Image Non-Uniformity Correction for 3-T Gd-EOB-DTPA-Enhanced MR Imaging of the Liver

Quantitative MR texture analysis for the differentiation of uterine smooth muscle tumors with high signal intensity on T2-weighted imaging

The purpose of this study was to distinguish leiomyosarcomas/smooth muscle tumors of uncertain malignant potential (STUMP) from leiomyomas with high signal intensity (SI) on T2-weighted imaging (T2WI) using quantitative MR texture analysis combined with patient characteristics and visual assessment. Thirty-one leiomyomas, 2 STUMPs, and 6 leiomyosarcomas showing high SI on T2WI were included. First, we searched for differences in patient characteristics and visual assessment between leiomyomas and leiomyosarcomas/STUMPs. We also compared the MR texture on T2WI and the apparent diffusion coefficient (ADC) to identify differences between leiomyomas and leiomyosarcomas/STUMPs. In the univariate analysis, significant differences between leiomyomas and leiomyosarcomas/STUMPs were observed in age, menopausal status, margin, hemorrhage, long diameter, T2-variance, T2-volume, ADC-variance, ADC-entropy, ADC-uniformity, ADC-90th and 95th percentile values, and ADC-volume (P < .05, respectively). There were significantly more postmenopausal patients with leiomyosarcomas/STUMPs than with leiomyomas, and leiomyosarcomas/STUMPs had more irregular margins, more frequent presence of hemorrhage and exhibited larger tumor diameters, T2-volume, T2-variance, ADC-volume, ADC-variance, ADC-entropy, and higher ADC-90th and 95th percentile values but lower ADC-uniformity. Multivariate analyses revealed that the independent differentiators were menopausal status, hemorrhage and ADC-entropy (P < .05, respectively). The area under the curve obtained by combining the 3 items was 0.980. The best cutoff value for ADC-entropy was 9.625 (sensitivity: 100%, specificity: 58%). The combination of menopausal status, hemorrhage, and ADC-entropy can help accurately distinguish leiomyosarcomas/STUMPs from leiomyomas with high SI on T2WI; however, external validation in a larger population is required because of the small sample size of our study.

Comparison of liver scintigraphy and the liver-spleen contrast in Gd-EOB-DTPA-enhanced MRI on liver function tests

The liver-spleen contrast (LSC) using hepatobiliary-phase images could replace the receptor index (LHL15) in liver scintigraphy; however, few comparative studies exist. This study aimed to verify the convertibility from LSC into LHL15. In 136 patients, the LSC, not at 20 min, but at 60 min after injecting gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid was compared with the LHL15, albumin–bilirubin (ALBI) score, and the related laboratory parameters. The LHL15 was also compared with their biochemical tests. The correlation coefficients of LSC with LHL15, ALBI score, total bilirubin, and albumin were 0.740, –0.624, –0.606, and 0.523 (P < 0.00001), respectively. The correlation coefficients of LHL15 with ALBI score, total bilirubin, and albumin were –0.647, –0.553, and 0.569 (P < 0.00001), respectively. The linear regression equation on the estimated LHL15 (eLHL15) from LSC was eLHL15 = 0.460 · LSC + 0.727 (P < 0.00001) and the coefficient of determination was 0.548. Regarding a contingency table using imaging-based clinical stage classification, the degree of agreement between eLHL15 and LHL15 was 65.4%, and Cramer's V was 0.568 (P < 0.00001). Therefore, although the LSC may be influenced by high total bilirubin, the eLHL15 can replace the LSC as an index to evaluate liver function.

Utility of Stack-of-stars Acquisition for Hepatobiliary Phase Imaging without Breath-holding

Purpose:

Post-contrast liver magnetic resonance imaging is typically performed with breath-hold 3D gradient echo sequences. However, breath-holding for >10 s is difficult for some patients. In this study, we compared the quality of hepatobiliary phase (HBP) imaging without breath-holding using the prototype pulse sequences stack-of-stars liver acquisition with volume acceleration (LAVA) (LAVA Star) with or without navigator echoes (LAVA Star_navi+ and LAVA Star_navi−) and Cartesian LAVA with navigator echoes (Cartesian LAVA_navi+).

Methods:

Seventy-two patients were included in this single-center, retrospective, cross-sectional study. HBP imaging using the three LAVA sequences (Cartesian LAVA_navi+, LAVA Star_navi−, and LAVA Star_navi+) without breath-holding was performed for all patients using a 3T magnetic resonance system. Two independent radiologists qualitatively analyzed (overall image quality, liver edge sharpness, hepatic vein clarity, streak artifacts, and respiratory motion/pulsation artifacts) HBP images taken by the three sequences using a five-point scale. Quantitative evaluations were also performed by calculating the liver-to-spleen, -lesion, and -portal vein (PV) signal intensity ratios. The results were compared between the three sequences using the Friedman test.

Results:

LAVA Star_navi+ showed the best image quality and hepatic vein clarity (P < 0.0001). LAVA Star_navi− showed the lowest image quality (P < 0.0001–0.0106). LAVA Star_navi+ images showed fewer streak artifacts than LAVA Star_navi− images (P < 0.0001), while Cartesian LAVA_navi+ images showed no streak artifacts. Cartesian LAVA_navi+ images showed stronger respiratory motion/pulsation artifacts than the others (P < 0.0001). LAVA Star_navi− images showed the highest liver-to-spleen ratios (P < 0.0001–0.0005). Cartesian LAVA_navi+ images showed the lowest liver-to-lesion and -PV ratios (P < 0.0001–0.0108).

Conclusion:

In terms of image quality, the combination of stack-of-stars acquisition and navigator echoes is the best for HBP imaging without breath-holding.

Motion Artifact Reduction Using a Convolutional Neural Network for Dynamic Contrast Enhanced MR Imaging of the Liver

Purpose:

To improve the quality of images obtained via dynamic contrast enhanced MRI (DCE-MRI), which contain motion artifacts and blurring using a deep learning approach.

Materials and Methods:

A multi-channel convolutional neural network-based method is proposed for reducing the motion artifacts and blurring caused by respiratory motion in images obtained via DCE-MRI of the liver. The training datasets for the neural network included images with and without respiration-induced motion artifacts or blurring, and the distortions were generated by simulating the phase error in k-space. Patient studies were conducted using a multi-phase T₁-weighted spoiled gradient echo sequence for the liver, which contained breath-hold failures occurring during data acquisition. The trained network was applied to the acquired images to analyze the filtering performance, and the intensities and contrast ratios before and after denoising were compared via Bland–Altman plots.

Results:

The proposed network was found to be significantly reducing the magnitude of the artifacts and blurring induced by respiratory motion, and the contrast ratios of the images after processing via the network were consistent with those of the unprocessed images.

Conclusion:

A deep learning-based method for removing motion artifacts in images obtained via DCE-MRI of the liver was demonstrated and validated.

Uniform combined reconstruction of multichannel 7T knee MRI receive coil data without the use of a reference scan

BACKGROUND

MR image intensity nonuniformity is often observed at 7T. Reference scans from the body coil used for uniformity correction at lower field strengths are typically not available at 7T.

PURPOSE

To evaluate the efficacy of a novel algorithm, Uniform Combined Reconstruction (UNICORN), to correct receive coil-induced nonuniformity in musculoskeletal 7T MRI without the use of a reference scan.

STUDY TYPE

Retrospective image analysis study.

SUBJECTS

MRI data of 20 subjects was retrospectively processed offline. Field Strength/Sequence: Knees of 20 subjects were imaged at 7T with a single-channel transmit, 28-channel phased-array receive knee coil. A turbo-spin-echo sequence was used to acquire 33 series of images.

ASSESSMENT

Three fellowship-trained musculoskeletal radiologists with cumulative experience of 42 years reviewed the images. The uniformity, contrast, signal-to-noise ratio (SNR), and overall image quality were evaluated for images with no postprocessing, images processed with N4 bias field correction algorithm, and the UNICORN algorithm.

STATISTICAL TESTS

Intraclass correlation coefficient (ICC) was used for measuring the interrater reliability. ICC and 95% confidence intervals (CIs) were calculated using the R statistical package employing a two-way mixed-effects model based on a mean rating (k = 3) for absolute agreement. The Wilcoxon signed-rank test with continuity correction was used for analyzing the overall image quality scores.

RESULTS

UNICORN was preferred among the three methods evaluated for uniformity in 97.9% of the pooled ratings, with excellent interrater agreement (ICC of 0.98, CI 0.97-0.99). UNICORN was also rated better than N4 for contrast and equivalent to N4 in SNR with ICCs of 0.80 (CI 0.72-0.86) and 0.67 (CI 0.54-0.77), respectively. The overall image quality scores for UNICORN were significantly higher than N4 (P < 6 × 10^-13 ), with good to excellent interrater agreement (ICC 0.90, CI 0.86-0.93).

DATA CONCLUSION

Without the use of a reference scan, UNICORN provides better image uniformity, contrast, and overall image quality at 7T compared with the N4 bias field-correction algorithm.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1534-1544.

A quantitative experimental phantom study on MRI image uniformity

OBJECTIVES

Our goal was to assess MR image uniformity by investigating aspects influencing said uniformity via a method laid out by the National Electrical Manufacturers Association (NEMA).

METHODS

Six metallic materials embedded in a glass phantom were scanned (i.e. Au, Ag, Al, Au-Ag-Pd alloy, Ti and Co-Cr alloy) as well as a reference image. Sequences included spin echo (SE) and gradient echo (GRE) scanned in three planes (i.e. axial, coronal, and sagittal). Moreover, three surface coil types (i.e. head and neck, Brain, and temporomandibular joint coils) and two image correction methods (i.e. surface coil intensity correction or SCIC, phased array uniformity enhancement or PURE) were employed to evaluate their effectiveness on image uniformity. Image uniformity was assessed using the National Electrical Manufacturers Association peak-deviation non-uniformity method.

RESULTS

Results showed that temporomandibular joint coils elicited the least uniform image and brain coils outperformed head and neck coils when metallic materials were present. Additionally, when metallic materials were present, spin echo outperformed gradient echo especially for Co-Cr (particularly in the axial plane). Furthermore, both SCIC and PURE improved image uniformity compared to uncorrected images, and SCIC slightly surpassed PURE when metallic metals were present. Lastly, Co-Cr elicited the least uniform image while other metallic materials generally showed similar patterns (i.e. no significant deviation from images without metallic metals).

CONCLUSIONS

Overall, a quantitative understanding of the factors influencing MR image uniformity (e.g. coil type, imaging method, metal susceptibility, and post-hoc correction method) is advantageous to optimize image quality, assists clinical interpretation, and may result in improved medical and dental care.