1,236 research outputs found
Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group
This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available give rise to the need in the neuroimaging community for guidelines on implementation. This article describes relevant considerations and provides specific implementation recommendations for all steps in QSM data acquisition, processing, analysis, and presentation in scientific publications. We recommend that data be acquired using a monopolar 3D multi-echo GRE sequence, that phase images be saved and exported in DICOM format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields should be removed within the brain mask using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of whole brain as a region of interest in the analysis, and QSM results should be reported with - as a minimum - the acquisition and processing specifications listed in the last section of the article. These recommendations should facilitate clinical QSM research and lead to increased harmonization in data acquisition, analysis, and reporting
Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging
The value of in vivo preclinical diffusion MRI (dMRI) is substantial.
Small-animal dMRI has been used for methodological development and validation,
characterizing the biological basis of diffusion phenomena, and comparative
anatomy. Many of the influential works in this field were first performed in
small animals or ex vivo samples. The steps from animal setup and monitoring,
to acquisition, analysis, and interpretation are complex, with many decisions
that may ultimately affect what questions can be answered using the data. This
work aims to serve as a reference, presenting selected recommendations and
guidelines from the diffusion community, on best practices for preclinical dMRI
of in vivo animals. In each section, we also highlight areas for which no
guidelines exist (and why), and where future work should focus. We first
describe the value that small animal imaging adds to the field of dMRI,
followed by general considerations and foundational knowledge that must be
considered when designing experiments. We briefly describe differences in
animal species and disease models and discuss how they are appropriate for
different studies. We then give guidelines for in vivo acquisition protocols,
including decisions on hardware, animal preparation, imaging sequences and data
processing, including pre-processing, model-fitting, and tractography. Finally,
we provide an online resource which lists publicly available preclinical dMRI
datasets and software packages, to promote responsible and reproducible
research. An overarching goal herein is to enhance the rigor and
reproducibility of small animal dMRI acquisitions and analyses, and thereby
advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl
An Open Resource for Non-human Primate Imaging
Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets
The minimal preprocessing pipelines for the Human Connectome Project
The Human Connectome Project (HCP) faces the challenging task of bringing multiple magnetic resonance imaging (MRI) modalities together in a common automated preprocessing framework across a large cohort of subjects. The MRI data acquired by the HCP differ in many ways from data acquired on conventional 3 Tesla scanners and often require newly developed preprocessing methods. We describe the minimal preprocessing pipelines for structural, functional, and diffusion MRI that were developed by the HCP to accomplish many low level tasks, including spatial artifact/distortion removal, surface generation, cross-modal registration, and alignment to standard space. These pipelines are specially designed to capitalize on the high quality data offered by the HCP. The final standard space makes use of a recently introduced CIFTI file format and the associated grayordinate spatial coordinate system. This allows for combined cortical surface and subcortical volume analyses while reducing the storage and processing requirements for high spatial and temporal resolution data. Here, we provide the minimum image acquisition requirements for the HCP minimal preprocessing pipelines and additional advice for investigators interested in replicating the HCP's acquisition protocols or using these pipelines. Finally, we discuss some potential future improvements to the pipelines
PET/MRI ๋ฐ MR-IGRT๋ฅผ ์ํ MRI ๊ธฐ๋ฐ ํฉ์ฑ CT ์์ฑ์ ํ๋น์ฑ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ์๊ณผ๋ํ ์๊ณผํ๊ณผ, 2020. 8. ์ด์ฌ์ฑ.Over the past decade, the application of magnetic resonance imaging (MRI) in the field of diagnosis and treatment has increased. MRI provides higher soft-tissue contrast, especially in the brain, abdominal organ, and bone marrow without the expose of ionizing radiation. Hence, simultaneous positron emission tomography/MR (PET/MR) system and MR-image guided radiation therapy (MR-IGRT) system has recently been emerged and currently available for clinical study.
One major issue in PET/MR system is attenuation correction from MRI scans for PET quantification and a similar need for the assignment of electron densities to MRI scans for dose calculation can be found in MR-IGRT system. Because the MR signals are related to the proton density and relaxation properties of tissue, not to electron density. To overcome this problem, the method called synthetic CT (sCT), a pseudo CT derived from MR images, has been proposed. In this thesis, studies on generating synthetic CT and investigating the feasibility of using a MR-based synthetic CT for diagnostic and radiotherapy application were presented.
Firstly, MR image-based attenuation correction (MR-AC) method using level-set segmentation for brain PET/MRI was developed. To resolve conventional inaccuracy MR-AC problem, we proposed an improved ultrashort echo time MR-AC method that was based on a multiphase level-set algorithm with main magnetic field inhomogeneity correction. We also assessed the feasibility of level-set based MR-AC method, compared with CT-AC and MR-AC provided by the manufacturer of the PET/MRI scanner.
Secondly, we proposed sCT generation from the low field MR images using 2D convolution neural network model for MR-IGRT system. This sCT images were compared to the deformed CT generated using the deformable registration being used in the current system. We assessed the feasibility of using sCT for radiation treatment planning from each of the patients with pelvic, thoraic and abdominal region through geometric and dosimetric evaluation.์ง๋ 10๋
๊ฐ ์ง๋จ ๋ฐ ์น๋ฃ๋ถ์ผ์์ ์๊ธฐ๊ณต๋ช
์์(Magnetic resonance imaging; MRI) ์ ์ ์ฉ์ด ์ฆ๊ฐํ์๋ค. MRI๋ CT์ ๋น๊ตํด ์ถ๊ฐ์ ์ธ ์ ๋ฆฌ๋ฐฉ์ฌ์ ์ ํผํญ์์ด ๋, ๋ณต๋ถ ๊ธฐ๊ด ๋ฐ ๊ณจ์ ๋ฑ์์ ๋ ๋์ ์ฐ์กฐ์ง ๋๋น๋ฅผ ์ ๊ณตํ๋ค. ๋ฐ๋ผ์ MRI๋ฅผ ์ ์ฉํ ์์ ์๋ฐฉ์ถ๋จ์ธต์ดฌ์(Positron emission tomography; PET)/MR ์์คํ
๊ณผ MR ์์ ์ ๋ ๋ฐฉ์ฌ์ ์น๋ฃ ์์คํ
(MR-image guided radiation therapy; MR-IGRT)์ด ์ง๋จ ๋ฐ ์น๋ฃ ๋ฐฉ์ฌ์ ๋ถ์ผ์ ๋ฑ์ฅํ์ฌ ์์์ ์ฌ์ฉ๋๊ณ ์๋ค.
PET/MR ์์คํ
์ ํ ๊ฐ์ง ์ฃผ์ ๋ฌธ์ ๋ PET ์ ๋ํ๋ฅผ ์ํ MRI ์ค์บ์ผ๋ก๋ถํฐ์ ๊ฐ์ ๋ณด์ ์ด๋ฉฐ, MR-IGRT ์์คํ
์์ ์ ๋ ๊ณ์ฐ์ ์ํด MR ์์์ ์ ์ ๋ฐ๋๋ฅผ ํ ๋นํ๋ ๊ฒ๊ณผ ๋น์ทํ ํ์์ฑ์ ์ฐพ์ ์ ์๋ค. ์ด๋ MR ์ ํธ๊ฐ ์ ์ ๋ฐ๋๊ฐ ์๋ ์กฐ์ง์ ์์ฑ์ ๋ฐ๋ ๋ฐ T1, T2 ์ด์ ํน์ฑ๊ณผ ๊ด๋ จ์ด ์๊ธฐ ๋๋ฌธ์ด๋ค. ์ด ๋ฌธ์ ๋ฅผ ๊ทน๋ณตํ๊ธฐ ์ํด, MR ์ด๋ฏธ์ง๋ก๋ถํฐ ์ ๋๋ ๊ฐ์์ CT์ธ ํฉ์ฑ CT๋ผ ๋ถ๋ฆฌ๋ ๋ฐฉ๋ฒ์ด ์ ์๋์๋ค. ๋ณธ ํ์๋
ผ๋ฌธ์์๋ ํฉ์ฑ CT ์์ฑ ๋ฐฉ๋ฒ ๋ฐ ์ง๋จ ๋ฐ ๋ฐฉ์ฌ์ ์น๋ฃ์ ์ ์ฉ์ ์ํ MR ์์ ๊ธฐ๋ฐ ํฉ์ฑ CT ์ฌ์ฉ์ ์์์ ํ๋น์ฑ์ ์กฐ์ฌํ์๋ค.
์ฒซ์งธ๋ก, ๋ PET/MR๋ฅผ ์ํ ๋ ๋ฒจ์
๋ถํ ์ ์ด์ฉํ MR ์ด๋ฏธ์ง ๊ธฐ๋ฐ ๊ฐ์ ๋ณด์ ๋ฐฉ๋ฒ์ ๊ฐ๋ฐํ์๋ค. MR ์ด๋ฏธ์ง ๊ธฐ๋ฐ ๊ฐ์ ๋ณด์ ์ ๋ถ์ ํ์ฑ์ ์ ๋ํ ์ค๋ฅ์ ๋ PET/MRI ์ฐ๊ตฌ์์ ๋ณ๋ณ์ ์๋ชป๋ ํ๋
์ผ๋ก ์ด์ด์ง๋ค. ์ด ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํด, ์๊ธฐ์ฅ ๋ถ๊ท ์ผ ๋ณด์ ์ ํฌํจํ ๋ค์ ๋ ๋ฒจ์
์๊ณ ๋ฆฌ์ฆ์ ๊ธฐ์ดํ ๊ฐ์ ๋ ์ด๋จํ ์์ฝ ์๊ฐ MR-AC ๋ฐฉ๋ฒ์ ์ ์ํ์๋ค. ๋ํ CT-AC ๋ฐ PET/MRI ์ค์บ๋ ์ ์กฐ์
์ฒด๊ฐ ์ ๊ณตํ MR-AC์ ๋น๊ตํ์ฌ ๋ ๋ฒจ์
๊ธฐ๋ฐ MR-AC ๋ฐฉ๋ฒ์ ์์์ ์ฌ์ฉ๊ฐ๋ฅ์ฑ์ ํ๊ฐํ์๋ค.
๋์งธ๋ก, MR-IGRT ์์คํ
์ ์ํ ์ฌ์ธต ์ปจ๋ณผ๋ฃจ์
์ ๊ฒฝ๋ง ๋ชจ๋ธ์ ์ฌ์ฉํ์ฌ ์ ํ๋ MR ์ด๋ฏธ์ง์์ ์์ฑ๋ ํฉ์ฑ CT ๋ฐฉ๋ฒ๋ฅผ ์ ์ํ์๋ค. ์ด ํฉ์ฑ CT ์ด๋ฏธ์ง๋ฅผ ๋ณํ ์ ํฉ์ ์ฌ์ฉํ์ฌ ์์ฑ๋ ๋ณํ CT์ ๋น๊ต ํ์๋ค. ๋ํ ๊ณจ๋ฐ, ํ๋ถ ๋ฐ ๋ณต๋ถ ํ์์์์ ๊ธฐํํ์ , ์ ๋์ ๋ถ์์ ํตํด ๋ฐฉ์ฌ์ ์น๋ฃ๊ณํ์์์ ํฉ์ฑ CT๋ฅผ ์ฌ์ฉ๊ฐ๋ฅ์ฑ์ ํ๊ฐํ์๋ค.Chapter 1. Introduction 1
1.1. Background 1
1.1.1. The Integration of MRI into Other Medical Devices 1
1.1.2. Chanllenges in the MRI Integrated System 4
1.1.3. Synthetic CT Generation 5
1.2. Purpose of Research 6
Chapter 2. MRI-based Attenuation Correction for PET/MRI 8
2.1. Background 8
2.2. Materials and Methods 10
2.2.1. Brain PET Dataset 19
2.2.2. MR-Based Attenuation Map using Level-Set Algorithm 12
2.2.3. Image Processing and Reconstruction 18
2.3. Results 20
2.4. Discussion 28
Chapter 3. MRI-based synthetic CT generation for MR-IGRT 30
3.1. Background 30
3.2. Materials and Methods 32
3.2.1. MR-dCT Paired DataSet 32
3.2.2. Synthetic CT Generation using 2D CNN 36
3.2.3. Data Analysis 38
3.3. Results 41
3.3.1. Image Comparison 41
3.3.2. Geometric Analysis 49
3.3.3. Dosimetric Analysis 49
3.4. Discussion 56
Chapter 4. Conclusions 59
Bibliography 60
Abstract in Korean (๊ตญ๋ฌธ ์ด๋ก) 64Docto
Quantitative Susceptibility Imaging of Tissue Microstructure Using Ultra-High Field MRI
This thesis has used ultra-high field (UHF) magnetic resonance imaging (MRI) to investigate the fundamental relationships between tissue microstructure and such susceptibility-based contrast parameters as the apparent transverse relaxation rate (R2*), the local Larmor frequency shift (LFS) and quantitative volume magnetic susceptibility (QS). The interaction of magnetic fields with biological tissues results in shifts in the LFS which can be used to distinguish underlying cellular architecture. The LFS is also linked to the relaxation properties of tissues in a gradient echo MRI sequence. Equally relevant, histological analysis has identified iron and myelin as two major sources of the LFS. As a result, computation of LFS and the associated volume magnetic susceptibility from MRI phase data may serve as a significant method for in vivo monitoring of changes in iron and myelin associated with normal, healthy aging, as well as neurological disease processes.
In this research, the cellular level underpinnings of the R2* and LFS signals were examined in a model rat brain system using 9.4 T MRI. The study was carried out using biophysical modeling and correlation with quantitative histology. For the first time, multiple biophysical modeling schemes were compared in both gray and white matter of excised rat brain tissue. Suprisingly, R2* dependence on tissue orientation has not been fully understood. Accordingly, scaling relations were derived for calculating the reversible, mesoscopic magnetic field component, R2\u27, of the apparent transverse relaxation rate from the orientation dependence in gray and white matter. Our results demonstrate that the orientation dependence of R2* and LFS in both white and cortical gray matter has a sinusoidal dependence on tissue orientation and a linear dependence on the volume fraction of myelin in the tissue.
A susceptibility processing pipeline was also developed and applied to the calculation of phase-combined LFS and QS maps. The processing pipeline was subsequently used to monitor myelin and iron changes in multiple sclerosis (MS) patients compared to healthy, age and gender-matched controls. With the use of QS and R2* mapping, evidence of statistically significant increases in iron deposition in sub-cortical gray matter, as well as myelin degeneration along the white matter skeleton, were identified in MS patients. The magnetic susceptibility-based MRI methods were then employed as potential clinical biomarkers for disease severity monitoring of MS. It was demonstrated that the combined use of R2* and QS, obtained from multi-echo gradient echo MRI, could serve as an improved metric for monitoring both gray and white matter changes in early MS
Multispectral segmentation of whole-brain MRI
Magnetic Resonance Imaging (MRI) is a widely used medical technology for diagnosis and detection of various tissue abnormalities, tumor detection, and in evaluation of either residual or recurrent tumors. This thesis work exploits MRI information acquired on brain tumor structure and physiological properties and uses a novel image segmentation technique to better delineate tissue differences.;MR image segmentation will be important in distinguishing between boundaries of different tissues in the brain. A segmentation software tool was developed that combines the different types of clinical MR images and presents them as a single colored image. This technique is based on the fuzzy c-means (FCM) clustering algorithm. The MR data sets are used to form five-dimensional feature vectors. These vectors are segmented by FCM into six tissue classes for normal brains and nine tissue classes for human brains with tumors. The segmented images are then compared with segmentation performed using Statistical Parametric Mapping (SPM2)---software that is commonly used for brain tissue segmentation. The results from segmenting the whole volume MRI using FCM show better distinction between tumor tissues than SPM2
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