Search

Purpose: To develop and validate a method for B0$$ {B}_0 $$ mapping for knee imaging using the quantitative Double‐Echo in Steady‐State (qDESS) exploiting the phase difference (Δθ$$ \Delta \theta $$) between the two echoes acquired. Contrary to a two‐gradient‐echo (2‐GRE) method, Δθ$$ \Delta \theta $$ depends only on the first echo time. Methods: Bloch simulations were applied to investigate robustness to noise of the proposed methodology and all imaging studies were validated with phantoms and in vivo simultaneous bilateral knee acquisitions. Two phantoms and five healthy subjects were scanned using qDESS, water saturation shift referencing (WASSR), and multi‐GRE sequences. ΔB0$$ \Delta {B}_0 $$ maps were calculated with the qDESS and the 2‐GRE methods and compared against those obtained with WASSR. The comparison was quantitatively assessed exploiting pixel‐wise difference maps, Bland–Altman (BA) analysis, and Lin's concordance coefficient (ρc$$ {\rho}_c $$). For in vivo subjects, the comparison was assessed in cartilage using average values in six subregions. Results: The proposed method for measuring ΔB0$$ \Delta {B}_0 $$ inhomogeneities from a qDESS acquisition provided ΔB0$$ \Delta {B}_0 $$ maps that were in good agreement with those obtained using WASSR. ΔB0$$ \Delta {B}_0 $$ρc$$ {\rho}_c $$ values were ≥$$ \ge $$ 0.98 and 0.90 in phantoms and in vivo, respectively. The agreement between qDESS and WASSR was comparable to that of a 2‐GRE method. Conclusion: The proposed method may allow B0 correction for qDESS T2$$ {T}_2 $$ mapping using an inherently co‐registered ΔB0$$ \Delta {B}_0 $$ map without requiring an additional B0 measurement sequence. More generally, the method may help shorten knee imaging protocols that require an auxiliary ΔB0$$ \Delta {B}_0 $$ map by exploiting a qDESS acquisition that also provides T2$$ {T}_2 $$ measurements and high‐quality morphological imaging. [ABSTRACT FROM AUTHOR]

Purpose: To develop a super‐resolution technique using convolutional neural networks for generating thin‐slice knee MR images from thicker input slices, and compare this method with alternative through‐plane interpolation methods. Methods: We implemented a 3D convolutional neural network entitled DeepResolve to learn residual‐based transformations between high‐resolution thin‐slice images and lower‐resolution thick‐slice images at the same center locations. DeepResolve was trained using 124 double echo in steady‐state (DESS) data sets with 0.7‐mm slice thickness and tested on 17 patients. Ground‐truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state‐of‐the‐art single‐image sparse‐coding super‐resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin‐slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground‐truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann‐Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability. Results: DeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and sparse‐coding super‐resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse‐coding super‐resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73). Conclusion: DeepResolve was capable of resolving high‐resolution thin‐slice knee MRI from lower‐resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Purpose: To demonstrate and evaluate the scan time and quantitative accuracy of simultaneous bilateral‐knee imaging compared with single‐knee acquisitions. Methods: Hardware modifications and safety testing was performed to enable MR imaging with two 16‐channel flexible coil arrays. Noise covariance and sensitivity‐encoding g‐factor maps for the dual‐coil‐array configuration were computed to evaluate coil cross‐talk and noise amplification. Ten healthy volunteers were imaged on a 3T MRI scanner with both dual‐coil‐array bilateral‐knee and single‐coil‐array single‐knee configurations. Two experienced musculoskeletal radiologists compared the relative image quality between blinded image pairs acquired with each configuration. Differences in T2 relaxation time measurements between dual‐coil‐array and single‐coil‐array acquisitions were compared with the standard repeatability of single‐coil‐array measurements using a Bland‐Altman analysis. Results: The mean g‐factors for the dual‐coil‐array configuration were low for accelerations up to 6 in the right–left direction, and minimal cross‐talk was observed between the two coil arrays. Image quality ratings of various joint tissues showed no difference in 89% (95% confidence interval: 85–93%) of rated image pairs, with only small differences (“slightly better” or “slightly worse”) in image quality observed. The T2 relaxation time measurements between the dual‐coil‐array configuration and the single‐coil configuration showed similar limits of agreement and concordance correlation coefficients (limits of agreement: −0.93 to 1.99 ms; CCC: 0.97 (95% confidence interval: 0.96–0.98)), to the repeatability of single‐coil‐array measurements (limits of agreement: −2.07 to 1.96 ms; CCC: 0.97 (95% confidence interval: 0.95–0.98)). Conclusion: A bilateral coil‐array setup can image both knees simultaneously in similar scan times as conventional unilateral knee scans, with comparable image quality and quantitative accuracy. This has the potential to improve the value of MRI knee evaluations. Magn Reson Med 80:529–537, 2018. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

Purpose To develop and optimize a multislice glycosaminoglycan (GAG) chemical exchange saturation transfer (GagCEST) sequence for volumetric imaging of articular cartilage, and to validate the sequence against T1ρ relaxation times in whole joint imaging of tibiotalar cartilage. Methods Ex vivo experiments were used to observe the effect of the number of partitions and shot TR on signal-to-noise ratio and measured GagCESTasym. GagCEST imaging of the entire tibiotalar joint was also performed on 10 healthy subjects. The measured GagCESTasym was compared and correlated with T1ρ relaxation times. Results Ex vivo studies showed a higher average GagCESTasym from articular cartilage on multislice acquisitions acquired with two or more partitions than observed with a single-slice acquisition. In healthy human subjects, an average GagCESTasym of 8.8 ± 0.7% was observed. A coefficient of variation of GagCESTasym across slices of less than 15% was seen for all subjects. Across subjects, a Pearson correlation coefficient of −0.58 was observed between the measured gagCESTasym and T1ρ relaxation times. Conclusions We demonstrated the feasibility and optimization of multislice GagCEST mapping of articular cartilage. Volumetric analysis and decreased scan times will help to advance the clinical utility of GagCEST imaging of articular cartilage. Magn Reson Med 77:1134-1141, 2017. © 2016 International Society for Magnetic Resonance in Medicine [ABSTRACT FROM AUTHOR]