Effect of simultaneous multislice acceleration on the quantitative measurements of diffusion-weighted imaging, intravoxel incoherent motion, and diffusion kurtosis imaging in hepatocellular carcinoma and abdominal organs

Introduction

Diffusion-weighted imaging (DWI) is a valuable magnetic resonance imaging (MRI) technique that is essential for the detection and diagnosis of abdominal diseases, especially carcinogenesis. For example, as the cellular density often increases during the development of hepatocellular carcinoma (HCC) (1), DWI can effectively reflect this process by characterizing the degree of diffusion restriction of water movement in tissues (2).

Among the quantitative diffusion parameters of DWI, the apparent diffusion coefficient (ADC) based on a monoexponential model is the most commonly used parameter to describe the overall diffusion restriction of water molecules. However, due to the complex structure of the human body, the diffusion of water molecules does not conform to the Gaussian distribution, and DWI is also influenced by microperfusion, rendering the monoexponential model and ADC less reliable in this regard. To address these limitations, diffusion kurtosis imaging (DKI) was developed to account for non-Gaussian diffusion (3), and the intravoxel incoherent motion (IVIM) technique was introduced to differentiate between water molecule diffusion and microperfusion effects via a biexponential model (4). Although previous studies have indicated the considerable potential of these diffusion models and have demonstrated that quantitative diffusion parameters can predict tumor aggressiveness and evaluate its treatment response (5,6), several practical challenges related to abdominal DWI remain (7). Notably, the relatively long acquisition time of DWI with multiple b values or advanced diffusion models may hamper its widespread application in clinical practice.

With recent advances in MRI, simultaneous multislice (SMS) acceleration has been developed to reduce scan time through the excitation and reconstruction of multiple slices simultaneously (8). The feasibility of SMS acceleration has been proven in abdominal DWI with the achievement of either rapid protocols or higher-fidelity imaging without signal-to-noise ratio penalties (9-13), supporting the value of combining SMS acceleration with DWI. Recently, several studies have been conducted to explore the application of SMS acceleration in different diffusion models (10,11,14). However, these studies included relatively small sample sizes (e.g., 8–67 volunteers and patients) and reported inconsistent findings (10,11,14,15). For example, Phi Van et al. found SMS-IVIM provided decreased perfusion fraction (f) values in upper abdominal organs (10), while Loh et al. and Xu et al. reported comparable quantitative diffusion parameters between SMS-IVIM and conventional (CON) sequences in the liver (11,14). Given the importance of quantitative diffusion parameters in the diagnosis and treatment evaluation of abdominal diseases, the impact of SMS acceleration on quantitative measurements in different diffusion models should be more conclusively determined.

Therefore, the purpose of this study was to ascertain whether SMS acceleration affects the quantitative measurements of monoexponential DWI, IVIM, and DKI in HCC and in the upper abdominal solid organs. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2894/rc).

Methods

Study population

This single-center prospective study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Institutional Review Board of West China Hospital, Sichuan University [No. 2021 (107)]. Informed consent was obtained from all participants. Patients admitted to West China Hospital, Sichuan University, between October 2021 and September 2023, who met the following criteria were enrolled: (I) age over 18 years; (II) clinically suspected of HCC according to previous medical history or examinations; (III) no prior treatment for focal liver lesion; and (IV) no MRI contraindications (e.g., claustrophobia). Patients were excluded for the following reasons: (I) tumor diameter less than 1 cm; (II) no pathological report or a lack of clinical diagnosis; (III) an interval between MRI and pathological examination >1 month; (IV) pathology other than HCC if pathological examination was performed; and (V) suboptimal image quality (e.g., severe artifacts). The flowchart of patient selection is provided in Figure 1.

Figure 1 Flowchart of patient selection. HCC, hepatocellular carcinoma; MRI, magnetic resonance imaging.

Imaging protocol

All patients underwent preoperative MRI examinations on a 3T scanner (MAGNETOM Skyra, Siemens Healthineers, Erlangen, Germany) with an 18-channel body coil and a 32-channel spine coil. All participants fasted for 6 hours before the examination and were positioned in the supine orientation with the head first. CON and SMS-accelerated diffusion-weighted sequences, including CON-monoexponential DWI, SMS-monoexponential DWI, CON-DKI, SMS-DKI, CON-IVIM, and SMS-IVIM, were conducted sequentially in a free-breathing manner. All diffusion-weighted sequences were acquired with single-shot echo-planar imaging. The detailed acquisition parameters are summarized in Table 1.

Image analysis and quantitative measurement

All images were transferred to a syngo.via Frontier workstation and analyzed by a prototype MR Body Diffusion toolbox (v. 1.4.0, Siemens Healthineers), yielding ADC for monoexponential DWI, mean kurtosis (MK), and mean diffusivity (MD) for DKI, as well as the pure diffusion coefficient (D) and f for IVIM. Notably, our IVIM protocol was not optimized for estimating the perfusion-related diffusion coefficient, as the second b value (30 s/mm²) was too high to reliably capture microcirculation effects (16). The IVIM parameters were calculated with a segmented fitting approach and estimated through linear fitting. Two experienced researchers (T. Yang and Z.Q., with 5 and 6 years of experience in liver MRI, respectively) independently performed region of interest (ROI) analysis for quantitative measurements (Figure 2), and the average of their measurements were used for analyses. The analyzed anatomic regions included the HCC lesion, liver parenchyma, spleen, pancreas, right kidney, and left kidney. For HCC lesions, free-hand ROIs were manually outlined along the tumor margin at the slice of the largest tumor diameter, with necrotic and hemorrhagic regions being excluded. For liver parenchyma, three circular ROIs with an average area of 50 pixels were placed on the right lobe at the level of the right portal vein, with major vessels and artifacts being avoided. For the spleen and pancreas, three ROIs with an average area of 40 pixels were placed at the anterior, middle, and posterior poles of the spleen at the level of the splenic hilum; similarly, 20 pixels were placed on the head, body, and tail of the pancreas. For both kidneys, three ROIs with an average area of 15 pixels were placed at the renal cortex at the midpole level of each kidney (17,18). The average of the quantitative diffusion parameters within the three ROIs of each organ was used for final data analysis.

Figure 2 Illustration of ROI placement in HCC (A), the liver (B), spleen (B), pancreas (C), right kidney (D), and left kidney (D) for ADC, DKI, and IVIM analysis. Yellow circles indicate ROI. ADC, apparent diffusion coefficient; DKI, diffusion kurtosis imaging; HCC, hepatocellular carcinoma; IVIM, intravoxel incoherent motion; ROI, region of interest.

Statistical analysis

Statistical differences between CON and SMS-accelerated diffusion parameters of HCC and upper abdominal solid organs were assessed with paired t-tests or Wilcoxon signed-rank tests, where appropriate. The normality of paired differences was rigorously assessed through (I) Shapiro-Wilk tests (α =0.05 threshold); (II) visual inspection of Q-Q plots; and (III) evaluation of skewness (>2) and kurtosis (>7). Parameters meeting all normality criteria were analyzed with paired t-tests; otherwise, Wilcoxon signed-rank tests were applied. Interreader agreement was assessed via intraclass correlation coefficients (ICCs), with ICCs 0.81–1.00 indicating excellent consistency, 0.61–0.80 good consistency, 0.41–0.60 moderate consistency, and 0.21–0.40 fair consistency, 0.00–0.20 poor consistency (19). MedCalc software (version 20.112; MedCalc Software, Ostend, Belgium; https://www.medcalc.org/) was used to perform the statistical analysis. Bonferroni correction was applied to adjust for multiple comparisons (6 organs × 6 parameters), with P values less than 0.008 (0.05/6) considered statistically significant. The Bland-Altman method was used to evaluate the distribution and concordance of quantitative parameters of HCC and upper abdominal organs between CON and SMS-accelerated sequences.

Results

Population demographics

A total of 113 patients (age 56.7±10.1 years; 101 males) comprising 129 HCCs (size 4.00±2.78 cm) were enrolled in our study, among whom 67 (67/113, 59.3%) had pathologically confirmed HCC. The etiologies of these patients included hepatitis B infection (95/113, 84.1%), hepatitis C infection (11/113, 9.7%), alcoholic hepatitis (2/113, 1.8%), liver steatosis (1/113, 0.9%), and unknown etiology (4/113, 3.5%). In the study cohort, 100 patients had solitary HCC. The baseline characteristics of the patients are listed in Table 2.

Acquisition time

The acquisition times of CON-monoexponential DWI, SMS-monoexponential DWI, CON-DKI, SMS-DKI, CON-IVIM, and SMS-IVIM are presented in Table 1. Compared with CON sequences, the SMS sequences demonstrated superior efficiency, with the acquisition time reduced by 37.35% for SMS-monoexponential DWI, 44.44% for SMS-DKI, and 30.69% for SMS-IVIM.

Comparison of quantitative measurements

The quantitative measurements for CON and SMS-accelerated diffusion-weighted sequences of HCC and the upper abdominal organs are shown in Table 3. The ADC values from SMS-DWI were significantly lower than those of CON-DWI in HCC [(1.02±0.34)×10⁻³ mm²/s vs. (1.08±0.36)×10⁻³ mm²/s; P=0.003]. Moreover, the f (%) values from SMS-DWI were significantly lower than those of CON-DWI in HCC [(27.2±11.4)% vs. (31.4±13.9)%; P<0.001], liver parenchyma [(29.29±7.99)% vs. (31.03±8.01)%; P=0.005], right kidney [(26.34±7.12)% vs. (28.97±9.00)%; P=0.002], and left kidney [(26.28±6.36)% vs. (29.47±8.32)%; P=0.005]. Other quantitative diffusion parameters did not differ significantly between CON and SMS-accelerated sequences (all P values >0.008). The representative cases are presented in Figures 3,4. Bland-Altman analyses (Figures 5,6) demonstrated good agreement of the quantitative measurements between CON and SMS-accelerated sequences, with acceptable absolute mean biases and most measurements within the limits of agreement.

Figure 3 CON (upper row) and SMS-accelerated (bottom row) diffusion-weighted images and quantitative maps in a 64-year-old female patient with HCC. The 3.0-cm HCC in the caudate lobe (yellow arrows) showed hyperintensity on CON-DWI (A) and SMS-DWI (E) with b values of 50 s/mm². The demonstrated quantitative maps were derived from DWI and DKI, including CON-ADC (B) and SMS-ADC (F), CON-MK (C), and SMS-MK (G), as well as CON-MD (D) and SMS-MD (H). ADC, apparent diffusion coefficient; CON, conventional; DKI, diffusion kurtosis imaging; DWI, diffusion-weighted imaging; HCC, hepatocellular carcinoma; MD, mean diffusivity; MK, mean kurtosis; SMS, simultaneous multislice.

Figure 4 CON (upper row) and SMS-accelerated (bottom row) diffusion-weighted images and quantitative maps in a 53-year-old male patient with HCC. The 5.0-cm HCC in the right liver lobe (yellow arrows) showed hyperintensity on CON-DWI (A) and SMS-DWI (D) with b values of 50 s/mm². The demonstrated quantitative maps were derived from IVIM, including CON-D (B) and SMS-D (E), as well as CON-f (C) and SMS-f (F). CON, conventional; D, pure diffusion coefficient; DWI, diffusion-weighted imaging; f, perfusion fraction; HCC, hepatocellular carcinoma; IVIM, intravoxel incoherent motion; SMS, simultaneous multislice.

Figure 5 Bland-Altman plots showing the agreement of quantitative measurements in HCC and liver parenchyma between CON and SMS-accelerated sequences. ADC, apparent diffusion coefficient; CON, conventional; D, pure diffusion coefficient; DKI, diffusion kurtosis imaging; DWI, diffusion-weighted imaging; f, perfusion fraction; HCC, hepatocellular carcinoma; IVIM, intravoxel incoherent motion; MD, mean diffusivity; MK, mean kurtosis; SD, standard deviation; SMS, simultaneous multislice.

Figure 6 Bland-Altman plots showing the agreement of quantitative measurements in the spleen, pancreas, right kidney, and left kidney between the CON and SMS-accelerated sequences. ADC, apparent diffusion coefficient; CON, conventional; D, pure diffusion coefficient; DKI, diffusion kurtosis imaging; DWI, diffusion-weighted imaging; f, perfusion fraction; IVIM, intravoxel incoherent motion; MD, mean diffusivity; MK, mean kurtosis; SD, standard deviation; SMS, simultaneous multislice.

Interreader agreement for quantitative measurements

As shown in Table 4, the interreader agreement of quantitative measurements for both CON and SMS-accelerated diffusion-weighted sequences was moderate to excellent. In CON sequences, the ICC of ADC ranged from 0.604 [95% confidence interval (CI): 0.232–0.822] to 0.847 (95% CI: 0.772–0.879), while in CON-DKI and CON-IVIM, the ICC ranged from 0.476 (95% CI: 0.054–0.754) to 0.723 (95% CI: 0.422–0.880). In SMS-DWI, the ICC of ADC ranged from 0.530 (95% CI: 0.126–0.783) to 0.888 (95% CI: 0.738–0.954), while in SMS-DKI and SMS-IVIM, the ICC ranged from 0.481 (95% CI: 0.16–0.656) to 0.746 (95% CI: 0.463–0.891).

Discussion

In this study, by analyzing quantitative parametric maps, we found that SMS acceleration can reduce scan time without affecting the quantitative measurements of the spleen and pancreas. However, the differences in ADC values between CON and SMS-accelerated monoexponential DWI in HCC, as well as the differences in f values between CON and SMS-accelerated IVIM in HCC, liver parenchyma, right kidney, and left kidney, should be noted.

In our study, lower ADC values were observed in HCC on SMS-monoexponential DWI as compared to CON-monoexponential DWI. This finding in HCC is in line with the study by Wu et al., who used an identical prototype SMS method and reported lower ADC values in HCC with SMS-monoexponential DWI (20). This phenomenon may be attributed to the shorter time of repetition (TR) in SMS acceleration compared to CON-monoexponential DWI (2,000 vs. 3,200 ms in our study, respectively) resulting in incomplete recovery of longitudinal magnetization, ultimately affecting the ADC image signals (8). ADC decreased with a shorter TR especially when the TR was less than 3,000 ms (21). Additionally, SMS excitation can introduce interslice signal modulation, further perturbing steady-state conditions and magnifying ADC biases (8). Moreover, different scanners, field strengths, b values, and acquisition schemes have been proven to affect ADC measurements (16,22,23). Given the above-described factors, future studies with standardized scanning protocols are needed to further validate our results and promote the clinical application of SMS acceleration.

When comparing IVIM-derived diffusion parameters, we performed IVIM-DWI with multiple b values with a 3-T magnet because previous studies indicate that the use of multiple b values can reduce the estimation error of quantitative parameters and that 3T IVIM-DWI is more reproducible than is the 1.5T version (24,25). Moreover, we found that D values were higher and f values were lower on SMS-accelerated sequence as compared to those of the CON sequence in HCC and the upper abdominal organs. Statistically significant differences in f values were noted in HCC, liver parenchyma, the right kidney, and the left kidney. These findings were consistent with those of Phi Van et al., who reported increased D values and decreased f values with SMS-IVIM as compared to CON-IVIM in the liver, pancreas, and kidneys (10). The higher D values and lower f values on SMS-IVIM in our study can be attributed to several causes. First, the use of a free-breathing scheme might have introduced aliasing effects and temporal signal fluctuations within slices, potentially leading to inaccuracies in diffusion parameters (26). Second, a longer time to echo (TE)—with effectively shorter T2 weighting—plays a critical role in IVIM measurements (27,28). T2 elongation in tissues such as HCC can lead to an underestimation of the f due to the differential T2 decay rates between the perfusion and diffusion compartments. The study by Führes et al. also found that f exhibits a strong TE dependence, increasing with longer TE (29). In SMS-IVIM, the simultaneous acquisition of multiple slices may exacerbate this effect by altering the effective TE or introducing variability in T2 weighting across slices, contributing to the observed reduction in f and elevation in D. Third, the reduced TR in SMS acceleration prevents complete longitudinal magnetization recovery of blood due to its long T1 relaxation time, resulting in partial saturation of the blood signal. This mechanism likely explains the lower f values observed in SMS-IVIM as compared to CON sequences (30). Thus, the D value, which reflects the pure molecular diffusion, may be exaggerated. Since the D value is computed first in the multistep IVIM parameter calculation, higher D values lead to lower f values in subsequent calculations (10).

The DKI-derived MD value and MK value can reflect the complexity of tissue microstructure and represent the true diffusion coefficient of the tissue, providing a more accurate reflection of the actual diffusion behavior of water molecules than the ADC value (31). Increased MK values and decreased MD values suggest abnormal diffusion behavior in the organs (31). Although there were no statistically significant differences between the quantitative parameters of SMS-DKI and CON-DKI in our study, the MK values tended to increase and the MD values decrease in HCC and the upper abdominal organs when the SMS-accelerated technique was applied. Hence, caution is warranted in the analysis of SMS-DKI-related clinical research.

The slice acceleration factor (AF) may also influence quantitative measurements. Theoretically, SMS acceleration can significantly shorten the scan time by increasing the AF (8). However, the increase in AF may lead to suboptimal image quality and inaccurate quantitative measurements, and SMS-DWI with an AF of 2 has been recommended as optimal previous studies (32-34). Furthermore, Xu et al. reported similar results for the IVIM and DKI parameters in abdominal imaging, with an optimal AF of 2 being adopted as a compromise between scan time reduction and quantitative measurement (11). Therefore, an AF of 2 can be a practical choice in clinical workflows for reducing scan time while ensuring image quality and reliable quantitative measurements. Moreover, our protocol employed a 1.5-mm gap and a slice thickness of 5 mm, which could theoretically introduce some degree of interslice signal contamination, thereby resulting in increase in the inaccuracy of the diffusion parameters. However, slices were acquired in an interleaved order in our protocol to ensure that any potential crosstalk occurred between nonadjacent slices, thereby minimizing signal contamination.

We acknowledged several limitations in this study. First, possible bias might have been introduced due to the single-center design and the use of a single type of MRI scanner; moreover, the DWI parameters might have been affected by a number of factors. Additional larger and multicenter studies are warranted to confirm our findings. Second, we excluded tumors less than 1 cm in size because they were inadequate for accurate measurement. Therefore, the results of our study may not be applicable to tumors of this size. Third, the majority of patients included in our study had liver cirrhosis, which might have affected the measurement of IVIM-derived parameters (35). Future studies should focus on normal liver parenchyma to validate our findings. Fourth, although we attempted to keep the ROI placement across scans uniform, inconsistency in the slices between different scans was inevitable. Fifth, our study conducted DWI only in a free-breathing manner because it is the most versatile approach for abdominal DWI (36). As a result, the applicability of our findings to other respiratory schemes may be limited. Sixth, SMS acquisitions may amplify B0 inhomogeneity artifacts due to simultaneous slice excitation (37), and although we used vendor-provided shimming and calibration scans, residual inhomogeneity could have led to small but systematic biases in quantitative parameters as compared to CON sequences. Seventh, the TE for SMS sequences differed from those in the corresponding CON sequences, which might have influenced the quantitative diffusion measurements. However, we used the system-recommended minimum TE to optimize image quality and reduce the scan time for all sequences, with slight variations arising from system and sequence constraints. Finally, not all HCCs were confirmed pathologically, but it is acceptable to diagnose HCC based on imaging and tumor markers according to the relevant guidelines (38).

Conclusions

Compared with CON sequences, SMS acceleration can reduce acquisition time without affecting most quantitative parameters in HCC and the upper abdominal organs. The observed differences in ADC and f values between CON and SMS sequences should be interpreted according to the corresponding clinical contexts. For applications requiring precise ADC quantification, such as treatment response assessment and ADC- or f threshold-guided management, the systematic bias introduced by SMS acceleration may warrant protocol harmonization or threshold adjustments. For borderline cases, confirmatory CON sequences may be considered.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2894/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2894/dss

Funding: This study was supported by the National Natural Science Foundation of China (grant No. 82302161); the Natural Science Foundation of Sichuan Province (grant No. 2024NSFSC1798); the China Postdoctoral Science Foundation (grant No. 2023M732464); the Postdoctorate Research Fund of West China Hospital, Sichuan University (grant No. 2024HXBH161); and the Development Project of Hainan Provincial Clinical Medical Center and Post- Doctoral Station Development Project of Sanya (grant No. 23CZ009).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2024-2894/coif). Ting Yin reports that she is an employee of Siemens Healthineers Ltd. throughout her involvement in the study. 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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Institutional Review Board of West China Hospital, Sichuan University [No. 2021 (107)]. Informed consent was obtained from 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/.

References

1. Gluskin JS, Chegai F, Monti S, Squillaci E, Mannelli L. Hepatocellular Carcinoma and Diffusion-Weighted MRI: Detection and Evaluation of Treatment Response. J Cancer 2016;7:1565-70. [Crossref] [PubMed]
2. Malayeri AA, El Khouli RH, Zaheer A, Jacobs MA, Corona-Villalobos CP, Kamel IR, Macura KJ. Principles and applications of diffusion-weighted imaging in cancer detection, staging, and treatment follow-up. Radiographics 2011;31:1773-91. [Crossref] [PubMed]
3. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005;53:1432-40. [Crossref] [PubMed]
4. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988;168:497-505. [Crossref] [PubMed]
5. Surov A, Pech M, Omari J, Fischbach F, Damm R, Fischbach K, Powerski M, Relja B, Wienke A. Diffusion-Weighted Imaging Reflects Tumor Grading and Microvascular Invasion in Hepatocellular Carcinoma. Liver Cancer 2021;10:10-24. [Crossref] [PubMed]
6. Li X, Zhang K, Shi Y, Wang F, Meng X. Correlations between the minimum and mean apparent diffusion coefficient values of hepatocellular carcinoma and tumor grade. J Magn Reson Imaging 2016;44:1442-7. [Crossref] [PubMed]
7. Ni P, Lin Y, Zhong Q, Chen Z, Sandrasegaran K, Lin C. Technical advancements and protocol optimization of diffusion-weighted imaging (DWI) in liver. Abdom Radiol (NY) 2016;41:189-202. [Crossref] [PubMed]
8. Taron J, Martirosian P, Erb M, Kuestner T, Schwenzer NF, Schmidt H, Honndorf VS, Weiß J, Notohamiprodjo M, Nikolaou K, Schraml C. Simultaneous multislice diffusion-weighted MRI of the liver: Analysis of different breathing schemes in comparison to standard sequences. J Magn Reson Imaging 2016;44:865-79. [Crossref] [PubMed]
9. Obele CC, Glielmi C, Ream J, Doshi A, Campbell N, Zhang HC, Babb J, Bhat H, Chandarana H. Simultaneous Multislice Accelerated Free-Breathing Diffusion-Weighted Imaging of the Liver at 3T. Abdom Imaging 2015;40:2323-30. [Crossref] [PubMed]
10. Phi Van VD, Becker AS, Ciritsis A, Reiner CS, Boss A. Intravoxel Incoherent Motion Analysis of Abdominal Organs: Application of Simultaneous Multislice Acquisition. Invest Radiol 2018;53:179-85. [Crossref] [PubMed]
11. Xu H, Zhang N, Yang DW, Ren A, Ren H, Zhang Q, Zhu JX, Li GJ, Yang ZH. Scan Time Reduction in Intravoxel Incoherent Motion Diffusion-Weighted Imaging and Diffusion Kurtosis Imaging of the Abdominal Organs: Using a Simultaneous Multislice Technique With Different Acceleration Factors. J Comput Assist Tomogr 2021;45:507-15. [Crossref] [PubMed]
12. Boss A, Barth B, Filli L, Kenkel D, Wurnig MC, Piccirelli M, Reiner CS. Simultaneous multi-slice echo planar diffusion weighted imaging of the liver and the pancreas: Optimization of signal-to-noise ratio and acquisition time and application to intravoxel incoherent motion analysis. Eur J Radiol 2016;85:1948-55. [Crossref] [PubMed]
13. Vasquez JA, Brown M, Woolsey M, Abdul-Ghani M, Katabathina V, Deng S, Blangero J, Clarke GD. Reproducibility and Repeatability of Intravoxel Incoherent Motion MRI Acquisition Methods in Liver. J Magn Reson Imaging 2024;60:1691-703. [Crossref] [PubMed]
14. Loh M, Führes T, Stuprich C, Benkert T, Bickelhaupt S, Uder M, Laun FB. Effect of simultaneous multislice imaging, slice properties, and repetition time on the measured magnetic resonance biexponential intravoxel incoherent motion in the liver. PLoS One 2024;19:e0306996. [Crossref] [PubMed]
15. Wu Y, Ye Z, Yang T, Yao S, Chen J, Yin T, Song B. Preoperative prediction of early recurrence in hepatocellular carcinoma using simultaneous multislice diffusion kurtosis imaging. Eur Radiol 2025; Epub ahead of print. [Crossref]
16. Chevallier O, Zhou N, Cercueil JP, He J, Loffroy R, Wáng YXJ. Comparison of tri-exponential decay versus bi-exponential decay and full fitting versus segmented fitting for modeling liver intravoxel incoherent motion diffusion MRI. NMR Biomed 2019;32:e4155. [Crossref] [PubMed]
17. Donati OF, Chong D, Nanz D, Boss A, Froehlich JM, Andres E, Seifert B, Thoeny HC. Diffusion-weighted MR imaging of upper abdominal organs: field strength and intervendor variability of apparent diffusion coefficients. Radiology 2014;270:454-63. [Crossref] [PubMed]
18. Ye Z, Yao S, Yang T, Li Q, Li Z, Song B. Abdominal Diffusion-Weighted MRI With Simultaneous Multi-Slice Acquisition: Agreement and Reproducibility of Apparent Diffusion Coefficients Measurements. J Magn Reson Imaging 2024;59:1170-8. [Crossref] [PubMed]
19. Zhang G, Sun H, Qian T, An J, Shi B, Zhou H, Liu Y, Peng X, Liu Y, Chen L, Jin Z. Diffusion-weighted imaging of the kidney: comparison between simultaneous multi-slice and integrated slice-by-slice shimming echo planar sequence. Clin Radiol 2019;74:325.e1-8. [Crossref] [PubMed]
20. Wu Y, Ye Z, Yang T, Yao S, Chen J, Yin T, Tang H, Song B. Simultaneous multislice echo-planar diffusion-weighted imaging (DWI) in patients with focal liver lesions: a comparative study with conventional DWI. Quant Imaging Med Surg 2024;14:6684-97. [Crossref] [PubMed]
21. Ogura A, Hayakawa K, Miyati T, Maeda F. Imaging parameter effects in apparent diffusion coefficient determination of magnetic resonance imaging. Eur J Radiol 2011;77:185-8. [Crossref] [PubMed]
22. Taouli B, Koh DM. Diffusion-weighted MR imaging of the liver. Radiology 2010;254:47-66. [Crossref] [PubMed]
23. Dale BM, Braithwaite AC, Boll DT, Merkle EM. Field strength and diffusion encoding technique affect the apparent diffusion coefficient measurements in diffusion-weighted imaging of the abdomen. Invest Radiol 2010;45:104-8. [Crossref] [PubMed]
24. Cui Y, Dyvorne H, Besa C, Cooper N, Taouli B. IVIM Diffusion-weighted Imaging of the Liver at 3.0T: Comparison with 1.5T. Eur J Radiol Open 2015;2:123-8. [Crossref] [PubMed]
25. Wang Q, Yu G, Qiu J, Lu W. Application of Intravoxel Incoherent Motion in Clinical Liver Imaging: A Literature Review. J Magn Reson Imaging 2024;60:417-40. [Crossref] [PubMed]
26. Xu J, Moeller S, Auerbach EJ, Strupp J, Smith SM, Feinberg DA, Yacoub E, Uğurbil K. Evaluation of slice accelerations using multiband echo planar imaging at 3 T. Neuroimage 2013;83:991-1001. [Crossref] [PubMed]
27. Ma FZ, Wáng YXJ T. (2) relaxation time elongation of hepatocellular carcinoma relative to native liver tissue leads to an underestimation of perfusion fraction measured by standard intravoxel incoherent motion magnetic resonance imaging. Quant Imaging Med Surg 2024;14:1316-22. [Crossref] [PubMed]
28. Wáng YXJ. An explanation for the triphasic dependency of apparent diffusion coefficient (ADC) on T2 relaxation time: the multiple T2 compartments model. Quant Imaging Med Surg 2025;15:3779-91. [Crossref] [PubMed]
29. Führes T, Riexinger AJ, Loh M, Martin J, Wetscherek A, Kuder TA, Uder M, Hensel B, Laun FB. Echo time dependence of biexponential and triexponential intravoxel incoherent motion parameters in the liver. Magn Reson Med 2022;87:859-71. [Crossref] [PubMed]
30. Lu H, Clingman C, Golay X, van Zijl PC. Determining the longitudinal relaxation time (T1) of blood at 3.0 Tesla. Magn Reson Med 2004;52:679-82. [Crossref] [PubMed]
31. Cao L, Chen J, Duan T, Wang M, Jiang H, Wei Y, Xia C, Zhou X, Yan X, Song B. Diffusion kurtosis imaging (DKI) of hepatocellular carcinoma: correlation with microvascular invasion and histologic grade. Quant Imaging Med Surg 2019;9:590-602. [Crossref] [PubMed]
32. Xu H, Zhang N, Yang DW, Ren A, Ren H, Zhang Q, Zhu JX, Li GJ, Yang ZH. Feasibility study of simultaneous multislice diffusion kurtosis imaging with different acceleration factors in the liver. BMC Med Imaging 2021;21:132. [Crossref] [PubMed]
33. Taron J, Martirosian P, Schwenzer NF, Erb M, Kuestner T, Weiß J, Othman A, Notohamiprodjo M, Nikolaou K, Schraml C. Scan time minimization in hepatic diffusion-weighted imaging: evaluation of the simultaneous multislice acceleration technique with different acceleration factors and gradient preparation schemes. MAGMA 2016;29:739-49. [Crossref] [PubMed]
34. Taron J, Martirosian P, Kuestner T, Schwenzer NF, Othman A, Weiß J, Notohamiprodjo M, Nikolaou K, Schraml C. Scan time reduction in diffusion-weighted imaging of the pancreas using a simultaneous multislice technique with different acceleration factors: How fast can we go? Eur Radiol 2018;28:1504-11. [Crossref] [PubMed]
35. Shiraga K, Ono K, Inuzuka R, Asakai H, Ookubo T, Shirayama A, Higashi K, Nakajima H. Intravoxel incoherent motion imaging has the possibility to detect liver abnormalities in young Fontan patients with good hemodynamics. Cardiol Young 2019;29:898-903. [Crossref] [PubMed]
36. Chen X, Qin L, Pan D, Huang Y, Yan L, Wang G, Liu Y, Liang C, Liu Z. Liver diffusion-weighted MR imaging: reproducibility comparison of ADC measurements obtained with multiple breath-hold, free-breathing, respiratory-triggered, and navigator-triggered techniques. Radiology 2014;271:113-25. [Crossref] [PubMed]
37. Anderson RJ, Poser BA, Stenger VA. Simultaneous multislice spectral-spatial excitations for reduced signal loss susceptibility artifact in BOLD functional MRI. Magn Reson Med 2014;72:1342-52. [Crossref] [PubMed]
38. Ayuso C, Rimola J, Vilana R, Burrel M, Darnell A, García-Criado Á, Bianchi L, Belmonte E, Caparroz C, Barrufet M, Bruix J, Brú C. Diagnosis and staging of hepatocellular carcinoma (HCC): current guidelines. Eur J Radiol 2018;101:72-81. [Crossref] [PubMed]