Esin Ozturk-Isik

High-Speed 3T MR Spectroscopic Imaging of Prostate With Flyback Echo-Planar Encoding

Prostate MR spectroscopic imaging (MRSI) at 3T may provide two‐fold higher spatial resolution over 1.5T, but this can result in longer acquisition times to cover the entire gland using conventional phase‐encoding. In this study, flyback echo‐planar readout trajectories were incorporated into a Malcolm Levitts composite‐pulse decoupling sequence (MLEV)–point‐resolved spectroscopy sequence (PRESS) to accelerate the acquisition of large array (16 × 16 × 8), high spatial (0.154 cm3) resolution MRSI data by eight‐fold to just 8.5 minutes. Artifact free, high‐quality MRSI data was obtained in nine prostate cancer patients. Easy data reconstruction and the robustness of the flyback echo‐planar encoding make this technique particularly suitable for the clinical setting. The short acquisition time provided by this method reduces the 3T prostate MRI/MRSI exam time, allows longer repetition times, and/or allows the acquisition of additional MR acquisitions within the same exam. J. Magn. Reson. Imaging 2007;25:1288–1292.

1 H Spectroscopic Imaging of Human Brain at 3 Tesla: Comparison of Fast Three-Dimensional Magnetic Resonance Spectroscopic Imaging Techniques

To investigate the signal‐to‐noise‐ratio (SNR) and data quality of time‐reduced three‐dimensional (3D) proton magnetic resonance spectroscopic imaging (1H MRSI) techniques in the human brain at 3 Tesla.

Assesment of perfusion in glial tumors with arterial spin labeling; comparison with dynamic susceptibility contrast method

PURPOSE Arterial spin labeling perfusion imaging (ASL-PI) is a non-invasive perfusion imaging method that can be used for evaluation and quantification of cerebral blood flow (CBF). Aim of our study was to evaluating the efficiency of ASL in histopathological grade estimation of glial tumors and comparing findings with dynamic susceptibility contrast perfusion imaging (DSC-PI) method. METHODS This study involved 33 patients (20 high-grade and 13 low-grade gliomas). Multiphase multislice pulsed ASL MRI sequence and a first-passage gadopentetate dimeglumine T2*-weighted gradient-echo single-shot echo-planar sequence were acquired for all the patients. For each patient, perfusion relative signal intensity (rSI), CBF and relative CBF (rCBF) on ASL-PI and relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) values on DSC-PI were determined. The relative signal intensity of each tumor was determined as the maximal SI within the tumor divided by SI within symetric region in the contralateral hemisphere on ASL-PI. rCBV and rCBF were calculated by deconvolution of an arterial input function. Relative values of the lesions were obtained by dividing the values to the normal appearing symmetric region on the contralateral hemisphere. For statistical analysis, Mann-Whitney ranksum test was carried out. Receiver operating characteristic curve (ROC) analysis was performed to assess the relationship between the rCBF-ASL, rSI-ASL, rCBV and rCBF ratios and grade of gliomas. Their cut-off values permitting best discrimination was calculated. The correlation between rCBV, rCBF, rSI-ASL and rCBF-ASL and glioma grade was assessed using Spearman correlation analysis. RESULTS There was a statistically significant difference between low and high-grade tumors for all parameters. Correlation analyses revealed significant positive correlations between rCBV and rCBF-ASL (r=0.81, p<0.001). However correlation between rCBF and rCBF-ASL was weaker (r=0.64, p<0.001). CONCLUSION Arterial spin labeling is an employable imaging technique for evaluating tumor perfusion non-invasively and may be useful in differentiating high and low grade gliomas.

3D 1H MRSI of brain tumors at 3.0 tesla using an eight-channel phased-array head coil†

To implement proton magnetic resonance spectroscopic imaging (1H MRSI) at 3 Tesla (3T) using an eight‐channel phased‐array head coil in a population of brain‐tumor patients.

Multi-Channel Metabolic Imaging, with SENSE reconstruction, of Hyperpolarized [1-13C] Pyruvate in a Live Rat at 3.0 tesla on a Clinical MR Scanner

We report metabolic images of (13)C, following injection of a bolus of hyperpolarized [1-(13)C] pyruvate in a live rat. The data were acquired on a clinical scanner, using custom coils for volume transmission and array reception. Proton blocking of all carbon resonators enabled proton anatomic imaging with the system body coil, to allow for registration of anatomic and metabolic images, for which good correlation was achieved, with some anatomic features (kidney and heart) clearly visible in a carbon image, without reference to the corresponding proton image. Parallel imaging with sensitivity encoding was used to increase the spatial resolution in the SI direction of the rat. The signal to noise ratio in was in some instances unexpectedly high in the parallel images; variability of the polarization among different trials, plus partial volume effects, are noted as a possible cause of this.

3D sensitivity encoded ellipsoidal MR spectroscopic imaging of gliomas at 3T.

PURPOSE The goal of this study was to implement time efficient data acquisition and reconstruction methods for 3D magnetic resonance spectroscopic imaging (MRSI) of gliomas at a field strength of 3T using parallel imaging techniques. METHODS The point spread functions, signal to noise ratio (SNR), spatial resolution, metabolite intensity distributions and Cho:NAA ratio of 3D ellipsoidal, 3D sensitivity encoding (SENSE) and 3D combined ellipsoidal and SENSE (e-SENSE) k-space sampling schemes were compared with conventional k-space data acquisition methods. RESULTS The 3D SENSE and e-SENSE methods resulted in similar spectral patterns as the conventional MRSI methods. The Cho:NAA ratios were highly correlated (P<.05 for SENSE and P<.001 for e-SENSE) with the ellipsoidal method and all methods exhibited significantly different spectral patterns in tumor regions compared to normal appearing white matter. The geometry factors ranged between 1.2 and 1.3 for both the SENSE and e-SENSE spectra. When corrected for these factors and for differences in data acquisition times, the empirical SNRs were similar to values expected based upon theoretical grounds. The effective spatial resolution of the SENSE spectra was estimated to be same as the corresponding fully sampled k-space data, while the spectra acquired with ellipsoidal and e-SENSE k-space samplings were estimated to have a 2.36-2.47-fold loss in spatial resolution due to the differences in their point spread functions. CONCLUSION The 3D SENSE method retained the same spatial resolution as full k-space sampling but with a 4-fold reduction in scan time and an acquisition time of 9.28 min. The 3D e-SENSE method had a similar spatial resolution as the corresponding ellipsoidal sampling with a scan time of 4:36 min. Both parallel imaging methods provided clinically interpretable spectra with volumetric coverage and adequate SNR for evaluating Cho, Cr and NAA.

T1 and T2 Metabolite Relaxation Times in Normal Brain at 3T and 7T

This study was designed to measure T1 and T2 relaxation times of the singlets in normal brain at 7T. Our results demonstrated that the T1 relaxation values of total creatine (tCr)-CH3 and N-acetyl aspartate (NAA) in the parietal white matter significantly increased at 7T compared to 3T, while the T1 of Choline-containing compounds (Cho) was similar between field strengths.

Unaliasing Lipid Contamination for MR Spectroscopic Imaging of Gliomas at 3T Using Sensitivity Encoding (SENSE)

3D magnetic resonance spectroscopic imaging (MRSI) has been successfully employed to extract information about brain tumor metabolism, such as cell membrane breakdown, cellular energetics, and neuronal integrity, through its ability to differentiate signals coming from choline (Cho), creatine (Cr), and N‐acetyl aspartate (NAA) molecules. The additional presence of lipids within subregions of the tumor may indicate cellular membrane breakdown due to cell death. Another potential source of lipids is subcutaneous fat, which may be excited with point‐resolved spectroscopy (PRESS) volume selection and aliased into the spectral field of view (FOV) due to the chemical shift artifact and the low bandwidth of the selection pulses. The purpose of our study was to employ a postprocessing method for unaliasing lipid resonances originating from in‐slice subcutaneous lipids from the 3D MRSI of gliomas at 3T, using an eight‐channel phased‐array coil and sensitivity encoding (SENSE). Magn Reson Med, 2006.

Elliptical magnetic resonance spectroscopic imaging with GRAPPA for imaging brain tumors at 3 T

Magnetic Resonance Spectroscopic Imaging (MRSI) is a technique for imaging spatial variation of metabolites and has been very useful in characterizing biochemical changes associated with disease as well as response to therapy in malignant pathologies. This work presents a self-calibrated undersampling to accelerate 3D elliptical MRSI and an extrapolation-reconstruction algorithm based on the GRAPPA method. The accelerated MRSI technique was tested in three volunteers and five brain tumor patients. Acceleration allowed larger spatial coverage and consequently, less lipid contamination in spectra, compared to fully sampled acquisition within the same scantime. Metabolite concentrations measured from the accelerated acquisitions were in good agreement with measurements obtained from fully sampled MRSI scans.

Final Gleason Score Prediction Using Discriminant Analysis and Support Vector Machine Based on Preoperative Multiparametric MR Imaging of Prostate Cancer at 3T

Objective. This study aimed at evaluating linear discriminant analysis (LDA) and support vector machine (SVM) classifiers for estimating final Gleason score preoperatively using multiparametric magnetic resonance imaging (mp-MRI) and clinical parameters. Materials and Methods. Thirty-three patients who underwent mp-MRI on a 3T clinical MR scanner and radical prostatectomy were enrolled in this study. The input features for classifiers were age, the presence of a palpable prostate abnormality, prostate specific antigen (PSA) level, index lesion size, and Likert scales of T2 weighted MRI (T2w-MRI), diffusion weighted MRI (DW-MRI), and dynamic contrast enhanced MRI (DCE-MRI) estimated by an experienced radiologist. SVM based recursive feature elimination (SVM-RFE) was used for eliminating features. Principal component analysis (PCA) was applied for data uncorrelation. Results. Using a standard PCA before final Gleason score classification resulted in mean sensitivities of 51.19% and 64.37% and mean specificities of 72.71% and 39.90% for LDA and SVM, respectively. Using a Gaussian kernel PCA resulted in mean sensitivities of 86.51% and 87.88% and mean specificities of 63.99% and 56.83% for LDA and SVM, respectively. Conclusion. SVM classifier resulted in a slightly higher sensitivity but a lower specificity than LDA method for final Gleason score prediction for prostate cancer for this limited patient population.