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- Ahlgren, André, et al.
(författare)
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A linear mixed perfusion model for tissue partial volume correction of perfusion estimates in dynamic susceptibility contrast MRI: : Impact on absolute quantification, repeatability, and agreement with pseudo-continuous arterial spin labeling
- 2017
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Ingår i: Magnetic Resonance in Medicine. - : Wiley. - 1522-2594 .- 0740-3194. ; 77:6, s. 2203-2214
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE: The partial volume effect (PVE) is an important source of bias in brain perfusion measurements. The impact of tissue PVEs in perfusion measurements with dynamic susceptibility contrast MRI (DSC-MRI) has not yet been well established. The purpose of this study was to suggest a partial volume correction (PVC) approach for DSC-MRI and to study how PVC affects DSC-MRI perfusion results.METHODS: A linear mixed perfusion model for DSC-MRI was derived and evaluated by way of simulations. Twenty healthy volunteers were scanned twice, including DSC-MRI, arterial spin labeling (ASL), and partial volume measurements. Two different algorithms for PVC were employed and assessed.RESULTS: Simulations showed that the derived model had a tendency to overestimate perfusion values in voxels with high fractions of cerebrospinal fluid. PVC reduced the tissue volume dependence of DSC-MRI perfusion values from 44.4% to 4.2% in gray matter and from 55.3% to 14.2% in white matter. One PVC method significantly improved the voxel-wise repeatability, but PVC did not improve the spatial agreement between DSC-MRI and ASL perfusion maps.CONCLUSION: Significant PVEs were found for DSC-MRI perfusion estimates, and PVC successfully reduced those effects. The findings suggest that PVC might be an important consideration for DSC-MRI applications. Magn Reson Med, 2016. © 2016 Wiley Periodicals, Inc.
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| 2. |
- Ahlgren, André
(författare)
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Methodological improvements in quantitative MRI : Perfusion estimation and partial volume considerations
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The magnetic resonance imaging (MRI) scanner is a remarkable medical imaging device, capable of producing detailed images of the inside of the body. In addition to imaging internal tissue structures, the scanner can also be used to measure various properties of the tissue. If a tissue property is measured in every image pixel, the resulting property image (the parameter map) can be displayed and used for medical interpretation — a concept referred to as ‘quantitative MRI’. Tissue properties that are commonly probed include traditional MR parameters such as T1, T2 and proton density, as well as functional parameters such as tissue perfusion, brain activation, diffusion and flow.Quantitative MRI relies on the continuous development of new and improved ways to acquire data with the scanner (pulse sequences), to model and analyze the data (postprocessing), and to interpret the output from a medical perspective. This thesis describes methods that have been developed with the specific aim to improve certain quantitative MRI techniques. In particular, the work is focused on improved analysis of perfusion MRI data, and ways to handle the partial volume issue.Constant delivery of oxygen and nutrients via the blood is vital for tissue viability. Perfusion MRI is designed to measure the properties of the local blood delivery, and perfusion images can be used as a marker for tissue health. Whereas rough estimates of perfusion properties can suffice in some cases, more accurate information can provide improved medical research and diagnostics. Most of the methods described in this work aim to provide tissue perfusion information with higher accuracy than previous approaches.One particular way to improve perfusion information is to account for the so-called partial volume effect. This means that limited image resolution implies that a single pixel may contain signal from more than one type of tissue. In other words, the signal can be mixed, and the calculated perfusion represents a mixture of the underlying perfusion of the different tissue types. By first using another quantitative MRI method that estimates the partial volume of each tissue type in every pixel (referred to as partial volume mapping), the partial volume effect can be corrected for by so-called partial volume correction.Partial volume mapping also relates to the field of MRI segmentation, that is, methods to segment an image into different tissue types and anatomical regions. This work also explores and expands a new partial volume mapping and segmentation method, referred to as fractional signal modeling, which seems to be exceptionally versatile and robust, as well as simple to implement and use. A general framework is laid out, with the hope of inspiring more researchers to adapt it and assess its value in different applications.In conclusion, this work improved the quantification in different perfusion MRI methods, as well as presented a new partial volume mapping method. The described methods will hopefully yield value in medical applications in the future.
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| 3. |
- Scherman Rydhög, Anna, et al.
(författare)
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Separating Blood and Water: Perfusion and Free Water Elimination from Diffusion MRI in the Human Brain.
- 2017
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Ingår i: NeuroImage. - : Elsevier BV. - 1053-8119. ; 156, s. 423-434
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Tidskriftsartikel (refereegranskat)abstract
- The assessment of the free water fraction in the brain provides important information about extracellular processes such as atrophy and neuroinflammation in various clinical conditions as well as in normal development and aging. Free water estimates from diffusion MRI are assumed to account for freely diffusing water molecules in the extracellular space, but may be biased by other pools of molecules in rapid random motion, such as the intravoxel incoherent motion (IVIM) of blood, where water molecules perfuse in the randomly oriented capillary network. The goal of this work was to separate the signal contribution of the perfusing blood from that of free-water and of other brain diffusivities. The influence of the vascular compartment on the estimation of the free water fraction and other diffusivities was investigated by simulating perfusion in diffusion MRI data. The perfusion effect in the simulations was significant, especially for the estimation of the free water fraction, and was maintained as long as low b-value data were included in the analysis. Two approaches to reduce the perfusion effect were explored in this study: (i) increasing the minimal b-value used in the fitting, and (ii) using a three-compartment model that explicitly accounts for water molecules in the capillary blood. Estimation of the model parameters while excluding low b-values reduced the perfusion effect but was highly sensitive to noise. The three-compartment model fit was more stable and additionally, provided an estimation of the volume fraction of the capillary blood compartment. The three-compartment model thus disentangles the effects of free water diffusion and perfusion, which is of major clinical importance since changes in these components in the brain may indicate different pathologies, i.e., those originating from the extracellular space, such as neuroinflammation and atrophy, and those related to the vascular space, such as vasodilation, vasoconstriction and capillary density. Diffusion MRI data acquired from a healthy volunteer, using multiple b-shells, demonstrated an expected non-zero contribution from the blood fraction, and indicated that not accounting for the perfusion effect may explain the overestimation of the free water fraction evinced in previous studies. Finally, the applicability of the method was demonstrated with a dataset acquired using a clinically feasible protocol with shorter acquisition time and fewer b-shells.
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