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Inverse Z-spectrum analysis for spillover-, MT-, and T1 -corrected steady-state pulsed CEST-MRI--application to pH-weighted MRI of acute stroke.

ABSTRACT: Endogenous chemical exchange saturation transfer (CEST) effects are always diluted by competing effects, such as direct water proton saturation (spillover) and semi-solid macromolecular magnetization transfer (MT). This leads to unwanted T2 and MT signal contributions that lessen the CEST signal specificity to the underlying biochemical exchange processes. A spillover correction is of special interest for clinical static field strengths and protons resonating near the water peak. This is the case for all endogenous CEST agents, such as amide proton transfer, -OH-CEST of glycosaminoglycans, glucose or myo-inositol, and amine exchange of creatine or glutamate. All CEST effects also appear to be scaled by the T1 relaxation time of water, as they are mediated by the water pool. This forms the motivation for simple metrics that correct the CEST signal. Based on eigenspace theory, we propose a novel magnetization transfer ratio (MTRRex ), employing the inverse Z-spectrum, which eliminates spillover and semi-solid MT effects. This metric can be simply related to Rex , the exchange-dependent relaxation rate in the rotating frame, and ka , the inherent exchange rate. Furthermore, it can be scaled by the duty cycle, allowing for simple translation to clinical protocols. For verification, the amine proton exchange of creatine in solutions with different agar concentrations was studied experimentally at a clinical field strength of 3 T, where spillover effects are large. We demonstrate that spillover can be properly corrected and that quantitative evaluation of pH and creatine concentration is possible. This proves that MTRRex is a quantitative and biophysically specific CEST-MRI metric. Applied to acute stroke induced in rat brain, the corrected CEST signal shows significantly higher contrast between the stroke area and normal tissue, as well as less B1 dependence, than conventional approaches.

SUBMITTER: Zaiss M

PROVIDER: S-EPMC4520220 | biostudies-other | 2014 Mar

REPOSITORIES: biostudies-other

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Project description:BackgroundParkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs).Methods155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models.ResultsOur VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.ConclusionThe SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.

Dataset Information

Inverse Z-spectrum analysis for spillover-, MT-, and T1 -corrected steady-state pulsed CEST-MRI--application to pH-weighted MRI of acute stroke.

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