Purpose To evaluate whether compartmental analysis by using hybrid multidimensional magnetic resonance (MR) imaging can be used to diagnose prostate cancer and determine its aggressiveness. Materials and Methods Twenty-two patients with prostate cancer underwent preoperative 3.0-T MR imaging. Axial images were obtained with hybrid multidimensional MR imaging by using all combinations of echo times (47, 75, 100 msec) and b values of 0, 750, 1500 sec/mm, resulting in a 3 × 3 array of data associated with each voxel. Volumes of the tissue components stroma, epithelium, and lumen were calculated by fitting the hybrid data to a three-compartment signal model, with distinct, paired apparent diffusion coefficient (ADC) and T2 values associated with each compartment. Volume fractions and conventional ADC and T2 were measured for regions of interest in sites of prostatectomy-verified malignancy (n = 28) and normal tissue (n = 71). Receiver operating characteristic (ROC) analysis was used to evaluate the performance of various parameters in differentiating prostate cancer from benign tissue. Results Compared with normal tissue, prostate cancer showed significantly increased fractional volumes of epithelium (23.2% ± 7.1 vs 48.8% ± 9.2, respectively) and reduced fractional volumes of lumen (26.4% ± 14.1 vs 14.0% ± 5.2) and stroma (50.5% ± 15.7 vs 37.2% ± 9.1) by using hybrid multidimensional MR imaging. The fractional volumes of tissue components show a significantly higher Spearman correlation coefficient with Gleason score (epithelium: ρ = 0.652, P = .0001; stroma: ρ = -0.439, P = .020; lumen: ρ = -0.390, P = .040) compared with traditional T2 values (ρ = -0.292, P = .132) and ADCs (ρ = -0.315, P = .102). The area under the ROC curve for differentiation of cancer from normal prostate was highest for fractional volume of epithelium (0.991), followed by fractional volumes of lumen (0.800) and stroma (0.789). Conclusion Fractional volumes of prostatic lumen, stroma, and epithelium change significantly when cancer is present. These parameters can be measured noninvasively by using hybrid multidimensional MR imaging and have the potential to improve the diagnosis of prostate cancer and determine its aggressiveness. RSNA, 2018 Online supplemental material is available for this article.

We present a Wiener filtering based algorithm for the elimination of motion artifacts present in Near Infrared (NIR) spectroscopy measurements. Until now, adaptive filtering was the only technique used in the noise cancellation in NIR studies. The results in this preliminary study revealed that the proposed method gives better estimates than the classical adaptive filtering approach without the need for additional sensor measurements. Moreover, this novel technique has the potential to filter out motion artifacts in functional near infrared (fNIR) signals, too.

A novel center-out 3D trajectory for sampling magnetic resonance data is presented. The trajectory set is based on a single Fermat spiral waveform, which is substantially undersampled in the center of k-space. Multiple trajectories are combined in a ''stacked cone'' configuration to give very uniform sampling throughout a ''hub,'' which is very efficient in terms of gradient performance and uniform trajectory spacing. The fermat looped, orthogonally encoded trajectories (FLORET) design produces less gradient-efficient trajectories near the poles, so multiple orthogonal hub designs are shown. These multihub designs oversample k-space twice with orthogonal trajectories, which gives unique properties but also doubles the minimum scan time for critical sampling of k-space. The trajectory is shown to be much more efficient than the conventional stack of cones trajectory, and has nearly the same signal-to-noise ratio efficiency (but twice the minimum scan time) as a stack of spirals trajectory. As a center-out trajectory, it provides a shorter minimum echo time than stack of spirals, and its spherical k-space coverage can dramatically reduce Gibbs ringing. Magn Reson Med 66:1303-1311,

Functional near-infrared spectroscopy (fNIR) is a neroimaging modality that enables continuous, noninvasive, and portable monitoring of changes in blood oxygenation and blood volume related to human brain function. Over the last decade, studies in the laboratory have established that fNIR spectroscopy provides a veridical measure of oxygenation and blood flow in the brain. Our recent findings indicate that fNIR can effectively monitor cognitive tasks such as attention, working memory, target categorization, and problem solving. These experimental outcomes compare favorably with functional magnetic resonance imaging (fMRI) studies, and in particular, with the blood oxygenation level dependent signal. Since fNIR can be implemented in the form of a wearable and minimally intrusive device, it has the capacity to monitor brain activity under real life conditions and in everyday environments. Moreover, the fNIR system is amenable to integration with other established physiological and neurobehavioral measures, including electroencephalogram, eye tracking, pupil reflex, heart rate variability, respiration, and electrodermal activity.