Quantitative Diffusion and T2 Mapping Using RF-Modulated Phase-Based Gradient Echo Imaging.

Early detection of disc degeneration and categorizing them is very important for the treatment plan and better prognosis. We aimed to assess the efficacy of apparent diffusion coefficient (ADC) mapping and T2 mapping in the early detection and grading of the severity of degenerative disc disease. In this review, the articles published until July 2022, from the PubMed database were used. All articles contained information about the application of ADC and T2 mapping in the detection of disc degeneration. A total of 40 articles were included in this review. We assessed each article for the feasibility of both techniques to categorize the degeneration. ADC values of nucleus pulposus (NP) showed a negative correlation with Pfirrmann grades and age. T2 values of NP and annulus fibrosus (AF) are significantly decreased with an increase in Pfirrmann grades. And also, as age increases, T2 values of NP and posterior AF decreased significantly. ADC and T2 mapping techniques are sensitive in the detection and categorization of disc degeneration. It is highly recommended to include quantitative magnetic resonance imaging techniques in routine spine imaging protocol in a patient with complaints of back pain.

Background After acute myocardial infarction (AMI), reperfusion injury is associated with microvascular lesions and myocardial edema. Purpose To evaluate the performance of apparent diffusion coefficient (ADC) quantification compared with T1 and T2 values in the detection of acute myocardial injury. Materials and Methods In this prospective study conducted from June 2016 to November 2018, participants without a history of heart failure or cardiomyopathy were enrolled after undergoing reperfusion for their first AMI. Quantitative T1 and T2 mapping were performed with a 1.5-T MRI scanner and compared with a fast free-breathing acquisition technique for ADC mapping (approximate duration, 3 minutes; five slices; spin-echo cardiac diffusion acquisition; b values, 0 and 200 sec/mm2; six diffusion-encoding directions; five repetitions). Quantitative ADC and unenhanced T1 and T2 values were compared in infarct, border, and remote regions by using Welch analysis of variance with Games-Howell post hoc test for pairwise comparisons. Results Thirty-four participants with AMI underwent MRI an average of 5 days ± 1.9 (standard deviation) after reperfusion. Mean ADC was markedly high in the infarcted regions (2.32 × 10-3 mm2/sec; 95% confidence interval [CI]: 2.28, 2.36) and moderately high in the border regions (1.91 ×10-3 mm2/sec; 95% CI: 1.89, 1.94; P < .001). In remote regions, mean ADC (1.62 ×10-3 mm2/sec; 95% CI: 1.59, 1.64) was comparable to that measured in vivo in healthy volunteers. Within the same regions of interest, although the measures showed similar trends in infarct and remote regions for T1 (mean, 1332 mec [95% CI: 1296, 1368] vs 1045 msec [95% CI: 1034, 1056]; P < .001) and T2 (72 msec [95% CI: 69, 75] vs 50 msec [95% CI: 49, 51]; P < .001), the magnitude of the differences among regions was greater when using ADC. Normalized signal differences between infarct and remote regions showed that diffusion-weighted MRI depicted edema 5.1 (P < .001) and 3.5 (P < .001) times greater than did T1 and T2 maps, respectively. Conclusion Multislice cardiac diffusion-weighted images could be acquired in those with acute myocardial injury. Quantitative apparent diffusion coefficient mapping showed greater differences among remote regions and lesions than did T1 or T2 mapping. © RSNA, 2020 See also the editorial by Lloyd and Farris in this issue.

This study aimed to determine the accuracy of apparent diffusion coefficient (ADC) and enhancement ratio (ER) in discriminating primary cerebral lymphomas (PCL) and glioblastomas. Circular regions of interest were randomly placed centrally within the largest solid-enhancing area of all lymphomas and glioblastomas on both post-contrast T1-weighted images and corresponding ADC maps. Regions of interest were also drawn in the contralateral hemisphere to obtain enhancement and ADC values of normal-appearing white matter. This helped us to calculate the ER and ADC ratio. Mean enhancement and ADC (mm2/s) values for PCL were 2220.56 ± 2948.30 and 712.00 ± 137.87, respectively. Mean enhancement and ADC values for glioblastoma were 1537.07 ± 1668.33 and 1037.93 ± 280.52, respectively. Differences in ADC values, ratios and ERs were all statistically significant between the two groups (p < 0.05). ADC values correctly predicted 71.4% of the lesions as glioblastoma and 83.3% as PCL (area under the curve (AUC) = 0.86 on receiver operating characteristic curve analysis). ADC ratios correctly predicted 85.7% of the lesions as glioblastoma and 100% as PCL (AUC = 0.93). ERs correctly predicted 71.4% of the lesions as glioblastoma and 88.9% as PCL (AUC = 0.92). The combination of ADC ratio and ER correctly predicted 100% tumour type in both patient subgroups. ADC values, ADC ratios and ERs may serve as useful variables to distinguish PCL from glioblastoma. The combination of ADC ratio and ER yielded the best results in identification of both patient subgroups.

To compare the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis derived from readout-segmented echo-planar imaging (RS-EPI) and single-shot echo-planar imaging (SS-EPI) diffusion-weighted imaging (DWI) in evaluating normal-sized lymph node metastasis (LNM) in cervical cancer. Seventy-six pathologically confirmed cervical cancer patients (stages IB and IIA) were enrolled, including 61 patients with non-LNM (group A) and 15 patients with normal-sized LNM (group B). The recorded tumor volume on T2-weighted imaging was the reference against which both DWIs were evaluated. Each ADC histogram parameter (including ADC max , ADC 90 , ADC median , ADC mean , ADC 10 , ADC min , ADC skewness , ADC kurtosis , and ADC entropy ) was compared between SS-EPI and RS-EPI and between the 2 groups. There was no significant difference in tumor volume between the 2 DWIs and T2-weighted imaging (both P > 0.05). Higher ADC max and ADC entropy but lower ADC 10 , ADC min and ADC skewness were found in SS-EPI than those in RS-EPI (all P < 0.05). For SS-EPI, lower ADC 90 and higher ADC kurtosis were found in group B than those in group A (both P < 0.05). For RS-EPI, lower ADC 90 and higher ADC kurtosis and ADC entropy were found in group B than those in group A (all P < 0.05). Readout-segmented echo-planar imaging ADC kurtosis showed the highest area under the curve of 0.792 in the differentiation of the 2 groups (sensitivity, 80%; specificity, 73.77%). Compared with SS-EPI, the ADC histogram parameters derived from RS-EPI were more accurate, and ADC kurtosis held great potential in differentiating normal-sized LNM in cervical cancer.

The apparent diffusion coefficient (ADC) of water should be sensitive to the cytotoxic edema triggered by energy failure during ischemia. Elevated values of T2. the nuclear MR transverse relaxation time of water, seen on T2 nuclear MR images detect vasogenic edema and infarcted areas. The temporal and spatial changes in ADC and T2 abnormalities after occlusion of the middle cerebral artery (MCAO) were therefore estimated by these two quantitative techniques. Permanent MCAO was performed on rats. Quantitative ADC and T2 maps of brain water were obtained, from which the ischemic volumes were calculated at various times up to 48 hours after MCAO. The areas of decreased ADC represented 36 +/- 7% of the final infarct volume (24 hours) at 0.5 hours and 64 +/- 4% at 5 hours after MCAO, suggesting that there is recruitment of peripheral areas with disturbed energy metabolism and cytotoxic edema. The ADC and T2 contours closely matched at 3.5, 24, and 48 hours after MCAO. T2 imaging can assess ischemic insults as well as ADC imaging, but only 3.5 hours after the onset of ischemia. Assessment of edematous swelling (approximately 24.5% of total infarcted volume) demonstrates that ADC and therefore T2 imaging detect all the tissue that will become infarcted approximately 7 hours after occlusion. The spread of ADC and T2 abnormalities would therefore stop at approximately 7 hours, and any further increase in volume observed on the images would be mainly due to edematous swelling.

To compare perfusion-weighted (PW) imaging and apparent diffusion coefficient (ADC) maps in prediction of infarct size and growth in patients with acute middle cerebral artery infarct. This study was approved by the local institutional review board. Written informed consent was obtained from all 80 patients. Subsequent infarct volume and growth on follow-up magnetic resonance (MR) images obtained within 6 days were compared with the predictions based on PW images by using a time-to-peak threshold greater than 4 seconds and ADC maps obtained less than 12 hours after middle cerebral artery infarct. ADC- and PW imaging-predicted infarct growth areas and infarct volumes were correlated with subsequent infarct growth and follow-up diffusion-weighted (DW) imaging volumes. The impact of MR imaging time delay on the correlation coefficient between the predicted and subsequent infarct volumes and individual predictions of infarct growth by using receiver operating characteristic curves were assessed. The infarct volume measurements were highly reproducible (concordance correlation coefficient [CCC] of 0.965 and 95% confidence interval [CI]: 0.949, 0.976 for acute DW imaging; CCC of 0.995 and 95% CI: 0.993, 0.997 for subacute DW imaging). The subsequent infarct volume correlated (P<.0001) with ADC- (ρ=0.853) and PW imaging- (ρ=0.669) predicted volumes. The correlation was higher for ADC-predicted volume than for PW imaging-predicted volume (P<.005), but not when the analysis was restricted to patients without recanalization (P=.07). The infarct growth correlated (P<.0001) with PW imaging-DW imaging mismatch (ρ=0.470) and ADC-DW imaging mismatch (ρ=0.438), without significant differences between both methods (P=.71). The correlations were similar among time delays with ADC-predicted volumes but decreased with PW imaging-based volumes beyond the therapeutic window. Accuracies of ADC- and PW imaging-based predictions of infarct growth in an individual prediction were similar (area under the receiver operating characteristic curve [AUC] of 0.698 and 95% CI: 0.585, 0.796 vs AUC of 0.749 and 95% CI: 0.640, 0.839; P=.48). The ADC-based method was as accurate as the PW imaging-based method for evaluating infarct growth and size in the subacute phase.

Hypoxia is a condition, common to most malignant tumors, where oxygen tension in the tissue is below the physiological level. Among consequences of tumor hypoxia is also altered cancer cell metabolism that contributes to cancer therapy resistance. Therefore, precise assessment of tumor hypoxia is important for monitoring the tumor treatment progression. In this study, we propose a simple model for prediction of hypoxic level in tumors based on multiparametric magnetic resonance imaging. The study was performed on B16F1 murine melanoma tumors ex vivo that were first magnetic resonance scanned and then analyzed for hypoxic level using hypoxia-inducable factor 1-alpha antibody staining. Each tumor was analyzed in identical sections and in identical regions of interest for pairs of hypoxic level and magnetic resonance values (apparent diffusion coefficient and T 2). This was followed by correlation analysis between hypoxic level and respective magnetic resonance values. A moderate correlation was found between hypoxic level and apparent diffusion coefficient (ρ = 0.56, P < .00001) and lower between hypoxic level and T 2 (ρ = 0.38, P < .00001). The data were analyzed further to obtain simple predictive models based on the multiple linear regression analysis of the measured hypoxic level (dependent variable) and apparent diffusion coefficient and T 2 (independent variables). Among the hypoxic level models, the most efficient was the 3-parameter model given by relation (HL = k ADC ADC + k T2 T 2 + b), where k ADC = 26%/µm2/ms, k T2 = 0.8%/ms, and b = −32%. The model can be used for calculation of the predicted hypoxic level map based on magnetic resonance–measured apparent diffusion coefficient and T 2 maps. Similar prediction models, based on tumor apparent diffusion coefficient and T 2 maps, can be done also for other tumor types in vivo and can therefore help in assessment of tumor treatment as well as to better understand the role of hypoxia in cancer progression.

This study aims for targeted biopsy validation of magnetic resonance fingerprinting (MRF) and diffusion mapping for characterizing peripheral zone (PZ) prostate cancer and noncancers. One hundred four PZ lesions in 85 patients who underwent magnetic resonance imaging were retrospectively analyzed with apparent diffusion coefficient (ADC) mapping, MRF, and targeted biopsy (cognitive or in-gantry). A radiologist blinded to pathology drew regions of interest on targeted lesions and visually normal peripheral zone on MRF and ADC maps. Mean T1, T2, and ADC were analyzed using linear mixed models. Generalized estimating equations logistic regression analyses were used to evaluate T1 and T2 relaxometry combined with ADC in differentiating pathologic groups. Targeted biopsy revealed 63 cancers (low-grade cancer/Gleason score 6 = 10, clinically significant cancer/Gleason score ≥7 = 53), 15 prostatitis, and 26 negative biopsies. Prostate cancer T1, T2, and ADC (mean ± SD, 1660 ± 270 milliseconds, 56 ± 20 milliseconds, 0.70 × 10 ± 0.24 × 10 mm/s) were significantly lower than prostatitis (mean ± SD, 1730 ± 350 milliseconds, 77 ± 36 milliseconds, 1.00 × 10 ± 0.30 × 10 mm/s) and negative biopsies (mean ± SD, 1810 ± 250 milliseconds, 71 ± 37 milliseconds, 1.00 × 10 ± 0.33 × 10 mm/s). For cancer versus prostatitis, ADC was sensitive and T2 specific with comparable area under curve (AUC; (AUCT2 = 0.71, AUCADC = 0.79, difference between AUCs not significant P = 0.37). T1 + ADC (AUCT1 + ADC = 0.83) provided the best separation between cancer and negative biopsies. Low-grade cancer T2 and ADC (mean ± SD, 75 ± 29 milliseconds, 0.96 × 10 ± 0.34 × 10 mm/s) were significantly higher than clinically significant cancers (mean ± SD, 52 ± 16 milliseconds, 0.65 ± 0.18 × 10 mm/s), and T2 + ADC (AUCT2 + ADC = 0.91) provided the best separation. T1 and T2 relaxometry combined with ADC mapping may be useful for quantitative characterization of prostate cancer grades and differentiating cancer from noncancers for PZ lesions seen on T2-weighted images.

Diffusion-weighted MRI based on single-shot echo planar imaging (EPI) has been established as a useful tool to study acute cerebral ischemia. However, EPI is prone to spatial distortion and ghosting artifacts. In this study, a pulse sequence for diffusion-weighted imaging (DWI) based on a single-shot spiral readout is presented. Using this technique, multislice apparent diffusion coefficient (ADC) mapping can be performed in an interleaved fashion with the same temporal resolution as EPI. Other advantages associated with ADC mapping by the single-shot spiral method include minimal ghosting artifacts, reduced spatial distortion, and capability to scan in arbitrary planes. This technique has been successfully tested in five normal volunteers and three stroke patients. It has been demonstrated that the single-shot spiral technique is capable of producing high quality DWI and ADC trace maps (128 x 128) in the axial, sagittal, and coronal planes, which facilitate clinical diagnosis.

To establish whether T2 relaxation and apparent diffusion coefficient (ADC) in normal prostate and tumour are related and to investigate the effects of glandular compression from an enlarged transition zone (TZ) on peripheral zone (PZ) T2 and ADC by correlating them with the peripheral zone fractional volume (PZFV). 48 consecutive patients prospectively underwent multiecho T2 weighted (T2W) (echo times 20, 40, 60, 80, 100 ms) and diffusion-weighted (b=0, 100, 300, 500, 800 s mm(-2)) endorectal MRI. In 43 evaluable patients, single slice whole PZ, TZ and tumour (focal hypointense signal on T2W images in a biopsy-positive octant) regions of interest were transferred to T2 and ADC maps by slice matching. T2 and ADC values were correlated, and PZ values were correlated with PZFV. T2 and ADC values were significantly different among groups [T2 mean±standard deviation (SD) PZ, 149±49 ms; TZ, 125±26 ms; tumour, 97±23 ms; PZ vs TZ, p=0.002; PZ vs tumour, p<0.0001; TZ vs tumour, p<0.0001; ADC×10(-6) mm(2) s(-1) mean±SD PZ, 1680±215; TZ, 1478±139; tumour, 1030±205; p<0.0001]. Significant positive correlations existed between T2 and ADC for PZ, TZ, PZ and TZ together, but not for tumour (r=0.515, p<0.0001; r=0.300, p=0.03; r=0.526, p<0.0001; and r=0.239, p=0.32, respectively). No significant correlation existed between PZFV and PZ T2 (r=0.10, p=0.5) or ADC (r=0.03, p=0.8). The correlation between T2 and ADC that exists in normal prostate is absent in tumour. PZ compression by an enlarged TZ does not alter PZ T2 or ADC to affect tumour-PZ contrast. Microstructural features of tumours alter diffusivity independently of their effects on T2 relaxation.

Background/Objectives: This study aimed to evaluate the diagnostic accuracy of Apparent Diffusion Coefficient (ADC) values, derived from Diffusion-Weighted Imaging (DWI), in differentiating benign and malignant ascites. Methods: This retrospective study included 150 patients (85 benign, 65 malignant) who underwent abdominal MRI. All patients were scanned on a DWI sequence (b-values: 0, 500, and 1000 s/mm2). Two experienced radiologists, blinded to clinical and cytological outcomes, measured the mean ADC (ADCmean) from three distinct ROIs and the minimum ADC (ADCmin) from the area of lowest signal intensity on the ADC map. The diagnostic performance of ADC parameters and the Serum-Ascites Albumin Gradient (SAAG) was assessed using Receiver Operating Characteristic (ROC) curve analysis. Results: The mean values of ADCmean (3162 ± 204 × 10-6 mm2/s) and ADCmin (2885 ± 148 × 10-6 mm2/s) in the malignant group were significantly lower than those in the benign group (3596 ± 239 and 3322 ± 218 × 10-6 mm2/s; p = 0.006 and p = 0.0016, respectively). Inter-observer agreement was good for both ADCmean (ICC = 0.844) and ADCmin (ICC = 0.879). In the ROC analysis, ADCmin demonstrated the highest diagnostic performance (AUC: 0.930). An optimal cut-off value for ADCmin of ≤ 2983 × 10-6 mm2/s yielded 81.5% sensitivity and 85.8% specificity. The diagnostic performance of ADCmin was found to be superior to that of ADCmean (AUC: 0.877) and SAAG (AUC: 0.919). Conclusions: ADC values derived from DWI, particularly ADCmin, represent a highly accurate, non-invasive, and reproducible biomarker for differentiating benign from malignant ascites. The identified ADCmin threshold provides quantitative parameter that can aid in patient triage, especially when cytology is inconclusive, potential surrogate for fluid characterization.

BackgroundRadiation-induced insufficiency fractures (IF) is frequently occult without fracture line, which may be mistaken as metastasis. Quantitative apparent diffusion coefficient (ADC) shows potential value for characterization of benign and malignant bone marrow diseases. The purpose of this study was to develop a nomogram based on multi-parametric ADCs in the differntiation of occult IF from bone metastasis after radiotherapy (RT) for cervical cancer.MethodsThis study included forty-seven patients with cervical cancer that showed emerging new bone lesions in RT field during the follow-up. Multi-parametric quantitative ADC values were measured for each lesion by manually setting region of interests (ROIs) on ADC maps, and the ROIs were copied to adjacent normal muscle and bone marrow. Six parameters were calculated, including ADCmean, ADCmin, ADCmax, ADCstd, ADCmean ratio (lesion/normal bone) and ADCmean ratio (lesion/muscle). For univariate analysis, receiver operating characteristic curve (ROC) analysis was performed to assess the performance. For combined diagnosis, a nomogram model was developed by using a multivariate logistic regression analysis.ResultsA total of 75 bone lesions were identified, including 48 occult IFs and 27 bone metastases. There were significant differences in the six ADC parameters between occult IFs and bone metastases (p < 0.05), the ADC ratio (lesion/ muscle) showed an optimal diagnostic efficacy, with an area under ROC (AUC) of 0.887, the sensitivity of 95.8%, the specificity of 81.5%, respectively. Regarding combined diagnosis, ADCstd and ADCmean ratio (lesion/muscle) were identified as independent factors and were selected to generate a nomogram model. The nomogram model showed a better performance, yielded an AUC of 0.92, the sensitivity of 91.7%, the specificity of 96.3%, positive predictive value (PPV) of 97.8% and negative predictive value (NPV) of 86.7%, respectively.ConclusionsMulti-parametric ADC values demonstrate potential value for differentiating occult IFs from bone metastasis, a nomogram based on the combination of ADCstd and ADCmean ratio (lesion/muscle) may provide an improved classification performance.

In cervical carcinoma, lymph node involvement is a key indicator of disease progression and a critical prognostic factor that influences staging and treatment. Accurate identification of metastatic lymph nodes is essential for optimal management. Diffusion-weighted MRI combined with apparent diffusion coefficient (ADC) mapping provides a non-invasive approach for evaluating tissue cellularity and may enhance the identification of metastatic lymph nodes, surpassing traditional size-based criteria. The purpose of the study is to assess the effectiveness of diffusion-weighted imaging (DWI) and ADC values in differentiating between metastatic and non-metastatic lymph nodes in patients with cervical cancer. The study focuses on imaging findings without histopathological confirmation, aiming to determine whether ADC measurements can reliably indicate nodal metastasis. Thirty patients with histologically confirmed cervical cancer (International Federation of Gynecology and Obstetrics (FIGO) stage IB to IIIC) were prospectively enrolled in a study with a duration of three months. All patients underwent pre-treatment pelvic MRI including DWI on a 1.5 T scanner (b-values: 50, 400, and 800 s/mm²). ADC maps were obtained, and the ADC values of lymph nodes were quantitatively analyzed. Lymph nodes were categorized as suspicious for metastasis based on MRI features, including a short-axis diameter of ≥10 mm, round shape, or the presence of necrosis. Nodes lacking these characteristics were considered benign. Histopathological confirmation of lymph nodes was not conducted. A sample size of 30 was calculated via Fisher's z-test formula for correlation (assuming r = 0.685, 99% confidence, 90% power). Of 30 patients (mean age -63.5 years, range 51-80), one-third had radiologic evidence of lymph node involvement on MRI (stage IIIC1 by the FIGO 2018 criteria), while the remainder had no enlarged nodes. DWI showed qualitatively restricted diffusion in nodes suspected of metastasis. Quantitatively, ADC values were markedly lower in suspicious (metastatic-appearing) nodes compared to non-suspicious nodes. The mean ADC of nodes deemed metastatic was approximately 0.90 × 10-3mm²/s, versus ~1.30 × 10-3mm²/s for benign nodes (p < 0.001). Notably, all nodes with ADC < 1.0 × 10-3mm²/s were in the metastatic-suspect group, whereas nodes with ADC > 1.0 × 10-3mm²/s were benign, suggesting a clear separation. Using an ADC threshold of ~1.0 × 10-3mm²/s to define positive nodes, the sensitivity for detecting metastatic nodes was ~93% and specificity ~100% in this cohort. DWI and ADC mapping demonstrated strong potential in differentiating metastatic from non-metastatic lymph nodes in cervical carcinoma patients. Metastatic nodes showed significantly lower ADC values (around or below 1.0 × 10-3mm²/s) compared to non-metastatic nodes. These findings indicate that ADC measurements can augment conventional MRI assessment of lymph nodes, providing a non-invasive indicator of nodal metastasis. This could be especially valuable when surgical nodal sampling is not performed.

To examine the clinicopathological features and identify the predictors of pathological upgrading in low-risk prostate cancer (PCa) patients without hypointense lesions on the apparent diffusion coefficient (ADC) map calculated from multiparametric magnetic resonance imaging. We reviewed the medical records of 1905 PCa patients who underwent radical prostatectomy between 2007 and 2015. All ADC images were graded using the five-grade Likert scale; the positive hypointense lesions were graded 4-5. We analyzed 256 patients with low-risk classifications according to D'Amico criteria. Patients were classified into two groups according to the pathologic upgrading in the surgical specimens. The predictive factors for pathologic upgrading were evaluated using amultivariate logistic regression analysis. In 256 patients with low-risk PCa, the percentage of positive cores [odds ratio (OR) 1.09; 95% confidence interval (CI) 1.02-1.16], the percentage of cancer in the positive cores (OR 1.07, 95% CI 1.03-1.12), and the presence of hypointensity on an ADC map (OR 2.28; 95% CI 1.23-4.22) were independent predictors of pathologic upgrading. Notably, 138 of low-risk patients (53.9%) had no hypointense lesions on an ADC map. Of these 138 patients, the percentage of positive cores (OR 1.09; 95% CI 1.01-1.18) and the percentage of cancer in the positive cores (OR 1.06; 95% CI 1.01-1.12) remained independent predictors of pathologic upgrading. In low-risk PCa patients without hypointense lesions on an ADC map, biopsy-related parameters such as the percentage of positive cores and the percentage of cancer in the positive cores were independent predictors of pathological upgrading following radical prostatectomy.

Accurate prediction of renal mass subtypes, along with the WHO/ISUP grade and pathological T (pT) stage of clear cell renal cell carcinoma (ccRCC), is crucial for optimal decision making. Our study aimed to investigate the feasibility and reproducibility of motion-robust radial T2 mapping in differentiating lipid-poor angiomyolipoma (MFAML) from RCC and characterizing the WHO/ISUP grade and pT stage of ccRCC. Finally, 92 patients undergoing renal radial T2 mapping and ZOOMit DWI were recruited. The T2 values and apparent diffusion coefficient (ADC) were analyzed. Correlation coefficients were calculated between ADC and T2 values. Notably, ccRCC exhibited higher T2 and ADC values than MFAML (p < 0.05). T2 values were lower in the higher WHO/ISUP grade and pT stage of ccRCC (all p < 0.05). ADC showed no significant difference for pT stage (p = 0.056). T2 values revealed a higher area under the curve (AUC) in evaluating the WHO/ISUP grade compared to ADC (0.936 vs. 0.817, p = 0.027). T2 values moderately positively correlated with ADC (r = 0.675, p < 0.001). In conclusion, quantitative motion-robust radial T2 mapping is feasible for characterizing solid renal masses and could provide additional value for multiparametric imaging in predicting WHO/ISUP grade and pT stage of ccRCC.