Speed and signal-to-noise ratio (SNR) are critical for localized magnetic resonance spectroscopy (MRS) of low-concentration metabolites. Matching voxels to anatomical compartments a priori yields better SNR than the spectra created by summing signals from constituent chemical-shift-imaging (CSI) voxels post-acquisition. Here, a new method of localized Spectroscopy using Linear Algebraic Modeling (SLAM) is presented, that can realize this additional SNR gain. Unlike prior methods, SLAM generates spectra from C signal-generating anatomic compartments utilizing a CSI sequence wherein essentially only the C central k-space phase-encoding gradient steps with highest SNR are retained. After MRI-based compartment segmentation, the spectra are reconstructed by solving a sub-set of linear simultaneous equations from the standard CSI algorithm. SLAM is demonstrated with one-dimensional CSI surface coil phosphorus MRS in phantoms, the human leg and the heart on a 3T clinical scanner. Its SNR performance, accuracy, sensitivity to registration errors and inhomogeneity, are evaluated. Compared to one-dimensional CSI, SLAM yielded quantitatively the same results 4-times faster in 24 cardiac patients and healthy subjects. SLAM is further extended with fractional phase-encoding gradients that optimize SNR and/or minimize both inter- and intra-compartmental contamination. In proactive cardiac phosphorus MRS of 6 healthy subjects, both SLAM and fractional-SLAM (fSLAM) produced results indistinguishable from CSI while preserving SNR gains of 36–45% in the same scan-time. Both SLAM and fSLAM are simple to implement and reduce the minimum scan-time for CSI, which otherwise limits the translation of higher SNR achievable at higher field strengths to faster scanning.

Destructive interference from phase fluctuations caused by motion during 1 H magnetic resonance spectroscopy (MRS) stimulated-echo acquisition mode (STEAM) and point-resolved spectroscopy (PRESS) acquisitions can significantly diminish the traditional ͌N-gain in signal-to-noise ratio (SNR) afforded by averaging N signals, especially in the torso. The SNR loss is highly variable among individuals, even when identical acquisition protocols are used. This paper presents a theory for the SNR loss, assuming that the phase fluctuates randomly. It is shown that SNR in conventional averaging is reduced by the factor sinc( ͌3/), where is the standard deviation (SD) of the phase. "Constructive averaging," whereby each individual acquisition is phase-corrected using the phase of a high-SNR peak before averaging, reverses the SNR loss from motioninduced dephasing, resulting in a {1/sinc( ͌3/)}-fold SNR improvement. It is also shown that basing phase corrections on an average of ͌N adjacent points both improves correction accuracy and effectively eliminates false signal artifacts when corrections are based on low-SNR peaks. The theory is validated over a sevenfold range of variation in signal loss due to motion observed in

Background: The timing of return to play after anterior cruciate ligament (ACL) reconstruction is still controversial due to uncertainty of true ACL graft state at the time of RTP. Recent work utilizing ultra-short echo T2* (UTE-T2*) magnetic resonance imaging (MRI) as a scanner-independent method to objectively and non-invasively assess the status of in vivo ACL graft remodeling has produced promising results. Purpose/Hypothesis: The purpose of this study was to prospectively and noninvasively investigate longitudinal changes in T2* within ACL autografts at incremental time points up to 12 months after primary ACL reconstruction in human patients. We hypothesized that (1) T2* would increase from baseline and initially exceed that of the intact contralateral ACL, followed by a gradual decline as the graft undergoes remodeling, and (2) remodeling would occur in a region-dependent manner. Study Design: Case series; Level of evidence, 4. Methods: Twelve patients (age range, 14-45 years) who underwent primary ACL reconstruction with semitendinosus tendon or bone–patellar tendon–bone autograft (with or without meniscal repair) were enrolled. Patients with a history of previous injury or surgery to either knee were excluded. Patients returned for UTE MRI at 1, 3, 6, 9, and 12 months after ACL reconstruction. Imaging at 1 month included the contralateral knee. MRI pulse sequences included high-resolution 3-dimensional gradient echo sequence and a 4-echo T2-UTE sequence (slice thickness, 1 mm; repetition time, 20 ms; echo time, 0.3, 3.3, 6.3, and 9.3 ms). All slices containing the intra-articular ACL were segmented from high-resolution sequences to generate volumetric regions of interest (ROIs). ROIs were divided into proximal/distal and core/peripheral sub-ROIs using standardized methods, followed by voxel-to-voxel registration to generate T2* maps at each time point. This process was repeated by a second reviewer for interobserver reliability. Statistical differences in mean T2* values and mean ratios of T2*inj/T2*intact (ie, injured knee to intact knee) among the ROIs and sub-ROIs were assessed using repeated measures and one-way analyses of variance. P < .05 represented statistical significance. Results: Twelve patients enrolled in this prospective study, 2 withdrew, and ultimately 10 patients were included in the analysis (n = 7, semitendinosus tendon; n = 3, bone–patellar tendon–bone). Interobserver reliability for T2* values was good to excellent (intraclass correlation coefficient, 0.84; 95% CI, 0.59-0.94; P < .001). T2* values increased from 5.5 ± 2.1 ms (mean ± SD) at 1 month to 10.0 ± 2.9 ms at 6 months ( P = .001), followed by a decline to 8.1 ± 2.0 ms at 12 months ( P = .129, vs 1 month; P = .094, vs 6 months). Similarly, mean T2*inj/T2*intact ratios increased from 62.8% ± 22.9% at 1 month to 111.1% ± 23.9% at 6 months ( P = .001), followed by a decline to 92.8% ± 29.8% at 12 months ( P = .110, vs 1 month; P = .086, vs 6 months). Sub-ROIs exhibited similar increases in T2* until reaching a peak at 6 months, followed by a gradual decline until the 12-month time point. There were no statistically significant differences among the sub-ROIs ( P > .05). Conclusion: In this preliminary study, T2* values for ACL autografts exhibited a statistically significant increase of 82% between 1 and 6 months, followed by an approximate 19% decline in T2* values between 6 and 12 months. In the future, UTE-T2* MRI may provide unique insights into the condition of remodeling ACL grafts and may improve our ability to noninvasively assess graft maturity before return to play.

NEURORADIOLOGYM ultiple sclerosis (MS) is the most common nontraumatic demyelinating neurologic disorder in young adults, affecting at least 2.5 million people worldwide (1). MRI is routinely used for both diagnosis and management of MS (2). A hallmark of MS is the presence of hyperintense lesions on images obtained with T2-weighted, proton density-weighted, and fluid-attenuated inversion recovery (FLAIR) MRI. Not all lesions seen on these images are active. Identification of active lesions is crucial for effective patient treatment (3). It is generally thought that active lesions show enhancement on T1-weighted MRI scans after the administration of gadolinium-based contrast agents (GBCAs). However, there are concerns about GBCA administration, including nephrogenic systemic fibrosis in patients with renal compromise (4) and longterm gadolinium deposition in various tissues (5)(6)(7)(8). This is particularly a concern in patients with MS, who undergo frequent imaging with GBCA administration for regular clinical follow-up, which may result in higher cumulative gadolinium deposition in tissues. While acknowledging Purpose: To evaluate whether deep learning can predict enhancing lesions on MRI scans obtained without the use of contrast material. Materials and Methods:This study involved prospective analysis of existing MRI data. A convolutional neural network was used for classification of enhancing lesions on unenhanced MRI scans. This classification was performed for each slice, and the slice scores were combined by using a fully connected network to produce participant-wise predictions. The network input consisted of 1970 multiparametric MRI scans from 1008 patients recruited from 2005 to 2009. Enhanced lesions on postcontrast T1-weighted images served as the ground truth. The network performance was assessed by using fivefold cross-validation. Statistical analysis of the network performance included calculation of lesion detection rates and areas under the receiver operating characteristic curve (AUCs).Results: MRI scans from 1008 participants (mean age, 37.7 years 6 9.7; 730 women) were analyzed. At least one enhancing lesion was observed in 519 participants. The sensitivity and specificity averaged across the five test sets were 78% 6 4.3 and 73% 6 2.7, respectively, for slice-wise prediction. The corresponding participant-wise values were 72% 6 9.0 and 70% 6 6.3. The diagnostic performances (AUCs) were 0.82 6 0.02 and 0.75 6 0.03 for slice-wise and participant-wise enhancement prediction, respectively. Conclusion:Deep learning used with conventional MRI identified enhanced lesions in multiple sclerosis from images from unenhanced multiparametric MRI with moderate to high accuracy.

Background Deep learning (DL) is a promising methodology for automatic detection of abnormalities in brain MRI. Purpose To automatically evaluate the quality of multicenter structural brain MRI images using an ensemble DL model based on deep convolutional neural networks (DCNNs). Study Type Retrospective. Population The study included 1064 brain images of autism patients and healthy controls from the Autism Brain Imaging Data Exchange (ABIDE) database. MRI data from 110 multiple sclerosis patients from the CombiRx study were included for independent testing. Sequence T1‐weighted MR brain images acquired at 3T. Assessment The ABIDE data were separated into training (60%), validation (20%), and testing (20%) sets. The ensemble DL model combined the results from three cascaded networks trained separately on the three MRI image planes (axial, coronal, and sagittal). Each cascaded network consists of a DCNN followed by a fully connected network. The quality of image slices from each plane was evaluated by the DCNN and the resultant image scores were combined into a volumewise quality rating using the fully connected network. The DL predicted ratings were compared with manual quality evaluation by two experts. Statistical Tests Receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, and positive (PPV) and negative (NPV) predictive values. Results The AUC, sensitivity, specificity, accuracy, PPV, and NPV for image quality evaluation of the ABIDE test set using the ensemble model were 0.90, 0.77, 0.85, 0.84, 0.42, and 0.96, respectively. On the CombiRx set the same model achieved performance of 0.71, 0.41, 0.84, 0.73, 0.48, and 0.80. Data Conclusion This study demonstrated the high accuracy of DL in evaluating image quality of structural brain MRI in multicenter studies. Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1260–1267.