Quantitative evaluation of focal liver lesions with T1 mapping using a phase-sensitive inversion recovery sequence on gadoxetic acid-enhanced MRI

Accelerated 2D radial Look-Locker T1 mapping using a deep learning-based rapid inversion recovery sampling technique

Efficient abdominal coverage with T1-mapping methods currently available in the clinic is limited by the breath hold period (BHP) and the time needed for T1 recovery. This work develops a T1-mapping framework for efficient abdominal coverage based on rapid T1 recovery curve (T1RC) sampling, slice-selective inversion, optimized slice interleaving, and a convolutional neural network (CNN)-based T1 estimation. The effect of reducing the T1RC sampling was evaluated by comparing T1 estimates from T1RC ranging from 0.63 to 2.0 s with reference T1 values obtained from T1RC = 2.5-5 s. Slice interleaving methodologies were evaluated by comparing the T1 variation in abdominal organs across slices. The repeatability of the proposed framework was demonstrated by performing acquisition on test subjects across imaging sessions. Analysis of in vivo data based on retrospectively shortening the T1RC showed that with the CNN framework, a T1RC = 0.84 s yielded T1 estimates without significant changes in mean T1 (p > 0.05) or significant increase in T1 variability (p > 0.48) compared to the reference. Prospectively acquired data using T1RC = 0.84 s, an optimized slice interleaving scheme, and the CNN framework enabled 21 slices in a 20 s BHP. Analyses across abdominal organs produced T1 values within 2% of the reference. Repeatability experiments yielded Pearson's correlation, repeatability coefficient, and coefficient of variation of 0.99, 2.5%, and 0.12%, respectively. The proposed T1 mapping framework provides full abdominal coverage within a single BHP.

Respiratory motion-corrected T1 mapping of the abdomen

OBJECTIVE

The purpose of this study was to investigate an approach for motion-corrected T1 mapping of the abdomen that allows for free breathing data acquisition with 100% scan efficiency.

MATERIALS AND METHODS

Data were acquired using a continuous golden radial trajectory and multiple inversion pulses. For the correction of respiratory motion, motion estimation based on a surrogate was performed from the same data used for T1 mapping. Image-based self-navigation allowed for binning and reconstruction of respiratory-resolved images, which were used for the estimation of respiratory motion fields. Finally, motion-corrected T1 maps were calculated from the data applying the estimated motion fields. The method was evaluated in five healthy volunteers. For the assessment of the image-based navigator, we compared it to a simultaneously acquired ultrawide band radar signal. Motion-corrected T1 maps were evaluated qualitatively and quantitatively for different scan times.

RESULTS

For all volunteers, the motion-corrected T1 maps showed fewer motion artifacts in the liver as well as sharper kidney structures and blood vessels compared to uncorrected T1 maps. Moreover, the relative error to the reference breathhold T1 maps could be reduced from up to 25% for the uncorrected T1 maps to below 10% for the motion-corrected maps for the average value of a region of interest, while the scan time could be reduced to 6-8 s.

DISCUSSION

The proposed approach allows for respiratory motion-corrected T1 mapping in the abdomen and ensures accurate T1 maps without the need for any breathholds.

Hepatocellular carcinoma: State of the art diagnostic imaging

Primary liver cancer is the fourth most common malignancy worldwide, with hepatocellular carcinoma (HCC) comprising up to 90% of cases. Imaging is a staple for surveillance and diagnostic criteria for HCC in current guidelines. Because early diagnosis can impact treatment approaches, utilizing new imaging methods and protocols to aid in differentiation and tumor grading provides a unique opportunity to drastically impact patient prognosis. Within this review manuscript, we provide an overview of imaging modalities used to screen and evaluate HCC. We also briefly discuss emerging uses of new imaging techniques that offer the potential for improving current paradigms for HCC characterization, management, and treatment monitoring.

Quantification of contrast agent uptake in the hepatobiliary phase helps to differentiate hepatocellular carcinoma grade

This study aimed to assess the degree of differentiation of hepatocellular carcinoma (HCC) using Gd-EOB-DTPA-assisted magnetic resonance imaging (MRI) with T1 relaxometry. Thirty-three solitary HCC lesions were included in this retrospective study. This study's inclusion criteria were preoperative Gd-EOB-DTPA-assisted MRI of the liver and a histopathological evaluation after hepatic tumor resection. T1 maps of the liver were evaluated to determine the T1 relaxation time and reduction rate between the native phase and hepatobiliary phase (HBP) in liver lesions. These findings were correlated with the histopathologically determined degree of HCC differentiation (G1, well-differentiated; G2, moderately differentiated; G3, poorly differentiated). There was no significant difference between well-differentiated (950.2 ± 140.2 ms) and moderately/poorly differentiated (1009.4 ± 202.0 ms) HCCs in the native T1 maps. After contrast medium administration, a significant difference (p ≤ 0.001) in the mean T1 relaxation time in the HBP was found between well-differentiated (555.4 ± 140.2 ms) and moderately/poorly differentiated (750.9 ± 146.4 ms) HCCs. For well-differentiated HCCs, the reduction rate in the T1 time was significantly higher at 0.40 ± 0.15 than for moderately/poorly differentiated HCCs (0.25 ± 0.07; p = 0.006). In conclusion this study suggests that the uptake of Gd-EOB-DTPA in HCCs is correlated with tumor grade. Thus, Gd-EOB-DTPA-assisted T1 relaxometry can help to further differentiation of HCC.

Emerging artificial intelligence applications in liver magnetic resonance imaging

Chronic liver diseases (CLDs) are becoming increasingly more prevalent in modern society. The use of imaging techniques for early detection, such as magnetic resonance imaging (MRI), is crucial in reducing the impact of these diseases on healthcare systems. Artificial intelligence (AI) algorithms have been shown over the past decade to excel at image-based analysis tasks such as detection and segmentation. When applied to liver MRI, they have the potential to improve clinical decision making, and increase throughput by automating analyses. With Liver diseases becoming more prevalent in society, the need to implement these techniques to utilize liver MRI to its full potential, is paramount. In this review, we report on the current methods and applications of AI methods in liver MRI, with a focus on machine learning and deep learning methods. We assess four main themes of segmentation, classification, image synthesis and artefact detection, and their respective potential in liver MRI and the wider clinic. We provide a brief explanation of some of the algorithms used and explore the current challenges affecting the field. Though there are many hurdles to overcome in implementing AI methods in the clinic, we conclude that AI methods have the potential to positively aid healthcare professionals for years to come.

Diffusion-weighted MRI and FLAIR sequence for differentiation of hydatid cysts and simple cysts in the liver

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DWI signal characteristics are useful in differentiating between hydatid cysts and simple cysts.

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ADC values (b600 and b1000) can distinguish hydatid cyst and simple cyst.

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FLAIR sequence contributes to the differentiation of type 2 hydatid and simple cysts.

Purpose

The contribution of DWI and FLAIR to the differential diagnosis of type 1, 2, and 3 hydatid cysts and simple liver cysts was investigated according to the Gharbi classification. This study is the first report using FLAIR sequence for the differential diagnosis of liver hydatid cysts in this regard.

Methods

A total of 82 hydatid cysts and 40 simple cysts were scanned with DWI (in b600-b1000 values) and FLAIR sequence. In 64 patients included in the study, a total of 122 cystic lesions were diagnosed histopathologically or during follow-up. FLAIR and DWI signal characteristics were evaluated, and ADC values were calculated.

Results

The mean ADC value of hydatid cysts on DWI (b600) was 3.07 ± 0.41 × 10⁻³ s/mm², while it was 3.91 ± 0.51 × 10⁻³ s/mm² for simple cysts and the difference was statistically significant (p < 0.05). On b1000 DWI, the mean ADC values of hydatid and simple cysts were 2.99 ± 0.38 × 10⁻³ s/mm² and 3.43 ± 0:29 × 10⁻³ s/mm², respectively (p < 0.05). The qualitative evaluation of the signal intensity on b600−1000 DWI demonstrated the difference between the simple and hydatid cyst groups (p < 0.05). Type 2 hydatid cysts alone were distinguished from type 2–3 hydatid and simple cysts by FLAIR (p < 0.05).

Conclusions

ADC values can distinguish between hydatid cyst and simple cyst. FLAIR contributes to the differentiation of type 2 hydatid and simple cysts.