalse kognitiivse arenguga, mida PMM2-CDG patsientide hulgas esineb harva. Eesti PMM2-CDG patsientidel oli kõige sagedasem variant PMM2 geenis p.Val131Met. Teiseks, esitasime tulemused PMM2-CDG eeldatava sageduse kohta, kasutades Tartu Ülikooli Eesti Geenivaramu andmeid. Leidsime viis erinevat PMM2 heterosügootset mutatsiooni. Kõige sagedasem geenivariant on p.Arg141His kandlussagedusega 1/224. p.Val131Met kandlussagedus on 1/449. Eeldatav PMM2-CDG sagedus Eestis on 1/77,000. Kolmandaks, kirjeldasime patsienti KGH alatüübiga SLC35A2-CDG ning võrdlesime tema fenotüüpi ja genotüüpi 14 rahvusvahelise patsiendi kliiniliste andmetega. Patsientidele on iseloomulik mittespetsiifiline neuroloogiline haigus üldise arengu hilistumise, lihashüpotoonia, krampide ning epileptilise entsefalopaatiaga, düsmorfsed tunnused ja lühike kasv. Lisaks võib transferriini IEF olla vale-negatiivne. Neljandaks, kirjeldasime multisüsteemsete kliiniliste sümptomitega ning uue, seni kirjeldamata KGH alatüübiga patsienti, kellel on KGH alatüübi põhjuseks tõenäoliselt haiguspõhjuslik homosügootne muutus STX5 geenis. Käesolev uuring näitas, et Eesti patsientide puhul on transferriini IEF on tulemuslik meetod KGH diagnostikas. Skriiningu rakendamine võimaldas lisada uusi kliinilisi ja epidemioloogilisi andmeid erinevate teadaolevate ning uue KGH alatüübi kohta.Congenital disorders of glycosylation (CDG) are an expanding group of inherited metabolic diseases caused by impaired synthesis and attachment of glycans on proteins and lipids. Disorders affecting the N-glycosylation pathway form the most common CDG subgroup, and the most common N-glycosylation disorder is PMM2-CDG. The symptoms of different CDG are often non-specific and multisystem. Serum transferrin isoelectric focusing (Tf IEF) is a routine method to screen CDG. The aim of this study was to implement Tf IEF in Estonian clinical practice and to study the presence of N-glycosylation defects among Estonian patients in a three-year screening period. Altogether, positive CDG screening with subsequent molecular confirmation was detected in six patients among 1230 subjects screened. First, the most frequent CDG in Estonia is PMM2-CDG as we diagnosed this disorder in four patients from two families. In one family, the siblings show a mild neurological phenotype with normal-borderline cognitive development, which has previously been seldom described. Among PMM2-CDG patients, the most common variant in PMM2 gene is p.Val131Met. Second, we reported the expected frequency of PMM2-CDG based on the Estonian population data. In this cohort, we identified five different heterozygous variants in PMM2 gene. The most frequent variant is p.Arg141His with carrier frequency 1/224. The carrier frequency for p.Val131Met based on the Estonian population data is 1/449. The expected frequency of PMM2-CDG is 1/77,000. Third, we described a patient with SLC35A2-CDG and compared his phenotype-genotype with 14 international SLC35A2-CDG patients. This type of CDG presents as a non-specific neurological syndrome with global developmental delay, hypotonia, seizures and epileptic encephalopathy, together with dysmorphic features and short stature. In addition, Tf IEF can show a normal profile. Fourth, we presented a patient with multisystem clinical CDG features and a novel type II CDG likely caused by homozygous variant in STX5. In conclusion, Tf IEF proved to be an effective method to detect CDG among Estonian patients. Our results led to many findings, which have helped to add new clinical and epidemiological data about different known types of CDG, but also to expand the group of CDG by the discovery of a new type of CDG