Narrative review of magnetic resonance imaging in quantifying liver iron load.

Objective: To summarize the research progress of magnetic resonance imaging (MRI) in quantifying liver iron load. Methods: To summarize the current status and progress of MRI technology in the quantitative study of liver iron load through reviewing the relevant literature at home and abroad. Results: Different MRI sequence examination techniques have formed a series of non-invasive methods for the examination of liver iron load. These techniques have important clinical significance in the imaging diagnosis of liver iron load. So far, the main MRI methods used to assess liver iron load are: signal intensity measurement method (signal intensity, SI) [signal intensity ratio (SIR) and difference in in-phase and out-of-phase signal intensity], T2/R2 measurement (such as FerriScan technique), ultra-short echo time (UTE) imaging technique, and susceptibility weighted imaging (including conventional susceptibility weighted imaging) (SWI), quantitative susceptibility mapping (QSM), T2*/R2* measurement, Dixon and its derivative techniques. Conclusion: MRI has become the first choice for the non-invasive examination of liver iron overload, and it is helpful to improve the early detection of liver injury, liver fibrosis, liver cirrhosis and liver cancer caused by liver iron overload.

Quantitative study of 3T MRI qDixon-WIP applied in pancreatic fat infiltration in patients with type 2 diabetes mellitus.

Objective: To investigate the application value of 3T MRI qDixon-WIP technique in the quantitative measurement of pancreatic fat content in patients with type 2 diabetes mellitus (T2DM). Methods: The 3T MRI qDixon-WIP sequence was used to scan the livers and the pancreas of 47 T2DM patients (experimental group) and 48 healthy volunteers (control group). Pancreatic fat fraction (PFF), hepatic fat fraction (HFF), Body mass index (BMI) ratio of pancreatic volume to body surface area (PVI) were measured. Total cholesterol (TC), subcutaneous fat area (SA), triglyceride (TG), abdominal visceral fat area (VA), high density lipoprotein (HDL-c), fasting blood glucose (FPC) and low-density lipoprotein (LDL-c) were collected. The relationship between the experimental group and the control group and between PFF and other indicators was compared. The differences of PFF between the control group and different disease course subgroups were also explored. Results: There was no significant difference in BMI between the experimental group and the control group (P=0.231). PVI, SA, VA, PFF and HFF had statistical differences (P<0.05). In the experimental group, PFF was highly positively correlated with HFF (r=0.964, P<0.001), it was moderately positively correlated with TG and abdominal fat area (r=0.676, 0.591, P<0.001), and it was weakly positively correlated with subcutaneous fat area (r=0.321, P=0.033). And it had no correlation with FPC, PVI, HDL-c, TC and LDL-c (P>0.05). There were statistical differences in PFF between the control group and the patients with different course of T2DM (P<0.05). There was no significant difference in PFF between T2DM patients with a disease course ≤1 year and those with a disease course <5 years (P>0.05). There were significant differences in PFF between the groups with a disease course of 1-5 years and those with a disease course of more than 5 years (P<0.001). Conclusion: PVI of T2DM patients is lower than normal, but SA, VA, PFF, HFF are higher than normal. The degree of pancreatic fat accumulation in T2DM patients with long disease course was higher than that in patients with short disease course. The qDixon-WIP sequence can provide an important reference for clinical quantitative evaluation of fat content in T2DM patients.

α- and ß-Genotyping of Thalassemia Patients Based on a Multimodal Liver MRI Radiomics Model: A Preliminary Study in Two Centers.

BACKGROUND: So far, there is no non-invasive method that can popularize the genetic testing of thalassemia (TM) patients on a large scale. The purpose of the study was to investigate the value of predicting the α- and ß- genotypes of TM patients based on a liver MRI radiomics model. METHODS: Radiomics features of liver MRI image data and clinical data of 175 TM patients were extracted using Analysis Kinetics (AK) software. The radiomics model with optimal predictive performance was combined with the clinical model to construct a joint model. The predictive performance of the model was evaluated in terms of AUC, accuracy, sensitivity, and specificity. RESULTS: The T2 model showed the best predictive performance: the AUC, accuracy, sensitivity, and specificity of the validation group were 0.88, 0.865, 0.875, and 0.833, respectively. The joint model constructed from T2 image features and clinical features showed higher predictive performance: the AUC, accuracy, sensitivity, and specificity of the validation group were 0.91, 0.846, 0.9, and 0.667, respectively. CONCLUSION: The liver MRI radiomics model is feasible and reliable for predicting α- and ß-genotypes in TM patients.

Comparative Study on the Measurement of Liver LICdw between Ferriscan and T2* Based LICdw Obtained by Different Software's.

Objective: To explore the relationship between the liver iron concentration (LICF) from FerriScan and T2* based LIC obtained by Circle Cardiovascular Imaging CVI42 (CVI42), CMRtools/Thalassemia Tools (CMRtools), and Excel spreadsheet (Excel). Methods: Liver T2* values in 78 thalassemia patients were measured using CVI42, CMRtools, and Excel. Then the Garbowski formula was used to obtain LIC from T2*. Finally, the relationship of the LIC measured by the above three software and the LICF were compared. Results: There was no statistical difference between the T2* values measured by CVI42, CMRtools, and Excel (P>0.05), but there was a high degree of consistency between them (P<0.001), and there was a high linear positive correlation between them (P<0.001). There was no statistical difference between the LIC clinical grading results of CVI42, CMRtools, and Excel and LICF grading results (P>0.05), and they were highly consistent (P<0.001). Conclusion: The liver T2* values measured by CVI42, CMRtools, and Excel are equivalent. The LIC measured by CVI42, CMRtools, and Excel is equivalent to the LICF.