Magnetic resonance elastography for prostate cancer: advancing diagnostic accuracy, tumor characterization, and clinical applications

Introduction

Prostate cancer (PCa) is the second most common cancer in men and the fifth leading cause of cancer mortality globally, according to the Global Cancer Statistics 2024 (1). Current PCa diagnostics face two major gaps: (I) difficulty in reliably distinguishing indolent from clinically significant tumors; and (II) lack of imaging methods that directly assess tissue biomechanics, both of which limit accurate risk stratification and treatment planning. Conventional diagnostic methods, including prostate-specific antigen (PSA) testing, digital rectal examination, and systematic biopsies, are commonly employed. Still, these modalities have low specificity and can miss clinically significant lesions (2). Multiparametric magnetic resonance imaging (mpMRI) has revolutionized the diagnosis and risk stratification of PCa, facilitated better detection and localization of suspicious lesions using the Prostate Imaging Reporting and Data System (PI-RADS) (3,4).

All these advances, apart from mpMRI, are not sensitive in differentiating between aggressive and indolent tumors and evaluating the mechanical properties of tissues, which may be crucial in determining tumor biology (5). Magnetic resonance elastography (MRE) is a magnetic resonance imaging (MRI)-based imaging method that quantifies the non-invasive stiffness of tissues by measuring the mechanical propagation and generation of shear waves, and then back-calculating quantitative maps of stiffness (elastograms). As a biomechanical information source, MRE offers an imaging surrogate biomarker for cancer detection and typing (6-10). MRE was first implemented for liver applications and has also shown promise in evaluating solid tumors, including those in the brain, breast, and prostate (11-14). MRE can image such differences in stiffness and give useful information to differentiate benign from malignant lesions (15). The principles, current uses, and potential future directions of MRE in PCa imaging are summarized in this study. Technical advancements like tomoelastography, interaction with current diagnostic frameworks like PI-RADS, and the possible contribution of more recent methodologies like radiomics and machine learning are given particular attention (16). MRE is not yet routinely used in the clinical setting but has excellent potential as an adjunct imaging modality with the potential to enhance PCa diagnosis, risk stratification, and incorporation into precision oncology pathways in the near future.

Principles of MRE

MRE is a non-invasive, state-of-the-art medical imaging modality that employs the propagation of shear waves within soft tissue to identify tissue stiffness (17). MRE, which was initially targeted for implementation by Muthupillai et al. in 1995, has been increasingly applied in organ imaging such as liver, brain, breast, and prostate imaging (18).

The wave with stiffer tissues possesses longer wavelengths and propagates more quickly. Mechanical properties such as the Young’s modulus and shear modulus can be measured by MRE. Multifrequency MRE (using multiple wave frequencies for greater precision) and three-dimensional (3D) MRE (for measuring 3D stiffness) are some of the advancements the technology has undergone over the years (17). To address challenges in the deep pelvic position and limited prostate size, special wave drivers (including transperineal, transrectal, and transpelvic systems) and adapted inversion algorithms like multifrequency tomoelastography have been developed. These technological developments improved feasibility, image quality, and diagnostic accuracy in ex vivo and in vivo prostate MRE assessments (19).

Stepwise process of MRE

Step 1: shear wave generation in MRE utilizes an external driver to apply low-frequency mechanical vibrations (20–200 Hz) to the body surface in order to generate shear waves within the underlying tissue (Figure 1). Step 2: motion-sensitive MRI acquisition involves motion-encoding gradient (MEG) sequences of the MRI scanner being applied for imaging shear wave propagation in real time. Step 3: stiffness mapping (Elastogram Generation) is where wave data that had been acquired are mathematically inverted by algorithms to create elastograms—color-coded quantitative maps of tissue stiffness in kilopascals (kPa) (17,18).

Figure 1 Principles of magnetic resonance elastography. (A) Patient setup: the patient lies inside the MRI scanner, where phase-sensitive sequences are applied. (B) Vibration generation: an external mechanical driver is applied to the pelvis to produce vibrations that are transmitted to the prostate. (C) Shear wave propagation: shear waves travel through the prostate; stiffer tumors transmit waves faster and with longer wavelengths compared to benign tissue. (D) Stiffness map (elastogram): inversion algorithms reconstruct quantitative color-coded stiffness maps, where malignant lesions appear stiffer (red/yellow) than benign tissue (blue). MRI, magnetic resonance imaging.

Diagnostic performance and clinical evidence

PCa can be identified, characterized, and graded using MRE, according to a growing body of clinical and experimental studies. Because the mechanical stiffness of malignant prostate tissue is higher than that of normal parenchyma or benign prostatic hyperplasia (BPH), MRE provides a non-invasive diagnostic tool for PCa.

Stiffness differentiation

One of the principal applications of MRE in PCa is differentiating malignant from benign prostate tissue based on stiffness measurements. Malignant tumors tend to be stiffer than benign tumors due to the changes in the structure of the surrounding tissue. While individual cancer cells are softer and more deformable than normal cells, tumors, on average, are stiffer because of enhanced deposition of collagen, abnormal vascularity, and elevated interstitial fluid pressure. These stromal and extracellular alterations are the origins of the biomechanical signature sensed by MRE (20).

Using a pelvic acoustic driver, Kim et al. prospectively studied 75 patients in an in vivo study and established a cutoff stiffness of 4.2 kPa between PCa and benign tissue. Accuracy was 87% at the cutoff, sensitivity was 73%, specificity was 99%, and area under the curve (AUC) was 0.839 (Table 1). Stiffness was also related to International Society of Urological Pathology (ISUP) grade, suggesting a possible role in tumor grading. However, the overlap of PCa and BPH stiffness values reduced discrimination accuracy to 62% (AUC 0.598), suggesting the challenge in differentiating between these two conditions (21). Deng et al. measured 35 patients with lower urinary tract symptoms (LUTS) and an external driver at 60–150 Hz frequencies and showed enhanced lesion visibility and diagnostic accuracy with higher frequency (AUC 0.773–0.944) (19). These findings proved the clinical utility of MRE in vivo, with technical heterogeneity across driver systems and frequency conditions.

Ex vivo findings confirmed that malignant tissue is stiffer but with larger absolute values. Reiter et al. studied 14 specimens of prostatectomy at 9.4 T and reported a mean stiffness of 10.8±4.6 kPa in cancer and 5.4±4.4 kPa in normal tissue with a cutoff of 10.7 kPa (AUC =0.81). As illustrated in Figure 2, multi-sequence MRI and elastographic mapping provide detailed spatial localization and tumor stiffness visualization in both in vivo and ex vivo settings (22). A cutoff of 10.67 kPa produced an AUC of 0.81, demonstrating strong diagnostic performance under high-frequency ex vivo conditions. In vivo applicability was also confirmed by Lee et al. with the study of 31 patients with prostatic disease proven by biopsy. MRE was technically feasible in all cases (100%). The stiffness was significantly higher in PCa lesions (5.99±1.46 kPa; n=19) than in BPH (4.67±1.54 kPa; n=10; P=0.045). Occasionally, small cancers (<1 cm) were overlooked, but good diagnostic accuracy (sensitivity 76%, accuracy 61%) and excellent reproducibility [intraclass correlation coefficient (ICC) =0.95] were attained by MRE (23).

Figure 2 An axial plane through the midgland of a patient with prostate cancer. In vivo: (A,B) T2 TSE image with an extensive hypointensity in the left peripheral zone (arrows) and in the right and left transition zone (arrowheads). (C) Diffusion-weighted imaging with b =2,000 s/mm². Arrows indicate the primary lesion area (tumor region). Arrowheads point to surrounding non-tumorous tissue. The markedly hyperintense signal corresponds to the low T2 signal (A,B). (D) No abnormal signal increase is seen in the dynamic contrast-enhanced sequence. Ex vivo: (E) magnitude image. (F) Phase image scaled to shear wave displacement. (G) The marked-up HE image depicts a tumor with a Gleason score of 3+4=7 (green, Gleason pattern 3; blue, Gleason pattern 4). (H) Map of storage modulus. Arrows indicate the primary lesion area (tumor region). Arrowheads point to surrounding non-tumorous tissue. Reprinted with permission under CC BY 4.0 from Reiter et al. [2020] (22). DCE, dynamic contrast-enhanced; DWI, diffusion-weighted imaging; HE, hematoxylin and eosin; TSE, turbo spin echo.

Sahebjavaher et al. examined prostatectomy samples and showed significantly higher storage (69±21 kPa) and loss (58±17 kPa) moduli in cancerous tissues than in normal tissue. MRE alone (AUC ~0.75) was superior to diffusion-weighted imaging (DWI) (AUC =0.68), and performance was further improved when combined (AUC =0.82). However, fixation of tissue increasingly increased the stiffness (+0.2 kPa/h), with a decrease in potential for translation to the clinic (24). Across research, malignant prostate tissue is always stiffer than benign or normal tissue. Most in vivo research suggests workable thresholds of ~4–5 kPa for differentiating between cancer and non-cancerous tissue, while ex vivo research will report larger values (~10–11 kPa). Heterogeneity in drivers, acquisition rates, and study design emphasizes the necessity for multicenter trials with standardization before clinical use.

PI-RADS and mpMRI integration

mpMRI, according to the PI-RADS, is today’s reference standard for PCa detection. However, PI-RADS 3 lesions (25) remain a gray area of diagnosis and often lead to unnecessary biopsies or the missed clinically significant cancers. MRE provides quantitative stiffness maps that can be employed to complement mpMRI by maximizing lesion characterization and confidence in the diagnosis. In a large prospective cohort of 208 patients (73 PCa, 82 benign prostatic disease, 53 healthy controls), incorporating tomoelastography into mpMRI substantially enhanced diagnostic performance. Specificity for ruling out non-PCa cases was improved from 77% to 95%, and the overall AUC was improved from 0.85 to 0.95 with high sensitivity (~92%). MRE also differentiated peripheral-zone cancers from chronic prostatitis (AUC =0.94) and transition-zone cancers from BPH (AUC =0.91) (4). Early feasibility work had demonstrated the technical potential of transurethral MRE for prostate stiffness mapping, supporting its complementary value to mpMRI (26).

To put the concept into perspective, we supply a diagram of the workflow (Figure 3) based on current evidence about combining MRE and mpMRI. It outlines stepwise work from patient preparation and simultaneous selection of mpMRI—that is, T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), DWI, and MRE, with vibration and tracking of shear wave motion. This technique produces the quantitative parametric maps of apparent diffusion coefficient (ADC) and volume transfer constant (Ktrans), and stiffness elastograms. These are co-registered thereafter for joint image analysis, ultimately facilitating clinical reporting and decision-making. It is established in other research work that the combination of MRE with mpMRI significantly enhances lesion characterization, especially for indeterminate PI-RADS category 3 cases. It enhances specificity too and provides quantitative biomechanical data that are additional to the functional MRI parameters (4,21,22).

Figure 3 Workflow integrating mpMRI with MRE (mpMRI: T1, T2, DWI, etc.). The combined methodology produces co-registered maps (ADC, Ktrans, and stiffness elastograms) with elevated specificity and additional biomechanical data besides traditional MRI parameters. ADC, apparent diffusion coefficient; DWI, diffusion-weighted imaging; mpMRI, multiparametric magnetic resonance imaging; MRE, magnetic resonance elastography; MRI, magnetic resonance imaging.

Tumor aggressiveness and grading

The aggressiveness of the tumor should be assessed in the case management of PCa because it decides whether or not patients will receive conservative surveillance or definitive treatment. MRE quantifies tissue stiffness and viscosity that have been investigated as imaging biomarkers for tumor grade, metastatic risk, and pathologic outcome (4,27). In a prospective series of 208 patients, PCa tissue had higher shear-wave velocities (~3.4 m/s) and phase angles (~1.3 rad) compared to benign disease (2.6 m/s, 1.0 rad) and controls (2.2 m/s, 0.8 rad). Inclusion of stiffness increased diagnosis (AUC 0.95 versus 0.85 using PI-RADS v2.1). Subgroup analysis showed excellent discrimination between central-zone PCa and BPH (AUC =0.91), but extremely poor discrimination between peripheral-zone cancer and prostatitis (AUC =0.50). Age-dependent hardening of the healthy central zone of men was also reported (4). Hu et al. demonstrated that preoperative MRE predicts lymph node metastasis with an AUC of 0.982, which suggests a potential future application in nodal staging and systemic risk stratification (27). Such findings indicated that stiffness parameters can also offer prognostic information, in addition to local tumor detection. However, current evidence is limited by single-center design, modest sample sizes, and heterogeneous protocols. Stiffness may also be influenced by non-neoplastic processes (e.g., inflammation, fibrosis), and small lesions may be underestimated. A summary of study design, key findings, limitations, and the type of MRE drivers employed across the selected studies is provided in Table 2.

Clinical applications and utility of MRE in PCa

In PCa diagnosis and treatment, MRE is fast emerging as a promising adjunct to standard mpMRI. Its capacity for measuring the degree of stiffness of the tissue provides a biomechanic biomarker, complemented by structural and functional MRI sequences, for enhanced localization of lesions, assessment of the level of risk, and follow-up treatment monitoring (21).

Early detection and risk stratification

It is important to detect clinically significant PCa (csPCa) at an early and accurate stage, and this is the basis for risk stratification and recommendations for biopsy or definitive therapy. MRE provides quantitative stiffness maps of tissue that may complement mpMRI in this function. Deng et al. (n=35 patients with LUTS) showed vibration frequencies of elevated level (120–150 Hz) improved lesion visibility and diagnostic performance, with AUCs to 0.944 (19). Together, these results suggest that MRE can additionally personalize patient selection for biopsy and more accurately risk-stratify, prevent unnecessary invasive procedures, and maintain high detection rates for csPCa.

Precision lesion localization for intervention

For surgical planning, focal therapies, and the success of biopsy, the accurate tumor location in the prostate must be determined. With objective stiffness mapping, as provided by MRE, biopsy needles can be targeted toward the most probable cancer locations. Reiter et al. also validated this with ex vivo radical prostatectomy samples, with good correlation between regions of elevated stiffness (G' >10.8 kPa) and tumor locations as confirmed by the pathologist. With the integration of MRI-ultrasound fusion technology, in vivo MRE would have the potential for greater tumor detection in difficult-to-approach areas, such as the anterior and apical prostate, in which conventional imaging has diminished sensitivity (22).

Assessment of tumor grade and aggressiveness

Preliminary studies indicate that MRE stiffness values may correlate with ISUP grade; however, results remain inconsistent due to small sample sizes and heterogeneous protocols, according to studies. One such study by Kim et al. found PCa grade and in vivo stiffness values to have a positive correlation, and that MRE may help differentiate clinically significant tumor glands, although findings remain preliminary and require validation in larger cohorts (21). Sahebjavaher et al. improved on diagnostic performance by integrating the use of DWI with MRE, resulting in an AUC of 0.82 for cancer vs. healthy prostate tissue in the peripheral zone differentiation (24).

Challenges and limitations of MRE in PCa

Although there are positive findings in a series of studies, their clinical application in PCa has been limited due to certain technical, logistical, and biological restrictions.

Technical complexity and standardization issues

One of the biggest challenges to clinical application of prostate MRE is the technical challenge of acquiring and measuring reliable stiffness maps inside deep pelvic anatomy. Being significantly different from liver or brain MRE, by which wave-wave transmission is straightforward, the motion and positioning of the prostate cause a challenge with wave transmission and motion encoding (7,28). Various driver systems—i.e., transperineal, transrectal, transurethral actuators, and pelvic external drivers—have been explored with conflicting image quality, patient acceptability, and reproducibility (7,26,29,30). There is no universally accepted acquisition protocol or reconstruction algorithm for prostate MRE today. Stiffness measurements vary between centers due to differences in driver design, vibration frequency, and inversion schemes (21,23). Most published research has been conducted at single centers with small sample sizes, and unlike liver MRE, comprehensive multicenter validation studies have not yet been performed. The lack of standardized techniques and multicenter data impedes the development of reliable diagnostic thresholds and makes large-scale clinical implementation difficult. Achieving consensus on protocols and conducting multicenter studies with future prospects will be crucial for translating prostate MRE into clinical practice.

Motion and artifact sensitivity

One of the main limitations is the motion artifact sensitivity of MRE. Such physiological motion can create unwanted motion on imaging since the prostate lies adjacent to the rectum and bladder, which are physiologically moving due to processes like bladder filling and bowel peristalsis (31). The elastograms can be corrupted by motion, which would reduce the accuracy and reliability of stiffness measurement (21,23). In addition, patient cooperation is crucial to the quality of images obtained in MRE. The images obtained can be smeared or blurred if the patient is not able to keep still during the imaging procedure, which could result in non-diagnostic images and repeat imaging (29,32).

Biological complexity and interpretation of results

It is not only cancer that changes the stiffness of prostate tissue; inflammation, fibrosis, BPH, and endocrine change do as well. These contributions may complicate the interpretation of MRE results. For example, inflammation or fibrosis stiffens tissue independent of cancer and causes false positives. Small low-grade tumors, on the other hand, are unlikely to significantly change tissue stiffness, causing false negatives. Further, PCa heterogeneity—a common multifocal and heterogeneous stiffness—also impedes MRE result interpretation. It remains challenging to distinguish between clinically significant and insignificant lesions based on stiffness alone (4,21,22).

Implications for future research and research gaps

MRE has great promise for non-invasive PCa imaging, but seminal advances are necessary to achieve clinical implementation. High priority is assigned to multicenter validation to substantiate diagnostic thresholds and reproducibility (21,23), and prospective studies evaluating the incremental contribution of MRE compared to mpMRI for indeterminate PI-RADS 3 lesions (25), and follow-up treatment monitoring. The combination of radiomics and artificial intelligence (AI) with stiffness analysis may improve lesion detection, grading, and long-term follow-up.

AI-assisted work and integration with machine learning and radiomics

New breakthroughs in AI and radiomics are revolutionizing PCa imaging and can boost the clinical value of MRE. AI models facilitate computerized segmentation of the prostate and zonal anatomy, high-dimensional imaging feature extraction, and construction of prediction models to identify cancer and estimate risk. For instance, Aldoj et al. utilized a Dense U-Net architecture to MRE-acquired stiffness maps and attained a Dice similarity of 0.93 for computerized prostate segmentation (33). Indicating the possibility to combine MRE information with deep learning for labelable and reproducible anatomic labeling. Radiomic measurement of stiffness maps can permit the extraction of texture and heterogeneity descriptors potentially related to aggressiveness and Gleason grade of cancers (21,34). Machine learning-driven classifiers trained with the described features hold initial promise to identify clinically relevant PCa and benign prostatitis and to forecast therapeutic response. These methods will achieve clinical utility only with follow-on studies involving large, heterogeneous populations and external validation to remain robust and generalize to all patients.

Application in longitudinal monitoring, treatment response, and tumor microenvironment

MRE provides a non-invasive, quantitative means to longitudinally monitor prostate tumors and measure both pre-treatment tissue properties and therapy effects. MRE quantitation of stiffness enables practitioners to measure therapeutic benefit for regimens like radiation therapy, hormone ablation, or focal ablation. Such inconsistency makes it permissible to utilize MRE as an early therapy response biomarker, potentially leading to clinically earlier decisions compared to traditional imaging (4,35). MRE also offers valuable information regarding the microenvironment of the tumor. Tissue stiffness is not only dictated by cancerous cells but is equally dictated by immune cell invasion, fibrosis, and extracellular matrix remodeling. Present data have highlighted the connection between cancer behavior and immune cell dynamics and biomechanical changes. By detecting these changes, MRE can potentially allow non-invasive monitoring of the success of immunotherapy as well as stiffness-related biomarkers for personalized cancer therapy (21,36). In addition, MRE can help to differentiate recurrence/residual tumor from therapy change. For instance, radiation-/ablation-induced fibrosis exhibits moderately increased stiffness, whereas tissue from a viable tumor exhibits heterogeneously increased stiffness (35). The latter is particularly valuable for focal therapies such as high-intensity focused ultrasound or laser ablation, for which precise imaging of the edge of the tumor to prevent damage to overlying normal tissue is beneficial. Transurethral MRE has also indicated its capacity to characterize lesions smaller than 4.75 mm at frequencies above 200 Hz with high spatial resolution sufficient for pre-treatment planning and precise post-treatment evaluation (37). In summary, by integrating longitudinal stiffness measurement with microenvironmental examination and high-resolution imaging of lesions, MRE is an effective modality in PCa treatment with monitoring of therapy, early detection of recurrence, and optimization of precision focal therapy (21,35).

Technical innovations and driver design

Improving driver technologies for larger penetration, more comfort, and more consistent wave propagation is another area of concern in earnest. Novel transperineal and transrectal driver systems continue to be under test for optimal performance in prostate imaging. Novel driver systems, such as transurethral and endorectal actuators, permit shear waves with higher frequency to propagate more effectively to the prostate. These interventions enhance spatial resolution and enable the detection of small, focal tumors, rendering MRE even more precise for deep-seated lesions (20). Future developments can be manifested in flexible wearable devices and motion-corrected MRE sequences, enhancing imaging reliability (7,15,20,21,29).

Conclusions

MRE has emerged as a promising, non-invasive imaging technique with significant potential in the diagnosis, characterization, and management of PCa. By quantitatively assessing tissue stiffness, MRE complements traditional imaging modalities such as mpMRI, offering valuable biomechanical insights that may enhance tumor detection, localization, and grading. The development of advanced techniques such as tomoelastography, radiomics-based modeling, and hybrid imaging approaches have further improved the sensitivity and specificity of MRE in differentiating benign from malignant prostate lesions. Moreover, its applications in monitoring treatment response and exploring tumor microenvironmental properties open new avenues for personalized cancer care.

Despite its advantages, challenges such as technical standardization, validation through multicenter studies, and integration into clinical workflows remain. Addressing these limitations and expanding research into AI integration and longitudinal monitoring will be pivotal to MRE’s future role in routine PCa management. In conclusion, MRE measures prostate stiffness quantitatively to improve differentiation of benign and malignant tissue and improve mpMRI performance, especially for PI-RADS 3 cancers. Early evidence favors application to the grading of tumors and to predicting lymph node metastases and monitoring therapy. Widespread adoption is delayed by a lack of standardized protocols and multicenter validation. The future is headed towards consensus protocols for imaging, large clinical trials, and incorporation with radiomics and AI to provide automatic, personalized care for PCa.

Acknowledgments

None.

Footnote

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-495/prf

Funding: This study was supported by Yunnan Special Funds for High-Level Health Technology Talent Cultivation (No. D-2024058), Yunnan Fundamental Research Kunming Medical University Projects (No. 202301AY070001-243), the First-Class Discipline Team of Kunming Medical University (No. 2024XKTDTS03).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-495/coif). The 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.

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. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63. [Crossref] [PubMed]
2. Pinto F, Totaro A, Calarco A, et al. Imaging in prostate cancer diagnosis: present role and future perspectives. Urol Int 2011;86:373-82. [Crossref] [PubMed]
3. Turkbey B, Rosenkrantz AB, Haider MA, et al. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol 2019;76:340-51. [Crossref] [PubMed]
4. Li M, Guo J, Hu P, et al. Tomoelastography Based on Multifrequency MR Elastography for Prostate Cancer Detection: Comparison with Multiparametric MRI. Radiology 2021;299:362-70. [Crossref] [PubMed]
5. Bruno F, Arrigoni F, Mariani S, et al. Advanced magnetic resonance imaging (MRI) of soft tissue tumors: techniques and applications. Radiol Med 2019;124:243-52. [Crossref] [PubMed]
6. Sack I. Magnetic resonance elastography from fundamental soft-tissue mechanics to diagnostic imaging. Nature Reviews Physics 2023;5:25-42. [Crossref]
7. Wang J, Deng Y, Jondal D, et al. New and Emerging Applications of Magnetic Resonance Elastography of Other Abdominal Organs. Top Magn Reson Imaging 2018;27:335-52. [Crossref] [PubMed]
8. Aunan-Diop JS, Pedersen CB, Halle B, et al. Magnetic resonance elastography in normal pressure hydrocephalus-a scoping review. Neurosurg Rev 2022;45:1157-69. [Crossref] [PubMed]
9. Higuchi M, Tamaki N, Kurosaki M, et al. Longitudinal association of magnetic resonance elastography-associated liver stiffness with complications and mortality. Aliment Pharmacol Ther 2022;55:292-301. [Crossref] [PubMed]
10. Ajmera V, Kim BK, Yang K, et al. Liver Stiffness on Magnetic Resonance Elastography and the MEFIB Index and Liver-Related Outcomes in Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis of Individual Participants. Gastroenterology 2022;163:1079-1089.e5. [Crossref] [PubMed]
11. Yin M, Talwalkar JA, Glaser KJ, et al. Assessment of hepatic fibrosis with magnetic resonance elastography. Clin Gastroenterol Hepatol 2007;5:1207-1213.e2. [Crossref] [PubMed]
12. Murphy MC, Huston J 3rd, Jack CR Jr, et al. Measuring the characteristic topography of brain stiffness with magnetic resonance elastography. PLoS One 2013;8:e81668. [Crossref] [PubMed]
13. Sack I, Beierbach B, Hamhaber U, et al. Non-invasive measurement of brain viscoelasticity using magnetic resonance elastography. NMR Biomed 2008;21:265-71. [Crossref] [PubMed]
14. Sinkus R, Lorenzen J, Schrader D, et al. High-resolution tensor MR elastography for breast tumour detection. Phys Med Biol 2000;45:1649-64. [Crossref] [PubMed]
15. Pagé G, Garteiser P, Van Beers BE. Magnetic resonance elastography of malignant tumors. Front Phys 2022;10:910036. [Crossref]
16. Dhiman A, Kumar V, Das CJ. Quantitative magnetic resonance imaging in prostate cancer: A review of current technology. World J Radiol 2024;16:497-511. [Crossref] [PubMed]
17. Mariappan YK, Glaser KJ, Ehman RL. Magnetic resonance elastography: a review. Clin Anat 2010;23:497-511. [Crossref] [PubMed]
18. Muthupillai R, Lomas DJ, Rossman PJ, et al. Magnetic resonance elastography by direct visualization of propagating acoustic strain waves. Science 1995;269:1854-7. [Crossref] [PubMed]
19. Deng Y, Yi Z, Zhang T, et al. Magnetic resonance elastography of the prostate in patients with lower urinary tract symptoms: feasibility of the modified driver at high multi-frequencies. Abdom Radiol (NY) 2022;47:399-408. [Crossref] [PubMed]
20. Pepin KM, Ehman RL, McGee KP. Magnetic resonance elastography (MRE) in cancer: Technique, analysis, and applications. Prog Nucl Magn Reson Spectrosc 2015;90-91:32-48. [Crossref] [PubMed]
21. Kim SH, Kim JY, Hwang MJ. Magnetic Resonance Elastography for the Detection and Classification of Prostate Cancer. Cancers (Basel) 2024;16:3494. [Crossref] [PubMed]
22. Reiter R, Majumdar S, Kearney S, et al. Prostate cancer assessment using MR elastography of fresh prostatectomy specimens at 9.4 T. Magn Reson Med 2020;84:396-404. [Crossref] [PubMed]
23. Lee HJ, Cho SB, Lee JK, et al. The feasibility of MR elastography with transpelvic vibration for localization of focal prostate lesion. Sci Rep 2024;14:3864. [Crossref] [PubMed]
24. Sahebjavaher RS, Nir G, Gagnon LO, et al. MR elastography and diffusion-weighted imaging of ex vivo prostate cancer: quantitative comparison to histopathology. NMR Biomed 2015;28:89-100. [Crossref] [PubMed]
25. Schoots IG. MRI in early prostate cancer detection: how to manage indeterminate or equivocal PI-RADS 3 lesions? Transl Androl Urol 2018;7:70-82. [Crossref] [PubMed]
26. Arani A, Plewes D, Krieger A, et al. The feasibility of endorectal MR elastography for prostate cancer localization. Magn Reson Med 2011;66:1649-57. [Crossref] [PubMed]
27. Hu B, Deng Y, Chen J, et al. Evaluation of MR elastography for prediction of lymph node metastasis in prostate cancer. Abdom Radiol (NY) 2021;46:3387-400. [Crossref] [PubMed]
28. Hirsch S, Braun J, Sack I. Magnetic Resonance Elastography: Physical Background And Medical Applications. Wiley; 2016.
29. Dittmann F, Reiter R, Guo J, et al. Tomoelastography of the prostate using multifrequency MR elastography and externally placed pressurized-air drivers. Magn Reson Med 2018;79:1325-33. [Crossref] [PubMed]
30. Chopra R, Arani A, Huang Y, et al. In vivo MR elastography of the prostate gland using a transurethral actuator. Magn Reson Med 2009;62:665-71. [Crossref] [PubMed]
31. Aphinives C, Kiatsayompoo W, Eurboonyanun K, et al. Tissue stiffness in BPH patients from magnetic resonance elastography. Egyptian Journal of Radiology and Nuclear Medicine 2021;52:294. [Crossref]
32. Sahebjavaher RS, Baghani A, Honarvar M, et al. Transperineal prostate MR elastography: initial in vivo results. Magn Reson Med 2013;69:411-20. [Crossref] [PubMed]
33. Aldoj N, Biavati F, Dewey M, et al. Fully automated quantification of in vivo viscoelasticity of prostate zones using magnetic resonance elastography with Dense U-net segmentation. Sci Rep 2022;12:2001. [Crossref] [PubMed]
34. Dong H, White RD, Kolipaka A. Advances and Future Direction of Magnetic Resonance Elastography. Top Magn Reson Imaging 2018;27:363-84. [Crossref] [PubMed]
35. McGrath DM, Lee J, Foltz WD, et al. MR elastography to measure the effects of cancer and pathology fixation on prostate biomechanics, and comparison with T (1), T (2) and ADC. Phys Med Biol 2017;62:1126-48. [Crossref] [PubMed]
36. Silva MM Jr, Matheus WE, Garcia PV, et al. Characterization of reactive stroma in prostate cancer: involvement of growth factors, metalloproteinase matrix, sexual hormones receptors and prostatic stem cells. Int Braz J Urol 2015;41:849-58. [Crossref] [PubMed]
37. Arani A, Plewes D, Chopra R. Transurethral prostate magnetic resonance elastography: prospective imaging requirements. Magn Reson Med 2011;65:340-9. [Crossref] [PubMed]