Optimization of motion-corrected liver diffusion-weighted imaging at 3 Tesla (3T): incorporating complex averaging and reparametrized sinc fatsat pulse with optimized water excitation pulse

Introduction

Diffusion-weighted imaging (DWI) has emerged as a valuable tool in clinical practice, providing a non-invasive and contrast-media free assessment of the Brownian motion of water molecules (1). The restricted motion of water in the presence of cellular structures and edema makes DWI a powerful technique for evaluating cellular density (2-4) and detecting liver lesions (5-8). For the DW sequence, the b-value represents an indicator of water diffusion capability and determines the degree of diffusion weighting. Furthermore, DWI has demonstrated utility in monitoring treatment response in liver diseases (9,10) and estimating the apparent diffusion coefficient (ADC) for qualitative and quantitative assessment (11-13).

In liver DWI, single-shot echo-planar imaging (SS-EPI) sequences are commonly employed due to their rapid acquisition and reduced susceptibility to respiratory and cardiac motion artifacts (14). Free-breathing (FB) without respiratory triggering is commonly used in clinical practice due to its high signal-to-noise ratio (SNR)-time efficiency. However, the motion occurring between different b-values or averages can compromise the quality of the resulting DWI images and ADC maps. This can lead to image blurring, less reliable ADC maps, and consequently reduced lesion detection rates. Various respiratory compensation techniques, such as breath-holding (BH) and respiratory/navigator-triggered (NT) acquisition, have been investigated to improve ADC measurement reproducibility, SNR, and image quality in liver DWI (15-18). However, the implementation of BH decreases patient comfort, whereas respiratory/NT prolongs the measurement time. To address this limitation, a novel SS-EPI DWI sequence with motion correction (19) was developed for abdominal application. Additionally, complex averaging (20), and a combination of a reparametrized sinc fatsat pulse with an optimized water excitation pulse was proposed for further improving the image qualities of DWI (21,22).

Hence, the objective of this study was to develop an FB-optimized liver DWI protocol incorporating motion correction, complex averaging, and a novel fat suppression scheme, and to assess its image quality in comparison to various DWI protocols. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-340/rc).

Methods

Study participants

This prospective, cross-sectional study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and approved by the Fujian Medical University Union Hospital (No. 2023-KY018), and written informed consent was provided by all participants. A total of 42 healthy volunteers [22 males and 20 females, mean age, 28.7±4.2 years (range, 22–39 years)] were enrolled for magnetic resonance (MR) imaging of the liver at Fujian Medical University Union Hospital between January 2023 and March 2023. The inclusion criteria for this study were as follows: adults with no history of liver disease. The exclusion criteria were as follows: (I) imaging evidence of liver disease, including focal or diffuse liver lesions detected by liver MR imaging (MRI); (II) contraindications to MRI; (III) physical complaints during examination. The enrollment process of healthy volunteers is shown in Figure 1.

Figure 1 Flow diagram shows the enrollment of healthy volunteers. MRI, magnetic resonance imaging; NT, navigator trigger; FB, free breathing; MOCO, motion-corrected; DWI, diffusion-weighted imaging; SPAIR, spectral attenuated inversion recovery; cDWI, conventional diffusion-weighted imaging.

MRI protocol

MRI examinations were conducted using a 3 Tesla (3T) MR scanner (MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany), with the utilization of an 18-channel body matrix coil combined with a 32-channel spine matrix coil. All four DWI sequences, which encompassed three newly developed motion-corrected (MOCO)-DWI sequences and an FB conventional DWI (FB cDWI), were performed in a random order. The detailed scan parameters are presented in Table 1. For the three novel MOCO-DWI sequences, a non-rigid in-plane registration technique, named the inverse consistent deformable registration algorithm (19), was employed between averages to mitigate motion-related blurring. Following complex-valued averaging to reduce Rician noise bias (17), the same registration algorithm was performed for different b-values. Fat suppression was achieved by combining a binomial water excitation pulse (1-3-3-1 scheme, monopolar encoding) with a reparametrized sinc-shaped spectral fatsat pulse, or alternatively, a conventional spectral attenuated inversion recovery (SPAIR) pulse. In the case of cDWI, fat suppression was implemented using a conventional SPAIR pulse, and neither motion correction nor standard magnitude-based averaging was applied.

Image analysis

Qualitative image analysis

Three board-certified abdominal radiologists, with 15, 20, and 30 years of experience in abdominal MRI, respectively, performed an independent evaluation of all echo-planar imaging (EPI) DWI images. The images were anonymized and presented to the reviewers in a random order, without any information regarding the acquisition methods or imaging parameters. For follow-up analysis, the radiologists assessed the overall image quality and sharpness of liver contours at b-values of 0 and 1,000 s/mm² using a 5-point Likert scale, transforming the qualitative evaluation into a specific quantitative value: 1= nondiagnostic quality; 2= substantial deficits in image quality; 3= moderate image quality; 4= good image quality; and 5= excellent image quality. They also rated the severity of artifacts at b-values of 0 and 1,000 s/mm² as follows: 1= not diagnostic; 2= severe artifacts; 3= moderate artifacts; 4= mild artifacts; and 5= no artifacts. Furthermore, the effect of the heartbeat in the left liver lobe only at b-values of 1,000 s/mm² was rated as 1= nondiagnostic quality; 2= substantial impact on image quality; 3= moderate impact on image quality; 4= little impact on image quality; and 5= no artifacts.

Quantitative image analysis

A reader with eight years of experience conducted quantitative analysis on the source images and assessed the liver SNR at a b-value of 1,000 s/mm². The reader, who was blinded to the acquisition scheme, placed round regions of interest (ROIs) with an area of 1.5–2.5 cm² in the right and left liver lobes, carefully avoiding blood vessels, artifacts, and lesions. Three representative sections were selected for each DWI sequence in the superior-inferior direction. The central section corresponded to the level of the right portal vein, whereas the superior and inferior sections were obtained three or four section levels above or below the central section.

The reader measured the mean signal intensity (SI) of the liver ROIs and the SI of an equally sized ROI placed in the background (air) region of the corresponding section, close to the liver ROI site, ensuring the absence of prominent artifacts. This procedure was repeated, with similar background ROIs placed in the exact position across all other image slices. For the calculation of SNR, the following equation was utilized (23):

SNR=SI/(Rf∗SDbg)[1]

Where SI represents the SI of a specific ROI, standard deviation of background (SDbg) corresponds to the standard deviation of the selected artifact-free ROI placed on the background (air) of the slice, and Rf denotes the Rayleigh correction factor (0.655) used to convert the Rayleigh distribution of MRI signals in the vicinity of 0 (background) to a Gaussian distribution. In each volunteer, the location and size of the ROI remained consistent across all EPI DWI sequences (b=1,000 s/mm²) and their corresponding ADC maps.

Statistical analysis

The statistical analysis for this study involved both qualitative and quantitative parameters. The Friedman test, a nonparametric test, was used to compare qualitative parameters among the four sequences. For quantitative parameters, such as ADC and SNRs, normality was assessed using the Kolmogorov-Smirnov test, and variance homogeneity was assessed using Levene’s test.

For the quantitative analysis, the independent samples t-test was used to compare ADC differences between the left and right lobes of the liver. One-way analysis of variance (ANOVA) test was performed to compare ADC differences among the superior, middle, and inferior levels of the liver within each technique. The SNRs of the four techniques were compared using the ANOVA test, followed by Dunn-Bonferroni post-hoc test for significant pairwise comparisons. SNRs of the left and right lobes of the liver, as well as the superior, middle, and inferior levels of the liver within each technique, were also compared using independent samples t-test and ANOVA test, separately.

Regarding the qualitative analysis, the Friedman test was used to compare subjective scores among the four techniques. The Dunn-Bonferroni post-hoc test was applied to adjust for all significant pairwise comparisons. Inter-reader agreement for scores was measured using Fleiss kappa statistics, with values ranging from poor to excellent indicating the level of agreement between readers.

The statistical analysis was conducted using the software SPSS 26.0 (IBM Corp., Armonk, NY, USA). Differences were considered statistically significant when the two-sided P values were less than 0.05.

Results

All DWI sequences were successfully acquired in all cases. The mean scan time for the NT MOCO-DWI (136.15±14.23 s) sequence was not significantly different compared to the FB MOCO-DWI, FB MOCO-DWI SPAIR, and FB cDWI sequences (138 s) (P=0.538).

Regarding the comparison of ADC values among the four techniques, for the left–right direction, the ADC values of the left lobe were higher than those of the right lobe in all four techniques (FB MOCO-DWI: P<0.001, NT MOCO-DWI: P=0.003, FB MOCO-DWI SPAIR: P=0.007, FB cDWI: P=0.012). However, for the superior-inferior direction, all four techniques showed no significant difference (FB MOCO-DWI: P=0.455, NT MOCO-DWI: P=0.292, FB MOCO-DWI SPAIR: P=0.457, FB cDWI: P=0.670) (Table 2).

In terms of SNRs, FB MOCO-DWI demonstrated significantly higher SNRs of the liver compared to the other techniques (P<0.001), including the whole liver, left lobe, right lobe, and superior, middle, and inferior levels of the liver (Table 3, Figure 2). There were no significant differences in SNRs between the left and right lobes for all four techniques. In the superior-inferior direction, the SNRs of the inferior level of the liver were higher than those of the superior level in NT MOCO-DWI (P=0.041) (Table 4).

Figure 2 A violin plot of SNR in whole liver measured with different DWI sequences. SNR, signal-to-noise ratio; FB, free breathing; MOCO, motion-corrected; DWI, diffusion-weighted imaging; NT, navigator trigger; SPAIR, spectral attenuated inversion recovery; cDWI, conventional diffusion-weighted imaging.

Regarding image quality assessment, there was excellent agreement among the three readers (Fleiss Kappa value: 0.674–0.941). All readers consistently rated FB MOCO-DWI as having better overall image quality, sharper liver contour, milder artifacts, and less cardiac impact compared to the other techniques (P<0.001) (Table 5, Figure 3).

Figure 3 Comparisons of subjective image analysis and SNRs of the four EPI DWI (A) with the corresponding ADC maps (B) at b=1,000 s/mm². FB MOCO-DWI demonstrated significantly higher SNRs of the liver compared to the other techniques (P<0.001), including the whole liver, left lobe, right lobe, and superior, middle, and inferior levels of the liver, and demonstrated significantly better the overall image quality, the liver contour’s sharpness, the artifacts’ severity, and the heartbeat’s effects compared to the other DWI sequences, except for NT MOCO-DWI. FB, free breathing; MOCO, motion-corrected; DWI, diffusion-weighted imaging; NT, navigator trigger; SPAIR, spectral attenuated inversion recovery; cDWI, conventional diffusion-weighted imaging; SNR, signal-to-noise ratio; EPI, echo-planar imaging; ADC, apparent diffusion coefficient.

Discussion

In this study, the researchers aimed to evaluate a novel MOCO liver diffusion imaging method to address motion artifacts and improve image quality. They compared four EPI DWI protocols in terms of acquisition time, SNR, and image quality. The results showed that the SNR of the liver varied significantly among the four sequences, with the FB MOCO-DWI using optimized water excitation pulse performing the best.

The use of motion correction and complex averaging in MOCO-DWI contributed to an increase in SNR compared to FB cDWI. Previous studies have also reported the benefits of MOCO-DWI in improving SNR, such as in diffusion-weighted MRI of prostate cancer (20). The authors additionally attributed the higher SNR in FB MOCO-DWI to the application of fat suppression using a binomial water excitation pulse combined with a sinc-shaped spectral fatsat pulse, which did not cause signal reduction compared to using a SPAIR pulse. The SPAIR pulse’s spectral width can lead to partial water saturation and decreased SNR.

Comparing FB MOCO-DWI with NT MOCO-DWI, the former showed a significantly higher SNR in the liver. This can be attributed to the larger average number used in FB MOCO-DWI, which not only improves the SNR but also increases the likelihood of acquiring data during the diastole phase, reducing signal loss related to cardiac motion, especially in the left lobe of the liver (24). Despite similar acquisition times for the two MOCO-DWI sequences, FB MOCO-DWI was more time-efficient than NT MOCO-DWI because it acquired three b-values compared to two b-values in NT MOCO-DWI. However, it is important to note that the additional b500 image in FB MOCO-DWI does not impact the ADC calculation, as it lies between the b0 and b1,000 values (25). Overall, the study demonstrated the advantages of the FB MOCO-DWI sequence with optimized water excitation pulse in terms of SNR and image quality, making it a promising approach for liver diffusion imaging.

In liver imaging with DWI, respiratory and cardiac motion-induced artifacts can still be problematic, especially at 3T. To address these challenges, respiratory triggers (RTs) (18,26,27) and NTs (18,28) have been utilized. Previous studies have shown that RT and NT DWI can improve image sharpness compared to untriggered FB DWI in healthy volunteers (29). Several published reports have suggested that RT or NT acquisition can enhance liver lesion detection compared to FB acquisition, which is more susceptible to respiratory misregistration (26,30). However, a study by Choi et al. (16) found that FB and NT DWI had equal performance in assessing lesion detection and characterization for 3.0T hepatic MRI, with FB acquisition being more time-efficient than NT acquisition. A liver intravoxel incoherent motion (IVIM) imaging study by Li et al. (31) based on 22 healthy participants reported that FB can be used to replace RT due to slightly better reproducibility, while saving approximately half of the image data acquisition time. Wang et al. (32) demonstrated that an FB technique for simultaneous water-fat separation and T1 mapping of the whole liver (SWALI) in a single scan can achieve 2.5 mm isotropic spatial resolution, superior to conventional liver T1 mapping techniques under BH conditions.

In the present study, the results showed that FB MOCO-DWI demonstrated significantly better in terms of the overall image quality, the liver contour sharpness, the artifact severity, and the heartbeat effects compared to the other DWI sequences except NT MOCO-DWI. The motion-correction employed in FB MOCO-DWI contributed to reducing motion-induced artifacts. Additionally, the use of complex averaging technique and the new fat suppression scheme provided higher SNR and more homogeneity for liver signal and improved visualization of liver contours. Although there was a statistically significantly higher SNR at b=1,000 s/mm² for FB MOCO-DWI compared to NT MOCO-DWI, this difference was not statistically significant in qualitative analysis. This may be attributed to the fact that NT acquisition mitigates respiratory motion-induced artifacts, resulting in less pronounced differences in appearance between the two techniques. Overall, the study highlights the benefits of FB MOCO-DWI in terms of improved image quality and reduced motion artifacts.

In this study, the mean ADC values were found to decrease from the left to the right hepatic lobe, which was consistent with previous studies (33,34). This observation suggests that cardiac motion can influence the ADC values, particularly in the left hepatic lobe (35,36). However, no statistically significant differences in mean ADC values were observed in the cranial to caudal directions among the four DWI sequences.

It is important to acknowledge the limitations of the study. Firstly, the diagnostic performance of the new MOCO-DWI technique was not evaluated in clinical practice, so its effectiveness in detecting and characterizing liver lesions remains to be determined. Secondly, the study did not assess the reproducibility of ADC values in each of the four sequences, which would provide insights into the reliability and consistency of the measurements. Lastly, the new MOCO-DWI sequence using SS-EPI is still susceptible to susceptibility artifacts, resulting in geometric distortion, signal attenuation, and image blurring (37). These artifacts can potentially affect the accuracy and interpretation of the images.

In conclusion, the proposed EPI DWI technique, incorporating motion correction, complex averaging, and a modified fat suppression scheme, demonstrated clinical feasibility for liver MRI. Among the four sequences evaluated, FB MOCO-DWI showed superior SNR and excellent image quality, making it a recommended choice for liver DW imaging at 3T in clinical routine.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-340/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-340/coif). C.F. is an employee of Siemens Shenzhen Magnetic Resonance Ltd. G.L. is an employee of Siemens Healthineers Ltd. T.B. is an employee of Siemens Healthineers AG. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by Fujian Medical University Union Hospital (No. 2023-KY018) and informed consent was provided by all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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