Magnetic resonance imaging
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To identify texture features of multiparametric MRI (mp-MRI) for pre-treatment prediction of bone metastases (BM) in patients with prostate cancer (PCa). ⋯ Multiparametric MRI-based texture feature was significant predictor for BM in PCa. Clinical risk factors combined with MRI-based texture feature could further improve the prediction performance, which provide an illustrative example of precision medicine and may affect treatment strategies.
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This study sought to determine the feasibility of using Simultaneous Non-contrast Angiography and intraPlaque Hemorrhage (SNAP) to detect the lipid-rich/necrotic core (LRNC), and develop a machine learning based algorithm to segment plaque components on SNAP images. ⋯ The RF is the overall best classifier for our proposed method, and the feasibility of using SNAP to identify plaque components, including LRNC, IPH, CA, and FT has been validated. The proposed segmentation method using a single SNAP sequence might be a promising tool for atherosclerotic plaque components assessment.
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Magnetic resonance imaging (MRI) is an important tool for analysis of deep brain grey matter structures. However, analysis of these structures is limited due to low intensity contrast typically found in whole brain imaging protocols. Herein, we propose a big data registration-enhancement (BDRE) technique to augment the contrast of deep brain structures using an efficient large-scale non-rigid registration strategy. ⋯ BDRE significantly improves mean segmentation accuracy over all methods tested for both thalamic nuclei (3 T imaging: 9.1%; 7 T imaging: 15.6%; DN: 6.9%; SR: 16.2%) and hippocampal subfields (3 T T1 only: 8.7%; DN: 8.4%; SR: 8.6%). We also present DSC performance for each thalamic nucleus and hippocampal subfield and show that BDRE can help MA segmentation for individual thalamic nuclei and hippocampal subfields. This work will enable large-scale analysis of clinically relevant deep brain structures from commonly acquired T1 images.
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Magnetic resonance elastography (MRE) can be used to noninvasively resolve the displacement pattern of induced mechanical waves propagating in tissue. The goal of this study is to establish an ergonomically flexible passive-driver design for brain MRE, to evaluate the reproducibility of MRE tissue-stiffness measurements, and to investigate the relationship between tissue-stiffness measurements and driver frequencies. An ergonomically flexible passive pillow-like driver was designed to induce mechanical waves in the brain. ⋯ The ranges of within-subject coefficients of variation for white matter, gray matter, and whole-brain shear-stiffness measurements for the three frequencies were 1.8-3.5% (60 Hz), 4.7-6.0% (50 Hz), and 3.7-4.1% (40 Hz). An ergonomic pneumatic pillow-like driver is feasible for highly reproducible in vivo evaluation of brain-tissue shear stiffness. Brain-tissue shear-stiffness values were frequency-dependent, thus emphasizing the importance of standardizing MRE acquisition protocols in multi-center studies.
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Comparative Study
Assessing liver fibrosis in chronic hepatitis B using MR extracellular volume measurements: Comparison with serum fibrosis indices.
To evaluate the diagnostic value of liver extracellular volume (ECVliver) measurement by equilibrium MR in staging liver fibrosis in chronic hepatitis B (CHB) patients, and to compare its performance with serum fibrosis indices. ⋯ MR ECVliver provides a promising noninvasive tool in staging liver fibrosis for CHB patients, superior to the fibrosis indices of APRI and FIB-4.