Usefulness of Breath-Hold Fat-Suppressed T2-Weighted Images With Deep Learning-Based Reconstruction of the Liver: Comparison to Conventional Free-Breathing Turbo Spin Echo

Deep Learning Reconstruction of Prospectively Accelerated MRI of the Pancreas: Clinical Evaluation of Shortened Breath-Hold Examinations With Dixon Fat Suppression

OBJECTIVE

Deep learning (DL)-enabled magnetic resonance imaging (MRI) reconstructions can enable shortening of breath-hold examinations and improve image quality by reducing motion artifacts. Prospective studies with DL reconstructions of accelerated MRI of the upper abdomen in the context of pancreatic pathologies are lacking. In a clinical setting, the purpose of this study is to investigate the performance of a novel DL-based reconstruction algorithm in T1-weighted volumetric interpolated breath-hold examinations with partial Fourier sampling and Dixon fat suppression (hereafter, VIBE-Dixon DL ). The objective is to analyze its impact on acquisition time, image sharpness and quality, diagnostic confidence, pancreatic lesion conspicuity, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR).

METHODS

This prospective single-center study included participants with various pancreatic pathologies who gave written consent from January 2023 to September 2023. During the same session, each participant underwent 2 MRI acquisitions using a 1.5 T scanner: conventional precontrast and postcontrast T1-weighted VIBE acquisitions with Dixon fat suppression (VIBE-Dixon, reference standard) using 4-fold parallel imaging acceleration and 6-fold accelerated VIBE-Dixon acquisitions with partial Fourier sampling utilizing a novel DL reconstruction tailored to the acquisition. A qualitative image analysis was performed by 4 readers. Acquisition time, image sharpness, overall image quality, image noise and artifacts, diagnostic confidence, as well as pancreatic lesion conspicuity and size were compared. Furthermore, a quantitative analysis of SNR and CNR was performed.

RESULTS

Thirty-two participants were evaluated (mean age ± SD, 62 ± 19 years; 20 men). The VIBE-Dixon DL method enabled up to 52% reduction in average breath-hold time (7 seconds for VIBE-Dixon DL vs 15 seconds for VIBE-Dixon, P < 0.001). A significant improvement of image sharpness, overall image quality, diagnostic confidence, and pancreatic lesion conspicuity was observed in the images recorded using VIBE-Dixon DL ( P < 0.001). Furthermore, a significant reduction of image noise and motion artifacts was noted in the images recorded using the VIBE-Dixon DL technique ( P < 0.001). In addition, for all readers, there was no evidence of a difference in lesion size measurement between VIBE-Dixon and VIBE-Dixon DL . Interreader agreement between VIBE-Dixon and VIBE-Dixon DL regarding lesion size was excellent (intraclass correlation coefficient, >90). Finally, a statistically significant increase of pancreatic SNR in VIBE-DIXON DL was observed in both the precontrast ( P = 0.025) and postcontrast images ( P < 0.001). Also, an increase of splenic SNR in VIBE-DIXON DL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images ( P = 0.34 and P = 0.003, respectively). Similarly, an increase of pancreas CNR in VIBE-DIXON DL was observed in both the precontrast and postcontrast images, but only reaching statistical significance in the postcontrast images ( P = 0.557 and P = 0.026, respectively).

CONCLUSIONS

The prospectively accelerated, DL-enhanced VIBE with Dixon fat suppression was clinically feasible. It enabled a 52% reduction in breath-hold time and provided superior image quality, diagnostic confidence, and pancreatic lesion conspicuity. This technique might be especially useful for patients with limited breath-hold capacity.

Quantitative Liver Imaging in Children

ABSTRACT

In children and adults, quantitative imaging examinations determine the effectiveness of treatment for liver disease. However, pediatric liver disease differs in presentation from liver disease in adults. Children also needed to be followed for a longer period from onset and have less control of their bodies, showing more movement than adults during imaging examinations, which leads to a greater need for sedation. Thus, it is essential to appropriately tailor and accurately perform noninvasive imaging tests in these younger patients. This article is an overview of updated imaging techniques used to assess liver disease quantitatively in children. The common initial imaging study for diffuse liver disease in pediatric patients is ultrasound. In addition to preexisting echo analysis, newly developed attenuation imaging techniques have been introduced to evaluate fatty liver. Ultrasound elastography is also now actively used to evaluate liver conditions, and the broad age spectrum of the pediatric population requires caution to be taken even in the selection of probes. Magnetic resonance imaging (MRI) is another important imaging tool used to evaluate liver disease despite requiring sedation or anesthesia in young children because it allows quantitative analysis with sequences such as fat analysis and MR elastography. In addition to ultrasound and MRI, we review quantitative imaging methods specifically for fatty liver, Wilson disease, biliary atresia, hepatic fibrosis, Fontan-associated liver disease, autoimmune hepatitis, sinusoidal obstruction syndrome, and the transplanted liver. Lastly, concerns such as growth and motion that need to be addressed specifically for children are summarized.

Comparison of image quality and lesion conspicuity between conventional and deep learning reconstruction in gadoxetic acid-enhanced liver MRI

OBJECTIVE

To compare the image quality and lesion conspicuity of conventional vs deep learning (DL)-based reconstructed three-dimensional T1-weighted images in gadoxetic acid-enhanced liver magnetic resonance imaging (MRI).

METHODS

This prospective study (NCT05182099) enrolled participants scheduled for gadoxetic acid-enhanced liver MRI due to suspected focal liver lesions (FLLs) who provided signed informed consent. A liver MRI was conducted using a 3-T scanner. T1-weighted images were reconstructed using both conventional and DL-based (AIRTM Recon DL 3D) reconstruction algorithms. Three radiologists independently reviewed the image quality and lesion conspicuity on a 5-point scale.

RESULTS

Fifty participants (male = 36, mean age 62 ± 11 years) were included for image analysis. The DL-based reconstruction showed significantly higher image quality than conventional images in all phases (3.71-4.40 vs 3.37-3.99, p < 0.001 for all), as well as significantly less noise and ringing artifacts than conventional images (p < 0.05 for all), while also showing significantly altered image texture (p < 0.001 for all). Lesion conspicuity was significantly higher in DL-reconstructed images than in conventional images in the arterial phase (2.15 [95% confidence interval: 1.78, 2.52] vs 2.03 [1.65, 2.40], p = 0.036), but no significant difference was observed in the portal venous phase and hepatobiliary phase (p > 0.05 for all). There was no significant difference in the figure-of-merit (0.728 in DL vs 0.709 in conventional image, p = 0.474).

CONCLUSION

DL reconstruction provided higher-quality three-dimensional T1-weighted imaging than conventional reconstruction in gadoxetic acid-enhanced liver MRI.

CRITICAL RELEVANCE STATEMENT

DL reconstruction of 3D T1-weighted images improves image quality and arterial phase lesion conspicuity in gadoxetic acid-enhanced liver MRI compared to conventional reconstruction.

KEY POINTS

DL reconstruction is feasible for 3D T1-weighted images across different spatial resolutions and phases. DL reconstruction showed superior image quality with reduced noise and ringing artifacts. Hepatic anatomic structures were more conspicuous on DL-reconstructed images.

Ultra-High-Resolution T2-Weighted PROPELLER MRI of the Rectum With Deep Learning Reconstruction: Assessment of Image Quality and Diagnostic Performance

OBJECTIVE

The aim of this study was to evaluate the impact of ultra-high-resolution acquisition and deep learning reconstruction (DLR) on the image quality and diagnostic performance of T2-weighted periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging of the rectum.

MATERIALS AND METHODS

This prospective study included 34 patients who underwent magnetic resonance imaging (MRI) for initial staging or restaging of rectal tumors. The following 4 types of oblique axial PROPELLER images perpendicular to the tumor were obtained: a standard 3-mm slice thickness with conventional reconstruction (3-CR) and DLR (3-DLR), and 1.2-mm slice thickness with CR (1.2-CR) and DLR (1.2-DLR). Three radiologists independently evaluated the image quality and tumor extent by using a 5-point scoring system. Diagnostic accuracy was evaluated in 22 patients with rectal cancer who underwent surgery after MRI without additional neoadjuvant therapy (median interval between MRI and surgery, 22 days). The signal-to-noise ratio and tissue contrast were measured on the 4 types of PROPELLER imaging.

RESULTS

1.2-DLR imaging showed the best sharpness, overall image quality, and rectal and lesion conspicuity for all readers ( P < 0.01). Of the assigned scores for tumor extent, extramural venous invasion (EMVI) scores showed moderate agreement across the 4 types of PROPELLER sequences in all readers (intraclass correlation coefficient, 0.60-0.71). Compared with 3-CR imaging, the number of cases with MRI-detected extramural tumor spread was significantly higher with 1.2-DLR imaging (19.0 ± 2.9 vs 23.3 ± 0.9, P = 0.03), and the number of cases with MRI-detected EMVI was significantly increased with 1.2-CR, 3-DLR, and 1.2-DLR imaging (8.0 ± 0.0 vs 9.7 ± 0.5, 11.0 ± 2.2, and 12.3 ± 1.7, respectively; P = 0.02). For the diagnosis of histopathologic extramural tumor spread, 3-CR and 1.2-CR had significantly higher specificity than 3-DLR and 1.2-DLR imaging (0.75 and 0.78 vs 0.64 and 0.58, respectively; P = 0.02), and only 1.2-CR had significantly higher accuracy than 3-CR imaging (0.83 vs 0.79, P = 0.01). The accuracy of MRI-detected EMVI with reference to pathological EMVI was significantly lower for 3-CR and 3-DLR compared with 1.2-CR (0.77 and 0.74 vs 0.85, respectively; P < 0.01), and was not significantly different between 1.2-CR and 1.2-DLR (0.85 vs 0.80). Using any pathological venous invasion as the reference standard, the accuracy of MRI-detected EMVI was significantly the highest with 1.2-DLR, followed by 1.2-CR, 3-CR, and 3-DLR (0.71 vs 0.67 vs 0.59 vs 0.56, respectively; P < 0.01). The signal-to-noise ratio was significantly highest with 3-DLR imaging ( P < 0.05). There were no significant differences in tumor-to-muscle contrast between the 4 types of PROPELLER imaging.

CONCLUSIONS

Ultra-high-resolution PROPELLER T2-weighted imaging of the rectum combined with DLR improved image quality, increased the number of cases with MRI-detected extramural tumor spread and EMVI, but did not improve diagnostic accuracy with respect to pathology in rectal cancer, possibly because of false-positive MRI findings or false-negative pathologic findings.

Utility of Thin-slice Fat-suppressed Single-shot T2-weighted MR Imaging with Deep Learning Image Reconstruction as a Protocol for Evaluating the Pancreas

PURPOSE

To compare the utility of thin-slice fat-suppressed single-shot T2-weighted imaging (T2WI) with deep learning image reconstruction (DLIR) and conventional fast spin-echo T2WI with DLIR for evaluating pancreatic protocol.

METHODS

This retrospective study included 42 patients (mean age, 70.2 years) with pancreatic cancer who underwent gadoxetic acid-enhanced MRI. Three fat-suppressed T2WI, including conventional fast-spin echo with 6 mm thickness (FSE 6 mm), single-shot fast-spin echo with 6 mm and 3 mm thickness (SSFSE 6 mm and SSFSE 3 mm), were acquired for each patient. For quantitative analysis, the SNRs of the upper abdominal organs were calculated between images with and without DLIR. The pancreas-to-lesion contrast on DLIR images was also calculated. For qualitative analysis, two abdominal radiologists independently scored the image quality on a 5-point scale in the FSE 6 mm, SSFSE 6 mm, and SSFSE 3 mm with DLIR.

RESULTS

The SNRs significantly improved among the three T2-weighted images with DLIR compared to those without DLIR in all patients (P < 0.001). The pancreas-to-lesion contrast of SSFSE 3 mm was higher than those of the FSE 6 mm (P < 0.001) and tended to be higher than SSFSE 6 mm (P = 0.07). SSFSE 3 mm had the highest image qualities regarding pancreas edge sharpness, pancreatic duct clarity, and overall image quality, followed by SSFSE 6 mm and FSE 6 mm (P < 0.0001).

CONCLUSION

SSFSE 3 mm with DLIR demonstrated significant improvements in SNRs of the pancreas, pancreas-to-lesion contrast, and image quality more efficiently than did SSFSE 6 mm and FSE 6 mm. Thin-slice fat-suppressed single-shot T2WI with DLIR can be easily implemented for pancreatic MR protocol.

Feasibility of the application of deep learning-reconstructed ultra-fast respiratory-triggered T2-weighted imaging at 3 T in liver imaging

OBJECTIVE

The evaluate the feasibility of a novel deep learning-reconstructed ultra-fast respiratory-triggered T2WI sequence (DL-RT-T2WI) In liver imaging, compared with respiratory-triggered Arms-T2WI (Arms-RT-T2WI) and respiratory-triggered FSE-T2WI (FSE-RT-T2WI) sequences.

METHODS

71 patients with liver lesions underwent 3-T MRI and were prospectively enrolled. Two readers independently analyzed images acquired with DL-RT-T2WI, Arms-RT-T2WI, and FSE-RT-T2WI. The qualitative evaluation indicators, including overall image quality (OIQ), sharpness, noise, artifacts, lesion detectability (LC), lesion characterization (LD), cardiacmotion-related signal loss (CSL), and diagnostic confidence (DC), were evaluated in two readers, and further statistically compared using paired Wilcoxon rank-sum test among three sequences.

RESULTS

176 lesions were detected in DL-RT-T2W and Arms-RT-T2WI, and 175 were detected in FSE-RT-T2WI. The acquisition time of DL-RT-T2WI was improved by 4.8-7.9 folds compared to the other two sequences. The OIQ was scored highest for DL-RT-T2WI (R1, 4.61 ± 0.52 and R2, 4.62 ± 0.49), was significantly superior to Arms-RT-T2WI (R1, 4.30 ± 0.66 and R2, 4.34 ± 0.69) and FSE-RT-T2WI (R1, 3.65 ± 1.08 and R2, 3.75 ± 1.01). Artifacts and sharpness scored highest for DL-RT-T2WI, followed by Arms-RT-T2WI, and were lowest for FSE-RT-T2WI in both two readers. Noise and CSL for DL-RT-T2WI scored similar to Arms-RT-T2WI (P > 0.05) and were significantly superior to FSE-RT-T2WI (P < 0.001). Both LD and LC for DL-RT-T2WI were significantly superior to Arms-RT-T2WI and FSE-RT-T2WI in two readers (P < 0.001). DC for DL-RT-T2WI scored best, significantly superior to Arms-RT-T2WI (P < 0.010) and FSE-RT-T2WI (P < 0.001).

CONCLUSIONS

The novel ultra-fast DL-RT-T2WI is feasible for liver imaging and lesion characterization and diagnosis, not only offers a significant improvement in acquisition time but also outperforms Arms-RT-T2WI and FSE-RT-T2WI concerning image quality and DC.

Deep learning reconstruction for turbo spin echo to prospectively accelerate ankle MRI: A multi-reader study

PURPOSE

To evaluate a deep learning reconstruction for turbo spin echo (DLR-TSE) sequence of ankle magnetic resonance imaging (MRI) in terms of acquisition time, image quality, and lesion detectability by comparing with conventional TSE.

METHODS

Between March 2023 and May 2023, patients with an indication for ankle MRI were prospectively enrolled. Each patient underwent a conventional TSE protocol and a prospectively undersampled DLR-TSE protocol. Four experienced radiologists independently assessed image quality using a 5-point scale and reviewed structural abnormalities. Image quality assessment included overall image quality, differentiation of anatomic details, diagnostic confidence, artifacts, and noise. Interchangeability analysis was performed to evaluate the equivalence of DLR-TSE relative to conventional TSE for detection of structural pathologies.

RESULTS

In total, 56 patients were included (mean age, 32.6 ± 10.6 years; 35 men). The DLR-TSE (233 s) protocol enabled a 57.4 % reduction in total acquisition time, compared with the conventional TSE protocol (547 s). DLR-TSE images had superior overall image quality, fewer artifacts, and less noise (all P < 0.05), compared with conventional TSE images, according to mean ratings by the four readers. Differentiation of anatomic details, diagnostic confidence, and assessments of structural abnormalities showed no differences between the two techniques (P > 0.05). Furthermore, DLR-TSE demonstrated diagnostic equivalence with conventional TSE, based on interchangeability analysis involving all analyzed structural abnormalities.

CONCLUSION

DLR can prospectively accelerate conventional TSE to a level comparable with a 4-minute comprehensive examination of the ankle, while providing superior image quality and similar lesion detectability in clinical practice.

Deep Learning-Accelerated Liver Diffusion-Weighted Imaging: Intraindividual Comparison and Additional Phantom Study of Free-Breathing and Respiratory-Triggering Acquisitions

OBJECTIVES

Deep learning-reconstructed diffusion-weighted imaging (DL-DWI) is an emerging promising time-efficient method for liver evaluation, but analyses regarding different motion compensation strategies are lacking. This study evaluated the qualitative and quantitative features, sensitivity for focal lesion detection, and scan time of free-breathing (FB) DL-DWI and respiratory-triggered (RT) DL-DWI compared with RT conventional DWI (C-DWI) in the liver and a phantom.

MATERIALS AND METHODS

Eighty-six patients indicated for liver MRI underwent RT C-DWI, FB DL-DWI, and RT DL-DWI with matching imaging parameters other than the parallel imaging factor and number of averages. Two abdominal radiologists independently assessed qualitative features (structural sharpness, image noise, artifacts, and overall image quality) using a 5-point scale. The signal-to-noise ratio (SNR) along with the apparent diffusion coefficient (ADC) value and its standard deviation (SD) were measured in the liver parenchyma and a dedicated diffusion phantom. For focal lesions, per-lesion sensitivity, conspicuity score, SNR, and ADC value were evaluated. Wilcoxon signed rank test and repeated-measures analysis of variance with post hoc test revealed the difference in DWI sequences.

RESULTS

Compared with RT C-DWI, the scan times for FB DL-DWI and RT DL-DWI were reduced by 61.5% and 23.9%, respectively, with statistically significant differences between all 3 pairs (all P 's < 0.001). Respiratory-triggered DL-DWI showed a significantly sharper liver margin, less image noise, and more minor cardiac motion artifact compared with RT C-DWI (all P 's < 0.001), whereas FB DL-DWI showed more blurred liver margins and poorer intrahepatic vessels demarcation than RT C-DWI. Both FB- and RT DL-DWI showed significantly higher SNRs than RT C-DWI in all liver segments (all P 's < 0.001). There was no significant difference in overall ADC values across DWI sequences in the patient or phantom, with the highest value recorded in the left liver dome by RT C-DWI. The overall SD was significantly lower with FB DL-DWI and RT DL-DWI than RT C-DWI (all P 's ≤ 0.003). Respiratory-triggered DL-DWI showed a similar per-lesion sensitivity (0.96; 95% confidence interval, 0.90-0.99) and conspicuity score to those of RT C-DWI and significantly higher SNR and contrast-to-noise ratio values ( P ≤ 0.006). The per-lesion sensitivity of FB DL-DWI (0.91; 95% confidence interval, 0.85-0.95) was significantly lower than that of RT C-DWI ( P = 0.001), with a significantly lower conspicuity score.

CONCLUSIONS

Compared with RT C-DWI, RT DL-DWI demonstrated superior SNR, comparable sensitivity for focal hepatic lesions, and reduced acquisition time, making it a suitable alternative to RT C-DWI. Despite FB DL-DWI's weakness in motion-related challenges, further refinement could potentiate FB DL-DWI in the context of abbreviated screening protocols, where time efficiency is a high priority.

Clinical applications of artificial intelligence in liver imaging

This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.