Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences.

Acquisition of the intestinal microbiota begins at birth, and a stable microbial community develops from a succession of key organisms. Disruption of the microbiota during maturation by low-dose antibiotic exposure can alter host metabolism and adiposity. We now show that low-dose penicillin (LDP), delivered from birth, induces metabolic alterations and affects ileal expression of genes involved in immunity. LDP that is limited to early life transiently perturbs the microbiota, which is sufficient to induce sustained effects on body composition, indicating that microbiota interactions in infancy may be critical determinants of long-term host metabolic effects. In addition, LDP enhances the effect of high-fat diet induced obesity. The growth promotion phenotype is transferrable to germ-free hosts by LDP-selected microbiota, showing that the altered microbiota, not antibiotics per se, play a causal role. These studies characterize important variables in early-life microbe-host metabolic interaction and identify several taxa consistently linked with metabolic alterations. PAPERCLIP:

Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods.

PURPOSE: To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods. METHODS: A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE-MRI data of 53 women with malignant (n = 25) or benign (n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE-MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k-space data and assessing the impact of the B 1 + $$ {\mathrm{B}}_1^{+} $$ field inhomogeneity on estimated parameters. RESULTS: The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy of v p $$ {v}_p $$ and PS $$ \mathrm{PS} $$ increase to about 33% and 51% without correction for B 1 + $$ {\mathrm{B}}_1^{+} $$ field. CONCLUSION: We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.

Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function.

PURPOSE: In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to be a significant contributor to uncertainty in the estimation of kinetic parameters. This study is to assess the feasibility of using a deep learning network to estimate local Capillary Input Function (CIF) to estimate blood-brain barrier (BBB) permeability, while reducing the required scan time. MATERIALS AND METHOD: A total of 13 healthy subjects (younger (<40 y/o): 8, older (> 67 y/o): 5) were recruited and underwent 25-min DCE-MRI scans. The 25 min data were retrospectively truncated to 10 min to simulate a reduced scan time of 10 min. A deep learning network was trained to predict the CIF using simulated tissue contrast dynamics with two vascular transport models. The BBB permeability (PS) was measured using 3 methods: (i) Ca-25min, using DCE-MRI data of 25 min with individually sampled AIF (Ca); (ii) Ca-10min, using truncated 10min data with AIF (Ca); and (iii) Cp-10min, using truncated 10 min data with CIF (Cp). The PS estimates from the Ca-25min method were used as reference standard values to assess the accuracy of the Ca-10min and Cp-10min methods in estimating the PS values. RESULTS: When compared to the reference method(Ca-25min), the Ca-10min and Cp-10min methods resulted in an overestimation of PS by 217 ± 241 % and 48.0 ± 30.2 %, respectively. The Bland Altman analysis showed that the mean difference from the reference was 8.85 ± 1.78 (x10-4 min-1) with the Ca-10min, while it was reduced to 1.63 ± 2.25 (x10-4 min-1) with the Cp-10min, resulting in an average reduction of 81%. The limits of agreement also reduced by up to 39.2% with the Cp-10min. We found a 75% increase of BBB permeability in the gray matter and a 35% increase in the white matter, when comparing the older group to the younger group. CONCLUSIONS: We demonstrated the feasibility of estimating the capillary-level input functions using a deep learning network. We also showed that this method can be used to estimate subtle age-related changes in BBB permeability with reduced scan time, without compromising accuracy. Moreover, the trained deep learning network can automatically select CIF, reducing the potential uncertainty resulting from manual user-intervention.

Time-dependent diffusivity and kurtosis in phantoms and patients with head and neck cancer.

PURPOSE: To assess the reliability of measuring diffusivity, diffusional kurtosis, and cellular-interstitial water exchange time with long diffusion times (100-800 ms) using stimulated-echo DWI. METHODS: Time-dependent diffusion MRI was tested on two well-established diffusion phantoms and in 5 patients with head and neck cancer. Measurements were conducted using an in-house diffusion-weighted STEAM-EPI pulse sequence with multiple diffusion times at a fixed TE on three scanners. We used the weighted linear least-squares fit method to estimate time-dependent diffusivity, D ( t ) $$ D(t) $$ , and diffusional kurtosis, K ( t ) $$ K(t) $$ . Additionally, the Kärger model was used to estimate cellular-interstitial water exchange time ( τ ex $$ {\tau}_{ex} $$ ) from K ( t ) $$ K(t) $$ . RESULTS: Diffusivity measured by time-dependent STEAM-EPI measurements and commercial SE-EPI showed comparable results with R2 above 0.98 and overall 5.4 ± 3.0% deviation across diffusion times. Diffusional kurtosis phantom data showed expected patterns: constant D $$ D $$ and K $$ K $$  = 0 for negative controls and slow varying D $$ D $$ and K $$ K $$ for samples made of nanoscopic vesicles. Time-dependent diffusion MRI in patients with head and neck cancer found that the Kärger model could be considered valid in 72% ± 23% of the voxels in the metastatic lymph nodes. The median cellular-interstitial water exchange time estimated for lesions was between 58.5 ms and 70.6 ms. CONCLUSIONS: Based on two well-established diffusion phantoms, we found that time-dependent diffusion MRI measurements can provide stable diffusion and kurtosis values over a wide range of diffusion times and across multiple MRI systems. Moreover, estimation of cellular-interstitial water exchange time can be achieved using the Kärger model for the metastatic lymph nodes in patients with head and neck cancer.

Simultaneous estimation of the cellular water exchange rate, intracellular volume fraction, and longitudinal relaxation rate in cancer cells.

The purpose of the current study was to investigate the feasibility of simultaneously estimating the cellular water efflux rate ( k ie ), intracellular longitudinal relaxation rate ( R 10 i ), and intracellular volume fraction ( v i ) of a cell suspension using multiple samples with different gadolinium concentrations. Numerical simulation studies were conducted to assess the uncertainty in the estimation of k ie , R 10 i , and v i from saturation recovery data using single (SC) or multiple concentrations (MC) of gadolinium-based contrast agent (GBCA). In vitro experiments with 4 T1 murine breast cancer and SCCVII squamous cell cancer models were conducted at 11 T to compare parameter estimation using the SC protocol with that using the MC protocol. The cell lines were challenged with a Na+ /K+ -ATPase inhibitor, digoxin, to assess the treatment response in terms of k ie , R 10 i , and v i . Data analysis was conducted using the two-compartment exchange model for parameter estimation. The simulation study data demonstrate that the MC method, compared with the SC method, reduces the uncertainty of the estimated k ie by decreasing the interquartile ranges from 27.3% ± 3.7% to 18.8% ± 5.1% and the median differences from ground truth from 15.0% ± 6.3% to 7.2% ± 4.2%, while estimating R 10 i and v i simultaneously. In the cell studies, the MC method demonstrated reduced uncertainty in overall parameter estimation compared with the SC approach. MC method-measured parameter changes in cells treated with digoxin increased R 10 i by 11.7% (p = 0.218) and k ie by 5.9% (p = 0.234) for 4 T1 cells, respectively, and decreased R 10 i by 28.8% (p = 0.226) and k ie by 1.6% (p = 0.751) for SCCVII cells, respectively. v i did not change noticeably by the treatment. The results of this study substantiate the feasibility of using saturation recovery data of multiple samples with different GBCA concentrations for simultaneous measurement of the cellular water efflux rate, intracellular volume fraction, and intracellular longitudinal relaxation rate in cancer cells.

Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach.

PURPOSE: To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI. METHODS: A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy. RESULT: The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81. CONCLUSION: This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.

Bilateral gradient-echo spectroscopic imaging with correction of frequency variations for measurement of fatty acid composition in mammary adipose tissue.

PURPOSE: To develop a simultaneous dual-slab three-dimensional gradient-echo spectroscopic imaging (GSI) technique with frequency drift compensation for rapid (<6 min) bilateral measurement of fatty acid composition (FAC) in mammary adipose tissue. METHODS: A bilateral GSI sequence was developed using a simultaneous dual-slab excitation followed by 128 monopolar echoes. A short train of navigator echoes without phase or partition encoding was included at the beginning of each pulse repetition time period to correct for frequency variation caused by respiration and heating of the cryostat. Voxel-wise spectral fitting was applied to measure the areas of the lipid spectral peaks to estimate the number of double-bond (ndb), number of methylene-interrupted double-bond (nmidb), and chain length (cl). The proposed method was tested in an oil phantom and 10 postmenopausal women to assess the influence of the frequency variation on FAC estimation. RESULTS: The frequency drift observed over 5:27 min during the phantom scan was about 10 Hz. Phase correction based on the navigator reduced the median error of ndb, nmidb, and cl from 9.7%, 17.6%, and 3.2% to 2.1%, 9.5%, and 2.8%, respectively. The in vivo data showed a mean ± standard deviation frequency drift of 17.4 ± 2.5 Hz, with ripples at 0.3 ± 0.1 Hz. Our reconstruction algorithm successfully separated signals from the left and right breasts with negligible residual aliasing. Phase correction reduced the interquartile range within each subject's adipose tissue of ndb, nmidb, and cl by 18.4 ± 10.6%, 18.5 ± 13.9%, and 18.4 ± 10.6%, respectively. CONCLUSION: This study shows the feasibility of obtaining bilateral spectroscopic imaging data in the breast and that incorporation of a frequency navigator improves the estimation of FAC.

Measurement of cellular-interstitial water exchange time in tumors based on diffusion-time-dependent diffusional kurtosis imaging.

PURPOSE: To assess the feasibility of using diffusion-time-dependent diffusional kurtosis imaging (tDKI) to measure cellular-interstitial water exchange time (τex ) in tumors, both in animals and in humans. METHODS: Preclinical tDKI studies at 7 T were performed with the GL261 glioma model and the 4T1 mammary tumor model injected into the mouse brain. Clinical studies were performed at 3 T with women who had biopsy-proven invasive ductal carcinoma. tDKI measurement was conducted using a diffusion-weighted STEAM pulse sequence with multiple diffusion times (20-800 ms) at a fixed echo time, while keeping the b-values the same (0-3000 s/mm2 ) by adjusting the diffusion gradient strength. The tDKI data at each diffusion time t were used for a weighted linear least-squares fit method to estimate the diffusion-time-dependent diffusivity, D(t), and diffusional kurtosis, K(t). RESULTS: Both preclinical and clinical studies showed that, when diffusion time t ≥ 200 ms, D(t) did not have a noticeable change while K(t) decreased monotonically with increasing diffusion time in tumors and t ≥ 100 ms for the cortical ribbon of the mouse brain. The estimated τex averaged median and interquartile range (IQR) of GL261 and 4T1 tumors were 93 (IQR = 89) ms and 68 (78) ms, respectively. For the cortical ribbon, the estimated τex averaged median and IQR were 41 (34) ms for C57BL/6 and 30 (17) ms for BALB/c. For invasive ductal carcinoma, the estimated τex median and IQR of the two breast cancers were 70 (94) and 106 (92) ms. CONCLUSION: The results of this proof-of-concept study substantiate the feasibility of using tDKI to measure cellular-interstitial water exchange time without using an exogenous contrast agent.

Rapid dynamic contrast-enhanced MRI for small animals at 7T using 3D ultra-short echo time and golden-angle radial sparse parallel MRI.

PURPOSE: To develop a rapid dynamic contrast-enhanced MRI method with high spatial and temporal resolution for small-animal imaging at 7 Tesla. METHODS: An ultra-short echo time (UTE) pulse sequence using a 3D golden-angle radial sampling was implemented to achieve isotropic spatial resolution with flexible temporal resolution. Continuously acquired radial spokes were grouped into subsets for image reconstruction using a multicoil compressed sensing approach (Golden-angle RAdial Sparse Parallel; GRASP). The proposed 3D-UTE-GRASP method with high temporal and spatial resolutions was tested using 7 mice with GL261 intracranial glioma models. RESULTS: Iterative reconstruction with different temporal resolutions and regularization factors λ showed that, in all cases, the cost function decreased to less than 2.5% of its starting value within 20 iterations. The difference between the time-intensity curves of 3D-UTE-GRASP and nonuniform fast Fourier transform (NUFFT) images was minimal when λ was 1% of the maximum signal intensity of the initial NUFFT images. The 3D isotropic images were used to generate pharmacokinetic parameter maps to show the detailed images of the tumor characteristics in 3D and also to show longitudinal changes during tumor growth. CONCLUSION: This feasibility study demonstrated that the proposed 3D-UTE-GRASP method can be used for effective measurement of the 3D spatial heterogeneity of tumor pharmacokinetic parameters.

Estimation of cellular-interstitial water exchange in dynamic contrast enhanced MRI using two flip angles.

PURPOSE: To investigate the feasibility of using multiple flip angles in dynamic contrast enhanced (DCE) MRI to reduce the uncertainty in estimation of intracellular water lifetime (τi ). METHODS: Numerical simulation studies were conducted to assess the uncertainty in estimation of τi using dynamic contrast enhanced MRI with one or two flip angles. In vivo experiments with a murine brain tumor model were conducted at 7T using two flip angles. The in vivo data were used to compare τi estimation using the single-flip-angle (SFA) protocol with that using the double-flip-angle (DFA) protocol. Data analysis was conducted using the two-compartment exchange model combined with the three-site-two-exchange model for water exchange. RESULTS: In the numerical simulation studies with a range of contrast kinetic parameters and signal-to-noise ratio = 20, the median bias of τi estimation decreased from 72 ms with SFA to 65 ms with DFA, and the corresponding median inter-quartile range reduced from 523 ms to 156 ms. In the in vivo studies, τi estimation with SFA was not successful in most voxels in the tumors, as the estimated τi values reached the upper limit of the parameter range (2 s). In contrast, the estimated τi values with DFA were mostly between 0.2 and 1.5 s and homogeneously distributed spatially across the tumor. The τi estimation with DFA was less sensitive to arterial input function scaling but more sensitive to pre-contrast T1 than the other contrast kinetic parameters. CONCLUSION: This study results demonstrate the feasibility of using multiple flip angles to encode the post-contrast time-intensity curve with different weighting of water exchange effect to reduce the uncertainty in τi estimation.

Quantifying myofiber integrity using diffusion MRI and random permeable barrier modeling in skeletal muscle growth and Duchenne muscular dystrophy model in mice.

PURPOSE: To measure the microstructural changes during skeletal muscle growth and progressive pathologies using the random permeable model with diffusion MRI, and compare findings to conventional imaging modalities such as three-point Dixon and T2 imaging. METHODS: In vivo and ex vivo DTI experiments with multiple diffusion times (20-700 ms) were completed on wild-type (n = 22) and muscle-dystrophic mdx mice (n = 8) at various developmental time points. The DTI data were analyzed with the random permeable model framework that provides estimates of the unrestricted diffusion coefficient (D0 ), membrane surface-to-volume ratio (S/V), and membrane permeability (κ). In addition, the MRI experiments included conventional measures, such as tissue fat fractions and T2 relaxation. RESULTS: During normal muscle growth between week 4 and week 13, the in vivo S/V, fractional anisotropy, and fat fraction correlated positively with age (ρ = 0.638, 0.664, and 0.686, respectively), whereas T2 correlated negatively (ρ = -0.847). In mdx mice, all DTI random permeable model parameters and fat fraction had significant positive correlation with age, whereas fractional anisotropy and T2 did not have significant correlation with age. Histological measurements of the perimeter-to-area ratio served as a proxy for the model-derived S/V in the cylindrical myofiber geometry, and had a significant correlation with the ex vivo S/V (r = 0.71) as well as the in vivo S/V (r = 0.56). CONCLUSION: The present study demonstrates that DTI at multiple diffusion times with the random permeable model analysis allows for noninvasively quantifying muscle fiber microstructural changes during both normal muscle growth and disease progression. Future studies can apply our technique to evaluate current and potential treatments to muscle myopathies.

Measurement of blood-brain barrier permeability using dynamic contrast-enhanced magnetic resonance imaging with reduced scan time.

PURPOSE: To investigate the feasibility of measuring the subtle disruption of blood-brain barrier (BBB) using DCE-MRI with a scan duration shorter than 10 min. METHODS: The extended Patlak-model (EPM) was introduced to include the effect of plasma flow (Fp ) in the estimation of vascular permeability-surface area product (PS). Numerical simulation studies were carried out to investigate how the reduction in scan time affects the accuracy in estimating contrast kinetic parameters. DCE-MRI studies of the rat brain were conducted with Fisher rats to confirm the results from the simulation. Intracranial F98 glioblastoma models were used to assess areas with different levels of permeability. In the normal brain tissues, the Patlak model (PM) and EPM were compared, whereas the 2-compartment-exchange-model (TCM) and EPM were assessed in the peri-tumor and the tumor regions. RESULTS: The simulation study results demonstrated that scan time reduction could lead to larger bias in PS estimated by PM (>2000%) than by EPM (<47%), especially when Fp is low. When Fp was high as in the gray matter, the bias in PM-PS (>900%) were larger than that in EPM-PS (<42%). The animal study also showed similar results, where the PM parameters were more sensitive to the scan duration than the EPM parameters. It was also demonstrated that, in the peri-tumor region, the EPM parameters showed less change by scan duration than the TCM parameters. CONCLUSION: The results of this study suggest that EPM can be used to measure PS with a scan duration of 10 min or less.

Feasibility analysis of early temporal kinetics as a surrogate marker for breast tumor type, grade, and aggressiveness.

BACKGROUND: Screening breast MRI has been shown to preferentially detect high-grade ductal carcinoma in situ (DCIS) and invasive carcinoma, likely due to increased angiogenesis resulting in early initial uptake of contrast. As interest grows in abbreviated screening breast MRI (AB-MRI), markers of early contrast washin that can predict tumor grade and potential aggressiveness are of clinical interest. PURPOSE: To evaluate the feasibility of using the initial enhancement ratio (IER) as a surrogate marker for tumor grade, hormone receptor status, and prognostic markers, as an initial step to being incorporated into AB-MRI. STUDY TYPE: Retrospective. SUBJECTS: In all, 162 women (mean 55.0 years, range 32.8-87.7 years) with 187 malignancies imaged January 2012-November 2015. FIELD STRENGTH/SEQUENCE: Images were acquired at 3.0T with a T1 -weighted gradient echo fat-suppressed-volume interpolated breath-hold sequence. ASSESSMENT: Subjects underwent dynamic contrast-enhanced breast MRI with a 7-channel breast coil. IER (% signal increase over baseline at the first postcontrast acquisition) was assessed and correlated with background parenchymal enhancement, washout curves, stage, and final pathology. STATISTICAL TESTS: Chi-square test, Spearman rank correlation, Mann-Whitney U-tests, Bland-Altman analysis, and receiver operating characteristic curve analysis. RESULTS: IER was higher for invasive cancer than for DCIS (R1/R2, P < 0.001). IER increased with tumor grade (R1: r = 0.56, P < 0.001, R2: r = 0.50, P < 0.001), as ki-67 increased (R1: r = 0.35, P < 0.001; R2 r = 0.35, P < 0.001), and for node-positive disease (R1/R2, P = 0.001). IER was higher for human epidermal growth factor receptor two-positive and triple negative cancers than for estrogen receptor-positive / progesterone receptor-positive tumors (R1 P < 0.001-0.002; R2 P = 0.0.001-0.011). IER had higher sensitivity (80.6% vs. 75.5%) and specificity (55.8% vs. 48.1%) than washout curves for positive nodes, higher specificity (48.1% vs. 36.5%) and positive predictive value (70.2% vs. 66.7%) for high ki-67, and excellent interobserver agreement (intraclass correlation coefficient = 0.82). DATA CONCLUSION: IER, a measurement of early contrast washin, is associated with higher-grade malignancies and tumor aggressiveness and might be potentially incorporated into an AB-MRI protocol. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1692-1700.

Voxelwise analysis of simultaneously acquired and spatially correlated 18 F-fluorodeoxyglucose (FDG)-PET and intravoxel incoherent motion metrics in breast cancer.

PURPOSE: Diffusion-weighted imaging (DWI) and 18 F-fluorodeoxyglucose-positron emission tomography (18 F-FDG-PET) independently correlate with malignancy in breast cancer, but the relationship between their structural and metabolic metrics is not completely understood. This study spatially correlates diffusion, perfusion, and glucose avidity in breast cancer with simultaneous PET/MR imaging and compares correlations with clinical prognostics. METHODS: In this Health Insurance Portability and Accountability Act-compliant prospective study, with written informed consent and approval of the institutional review board and using simultaneously acquired FDG-PET and DWI, tissue diffusion (Dt ), and perfusion fraction (fp ) from intravoxel incoherent motion (IVIM) analysis were registered to FDG-PET within 14 locally advanced breast cancers. Lesions were analyzed using 2D histograms and correlation coefficients between Dt , fp , and standardized uptake value (SUV). Correlations were compared with prognostics from biopsy, metastatic burden from whole-body PET, and treatment history. RESULTS: SUV||Dt correlation coefficient significantly distinguished treated (0.11 ± 0.24) from nontreated (-0.33 ± 0.26) patients (P = 0.005). SUV||fp correlations were on average negative for the whole cohort (-0.17 ± 0.13). CONCLUSION: Simultaneously acquired and registered FDG-PET/DWI allowed quantifiable descriptions of breast cancer microenvironments that may provide a framework for monitoring and predicting response to treatment. Magn Reson Med 78:1147-1156, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Simultaneous measurement of T1 /B1 and pharmacokinetic model parameters using active contrast encoding (ACE)-MRI.

The aim of this study was to assess the feasibility of combining dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) with the measurement of the radiofrequency (RF) transmit field B1 and pre-contrast longitudinal relaxation time T10 . A novel approach has been proposed to simultaneously estimate B1 and T10 from a modified DCE-MRI scan that actively encodes the washout phase of the curve with different amounts of T1 and B1 weighting using multiple flip angles and repetition times, hence referred to as active contrast encoding (ACE)-MRI. ACE-MRI aims to simultaneously measure B1 and T10 , together with contrast kinetic parameters, such as the transfer constant Ktrans , interstitial space volume fraction ve and vascular space volume fraction vp . The proposed method was tested using numerical simulations and in vivo studies with mouse models of breast cancer implanted in the flank and mammary fat pad, and glioma in the brain. In the numerical simulation study with a signal-to-noise ratio of 10, both B1 and T10 were estimated accurately with errors of 5.1 ± 3.5% and 12.3 ± 8.8% and coefficients of variation (CV) of 14.9 ± 8.6% and 15.0 ± 5.0%, respectively. Using the same ACE-MRI data, the kinetic parameters Ktrans , ve and vp were also estimated with errors of 14.2 ± 8.3% (CV = 13.5 ± 4.6%), 14.7 ± 9.9% (CV = 13.3 ± 4.5%) and 14.0 ± 9.3% (CV = 14.0 ± 4.5%), respectively. For the in vivo tumor data from 11 mice, voxel-wise comparisons between ACE-MRI and DCE-MRI methods showed that the mean differences for the five parameters were as follows: ΔKtrans  = 0.006 (/min), Δve  = 0.016, Δvp  = 0.000, ΔB1  = -0.014 and ΔT1  = -0.085 (s), which suggests a good agreement between the two methods. When compared with separately measured B1 and T10 , and DCE-MRI estimated kinetic parameters as a reference, the mean relative errors of ACE-MRI estimation were B1  = -0.3%, T10  = -8.5%, Ktrans  = 11.4%, ve  = 14.5% and vp  = 4.5%. This proof-of-concept study demonstrates that the proposed ACE-MRI method can be used to estimate B1 and T10 , together with contrast kinetic model parameters.

Separation of benign and malignant breast lesions using dynamic contrast enhanced MRI in a biopsy cohort.

PURPOSE: To assess the diagnostic utility of contrast kinetic analysis for breast lesions and background parenchyma of women undergoing MRI-guided biopsies, for whom standard clinical analysis had failed to separate benign and malignant lesions. MATERIALS AND METHODS: This study included 115 women who had indeterminate lesions based on routine diagnostic breast MRI exams and underwent an MRI (3 Tesla) -guided biopsy of one or more lesions suspicious for breast cancer. Breast dynamic contrast-enhanced (DCE)-MRI was performed using a radial stack-of-stars three-dimensional spoiled gradient echo pulse sequence and modified k-space weighted image contrast image reconstruction. Contrast kinetic model analysis was conducted to characterize the contrast enhancement patterns measured in lesions and background parenchyma (BP). The transfer rate (Ktrans ), interstitial volume fraction (ve ), and vascular volume fraction (vp ) estimated from the lesion and BP were used to separate malignant from benign lesions. RESULTS: The patients with malignant lesions had significantly (P < 0.05) higher median lesion-Ktrans (0.081 min-1 ), higher median BP-Ktrans (0.032 min-1 ), and BP-vp (0.020) than those without malignant lesions (0.056 min-1 , 0.017 min-1 and 0.012, respectively). The area under the receiver operating characteristic curve (AUC) of the BP-Ktrans (0.687) was highest among the single parameters and higher than that of the lesion-Ktrans (0.664). The combined logistic regression model of lesion-Ktrans , lesion-ve , BP-Ktrans , BP-ve , and BP-vp had the highest AUC of 0.730. CONCLUSION: Our results suggest that the contrast kinetic analysis of DCE-MRI data can be used to differentiate the malignant lesions from the benign and high-risk lesions among the indeterminate breast lesions recommended for MRI-guided biopsy exams. LEVEL OF EVIDENCE: 3 J. MAGN. RESON. IMAGING 2017;45:1385-1393.

Comparison of conventional DCE-MRI and a novel golden-angle radial multicoil compressed sensing method for the evaluation of breast lesion conspicuity.

PURPOSE: To compare a novel multicoil compressed sensing technique with flexible temporal resolution, golden-angle radial sparse parallel (GRASP), to conventional fat-suppressed spoiled three-dimensional (3D) gradient-echo (volumetric interpolated breath-hold examination, VIBE) MRI in evaluating the conspicuity of benign and malignant breast lesions. MATERIALS AND METHODS: Between March and August 2015, 121 women (24-84 years; mean, 49.7 years) with 180 biopsy-proven benign and malignant lesions were imaged consecutively at 3.0 Tesla in a dynamic contrast-enhanced (DCE) MRI exam using sagittal T1-weighted fat-suppressed 3D VIBE in this Health Insurance Portability and Accountability Act-compliant, retrospective study. Subjects underwent MRI-guided breast biopsy (mean, 13 days [1-95 days]) using GRASP DCE-MRI, a fat-suppressed radial "stack-of-stars" 3D FLASH sequence with golden-angle ordering. Three readers independently evaluated breast lesions on both sequences. Statistical analysis included mixed models with generalized estimating equations, kappa-weighted coefficients and Fisher's exact test. RESULTS: All lesions demonstrated good conspicuity on VIBE and GRASP sequences (4.28 ± 0.81 versus 3.65 ± 1.22), with no significant difference in lesion detection (P = 0.248). VIBE had slightly higher lesion conspicuity than GRASP for all lesions, with VIBE 12.6% (0.63/5.0) more conspicuous (P < 0.001). Masses and nonmass enhancement (NME) were more conspicuous on VIBE (P < 0.001), with a larger difference for NME (14.2% versus 9.4% more conspicuous). Malignant lesions were more conspicuous than benign lesions (P < 0.001) on both sequences. CONCLUSION: GRASP DCE-MRI, a multicoil compressed sensing technique with high spatial resolution and flexible temporal resolution, has near-comparable performance to conventional VIBE imaging for breast lesion evaluation. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;45:1746-1752.

Stimulated echo diffusion tensor imaging (STEAM-DTI) with varying diffusion times as a probe of breast tissue.

PURPOSE: To explore the application of diffusion tensor imaging (DTI) for breast tissue and breast pathologies using a stimulated-echo acquisition mode (STEAM) with variable diffusion times. MATERIALS AND METHODS: In this Health Insurance Portability and Accountability Act-compliant study, approved by the local institutional review board, eight patients and six healthy volunteers underwent an MRI examination at 3 Tesla including STEAM-DTI with several diffusion times ranging from 68.5 to 902.5 ms. A DTI model was fitted to the data for each diffusion time, and parametric maps of mean diffusivity, fractional anisotropy, axial diffusivity, and radial diffusivity were computed for healthy fibroglandular tissue (FGT) and lesions. The median value of radial diffusivity for FGT was fitted to a linear decay to obtain an estimation of the surface-to-volume ratio, from which the radial diameter was calculated. RESULTS: For healthy FGT, radial diffusivity presented a linear decay with the square root of the diffusion time resulting in a range of estimated radial diameters from 202 to 496 µm, while axial diffusivity presented a nearly time-independent diffusion. Residual fat signal was reduced at longer diffusion times due to the shorter T1 of fat. Residual fat signal to the overall signal in the healthy volunteers' FGT was found to range from 2.39% to 2.55% (shortest mixing time), and from 0.40% to 0.51% (longest mixing time) for the b500 images. CONCLUSION: The use of variable diffusion times may provide an in vivo noninvasive tool to probe diffusion lengths in breast tissue and breast pathology, and might aid by improving fat suppression at longer diffusion times. LEVEL OF EVIDENCE: 2 J. Magn. Reson. Imaging 2017;45:84-93.

In vivo quantification of demyelination and recovery using compartment-specific diffusion MRI metrics validated by electron microscopy.

There is a need for accurate quantitative non-invasive biomarkers to monitor myelin pathology in vivo and distinguish myelin changes from other pathological features including inflammation and axonal loss. Conventional MRI metrics such as T2, magnetization transfer ratio and radial diffusivity have proven sensitivity but not specificity. In highly coherent white matter bundles, compartment-specific white matter tract integrity (WMTI) metrics can be directly derived from the diffusion and kurtosis tensors: axonal water fraction, intra-axonal diffusivity, and extra-axonal radial and axial diffusivities. We evaluate the potential of WMTI to quantify demyelination by monitoring the effects of both acute (6weeks) and chronic (12weeks) cuprizone intoxication and subsequent recovery in the mouse corpus callosum, and compare its performance with that of conventional metrics (T2, magnetization transfer, and DTI parameters). The changes observed in vivo correlated with those obtained from quantitative electron microscopy image analysis. A 6-week intoxication produced a significant decrease in axonal water fraction (p<0.001), with only mild changes in extra-axonal radial diffusivity, consistent with patchy demyelination, while a 12-week intoxication caused a more marked decrease in extra-axonal radial diffusivity (p=0.0135), consistent with more severe demyelination and clearance of the extra-axonal space. Results thus revealed increased specificity of the axonal water fraction and extra-axonal radial diffusivity parameters to different degrees and patterns of demyelination. The specificities of these parameters were corroborated by their respective correlations with microstructural features: the axonal water fraction correlated significantly with the electron microscopy derived total axonal water fraction (ρ=0.66; p=0.0014) but not with the g-ratio, while the extra-axonal radial diffusivity correlated with the g-ratio (ρ=0.48; p=0.0342) but not with the electron microscopy derived axonal water fraction. These parameters represent promising candidates as clinically feasible biomarkers of demyelination and remyelination in the white matter.

Evaluation of Breast Lipid Composition in Patients with Benign Tissue and Cancer by Using Multiple Gradient-Echo MR Imaging.

Purpose To demonstrate the feasibility of the use of a rapid, noninvasive, in vivo imaging method to measure fatty acid fractions of breast adipose tissue during diagnostic breast magnetic resonance (MR) examinations and to investigate associations between fatty acid fractions in breast adipose tissue and breast cancer status by using this method. Materials and Methods The institutional review board approved this retrospective HIPAA-compliant study and informed consent was waived. Between July 2013 and September 2014, multiple-echo three-dimensional gradient-echo data were acquired for 89 women. Spectra were generated and used to estimate fractions of monounsaturated fatty acid (MUFA), polyunsaturated fatty acid (PUFA), and saturated fatty acid (SFA) in the breast adipose tissue. Analysis of covariance and exact Mann-Whitney tests were used to compare groups and the Spearman rank correlation coefficient was used to characterize the association of each imaging measure with each attribute. Results For postmenopausal women, MUFA was lower (0.38 ± 0.06 vs 0.46 ± 0.10; P < .05) and SFA was higher (0.31 ± 0.07 vs 0.19 ± 0.11; P < .05) for women with invasive ductal carcinoma than for those with benign tissue. No correlation was found between body mass index (BMI) and fatty acid fractions in breast adipose tissue. In women with benign tissue, postmenopausal women had a higher PUFA (0.35 ± 0.06 vs 0.27 ± 0.05; P < .01) and lower SFA (0.19 ± 0.11 vs 0.30 ± 0.12; P < .05) than premenopausal women. Conclusion There is a possible link between the presence of invasive ductal carcinoma and fatty acid fractions in breast adipose tissue for postmenopausal women in whom BMI values are not correlated with the fatty acid fractions. (©) RSNA, 2016 Online supplemental material is available for this article.