Introduction: Previous studies have proposed that the resolution limit (dmin) in diffusion MRI sc... more Introduction: Previous studies have proposed that the resolution limit (dmin) in diffusion MRI scales with the inverse cube root of the maximal gradient strength (gmax), according to dmin ∝ gmax with c = 3 [1, 2]. Current diffusion MRI protocols allow the diameters (d) of axons to be estimated in the human brain using clinical MRI scanners if wider than approximately 3-4 μm [3]. For axons with smaller diameters, the signal attenuation perpendicular to the axon becomes very low, so that their signal attenuation curves become inseparable from those that would arise if the diameter were zero [2]. This phenomenon gives rise to a resolution limit, below which structures of varying sizes cannot be distinguished [1, 2]. In this study, we present a careful analysis of the scaling laws of the resolution limit in diffusion MRI and show that as we move away from the approximations intrinsic to q-space analysis, c reduces to around two improving prospects for in vivo axon diameter estimation.
Tensor‐valued diffusion encoding provides more specific information than conventional diffusion‐w... more Tensor‐valued diffusion encoding provides more specific information than conventional diffusion‐weighted imaging (DWI), but has mainly been applied in neuroimaging studies. This study aimed to assess its potential for the imaging of prostate cancer (PCa).
Delineation of malformations of cortical development (MCD) is central in presurgical evaluation o... more Delineation of malformations of cortical development (MCD) is central in presurgical evaluation of drug‐resistant epilepsy. Delineation using magnetic resonance imaging (MRI) can be ambiguous, however, because the conventional T1‐ and T2‐weighted contrasts depend strongly on myelin for differentiation of cortical tissue and white matter. Variations in myelin content within both cortex and white matter may cause MCD findings on MRI to change size, become undetectable, or disagree with histopathology. The novel tensor‐valued diffusion MRI (dMRI) technique maps microscopic diffusion anisotropy, which is sensitive to axons rather than myelin. This work investigated whether tensor‐valued dMRI may improve differentiation of cortex and white matter in the delineation of MCD.
Diffusion MRI may enable non-invasive mapping of axonal microstructure. Most approaches infer axo... more Diffusion MRI may enable non-invasive mapping of axonal microstructure. Most approaches infer axon diameters from effects of time-dependent diffusion on the diffusion-weighted MR signal by modeling axons as straight cylinders. Axons do not, however, propagate in straight trajectories, and so far the impact of the axonal trajectory on diameter estimation has been insufficiently investigated. Here, we employ a toy model of axons, which we refer to as the undulating thin fiber model, to analyze the impact of undulating trajectories on the time dependence of diffusion. We study time-dependent diffusion in the frequency domain and characterize the diffusion spectrum by its height, width, and low-frequency behavior (power law exponent). Results show that microscopic orientation dispersion of the thin fibers is the main parameter that determines the characteristics of the diffusion spectra. At lower frequencies (longer diffusion times), straight cylinders and undulating thin fibers can have virtually identical spectra. If the straight-cylinder assumption is used to interpret data from undulating thin axons, the diameter is overestimated by an amount proportional to the undulation amplitude and microscopic orientation dispersion of the fibers. At higher frequencies (shorter diffusion times), spectra from cylinders and undulating thin fibers differ. The low-frequency behavior of the spectra from the undulating thin fibers may also differ from that of cylinders, because the power law exponent of undulating fibers can reach values below 2 for experimentally relevant frequency ranges. In conclusion, we argue that the non-straight nature of axonal trajectories should not be overlooked when analyzing and interpreting diffusion MRI data.
Purpose: To evaluate the feasibility of a 3-minute b-tensor encoding protocol for diffusion MRIba... more Purpose: To evaluate the feasibility of a 3-minute b-tensor encoding protocol for diffusion MRIbased assessment of the microscopic anisotropy and tissue heterogeneity in a wide range of intracranial tumors. Methods: B-tensor encoding was performed in 42 patients with intracranial tumors (gliomas, meningiomas, ademonas, metastases). Microscopic anisotropy and tissue heterogeneity were evaluated by estimating the anisotropic kurtosis (MK A) and isotropic kurtosis (MK I), respectively. An extensive imaging protocol was compared with a faster 3-minute protocol. Results: The fast imaging protocol yielded parameters with characteristics in terms of bias and precision similar to the full protocol. Glioblastomas had lower microscopic anisotropy than meningiomas (MK A = 0.29±0.06 versus 0.45±0.08, p = 0.003). Metastases had higher tissue heterogeneity (MK I = 0.57±0.07) than both the glioblastomas (0.44±0.06 p < 0.001) and meningiomas (0.46±0.06, p = 0.03). Conclusion: Evaluation of the microscopic anisotropy and tissue heterogeneity in intracranial tumor patients is feasible in clinically relevant times frames.
O2-05-04 IN COGNITIVELY NORMAL INDIVIDUALS, THE BEST CONCORDANCE BETWEEN CSF BIOMARKERS AND PETAM... more O2-05-04 IN COGNITIVELY NORMAL INDIVIDUALS, THE BEST CONCORDANCE BETWEEN CSF BIOMARKERS AND PETAMYLOID IS OBSERVEDWITHTHECOMBINATIONOFBAMYLOID1-42ANDPHOSPHORYLATEDTAU Foudil Lamari, St ephane Epelbaum, Hugo Bertin, Harald Hampel, Hovagim Bakardjian Sr., Francis Nyasse, Imen Benyounes, Simone Lista, Fouzi Mestari, Claude Jardel, Bruno Dubois, Marie-Odile Habert, INSIGHT-preAD Study Group, AP-HP, UF Biochimie des Maladies Neuro-m etaboliques, Service de Biochimie M etabolique, Groupe Hospitalier Piti e-Salpêtri ere, Paris, France; Sorbonne University, UPMC, Paris, France; Sorbonne Universit es, Universit e Pierre et Marie Curie (UPMC) Paris 6, INSERM, CNRS, Institut du cerveau et de la moelle (ICM), D epartement de Neurologie, Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), Hôpital Piti e-Salpêtri ere, Paris, France; Service de M edecine Nucl eaire, Hôpital de la Piti e-Salpêtri ere, AP-HP, Paris, France; CATI Multicenter Neuroimaging Platform, Paris, France; AXA Research Fund & UPMC Chair, Sorbonne Universit es, Universit e Pierre et Marie Curie (UPMC) Paris 06, INSERM, CNRS, Institut du Cerveau et de la Moelle (ICM), Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), Paris, France; Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), D epartement de Neurologie, Hôpital de la Piti e-Salpêtri ere, AP-HP, Paris, France; Sorbonne Universities, Universit e Pierre et Marie Curie-Paris 6, Paris, France; IHU-A-ICM Paris Institute of Translational Neurosciences, Hôpital Piti e-Salpêtri ere, Paris, France; ICM, Paris, France; IM2A Institute of Memory and Alzheimer’s Disease IHU-A-ICM Paris Institute of Translational Neurosciences Piti e-Salpêtri ere University Hospital, Paris, France; Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), Paris, France; Pierre et Marie Curie, Paris, France; APHP-PITIE Salpetriere University Hospital, Paris, France; AXA Research Fund & UPMC Chair; Sorbonne Universities, Pierre et Marie Curie University, Paris 06, Institute of Memory and Alzheimer’s Disease (IM2A) & Brain and Spine Institute (ICM)UMR S 1127, Hopital Piti e-Salpêtri ere, Paris, France; Sorbonne Universit es, Universit e Pierre et Marie Curie, Paris, France; APHP-PITIE Salpetriere University Hospital, Paris, France; Sorbonne University, UPMC, Paris, France; Sorbonne Universit es, Universit e Pierre et Marie Curie (UPMC) Paris 06, INSERM, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), Paris, France; Institut du Cerveau et de la Moelle Epini ere (ICM), Hôpital de la Piti e-Salpêtri ere, AP-HP, Paris, France; Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), ICM, Hôpital de la Piti e-Salpêtri ere, AP-HP, Universit e Paris 6, Paris, France; Sorbonne Universites, UPMC Univ Paris 06, INSERM U 1146, CNRS UMR 7371, Laboratoire d’Imagerie Biomedicale, Paris, France. Contact e-mail: [email protected]
Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularit... more Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encodingapplied along a single direction in each shot-tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensorvalued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/μm 2. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm 3. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems-even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m-and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.
The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical different... more The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically verified intracranial HGG (n = 18), LGG (n = 10), and MET (n = 15) were chosen. Preoperative magnetic resonance data included pre- and post-gadolinium contrast-enhanced T1-weighted fluid-attenuated inversion recover, cerebral blood flow (CBF), cerebral blood volume (CBV), fractional anisotropy, and apparent diffusion coefficient maps used for quantification of magnetic resonance biometrics by manual delineation of regions of interest. A binary logistic regression model was applied for multiparametric analysis and receiver operating characteristic (ROC) analysis. Statistically significant differences were found for normalized-ADC-tumor (nADC-T), normalized-CBF-tumor (nCBF-T), normalized-CBV-tumor (nCBV-T), and normalized-CBF-edema (nCBF-E) b...
Behavioral variant frontotemporal dementia (bvFTD) has been predominantly considered as a frontot... more Behavioral variant frontotemporal dementia (bvFTD) has been predominantly considered as a frontotemporal cortical disease, with limited direct investigation of frontal-subcortical connections. We aim to characterize the grey and white matter components of frontal-thalamic and frontal-striatal circuits in bvFTD. Twenty-four patients with bvFTD and 24 healthy controls underwent morphological and diffusion imaging. Subcortical structures were manually segmented according to published protocols. Probabilistic pathways were reconstructed separately from the dorsolateral, orbitofrontal and medial prefrontal cortex to the striatum and thalamus. Patients with bvFTD had smaller cortical and subcortical volumes, lower fractional anisotropy, and higher mean diffusivity metrics, which is consistent with disruptions in frontal-striatal-thalamic pathways. Unexpectedly, regional volumes of the striatum and thalamus connected to the medial prefrontal cortex were significantly larger in bvFTD (by 13...
In Parkinson's disease (PD), pathological microstructural changes occur that may be detected ... more In Parkinson's disease (PD), pathological microstructural changes occur that may be detected using diffusion magnetic resonance imaging (dMRI). However, there are few longitudinal studies that explore the effect of disease progression on diffusion indices. We prospectively included 76 patients with PD and 38 healthy controls (HC), all of whom underwent diffusion kurtosis imaging (DKI) as part of the prospective Swedish BioFINDER study at baseline and 2 years later. Annualized rates of change in DKI parameters, including fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK), were estimated in the gray matter (GM) by placing regions of interest (ROIs) in the basal ganglia and the thalamus, and in the white matter (WM) by tract-based spatial statistics (TBSS) analysis. When adjusting for potential confounding factors (age, gender, baseline-follow-up interval, and software upgrade of MRI scanner), only a decrease in FA in the putamen of PD patients (β = - 0.248, ...
BackgroundDiffusion kurtosis imaging (DKI) allows for assessment of diffusion influenced by micro... more BackgroundDiffusion kurtosis imaging (DKI) allows for assessment of diffusion influenced by microcellular structures. We analyzed DKI in suspected low-grade gliomas prior to histopathological diagnosis. The aim was to investigate if diffusion parameters in the perilesional normal-appearing white matter (NAWM) differed from contralesional white matter, and to investigate differences between glioma malignancy grades II and III and glioma subtypes (astrocytomas and oligodendrogliomas).Patients and methodsForty-eight patients with suspected low-grade glioma were prospectively recruited to this institutional review board-approved study and investigated with preoperative DKI at 3T after written informed consent. Patients with histologically proven glioma grades II or III were further analyzed (n=35). Regions of interest (ROIs) were delineated on T2FLAIR images and co-registered to diffusion MRI parameter maps. Mean DKI data were compared between perilesional and contralesional NAWM (stude...
In diffusion MRI (dMRI), microscopic diffusion anisotropy can be obscured by orientation dispersi... more In diffusion MRI (dMRI), microscopic diffusion anisotropy can be obscured by orientation dispersion. Separation of these properties is of high importance, since it could allow dMRI to non-invasively probe elongated structures such as neurites (axons and dendrites). However, conventional dMRI, based on single diffusion encoding (SDE), entangles microscopic anisotropy and orientation dispersion with intra-voxel variance in isotropic diffusivity. SDE-based methods for estimating microscopic anisotropy, such as the neurite orientation dispersion and density imaging (NODDI) method, must thus rely on model assumptions to disentangle these features. An alternative approach is to directly quantify microscopic anisotropy by the use of variable shape of the b-tensor. Along those lines, we here present the &#39;constrained diffusional variance decomposition&#39; (CODIVIDE) method, which jointly analyzes data acquired with diffusion encoding applied in a single direction at a time (linear tensor encoding, LTE) and in all directions (spherical tensor encoding, STE). We then contrast the two approaches by comparing neurite density estimated using NODDI with microscopic anisotropy estimated using CODIVIDE. Data were acquired in healthy volunteers and in glioma patients. NODDI and CODIVIDE differed the most in gray matter and in gliomas, where NODDI detected a neurite fraction higher than expected from the level of microscopic diffusion anisotropy found with CODIVIDE. The discrepancies could be explained by the NODDI tortuosity assumption, which enforces a connection between the neurite density and the mean diffusivity of tissue. Our results suggest that this assumption is invalid, which leads to a NODDI neurite density that is inconsistent between LTE and STE data. Using simulations, we demonstrate that the NODDI assumptions result in parameter bias that precludes the use of NODDI to map neurite density. With CODIVIDE, we found high levels of microscopic anisotropy in white matter, intermediate levels in structures such as the thalamus and the putamen, and low levels in the cortex and in gliomas. We conclude that accurate mapping of microscopic anisotropy requires data acquired with variable shape of the b-tensor.
The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of th... more The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of the variance of apparent diffusivities within a voxel. However, the link between the diffusional variance and the tissue heterogeneity is not well-established. To investigate this link we test the hypothesis that diffusional variance, caused by microscopic anisotropy and isotropic heterogeneity, is associated with variable cell eccentricity and cell density in brain tumors. We performed dMRI using a novel encoding scheme for diffusional variance decomposition (DIVIDE) in 7 meningiomas and 8 gliomas prior to surgery. The diffusional variance was quantified from dMRI in terms of the total mean kurtosis (MKT), and DIVIDE was used to decompose MKT into components caused by microscopic anisotropy (MKA) and isotropic heterogeneity (MKI). Diffusion anisotropy was evaluated in terms of the fractional anisotropy (FA) and microscopic fractional anisotropy (μFA). Quantitative microscopy was performed...
In Parkinson's disease (PD), pathological microstructural changes occur and such changes might be... more In Parkinson's disease (PD), pathological microstructural changes occur and such changes might be detected using diffusion magnetic resonance imaging (dMRI). However, it is unclear whether dMRI improves PD diagnosis or helps differentiating between phenotypes, such as postural instability gait difficulty (PIGD) and tremor dominant (TD) PD. We included 105 patients with PD and 44 healthy controls (HC), all of whom underwent dMRI as part of the prospective Swedish BioFINDER study. Diffusion kurtosis imaging (DKI) and neurite density imaging (NDI) analyses were performed using regions of interest in the basal ganglia, the thalamus, the pons and the midbrain as well as tractography of selected white matter tracts. In the putamen, the PD group showed increased mean diffusivity (MD) (p = .003), decreased fractional anisotropy (FA) (p = .001) and decreased mean kurtosis (MK), compared to HC (p = .024). High MD and a low MK in the putamen were associated with more severe motor and cognitive symptomatology (p < .05). Also, patients with PIGD exhibited increased MD in the putamen compared to the TD patients (p = .009). In the thalamus, MD was increased (p = .001) and FA was decreased (p = .032) in PD compared to HC. Increased MD and decreased FA correlated negatively with motor speed and balance (p < .05). In the superior longitudinal fasciculus (SLF), MD (p = .019) and f iso were increased in PD compared to HC (p = .03). These changes correlated negatively with motor speed (p < .002) and balance (p < .037). However, most of the observed changes in PD were also present in cases with either multiple system atrophy (n = 11) or progressive supranuclear palsy (n = 10). In conclusion, PD patients exhibit microstructural changes in the putamen, the thalamus, and the SLF, which are associated with worse disease severity. However, the dMRI changes are not sufficiently specific to improve the diagnostic work-up of PD. Longitudinal studies should evaluate whether dMRI measures can be used to track disease progression.
ABSTRACT In this work we present a semi automatic segmentation method for the cervical spine usin... more ABSTRACT In this work we present a semi automatic segmentation method for the cervical spine using diffusion tensor tractography. The quantification of DTI and DKI parameters is performed as a function of the anatomical position along the spine.
ABSTRACT In this work we present the first in vivo experiments employing magic angle spinning of ... more ABSTRACT In this work we present the first in vivo experiments employing magic angle spinning of the q-vector (qMAS) to map the microscopic anisotropy of the brain. This technique allows for the parameterization of anisotropy that is unaffected by the orientation dispersion. This means that the anisotropy is probed on a sub-voxel scale, and can potentially be useful in complex white matter geometries and gray matter, where conventional metrics such as FA are confounded by the tissue micro architecture.
comparative studies of the diffusional apparent exchange rate (AXR) at reduced scan times and sma... more comparative studies of the diffusional apparent exchange rate (AXR) at reduced scan times and smaller group sizes. Björn Lampinen, Filip Szczepankiewicz, Danielle van Westen, Pia Sundgren, Freddy Ståhlberg, Jimmy Lätt, and Markus Nilsson Department of Medical Radiation Physics, Lund University, Lund, Sweden, Department of Diagnostic Radiology, Lund University, Lund, Sweden, Center for Medical Imaging and Physiology, Lund University Hospital, Lund, Sweden, Lund University Bioimaging Center, Lund University, Lund, Sweden
Introduction: Previous studies have proposed that the resolution limit (dmin) in diffusion MRI sc... more Introduction: Previous studies have proposed that the resolution limit (dmin) in diffusion MRI scales with the inverse cube root of the maximal gradient strength (gmax), according to dmin ∝ gmax with c = 3 [1, 2]. Current diffusion MRI protocols allow the diameters (d) of axons to be estimated in the human brain using clinical MRI scanners if wider than approximately 3-4 μm [3]. For axons with smaller diameters, the signal attenuation perpendicular to the axon becomes very low, so that their signal attenuation curves become inseparable from those that would arise if the diameter were zero [2]. This phenomenon gives rise to a resolution limit, below which structures of varying sizes cannot be distinguished [1, 2]. In this study, we present a careful analysis of the scaling laws of the resolution limit in diffusion MRI and show that as we move away from the approximations intrinsic to q-space analysis, c reduces to around two improving prospects for in vivo axon diameter estimation.
Tensor‐valued diffusion encoding provides more specific information than conventional diffusion‐w... more Tensor‐valued diffusion encoding provides more specific information than conventional diffusion‐weighted imaging (DWI), but has mainly been applied in neuroimaging studies. This study aimed to assess its potential for the imaging of prostate cancer (PCa).
Delineation of malformations of cortical development (MCD) is central in presurgical evaluation o... more Delineation of malformations of cortical development (MCD) is central in presurgical evaluation of drug‐resistant epilepsy. Delineation using magnetic resonance imaging (MRI) can be ambiguous, however, because the conventional T1‐ and T2‐weighted contrasts depend strongly on myelin for differentiation of cortical tissue and white matter. Variations in myelin content within both cortex and white matter may cause MCD findings on MRI to change size, become undetectable, or disagree with histopathology. The novel tensor‐valued diffusion MRI (dMRI) technique maps microscopic diffusion anisotropy, which is sensitive to axons rather than myelin. This work investigated whether tensor‐valued dMRI may improve differentiation of cortex and white matter in the delineation of MCD.
Diffusion MRI may enable non-invasive mapping of axonal microstructure. Most approaches infer axo... more Diffusion MRI may enable non-invasive mapping of axonal microstructure. Most approaches infer axon diameters from effects of time-dependent diffusion on the diffusion-weighted MR signal by modeling axons as straight cylinders. Axons do not, however, propagate in straight trajectories, and so far the impact of the axonal trajectory on diameter estimation has been insufficiently investigated. Here, we employ a toy model of axons, which we refer to as the undulating thin fiber model, to analyze the impact of undulating trajectories on the time dependence of diffusion. We study time-dependent diffusion in the frequency domain and characterize the diffusion spectrum by its height, width, and low-frequency behavior (power law exponent). Results show that microscopic orientation dispersion of the thin fibers is the main parameter that determines the characteristics of the diffusion spectra. At lower frequencies (longer diffusion times), straight cylinders and undulating thin fibers can have virtually identical spectra. If the straight-cylinder assumption is used to interpret data from undulating thin axons, the diameter is overestimated by an amount proportional to the undulation amplitude and microscopic orientation dispersion of the fibers. At higher frequencies (shorter diffusion times), spectra from cylinders and undulating thin fibers differ. The low-frequency behavior of the spectra from the undulating thin fibers may also differ from that of cylinders, because the power law exponent of undulating fibers can reach values below 2 for experimentally relevant frequency ranges. In conclusion, we argue that the non-straight nature of axonal trajectories should not be overlooked when analyzing and interpreting diffusion MRI data.
Purpose: To evaluate the feasibility of a 3-minute b-tensor encoding protocol for diffusion MRIba... more Purpose: To evaluate the feasibility of a 3-minute b-tensor encoding protocol for diffusion MRIbased assessment of the microscopic anisotropy and tissue heterogeneity in a wide range of intracranial tumors. Methods: B-tensor encoding was performed in 42 patients with intracranial tumors (gliomas, meningiomas, ademonas, metastases). Microscopic anisotropy and tissue heterogeneity were evaluated by estimating the anisotropic kurtosis (MK A) and isotropic kurtosis (MK I), respectively. An extensive imaging protocol was compared with a faster 3-minute protocol. Results: The fast imaging protocol yielded parameters with characteristics in terms of bias and precision similar to the full protocol. Glioblastomas had lower microscopic anisotropy than meningiomas (MK A = 0.29±0.06 versus 0.45±0.08, p = 0.003). Metastases had higher tissue heterogeneity (MK I = 0.57±0.07) than both the glioblastomas (0.44±0.06 p < 0.001) and meningiomas (0.46±0.06, p = 0.03). Conclusion: Evaluation of the microscopic anisotropy and tissue heterogeneity in intracranial tumor patients is feasible in clinically relevant times frames.
O2-05-04 IN COGNITIVELY NORMAL INDIVIDUALS, THE BEST CONCORDANCE BETWEEN CSF BIOMARKERS AND PETAM... more O2-05-04 IN COGNITIVELY NORMAL INDIVIDUALS, THE BEST CONCORDANCE BETWEEN CSF BIOMARKERS AND PETAMYLOID IS OBSERVEDWITHTHECOMBINATIONOFBAMYLOID1-42ANDPHOSPHORYLATEDTAU Foudil Lamari, St ephane Epelbaum, Hugo Bertin, Harald Hampel, Hovagim Bakardjian Sr., Francis Nyasse, Imen Benyounes, Simone Lista, Fouzi Mestari, Claude Jardel, Bruno Dubois, Marie-Odile Habert, INSIGHT-preAD Study Group, AP-HP, UF Biochimie des Maladies Neuro-m etaboliques, Service de Biochimie M etabolique, Groupe Hospitalier Piti e-Salpêtri ere, Paris, France; Sorbonne University, UPMC, Paris, France; Sorbonne Universit es, Universit e Pierre et Marie Curie (UPMC) Paris 6, INSERM, CNRS, Institut du cerveau et de la moelle (ICM), D epartement de Neurologie, Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), Hôpital Piti e-Salpêtri ere, Paris, France; Service de M edecine Nucl eaire, Hôpital de la Piti e-Salpêtri ere, AP-HP, Paris, France; CATI Multicenter Neuroimaging Platform, Paris, France; AXA Research Fund & UPMC Chair, Sorbonne Universit es, Universit e Pierre et Marie Curie (UPMC) Paris 06, INSERM, CNRS, Institut du Cerveau et de la Moelle (ICM), Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), Paris, France; Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), D epartement de Neurologie, Hôpital de la Piti e-Salpêtri ere, AP-HP, Paris, France; Sorbonne Universities, Universit e Pierre et Marie Curie-Paris 6, Paris, France; IHU-A-ICM Paris Institute of Translational Neurosciences, Hôpital Piti e-Salpêtri ere, Paris, France; ICM, Paris, France; IM2A Institute of Memory and Alzheimer’s Disease IHU-A-ICM Paris Institute of Translational Neurosciences Piti e-Salpêtri ere University Hospital, Paris, France; Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), Paris, France; Pierre et Marie Curie, Paris, France; APHP-PITIE Salpetriere University Hospital, Paris, France; AXA Research Fund & UPMC Chair; Sorbonne Universities, Pierre et Marie Curie University, Paris 06, Institute of Memory and Alzheimer’s Disease (IM2A) & Brain and Spine Institute (ICM)UMR S 1127, Hopital Piti e-Salpêtri ere, Paris, France; Sorbonne Universit es, Universit e Pierre et Marie Curie, Paris, France; APHP-PITIE Salpetriere University Hospital, Paris, France; Sorbonne University, UPMC, Paris, France; Sorbonne Universit es, Universit e Pierre et Marie Curie (UPMC) Paris 06, INSERM, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), Paris, France; Institut du Cerveau et de la Moelle Epini ere (ICM), Hôpital de la Piti e-Salpêtri ere, AP-HP, Paris, France; Institut de la M emoire et de la Maladie d’Alzheimer (IM2A), ICM, Hôpital de la Piti e-Salpêtri ere, AP-HP, Universit e Paris 6, Paris, France; Sorbonne Universites, UPMC Univ Paris 06, INSERM U 1146, CNRS UMR 7371, Laboratoire d’Imagerie Biomedicale, Paris, France. Contact e-mail: [email protected]
Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularit... more Microstructure imaging techniques based on tensor-valued diffusion encoding have gained popularity within the MRI research community. Unlike conventional diffusion encodingapplied along a single direction in each shot-tensor-valued encoding employs diffusion encoding along multiple directions within a single preparation of the signal. The benefit is that such encoding may probe tissue features that are not accessible by conventional encoding. For example, diffusional variance decomposition (DIVIDE) takes advantage of tensor-valued encoding to probe microscopic diffusion anisotropy independent of orientation coherence. The drawback is that tensor-valued encoding generally requires gradient waveforms that are more demanding on hardware; it has therefore been used primarily in MRI systems with relatively high performance. The purpose of this work was to explore tensorvalued diffusion encoding on clinical MRI systems with varying performance to test its technical feasibility within the context of DIVIDE. We performed whole-brain imaging with linear and spherical b-tensor encoding at field strengths between 1.5 and 7 T, and at maximal gradient amplitudes between 45 and 80 mT/m. Asymmetric gradient waveforms were optimized numerically to yield b-values up to 2 ms/μm 2. Technical feasibility was assessed in terms of the repeatability, SNR, and quality of DIVIDE parameter maps. Variable system performance resulted in echo times between 83 to 115 ms and total acquisition times of 6 to 9 minutes when using 80 signal samples and resolution 2×2×4 mm 3. As expected, the repeatability, signal-to-noise ratio and parameter map quality depended on hardware performance. We conclude that tensor-valued encoding is feasible for a wide range of MRI systems-even at 1.5 T with maximal gradient waveform amplitudes of 33 mT/m-and baseline experimental design and quality parameters for all included configurations. This demonstrates that tissue features, beyond those accessible by conventional diffusion encoding, can be explored on a wide range of MRI systems.
The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical different... more The aim was to evaluate volume, diffusion, and perfusion metrics for better presurgical differentiation between high-grade gliomas (HGG), low-grade gliomas (LGG), and metastases (MET). For this retrospective study, 43 patients with histologically verified intracranial HGG (n = 18), LGG (n = 10), and MET (n = 15) were chosen. Preoperative magnetic resonance data included pre- and post-gadolinium contrast-enhanced T1-weighted fluid-attenuated inversion recover, cerebral blood flow (CBF), cerebral blood volume (CBV), fractional anisotropy, and apparent diffusion coefficient maps used for quantification of magnetic resonance biometrics by manual delineation of regions of interest. A binary logistic regression model was applied for multiparametric analysis and receiver operating characteristic (ROC) analysis. Statistically significant differences were found for normalized-ADC-tumor (nADC-T), normalized-CBF-tumor (nCBF-T), normalized-CBV-tumor (nCBV-T), and normalized-CBF-edema (nCBF-E) b...
Behavioral variant frontotemporal dementia (bvFTD) has been predominantly considered as a frontot... more Behavioral variant frontotemporal dementia (bvFTD) has been predominantly considered as a frontotemporal cortical disease, with limited direct investigation of frontal-subcortical connections. We aim to characterize the grey and white matter components of frontal-thalamic and frontal-striatal circuits in bvFTD. Twenty-four patients with bvFTD and 24 healthy controls underwent morphological and diffusion imaging. Subcortical structures were manually segmented according to published protocols. Probabilistic pathways were reconstructed separately from the dorsolateral, orbitofrontal and medial prefrontal cortex to the striatum and thalamus. Patients with bvFTD had smaller cortical and subcortical volumes, lower fractional anisotropy, and higher mean diffusivity metrics, which is consistent with disruptions in frontal-striatal-thalamic pathways. Unexpectedly, regional volumes of the striatum and thalamus connected to the medial prefrontal cortex were significantly larger in bvFTD (by 13...
In Parkinson's disease (PD), pathological microstructural changes occur that may be detected ... more In Parkinson's disease (PD), pathological microstructural changes occur that may be detected using diffusion magnetic resonance imaging (dMRI). However, there are few longitudinal studies that explore the effect of disease progression on diffusion indices. We prospectively included 76 patients with PD and 38 healthy controls (HC), all of whom underwent diffusion kurtosis imaging (DKI) as part of the prospective Swedish BioFINDER study at baseline and 2 years later. Annualized rates of change in DKI parameters, including fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK), were estimated in the gray matter (GM) by placing regions of interest (ROIs) in the basal ganglia and the thalamus, and in the white matter (WM) by tract-based spatial statistics (TBSS) analysis. When adjusting for potential confounding factors (age, gender, baseline-follow-up interval, and software upgrade of MRI scanner), only a decrease in FA in the putamen of PD patients (β = - 0.248, ...
BackgroundDiffusion kurtosis imaging (DKI) allows for assessment of diffusion influenced by micro... more BackgroundDiffusion kurtosis imaging (DKI) allows for assessment of diffusion influenced by microcellular structures. We analyzed DKI in suspected low-grade gliomas prior to histopathological diagnosis. The aim was to investigate if diffusion parameters in the perilesional normal-appearing white matter (NAWM) differed from contralesional white matter, and to investigate differences between glioma malignancy grades II and III and glioma subtypes (astrocytomas and oligodendrogliomas).Patients and methodsForty-eight patients with suspected low-grade glioma were prospectively recruited to this institutional review board-approved study and investigated with preoperative DKI at 3T after written informed consent. Patients with histologically proven glioma grades II or III were further analyzed (n=35). Regions of interest (ROIs) were delineated on T2FLAIR images and co-registered to diffusion MRI parameter maps. Mean DKI data were compared between perilesional and contralesional NAWM (stude...
In diffusion MRI (dMRI), microscopic diffusion anisotropy can be obscured by orientation dispersi... more In diffusion MRI (dMRI), microscopic diffusion anisotropy can be obscured by orientation dispersion. Separation of these properties is of high importance, since it could allow dMRI to non-invasively probe elongated structures such as neurites (axons and dendrites). However, conventional dMRI, based on single diffusion encoding (SDE), entangles microscopic anisotropy and orientation dispersion with intra-voxel variance in isotropic diffusivity. SDE-based methods for estimating microscopic anisotropy, such as the neurite orientation dispersion and density imaging (NODDI) method, must thus rely on model assumptions to disentangle these features. An alternative approach is to directly quantify microscopic anisotropy by the use of variable shape of the b-tensor. Along those lines, we here present the &#39;constrained diffusional variance decomposition&#39; (CODIVIDE) method, which jointly analyzes data acquired with diffusion encoding applied in a single direction at a time (linear tensor encoding, LTE) and in all directions (spherical tensor encoding, STE). We then contrast the two approaches by comparing neurite density estimated using NODDI with microscopic anisotropy estimated using CODIVIDE. Data were acquired in healthy volunteers and in glioma patients. NODDI and CODIVIDE differed the most in gray matter and in gliomas, where NODDI detected a neurite fraction higher than expected from the level of microscopic diffusion anisotropy found with CODIVIDE. The discrepancies could be explained by the NODDI tortuosity assumption, which enforces a connection between the neurite density and the mean diffusivity of tissue. Our results suggest that this assumption is invalid, which leads to a NODDI neurite density that is inconsistent between LTE and STE data. Using simulations, we demonstrate that the NODDI assumptions result in parameter bias that precludes the use of NODDI to map neurite density. With CODIVIDE, we found high levels of microscopic anisotropy in white matter, intermediate levels in structures such as the thalamus and the putamen, and low levels in the cortex and in gliomas. We conclude that accurate mapping of microscopic anisotropy requires data acquired with variable shape of the b-tensor.
The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of th... more The structural heterogeneity of tumor tissue can be probed by diffusion MRI (dMRI) in terms of the variance of apparent diffusivities within a voxel. However, the link between the diffusional variance and the tissue heterogeneity is not well-established. To investigate this link we test the hypothesis that diffusional variance, caused by microscopic anisotropy and isotropic heterogeneity, is associated with variable cell eccentricity and cell density in brain tumors. We performed dMRI using a novel encoding scheme for diffusional variance decomposition (DIVIDE) in 7 meningiomas and 8 gliomas prior to surgery. The diffusional variance was quantified from dMRI in terms of the total mean kurtosis (MKT), and DIVIDE was used to decompose MKT into components caused by microscopic anisotropy (MKA) and isotropic heterogeneity (MKI). Diffusion anisotropy was evaluated in terms of the fractional anisotropy (FA) and microscopic fractional anisotropy (μFA). Quantitative microscopy was performed...
In Parkinson's disease (PD), pathological microstructural changes occur and such changes might be... more In Parkinson's disease (PD), pathological microstructural changes occur and such changes might be detected using diffusion magnetic resonance imaging (dMRI). However, it is unclear whether dMRI improves PD diagnosis or helps differentiating between phenotypes, such as postural instability gait difficulty (PIGD) and tremor dominant (TD) PD. We included 105 patients with PD and 44 healthy controls (HC), all of whom underwent dMRI as part of the prospective Swedish BioFINDER study. Diffusion kurtosis imaging (DKI) and neurite density imaging (NDI) analyses were performed using regions of interest in the basal ganglia, the thalamus, the pons and the midbrain as well as tractography of selected white matter tracts. In the putamen, the PD group showed increased mean diffusivity (MD) (p = .003), decreased fractional anisotropy (FA) (p = .001) and decreased mean kurtosis (MK), compared to HC (p = .024). High MD and a low MK in the putamen were associated with more severe motor and cognitive symptomatology (p < .05). Also, patients with PIGD exhibited increased MD in the putamen compared to the TD patients (p = .009). In the thalamus, MD was increased (p = .001) and FA was decreased (p = .032) in PD compared to HC. Increased MD and decreased FA correlated negatively with motor speed and balance (p < .05). In the superior longitudinal fasciculus (SLF), MD (p = .019) and f iso were increased in PD compared to HC (p = .03). These changes correlated negatively with motor speed (p < .002) and balance (p < .037). However, most of the observed changes in PD were also present in cases with either multiple system atrophy (n = 11) or progressive supranuclear palsy (n = 10). In conclusion, PD patients exhibit microstructural changes in the putamen, the thalamus, and the SLF, which are associated with worse disease severity. However, the dMRI changes are not sufficiently specific to improve the diagnostic work-up of PD. Longitudinal studies should evaluate whether dMRI measures can be used to track disease progression.
ABSTRACT In this work we present a semi automatic segmentation method for the cervical spine usin... more ABSTRACT In this work we present a semi automatic segmentation method for the cervical spine using diffusion tensor tractography. The quantification of DTI and DKI parameters is performed as a function of the anatomical position along the spine.
ABSTRACT In this work we present the first in vivo experiments employing magic angle spinning of ... more ABSTRACT In this work we present the first in vivo experiments employing magic angle spinning of the q-vector (qMAS) to map the microscopic anisotropy of the brain. This technique allows for the parameterization of anisotropy that is unaffected by the orientation dispersion. This means that the anisotropy is probed on a sub-voxel scale, and can potentially be useful in complex white matter geometries and gray matter, where conventional metrics such as FA are confounded by the tissue micro architecture.
comparative studies of the diffusional apparent exchange rate (AXR) at reduced scan times and sma... more comparative studies of the diffusional apparent exchange rate (AXR) at reduced scan times and smaller group sizes. Björn Lampinen, Filip Szczepankiewicz, Danielle van Westen, Pia Sundgren, Freddy Ståhlberg, Jimmy Lätt, and Markus Nilsson Department of Medical Radiation Physics, Lund University, Lund, Sweden, Department of Diagnostic Radiology, Lund University, Lund, Sweden, Center for Medical Imaging and Physiology, Lund University Hospital, Lund, Sweden, Lund University Bioimaging Center, Lund University, Lund, Sweden
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Papers by Markus Nilsson