A novel alternative to classify tissues from T 1 and T 2 relaxation times for prostate MRI

Feasibility of using synthetic MRI to predict lymphatic vascular space invasion status in early-stage cervical cancer: added value to morphological MRI

OBJECTIVES

To investigate the feasibility of synthetic magnetic resonance imaging (syMRI) in predicting the lymphatic vascular space invasion (LVSI) status of early-stage cervical cancer, and its added value to morphological MRI.

MATERIALS AND METHODS

A total of 72 patients with pathology-confirmed early-stage cervical cancer were enrolled, and classified into LVSI- positive (n=41) and LVSI- negative (n=31) groups. Together with morphological parameters including gross tumor volume (GTV) and maximum tumor diameter (MTD), the T1, T2, and proton density (PD) values of the tumors were also measured and compared between two groups. Binary logistic regression analysis was used to identify the independent variable associated with LVSI. Receiver operating characteristic curve analyses and DeLong tests were used to evaluate and compare the performances of significant parameters or their combination in predicting LVSI.

RESULTS

LVSI- positive group showed significantly higher GTV (P=0.008) and MTD (P=0.019), and lower T1 (P<0.001) and PD values (P=0.041) than LVSI- negative group. However, no statistical significance was observed regarding the T2 values (P=0.331). Binary logistic regression indicated that T1 value (odds ratio [OR] = 0.993; P=0.001) and MTD (OR=1.903, P=0.027) were independent variables associated with LVSI in early cervical cancer. Optimal performance could be achieved [area under ROC curve (AUC) = 0.784; cut-off value = 0.56; sensitivity = 80.5%; specificity = 71.0%] when combining T1 and MTD for predicting LVSI. Its performance was significantly better than that of MTD alone (AUC, 0.784 vs 0.662, P=0.035).

CONCLUSION

syMRI might be a feasible approach, and it can provide added value to morphological MRI in predicting the LVSI status of early-stage cervical cancer.

Noninvasive ROS imaging and drug delivery monitoring in the tumor microenvironment

Reactive oxygen species (ROS) that are overproduced in certain tumors can be considered an indicator of oxidative stress levels in the tissue. Here, we report a magnetic resonance imaging (MRI)-based probe capable of detecting ROS levels in the tumor microenvironment (TME) using ROS-responsive manganese ion (Mn2+)-chelated, biotinylated bilirubin nanoparticles (Mn@bt-BRNPs). These nanoparticles are disrupted in the presence of ROS, resulting in the release of free Mn2+, which induces T1-weighted MRI signal enhancement. Mn@BRNPs show more rapid and greater MRI signal enhancement in high ROS-producing A549 lung carcinoma cells compared with low ROS-producing DU145 prostate cancer cells. A pseudo three-compartment model devised for the ROS-reactive MRI probe enables mapping of the distribution and concentration of ROS within the tumor. Furthermore, doxorubicin-loaded, cancer-targeting ligand biotin-conjugated Dox/Mn@bt-BRNPs show considerable accumulation in A549 tumors and also effectively inhibit tumor growth without causing body weight loss, suggesting their usefulness as a new theranostic agent. Collectively, these findings suggest that Mn@bt-BRNPs could be used as an imaging probe capable of detecting ROS levels and monitoring drug delivery in the TME with potential applicability to other inflammatory diseases.

Evaluation of magnetic resonance thermometry performance during MR-guided hyperthermia treatment of soft-tissue sarcomas in the lower extremities and pelvis

INTRODUCTION

This study evaluated the performance of magnetic resonance thermometry (MRT) during deep-regional hyperthermia (HT) in pelvic and lower-extremity soft-tissue sarcomas.

MATERIALS AND METHODS

17 pelvic (45 treatments) and 16 lower-extremity (42 treatments) patients underwent standard regional HT and chemotherapy. Pairs of double-echo gradient-echo scans were acquired during the MR protocol 1.4 s apart. For each pair, precision was quantified using phase data from both echoes ('dual-echo') or only one ('single-echo') in- or excluding body fat pixels in the field drift correction region of interest. The precision of each method was compared to that of the MRT approach using a built-in clinical software tool (SigmaVision). Accuracy was assessed in three lower-extremity patients (six treatments) using interstitial temperature probes. The Jaccard coefficient quantified pretreatment motion; receiver operating characteristic analysis assessed its predictability for acceptable precision (<1 °C) during HT.

RESULTS

Compared to the built-in dual-echo approach, single-echo thermometry improved the mean temporal precision from 1.32 ± 0.40 °C to 1.07 ± 0.34 °C (pelvis) and from 0.99 ± 0.28 °C to 0.76 ± 0.23 °C (lower extremities). With body fat-based field drift correction, single-echo mean accuracy improved from 1.4 °C to 1.0 °C. Pretreatment bulk motion provided excellent precision prediction with an area under the curve of 0.80-0.86 (pelvis) and 0.81-0.83 (lower extremities), compared to gastrointestinal air motion (0.52-0.58).

CONCLUSION

Single-echo MRT exhibited better precision than dual-echo MRT. Body fat-based field-drift correction significantly improved MRT accuracy. Pretreatment bulk motion showed improved prediction of acceptable MRT temporal precision over gastrointestinal air motion.

A review of PET attenuation correction methods for PET-MR

Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.

Pseudo-T2 mapping for normalization of T2-weighted prostate MRI

Objective

Signal intensity normalization is necessary to reduce heterogeneity in T2-weighted (T2W) magnetic resonance imaging (MRI) for quantitative analysis of multicenter data. AutoRef is an automated dual-reference tissue normalization method that normalizes transversal prostate T2W MRI by creating a pseudo-T2 map. The aim of this study was to evaluate the accuracy of pseudo-T2s and multicenter standardization performance for AutoRef with three pairs of reference tissues: fat/muscle (AutoRef_F), femoral head/muscle (AutoRef_FH) and pelvic bone/muscle (AutoRef_PB).

Materials and methods

T2s measured by multi-echo spin echo (MESE) were compared to AutoRef pseudo-T2s in the whole prostate (WP) and zones (PZ and TZ/CZ/AFS) for seven asymptomatic volunteers with a paired Wilcoxon signed-rank test. AutoRef normalization was assessed on T2W images from a multicenter evaluation set of 1186 prostate cancer patients. Performance was measured by inter-patient histogram intersections of voxel intensities in the WP before and after normalization in a selected subset of 80 cases.

Results

AutoRef_FH pseudo-T2s best approached MESE T2s in the volunteer study, with no significant difference shown (WP: p = 0.30, TZ/CZ/AFS: p = 0.22, PZ: p = 0.69). All three AutoRef versions increased inter-patient histogram intersections in the multicenter dataset, with median histogram intersections of 0.505 (original data), 0.738 (AutoRef_FH), 0.739 (AutoRef_F) and 0.726 (AutoRef_PB).

Discussion

All AutoRef versions reduced variation in the multicenter data. AutoRef_FH pseudo-T2s were closest to experimentally measured T2s.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10334-022-01003-9.

The Preoperative Diagnostic Performance of Multi-Parametric Quantitative Assessment in Rectal Carcinoma: A Preliminary Study Using Synthetic Magnetic Resonance Imaging

Objective

Synthetic MRI (SyMRI) can reconstruct different contrast-weighted images(T1, T2, PD) and has shorter scan time, easier post-processing and better reproducibility. Some studies have shown splendid correlation with conventional mapping techniques and no degradation in the quality of syMRI images compared with conventional MRI. It is crucial to select an individualized treatment plan based on the preoperative images of rectal carcinoma (RC). We tried to explore the feasibility of syMRI on T, N stage and extramural vascular invasion (EMVI) of rectal cancer.

Materials and Methods

A total of 100 patients (37 females and 63 males) diagnosed with rectal carcinoma were enrolled. All the patients underwent preoperative pelvic MR examinations including conventional MR sequence and synthetic MRI. Two radiologists evaluated the MRI findings of each rectal carcinoma and EMVI score in consensus. The values for T1, T2 relaxation times and PD value were measured in tumor(ROI-1) and pararectal fat space(ROI-2) and analyzed independently. A receiver operating characteristic (ROC) analysis was performed. Correlations between the T1, T2 and PD values and EMVI score were also evaluated.

Results

Compared with the normal rectal wall, the values of T1 and T2 relaxation times of the tumor were significantly higher (P <0.001). There was no statistically significant difference in the PD value (P >0.05). As for ROI, the ROI of pararectal fat space(ROI-2) had better significance than rectal cancer lesion (ROI-1). T2 value of ROI-1 and T1 value of ROI-2 were higher in the pEMVI positive group than in the negative group (P=0.002 and 0.001) and T1 value of ROI-2 had better performance with an AUC of 0.787, (95% CI:0.693- 0.882). T1 value, T2 value and PD value from ROI-2 were effective for both T and N stage of rectal cancer. High-grade pathological stage had showed higher T1 value (P_{T stage}=0.013,P_{N stage}=0.035), lower T2 value (P_{T stage}=0.025,P_{N stage}=0.034) and lower PD value (P_{T stage}=0.017). We also enrolled the characteristics with P < 0.05 in the combined model which had better diagnostic efficacy. A significant positive correlation was found between the T1 value of pararectal fat space(ROI-2) and EMVI score (r value = 0.519, P<0.001). The T2 value(r=0.213,P=0.049) and PD value(r=0.354,P=0.001) from ROI-1 was correlated with EMVI score. Correlation analysis did not show any significant associations between T2 value of tumor, T2, PD values of pararectal fat space and EMVI scores.

Conclusion

Synthetic MRI can provide multi-parameter quantitative image maps with a easier measurement and slightly shorter acquisition time compared with conventional MRI. The measurement of multi-parametric quantitative values contributes to diagnosing the tumor and evaluating T stage, N stage and EMVI. It has the potential to be used as a preoperative diagnostic and grading technique in rectal carcinoma.

[Material Investigation for the Development of Non-rigid Phantoms for CT-MRI Image Registration]

PURPOSE

In radiotherapy, deformable image registration (DIR) has been frequently used in different imaging examinations in recent years. However, no phantom has been established for quality assurance for DIR. In order to develop a non-rigid phantom for accuracy control between CT and MRI images, we investigated the suitability of 3D printing materials and gel materials in this study.

METHODS

We measured CT values, T₁ values, T₂ values, and the proton densities of 31 3D printer materials-purchased from three manufacturers-and one gel material. The dice coefficient after DIR was calculated for the CT-MRI images using a prototype phantom made of a gel material compatible with CT-MRI.

RESULTS

The CT number of the 3D printing materials ranged from -6.8 to 146.4 HU. On MRI, T₁ values were not measurable in most cases, whereas T₂ values were not measurable in all cases; proton density (PD) ranged from 2.51% to 4.9%. The gel material had a CT number of 111.16 HU, T₁ value of 813.65 ms, and T₂ value of 27.19 ms. The prototype phantom was flexible, and the usefulness of DIR with CT and MRI images was demonstrated using this phantom.

CONCLUSION

The CT number and T₁ and T₂ values of the gel material are close to those of the human body and may therefore be developed as a DIR verification phantom between CT and MRI. These findings may contribute to the development of non-rigid phantoms for DIR in the future.

Synthetic MRI for Radiotherapy Planning for Brain and Prostate Cancers: Phantom Validation and Patient Evaluation

Purpose

We aimed to evaluate the accuracy of T₁ and T₂ mappings derived from a multispectral pulse sequence (magnetic resonance image compilation, MAGiC^®) on 1.5-T MRI and with conventional sequences [gradient echo with variable flip angle (GRE-VFA) and multi-echo spin echo (ME-SE)] compared to the reference values for the purpose of radiotherapy treatment planning.

Methods

The accuracy of T₁ and T₂ measurements was evaluated with 2 coils [head and neck unit (HNU) and BODY coils] on phantoms using descriptive statistics and Bland–Altman analysis. The reproducibility and repeatability of T₁ and T₂ measurements were performed on 15 sessions with the HNU coil. The T₁ and T₂ synthetic sequences obtained by both methods were evaluated according to quality assurance (QA) requirements for radiotherapy. T₁ and T₂in vivo measurements of the brain or prostate tissues of two groups of five subjects were also compared.

Results

The phantom results showed good agreement (mean bias, 8.4%) between the two measurement methods for T₁ values between 490 and 2,385 ms and T₂ values between 25 and 400 ms. MAGiC^® gave discordant results for T₁ values below 220 ms (bias with the reference values, from 38% to 1,620%). T₂ measurements were accurately estimated below 400 ms (mean bias, 8.5%) by both methods. The QA assessments are in agreement with the recommendations of imaging for contouring purposes for radiotherapy planning. On patient data of the brain and prostate, the measurements of T₁ and T₂ by the two quantitative MRI (qMRI) methods were comparable (max difference, <7%).

Conclusion

This study shows that the accuracy, reproducibility, and repeatability of the multispectral pulse sequence (MAGiC^®) were compatible with its use for radiotherapy treatment planning in a range of values corresponding to soft tissues. Even validated for brain imaging, MAGiC^® could potentially be used for prostate qMRI.

Feasibility of Novel Three-Dimensional Magnetic Resonance Fingerprinting of the Prostate Gland: Phantom and Clinical Studies

Objective

To evaluate the feasibility of a new three-dimensional (3D) MR fingerprinting (MRF) technique for the prostate gland by conducting phantom and clinical studies.

Materials and Methods

The new 3D MRF technique used in this study enables quick data acquisition and has a high resolution. For the phantom study, the MRF T1 and T2 values in an in-house phantom were compared with those of gold-standard mapping methods using linear regression analysis. For the clinical study, we evaluated 90 patients who underwent prostate imaging with MRF for suspected prostate cancer between September 2019 and February 2020. The mean T1 and T2 values were compared in the peripheral zone, transition zone, and focal lesions using paired t tests. The differences in the T1 and T2 values according to cancer aggressiveness were evaluated using one-way analysis of variance.

Results

In the phantom study, the MRF T1 and T2 values showed a perfect correlation with the gold-standard T1 and T2 values (R > 0.99). In the clinical study, the T1 and T2 values in the peripheral zone were significantly higher than those in the transitional zone (p < 0.001, both). The T1 and T2 values in prostate cancer were significantly lower than those in the peripheral and transitional zones. The higher the grade of cancer, the lower the T2 values.

Conclusion

The T1 and T2 values obtained from the 3D MRF showed a perfect correlation with the gold standard values in the phantom study. Differences in the T1 and T2 values among the different zones of the prostate gland were identified using 3D MRF in patients.

The diagnostic performance of quantitative mapping in breast cancer patients: a preliminary study using synthetic MRI

Background

Previous studies have indicated that quantitative MRI (qMR) is beneficial for diagnosis of breast cancer. As a novel qMR technology, synthetic MRI (syMRI) may be advantageous by offering simultaneous generation of T1 and T2 mapping in one scan within a few minutes and without concern to the deposition of the gadolinium contrast agent in cell nucleus. In this study, the potential of quantitative mapping derived from Synthetic MRI (SyMRI) to diagnose breast cancer was investigated.

Methods

From April 2018 to May 2019, a total of 87 patients with suspicious breast lesions underwent both conventional and SyMRI before treatment. The quantitative metrics derived from SyMRI, including T1 and T2 values, were measured in breast lesions. The diagnostic performance of SyMRI was evaluated with unpaired Student’s t-tests, receiver operating characteristic curve analysis and multivariate logistic regression analysis. The AUCs of quantitative values were compared using Delong test.

Results

Among 77 patients who met the inclusion criteria, 48 were diagnosed with histopathological confirmed breast cancers, and the rest had benign lesions. The breast cancers showed significantly higher T1 (1611.61 ± 215.88 ms) values and lower T2 (80.93 ± 7.51 ms) values than benign lesions. The area under the ROC curve (AUC) values were 0.931 (95% CI: 0.874–0.989) and 0.883 (95% CI: 0.810–0.956) for T1 and T2 maps, respectively, in diagnostic discrimination between breast cancers and benign lesions. A slightly increased AUC of 0.978 (95% CI: 0.915–0.993) was achieved by combining those two relaxation-based quantitative metrics.

Conclusion

In conclusion, our preliminary study showed that the quantitative T1 and T2 values obtained by SyMRI could distinguish effectively between benign and malignant breast lesions, and T1 relaxation time showed the highest diagnostic efficiency. Furthermore, combining the two quantitative relaxation metrics further improved their diagnostic performance.

Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition

Objectives

To develop and evaluate an automated method for prostate T2-weighted (T2W) image normalization using dual-reference (fat and muscle) tissue.

Materials and methods

Transverse T2W images from the publicly available PROMISE12 (N = 80) and PROSTATEx (N = 202) challenge datasets, and an in-house collected dataset (N = 60) were used. Aggregate channel features object detectors were trained to detect reference fat and muscle tissue regions, which were processed and utilized to normalize the 3D images by linear scaling. Mean prostate pseudo T2 values after normalization were compared to literature values. Inter-patient histogram intersections of voxel intensities in the prostate were compared between our approach, the original images, and other commonly used normalization methods. Healthy vs. malignant tissue classification performance was compared before and after normalization.

Results

The prostate pseudo T2 values of the three tested datasets (mean ± standard deviation = 78.49 ± 9.42, 79.69 ± 6.34 and 79.29 ± 6.30 ms) corresponded well to T2 values from literature (80 ± 34 ms). Our normalization approach resulted in significantly higher (p < 0.001) inter-patient histogram intersections (median = 0.746) than the original images (median = 0.417) and most other normalization methods. Healthy vs. malignant classification also improved significantly (p < 0.001) in peripheral (AUC 0.826 vs. 0.769) and transition (AUC 0.743 vs. 0.678) zones.

Conclusion

An automated dual-reference tissue normalization of T2W images could help improve the quantitative assessment of prostate cancer.

Electronic supplementary material

The online version of this article (10.1007/s10334-020-00871-3) contains supplementary material, which is available to authorized users.

Fast and accurate compensation of signal offset for T2 mapping

OBJECTIVE

T₂ maps are more vendor independent than other MRI protocols. Multi-echo spin-echo signal decays to a non-zero offset due to imperfect refocusing pulses and Rician noise, causing T₂ overestimation by the vendor's 2-parameter algorithm. The accuracy of the T₂ estimate is improved, if the non-zero offset is estimated as a third parameter. Three-parameter Levenberg-Marquardt (LM) T₂ estimation takes several minutes to calculate, and it is sensitive to initial values. We aimed for a 3-parameter fitting algorithm that was comparably accurate, yet substantially faster.

METHODS

Our approach gains speed by converting the 3-parameter minimisation problem into an empirically unimodal univariate problem, which is quickly minimised using the golden section line search (GS).

RESULTS

To enable comparison, we propose a novel noise-masking algorithm. For clinical data, the agreement between the GS and the LM fit is excellent, yet the GS algorithm is two orders of magnitude faster. For synthetic data, the accuracy of the GS algorithm is on par with that of the LM fit, and the GS algorithm is significantly faster. The GS algorithm requires no parametrisation or initialisation by the user.

DISCUSSION

The new GS T₂ mapping algorithm offers a fast and much more accurate off-the-shelf replacement for the inaccurate 2-parameter fit in the vendor's software.

Automatic classification of tissues on pelvic MRI based on relaxation times and support vector machine

Tissue segmentation and classification in MRI is a challenging task due to a lack of signal intensity standardization. MRI signal is dependent on the acquisition protocol, the coil profile, the scanner type, etc. While we can compute quantitative physical tissue properties independent of the hardware and the sequence parameters, it is still difficult to leverage these physical properties to segment and classify pelvic tissues. The proposed method integrates quantitative MRI values (T1 and T2 relaxation times and pure synthetic weighted images) and machine learning (Support Vector Machine (SVM)) to segment and classify tissues in the pelvic region, i.e.: fat, muscle, prostate, bone marrow, bladder, and air. Twenty-two men with a mean age of 30±14 years were included in this prospective study. The images were acquired with a 3 Tesla MRI scanner. An inversion recovery-prepared turbo spin echo sequence was used to obtain T1-weighted images at different inversion times with a TR of 14000 ms. A 32-echo spin echo sequence was used to obtain the T2-weighted images at different echo times with a TR of 5000 ms. T1 and T2 relaxation times, synthetic T1- and T2-weighted images and anatomical probabilistic maps were calculated and used as input features of a SVM for segmenting and classifying tissues within the pelvic region. The mean SVM classification accuracy across subjects was calculated for the different tissues: prostate (94.2%), fat (96.9%), muscle (95.8%), bone marrow (91%) and bladder (82.1%) indicating an excellent classification performance. However, the segmentation and classification for air (within the rectum) may not always be successful (mean SVM accuracy 47.5%) due to the lack of air data in the training and testing sets. Our findings suggest that SVM can reliably segment and classify tissues in the pelvic region.

The feasibility of synthetic MRI in breast cancer patients: comparison of T2 relaxation time with multiecho spin echo T2 mapping method

OBJECTIVE

To compare the T2 relaxation times acquired with synthetic MRI to those of multi-echo spin-echo sequences and to evaluate the usefulness of synthetic MRI in the clinical setting.

METHODS

From January 2017 to May 2017, we included 51 patients with newly diagnosed breast cancer, who underwent additional synthetic MRI and multiecho spin echo (MESE) T2 mapping sequences. Synthetic MRI technique uses a multiecho and multidelay acquisition method for the simultaneous quantification of physical properties such as T1 and T2 relaxation times and proton density image map. A radiologist with 9 years of experience in breast imaging drew region of interests manually along the tumor margins on two consecutive axial sections including the center of tumor mass and in the fat tissue of contralateral breast on both synthetic T2 map and MESE T2 map images.

RESULTS

The mean T2 relaxation time of the cancer was 84.75 ms (± 15.54) by synthetic MRI and 90.35 ms (± 19.22) by MESE T2 mapping. The mean T2 relaxation time of the fat was 129.22 ms (± 9.53) and 102.11 ms (± 13.9), respectively. Bland-Altman analysis showed mean difference of 8.4 ms for the breast cancer and a larger mean difference of 27.8 ms for the fat tissue. Spearman's correlation test showed that there was significant positive correlation between synthetic MRI and MESE sequences for the cancer (r = 0.713, p < 0.001) and for the fat (r = 0.551, p < 0.001). The positive estrogen receptor and low histologic grade were associated with little differences between two methods (p = 0.02  and = 0.043, respectively).

CONCLUSION

T2 relaxation times of breast cancer acquired with synthetic MRI showed positive correlation with those of MESE T2 mapping. Synthetic MRI could be useful for the evaluation of tissue characteristics by simultaneous acquisition of several quantitative physical properties.

ADVANCES IN KNOWLEDGE

Synthetic MRI is useful for the evaluation of T2 relaxation times of the breast cancers.

Effects of Echo Time on IVIM Quantification of the Normal Prostate

The two-compartment intravoxel incoherent motion (IVIM) theory assumes that the transverse relaxation time is the same in both compartments. However, blood and tissue have different T2 values, and echo time (TE) may thus have an effect on the quantitative parameters of IVIM. The purpose of this study was to investigate the effects of TE on IVIM-DWI-derived parameters of the prostate. In total, 17 healthy volunteers underwent two repeat examinations. IVIM-DWI data were scanned 6 times with variable TE values of 60, 70, 80, 90, 100, and 120 ms. The ADC of a mono-exponential model and the D, D*, and f parameters of the IVIM model were calculated separately for each TE. Repeat measures were assessed by calculating the coefficient of variation and Bland-Altman limits of agreement for each parameter. Spearman's rho test was used to analyse relationships between IVIM indices and TE. Our results showed that TE had an effect on IVIM quantification, which should be kept constant in the examination protocol at each individual institution. Alternatively, an extended IVIM could be used to eliminate the effect of the TE value on the quantitative parameters of IVIM. This may be helpful for guiding clinical research, especially for longitudinal studies.