The results show that the proposed method provides positive contrast for the seeds and correctly differentiates them from other structures that appear similar to the seeds on conventional magnitude images.

MR spectroscopic (MRS) images from a large volume of brain can be obtained using a 3D echo-planar spectroscopic imaging (3D-EPSI) sequence. However, routine applications of 3D-EPSI are still limited by a long scan time. In this communication, a new approach termed "spectral phase-corrected generalized autocalibrating partially parallel acquisitions" (SPC-GRAPPA) is introduced for the reconstruction of 3D-EPSI data to accelerate data acquisition while preserving the accuracy of quantitation of brain metabolites. In SPC-GRAPPA, voxel-by-voxel spectral phase alignment between metabolite 3D-EPSI from individual coil elements is performed in the frequency domain, utilizing the whole spectrum from interleaved water reference 3D-EPSI for robust estimation of the zero-order phase correction. The performance of SPC-GRAPPA was compared with that of fully encoded 3D-EPSI and conventional GRAPPA. Analysis of whole-brain 3D-EPSI data reconstructed by SPC-GRAPPA demonstrates that SPC-GRAPPA with an acceleration factor of 1. Key words: phase correction; partial parallel imaging; echoplanar spectroscopic imaging; brain metabolites; short echo time Volumetric echo-planar spectroscopic imaging (3D-EPSI) is currently the most efficient strategy to obtain 3D maps of brain metabolite distributions (1). Compared to conventional spectroscopic imaging, sampling efficiency is gained with EPSI by simultaneously encoding the spectral dimension together with one spatial dimension, thus reducing the time-consuming spatial encoding in spectroscopic imaging to two dimensions. Although the acquisition speed of 3D-EPSI is one order of magnitude faster than that of conventional spectroscopic imaging methods, applications of 3D-EPSI are still limited by relatively long scan times, especially when high spatial resolution is desired. With the advent of multichannel phased-array coils, faster acquisition has become possible with parallel imaging that requires fewer numbers of phase-encoding (PE) steps for image reconstruction, albeit at the expense of a reduced signal-to-noise ratio (SNR) (2). Parallel imaging was initially applied to 2D spectroscopic imaging employing the sensitivity encoding (SENSE) method (3,4). However, phase cancellations reduced metabolite signal and introduced artifacts (2). To avoid phase cancellation, coherent phase alignment between the EPSI data from the individual coils in a phased array is required. According to the Fourier shift theorem, the phase alignment can be done in either the time domain or the frequency domain. In the time domain, phase alignment can be done by "time-shifting" the free induction decay (FID) signals from different coil elements until the FID signals start with the same phase, which is usually measured from the first sampling point of the FID signal. In the frequency domain, phase alignment can be achieved by maximizing the integral of the real component of each spectrum (5,6) from the different coil elements. Such spectral phase correction (SPC) for resonance peaks, which takes the entire 1 H sp...

ABSTRACT:Partial parallel imaging (PPI) techniques using array coils and multichannel receivers have become an effective approach to achieving fast magnetic resonance imaging (MRI). This article presents a Matlab toolbox called PULSAR (Parallel imaging Utilizing Localized Surface-coil Acquisition and Reconstruction) that can simulate the data acquisition and image reconstruction, and analyze performance of five common PPI techniques. PULSAR can simulate sensitivity functions of rectangular loop coils using a quasi-static model based on Biot-Savart's Law and undersampled multichannel data acquisition on a rectilinear k-space grid. In addition, PULSAR provides performance evaluation of the techniques based on artifact power (AP), signal-to-noise ratio (SNR), and computational complexity. In this article, the structure and functionality of the PULSAR toolbox are described. Examples using both the simulated and real four-channel and eight-channel data were used to demonstrate the utilities of the toolbox and to show that PULSAR is a convenient and effective means to study the PPI under different coil geometries, acquisition strategies, and reconstruction methods.

Road surface quality is essential for improving driving experience and reducing traffic accidents. Traditional road condition monitoring systems are limited in their temporal (speed) and spatial (coverage) responses needed for maintaining overall road quality. Several alternative systems have been proposed that utilize sensors mounted on vehicles. In particular, with the ubiquitous use of smartphones for navigation, smartphone-based road condition assessment has emerged as a promising new approach. In this paper, we propose to analyze different multiclass supervised machine learning techniques to effectively classify road surface conditions using accelerometer, gyroscope and GPS data collected from smartphones. Our work focuses on classification of three main class labels-smooth road, potholes, and deep transverse cracks. We hypothesize that using features from all three axes of the sensors provides more accurate results as compared to using features from only one axis. We also investigate the performance of deep neural networks to classify road conditions with and without explicit manual feature extraction. Our results indicate that models trained with features from all axes of the smartphone sensors outperform models that use only one axis. We also observe that the use of neural networks provides a significantly improved data classification. The machine learning approach discussed here can be implemented on a larger scale to monitor roads for defects that present a safety risk to commuters as well as to provide maintenance information to relevant authorities.

Multispectral autofluorescence lifetime imaging (maFLIM) can be used to clinically image a plurality of metabolic and biochemical autofluorescence biomarkers of oral epithelial dysplasia and cancer. This study tested the hypothesis that maFLIM-derived autofluorescence biomarkers can be used in machine-learning (ML) models to discriminate dysplastic and cancerous from healthy oral tissue. Clinical widefield maFLIM endoscopy imaging of cancerous and dysplastic oral lesions was performed at two clinical centers. Endoscopic maFLIM images from 34 patients acquired at one of the clinical centers were used to optimize ML models for automated discrimination of dysplastic and cancerous from healthy oral tissue. A computer-aided detection system was developed and applied to a set of endoscopic maFLIM images from 23 patients acquired at the other clinical center, and its performance was quantified in terms of the area under the receiver operating characteristic curve (ROC-AUC). Discrimination of dysplastic and cancerous from healthy oral tissue was achieved with an ROC-AUC of 0.81. This study demonstrates the capabilities of widefield maFLIM endoscopy to clinically image autofluorescence biomarkers that can be used in ML models to discriminate dysplastic and cancerous from healthy oral tissue. Widefield maFLIM endoscopy thus holds potential for automated in situ detection of oral dysplasia and cancer.

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual’s plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set.

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Chemical exchange saturation transfer (CEST) is an emerging MRI contrast mechanism that is capable of noninvasively imaging dilute CEST agents and local properties such as pH and temperature, augmenting the routine MRI methods. However, the routine CEST MRI includes a long RF saturation pulse followed by fast image readout, which is associated with high specific absorption rate and limited spatial resolution. In addition, echo planar imaging (EPI)-based fast image readout is prone to image distortion, particularly severe at high field. To address these limitations, we evaluated magnetization transfer (MT) prepared gradient echo (GRE) MRI for CEST imaging. We proved the feasibility using numerical simulations and experiments in vitro and in vivo. Then we optimized the sequence by serially evaluating the effects of the number of saturation steps, MT saturation power (B1), GRE readout flip angle (FA), and repetition time (TR) upon the CEST MRI, and further demonstrated the endogenous amide proton CEST imaging in rats brains (n = 5) that underwent permanent middle cerebral artery occlusion. The CEST images can identify ischemic lesions in the first 3 hours after occlusion. In summary, our study demonstrated that the readily available MT-prepared GRE MRI, if optimized, is CEST-sensitive and remains promising for translational CEST imaging.