OBJECTIVE: To assess T1 mapping performance in distinguishing between benign and malignant breast lesions and to explore its correlation with histopathologic features in breast cancer.
METHODS: This study prospectively enrolled 103 participants with a total of 108 lesions, including 25 benign and 83 malignant lesions. T1 mapping, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) were performed. Two radiologists independently outlined the ROIs and analyzed T1 and apparent diffusion coefficient (ADC) values for each lesion, assessing interobserver reliability with the intraclass correlation coefficient (ICC). T1 and ADC values were compared between benign and malignant lesions, across different histopathological characteristics (histological grades, estrogen, progesterone and HER2 receptors expression, Ki67, N status). Receiver operating characteristic (ROC) analysis and Pearson correlation coefficient (ρ) were performed.
RESULTS: T1 values showed statistically significant differences between benign and malignant groups (P < 0.001), with higher values in the malignant (1817.08 ms ± 126.64) compared to the benign group (1429.31 ms ± 167.66). In addition, T1 values significantly increased in the ER (-) group (P = 0.001). No significant differences were found in T1 values among HER2, Ki67, N status, and histological grades groups. Furthermore, T1 values exhibited a significant correlation (ρ) with ER (P < 0.01) and PR (P = 0.03). The AUC for T1 value in distinguishing benign from malignant lesions was 0.69 (95 % CI: 0.55 - 0.82, P = 0.005), and for evaluating ER status, it was 0.75 (95 % CI: 0.62 - 0.87, P = 0.002).
CONCLUSIONS: T1 mapping holds the potential as an imaging biomarker to assist in the discrimination of benign and malignant breast lesions and assessing the ER expression status in breast cancer.

OBJECTIVE: To characterize T1 relaxation times of the pancreas, liver, and spleen in children with and without abdominal pathology.
METHODS: This retrospective study included pediatric patients (< 18-years-old). T1 mapping was performed with a Modified Look-Locker Inversion Recovery sequence. Patients were grouped based on review of imaging reports and electronic medical records. The Kruskal-Wallis test with Dunn\'s multiple comparison was used to compare groups.
RESULTS: 220 participants were included (mean age: 11.4 ± 4.2 years (1.5 T); 10.9 ± 4.5 years (3 T)). Pancreas T1 (msec) was significantly different between subgroups at 1.5 T (p < 0.0001). Significant pairwise differences included: normal (median: 583; IQR: 561-654) vs. acute pancreatitis (731; 632-945; p = 0.0024), normal vs. chronic pancreatitis (700; 643-863; p = 0.0013), and normal vs. acute + chronic pancreatitis (1020; 897-1099; p < 0.0001). Pancreas T1 was also significantly different between subgroups at 3 T (p < 0.0001). Significant pairwise differences included: normal (779; 753-851) vs. acute pancreatitis (1087; 910-1259; p = 0.0012), and normal vs. acute + chronic pancreatitis (1226; 1025-1367; p < 0.0001). Liver T1 was significantly different between subgroups only at 3 T (p = 0.0011) with pairwise differences between normal (818, 788-819) vs. steatotic (959; 848-997; p = 0.0017) and normal vs. other liver disease (882; 831-904; p = 0.0455). Liver T1 was weakly correlated with liver fat fraction at 1.5 T (r = 0.39; 0.24-0.52; p < 0.0001) and moderately correlated at 3 T (r = 0.64; 0.49-0.76; p < 0.0001). There were no significant differences in splenic T1 relaxation times between subgroups.
CONCLUSIONS: Pancreas T1 relaxation times are higher at 1.5 T and 3 T in children with pancreatitis and liver T1 relaxation times are higher in children with steatotic and non-steatotic chronic liver disease at 3 T.

OBJECTIVE: Quantitative T1 mapping has the potential to replace biopsy for noninvasive diagnosis and quantitative staging of chronic liver disease. Conventional T1 mapping methods are confounded by fat and B 1 + $$ {B}_1^{+} $$ inhomogeneities, resulting in unreliable T1 estimations. Furthermore, these methods trade off spatial resolution and volumetric coverage for shorter acquisitions with only a few images obtained within a breath-hold. This work proposes a novel, volumetric (3D), free-breathing T1 mapping method to account for multiple confounding factors in a single acquisition.
METHODS: Free-breathing, confounder-corrected T1 mapping was achieved through the combination of non-Cartesian imaging, magnetization preparation, chemical shift encoding, and a variable flip angle acquisition. A subspace-constrained, locally low-rank image reconstruction algorithm was employed for image reconstruction. The accuracy of the proposed method was evaluated through numerical simulations and phantom experiments with a T1/proton density fat fraction phantom at 3.0 T. Further, the feasibility of the proposed method was investigated through contrast-enhanced imaging in healthy volunteers, also at 3.0 T.
RESULTS: The method showed excellent agreement with reference measurements in phantoms across a wide range of T1 values (200 to 1000 ms, slope = 0.998 (95% confidence interval (CI) [0.963 to 1.035]), intercept = 27.1 ms (95% CI [0.4 54.6]), r2 = 0.996), and a high level of repeatability. In vivo imaging studies demonstrated moderate agreement (slope = 1.099 (95% CI [1.067 to 1.132]), intercept = -96.3 ms (95% CI [-82.1 to -110.5]), r2 = 0.981) compared to saturation recovery-based T1 maps.
CONCLUSIONS: The proposed method produces whole-liver, confounder-corrected T1 maps through simultaneous estimation of T1, proton density fat fraction, and B 1 + $$ {B}_1^{+} $$ in a single, free-breathing acquisition and has excellent agreement with reference measurements in phantoms.

BACKGROUND: Extracellular volume fraction (ECV), measured with contrast-enhanced magnetic resonance imaging (CE-MRI), has been utilized to study myocardial fibrosis, but its role in peripheral artery disease (PAD) remains unknown. We hypothesized that T1 mapping and ECV differ between PAD patients and matched controls.
RESULTS: A total of 37 individuals (18 PAD patients and 19 matched controls) underwent 3.0T CE-MRI. Skeletal calf muscle T1 mapping was performed before and after gadolinium contrast with a motion-corrected modified look-locker inversion recovery (MOLLI) pulse sequence. T1 values were calculated with a three-parameter Levenberg-Marquardt curve fitting algorithm. ECV and T1 maps were quantified in five calf muscle compartments (anterior [AM], lateral [LM], and deep posterior [DM] muscle groups; soleus [SM] and gastrocnemius [GM] muscles). Averaged peak blood pool T1 values were obtained from the posterior and anterior tibialis and peroneal arteries. T1 values and ECV are heterogeneous across calf muscle compartments. Native peak T1 values of the AM, LM, and DM were significantly higher in PAD patients compared to controls (all p < 0.028). ECVs of the AM and SM were significantly higher in PAD patients compared to controls (AM: 26.4% (21.2, 31.6) vs. 17.3% (10.2, 25.1), p = 0.046; SM: 22.7% (19.5, 27.8) vs. 13.8% (10.2, 19.1), p = 0.020).
CONCLUSIONS: Native peak T1 values across all five calf muscle compartments, and ECV fractions of the anterior muscle group and the soleus muscle were significantly elevated in PAD patients compared with matched controls. Non-invasive T1 mapping and ECV quantification may be of interest for the study of PAD.

BACKGROUND: Accurately predicting the hepatocellular carcinoma (HCC) grade may facilitate the rational selection of treatment strategies. The diagnostic efficacy of the combination of Gadolinium ethoxybenzy diethylenetriamine pentaacetic (Gd-EOB-DTPA) enhancement T1 mapping and apparent diffusion coefficient (ADC) values in predicting HCC grade needs further validation.
OBJECTIVE: This study aimed to assess the capacity of Gd-EOB-DTPA-enhanced T1 mapping and ADC values, both individually and in combination, to discriminate between different grades of HCC.
METHODS: From July 2017 to February 2020, 96 patients (male, 83; mean age, 53.67 years; age range, 29-71 years) clinically diagnosed with HCC were included in the present study. All patients underwent Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI, including T1 mapping sequence) before surgery or biopsy. All the patients were categorized into 3 groups according to the pathological results (including 24 cases of well-differentiated HCCs, 59 cases of moderately differentiated HCCs, 13 cases of and poorly differentiated HCCs). The mean Gd-EOB-DTPA enhanced T1 values (ΔT1=[(T1pre-T1post)/T1pre]×100%) and ADC values between different grading groups of HCC were calculated and compared. The area under the characteristics curve (AUC), the diagnostic threshold, sensitivity, and specificity of ΔT1 and ADC for differential diagnosis were analyzed.
RESULTS: Mean ΔT1 was 58% for well-differentiated HCCs, 50% for moderately-differentiated HCCs, and 43% for poorly-differentiated HCCs. ΔT1 showed statistical differences between the groups (P<0.001). The mean ADC values of the 3 groups were 1.11×10-3 mm2/s, 0.91×10-3 mm2/s, and 0.80×10-3mm2/s, respectively. ADC showed statistical differences between the groups (P<0.001). In discriminating well- differentiated group from the moderately differentiated group, the AUC of ΔT1 was 0.751 (95% CI: 0.642, 0.859), the AUC of ADC was 0.782 (95% CI: 0.671, 0.894), the AUC of combined model was 0.811 (95% CI: 0.709, 0.914). In discriminating the poorly differentiated group from the moderately differentiated group, the AUC of ΔT1 was 0.768 (95% CI: 0.634, 0.902), the AUC of ADC was 0.754 (95% CI: 0.603, 0.904), and the AUC of the combined model was 0.841 (95% CI: 0.729, 0.953).
CONCLUSIONS: Gd-EOB-DTPA enhanced T1 mapping, and ADC values have complementary effects on the sensitivity and specificity for identifying different HCC grades. A combined model of Gd-EOB-DTPA-enhanced MRI T1 mapping and ADC values could improve diagnostic performance for predicting HCC grades.

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OBJECTIVE: To evaluate different cardiovascular magnetic resonance (CMR) parameters for the differentiation of light chain amyloidosis (AL) and transthyretin-related amyloidosis (ATTR).
RESULTS: In total, 75 patients, 53 with cardiac amyloidosis (20 patients with AL (66±12 years, 14 males [70%]) and 33 patients with ATTR (78±5 years, 28 males [88%])) were retrospectively analyzed regarding CMR parameters such as T1 and T2 mapping, extracellular volume (ECV), and late gadolinium enhancement (LGE) distribution patterns, and myocardial strain, and compared to a control cohort with other causes of left ventricular hypertrophy (LVH; 22 patients (53±16 years, 17 males [85%])). One way-ANOVA and receiver operating characteristic analysis were used for statistical analysis. ECV was the single best parameter to differentiate between cardiac amyloidosis and controls (area under the curve [AUC]: 0.97, 95% confidence intervals [CI]: 0.89-0.99, p<.0001, cutoff: >30%). T2 mapping was the best single parameter to differentiate between AL and ATTR amyloidosis (AL: 63±4 ms, ATTR: 58±2 ms, p<.001, AUC: 0.86, 95% CI: 0.74-0.94, cutoff: >61 ms). Subendocardial LGE was predominantly observed in AL patients (10/20 [50%] vs. 5/33 [15%]; p=.002). Transmural LGE was predominantly observed in ATTR patients (23/33 [70%] vs. 2/20 [10%]; p<.001). The diagnostic performance of T2 mapping to differentiate between AL and ATTR amyloidosis was further increased with the inclusion of LGE patterns (AUC: 0.96, 95% CI: 0.86-0.99]; p=.05).
CONCLUSIONS: ECV differentiates cardiac amyloidosis from other causes of LVH. T2 mapping combined with LGE differentiates AL from ATTR amyloidosis with high accuracy on a patient level.

The Instantaneous Signal Loss Simulation (InSiL) model is a promising alternative to the classical mono-exponential fitting of the Modified Look-Locker Inversion-recovery (MOLLI) sequence in cardiac T1 mapping applications, which achieves better accuracy and is less sensitive to heart rate (HR) variations. Classical non-linear least squares (NLLS) estimation methods require some parameters of the model to be fixed a priori in order to give reliable T1 estimations and avoid outliers. This introduces further bias in the estimation, reducing the advantages provided by the InSiL model. In this paper, a novel Bayesian estimation method using a hierarchical model is proposed to fit the parameters of the InSiL model. The hierarchical Bayesian modeling has a shrinkage effect that works as a regularizer for the estimated values, by pulling spurious estimated values toward the group-mean, hence reducing greatly the number of outliers. Simulations, physical phantoms, and in-vivo human cardiac data have been used to show that this approach estimates accurately all the InSiL parameters, and achieve high precision estimation of the T1 compared to the classical MOLLI model and NLLS InSiL estimation.

OBJECTIVE: Quantitative T1 mapping can be an essential tool for assessing tissue injury in multiple sclerosis (MS). We introduce T1-REQUIRE, a method that converts a single high-resolution anatomical 3D T1-weighted Turbo Field Echo (3DT1TFE) scan into a parametric T1 map that could be used for quantitative assessment of tissue damage. We present the accuracy and feasibility of this method in MS.
METHODS: 14 subjects with relapsing-remitting MS and 10 healthy subjects were examined. T1 maps were generated from 3DT1TFE images using T1-REQUIRE, which estimates T1 values using MR signal equations and internal tissue reference T1 values. Estimated T1 of lesions, white, and gray matter regions were compared with reference Inversion-Recovery Fast Field Echo T1 values and analyzed via correlation and Bland-Altman (BA) statistics.
RESULTS: 159 T1-weighted (T1W) hypointense MS lesions and 288 gray matter regions were examined. T1 values for MS lesions showed a Pearson\'s correlation of r = 0.81 (p < 0.000), R2 = 0.65, and Bias = 4.18%. BA statistics showed a mean difference of -53.95 ms and limits of agreement (LOA) of -344.20 and 236.30 ms. Non-lesional normal-appearing white matter had a correlation coefficient of r = 0.82 (p < 0.000), R2 = 0.67, Bias = 8.78%, mean difference of 73.87 ms, and LOA of -55.67 and 203.41 ms.
CONCLUSIONS: We demonstrate the feasibility of retroactively derived high-resolution T1 maps from routinely acquired anatomical images, which could be used to quantify tissue pathology in MS. The results of this study will set the stage for testing this method in larger clinical studies for examining MS disease activity and progression.

Cardiac magnetic resonance represents the gold standard imaging technique to assess cardiac volumes, wall thickness, mass, and systolic function but also to provide noninvasive myocardial tissue characterization across almost all cardiac diseases. In patients with cardiac amyloidosis, increased wall thickness of all heart chambers, a mildly reduced ejection fraction and occasionally pleural and pericardial effusion are the characteristic morphologic anomalies. The typical pattern after contrast injection is represented by diffuse areas of late gadolinium enhancement, which can be focal and patchy in very early stages, circumferential, and subendocardial in intermediate stages or even diffuse transmural in more advanced stages.

UNASSIGNED: Currently, there is a lack of an objective quantitative measure to comprehensively evaluate the inflammatory activity of axSpA, which poses certain challenges in accurately assessing the disease activity.
UNASSIGNED: To explore the value of combined-parameter models of sacroiliac joints (SIJs) MRI relaxometry and peripheral blood Mucosal-associated invariant T (MAIT) cells in evaluating the inflammatory activity of axial spondyloarthritis (axSpA).
UNASSIGNED: This retrospective clinical study included 88 axSpA patients (median age 31.0 (22.0, 41.8) years, 21.6% females) and 20 controls (median age 28.0 (20.5, 49.5) years, 40.0% females). The axSpA group was classified into active subgroup (n=50) and inactive subgroup (n=38) based on ASDAS-CRP. All participants underwent SIJs MRI examination including T1 and T2* mapping, and peripheral blood flow cytometry analysis of MAIT cells (defined as CD3+Vα7.2+CD161+) and their activation markers (CD69). The T1 and T2* values, as were the percentages of MAIT cells and CD69+MAIT cells were compared between different groups. Combined-parameter models were established using logistic regression, and ROC curves were employed to evaluate the diagnostic efficacy.
UNASSIGNED: The T1 values of SIJs and %CD69+MAIT cells in the axSpA group and its subgroup were higher than the control group (p<0.05), while %MAIT cells were lower than the control group (p<0.05). The T1 values and %CD69+MAIT cells correlated positively, while %MAIT cells correlated negatively, with the ASDAS-CRP (r=0.555, 0.524, -0.357, p<0.001). Between the control and axSpA groups, and between the inactive and active subgroups, the combined-parameter model T1 mapping+%CD69+MAIT cells has the best efficacy (AUC=0.959, 0.879, sensibility=88.6, 70%, specificity=95.0, 94.7%, respectively).
UNASSIGNED: The combined-parameter model T1 mapping+%CD69+MAIT cells allows a more accurate evaluation of the level of inflammatory activity.