Purpose
To evaluate diagnostic image quality of 3D late gadolinium enhancement (LGE) with high isotropic spatial resolution (∼1.4 mm3) images reconstructed from randomly undersampled k‐space using ...LOw‐dimensional‐structure Self‐learning and Thresholding (LOST).
Materials and Methods
We prospectively enrolled 270 patients (181 men; 55 ± 14 years) referred for myocardial viability assessment. 3D LGE with isotropic spatial resolution of 1.4 ± 0.1 mm3 was acquired at 1.5T using a LOST acceleration rate of 3 to 5. In a subset of 121 patients, 3D LGE or phase‐sensitive LGE were acquired with parallel imaging with an acceleration rate of 2 for comparison. Two readers evaluated image quality using a scale of 1 (poor) to 4 (excellent) and assessed for scar presence. The McNemar test statistic was used to compare the proportion of detected scar between the two sequences. We assessed the association between image quality and characteristics (age, gender, torso dimension, weight, heart rate), using generalized linear models.
Results
Overall, LGE detection proportions for 3D LGE with LOST were similar between readers 1 and 2 (16.30% vs. 18.15%). For image quality, readers gave 85.9% and 80.0%, respectively, for images categorized as good or excellent. Overall proportion of scar presence was not statistically different from conventional 3D LGE (28% vs. 33% P = 0.17 for reader 1 and 26% vs. 31% P = 0.37 for reader 2). Increasing subject heart rate was associated with lower image quality (estimated slope = –0.009 (P = 0.001)).
Conclusion
High‐resolution 3D LGE with LOST yields good to excellent image quality in >80% of patients and identifies patients with LV scar at the same rate as conventional 3D LGE.
Level of Evidence: 2
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2017;46:1829–1838.
Liver disease causes millions of deaths per year worldwide, and approximately half of these cases are due to cirrhosis, which is an advanced stage of liver fibrosis that can be accompanied by liver ...failure and portal hypertension. Early detection of liver fibrosis helps in improving its treatment and prevents its progression to cirrhosis. In this work, we present a novel noninvasive method to detect liver fibrosis from tagged MRI images using a machine learning‐based approach. Specifically, coronal and sagittal tagged MRI imaging are analyzed separately to capture cardiac‐induced deformation of the liver. The liver is manually delineated and a novel image feature, namely, the histogram of the peak strain (HPS) value, is computed from the segmented liver region and is used to classify the liver as being either normal or fibrotic. Classification is achieved using a support vector machine algorithm. The in vivo study included 15 healthy volunteers (10 males; age range 30–45 years) and 22 patients (15 males; age range 25–50 years) with liver fibrosis verified and graded by transient elastography, and 10 patients only had a liver biopsy and were diagnosed with a score of F3‐F4. The proposed method demonstrates the usefulness and efficiency of extracting the HPS features from the sagittal slices for patients with moderate fibrosis. Cross‐validation of the method showed an accuracy of 83.7% (specificity = 86.6%, sensitivity = 81.8%).
A novel method has been presented for detecting liver fibrosis in tagged MRI images. The method is based on extracting a new set of features representing the distribution of the peak strains in the liver. The results show that the proposed method can achieve an accuracy of 83.7% for patients with moderate to severe fibrosis.
Low scar-to-blood contrast in late gadolinium enhanced (LGE) MRI limits the visualization of scars adjacent to the blood pool. Nulling the blood signal improves scar detection but results in lack of ...contrast between myocardium and blood, which makes clinical evaluation of LGE images more difficult.
GB-LGE contrast is achieved through partial suppression of the blood signal using T
magnetization preparation between the inversion pulse and acquisition. The timing parameters of GB-LGE sequence are determined by optimizing a cost-function representing the desired tissue contrast. The proposed 3D GB-LGE sequence was evaluated using phantoms, human subjects (n = 45) and a swine model of myocardial infarction (n = 5). Two independent readers subjectively evaluated the image quality and ability to identify and localize scarring in GB-LGE compared to black-blood LGE (BB-LGE) (i.e., with complete blood nulling) and conventional (bright-blood) LGE.
GB-LGE contrast was successfully generated in phantoms and all in-vivo scans. The scar-to-blood contrast was improved in GB-LGE compared to conventional LGE in humans (1.1 ± 0.5 vs. 0.6 ± 0.4, P < 0.001) and in animals (1.5 ± 0.2 vs. -0.03 ± 0.2). In patients, GB-LGE detected more tissue scarring compared to BB-LGE and conventional LGE. The subjective scores of the GB-LGE ability for localizing LV scar and detecting papillary scar were improved as compared with both BB-LGE (P < 0.024) and conventional LGE (P < 0.001). In the swine infarction model, GB-LGE scores for the ability to localize LV scar scores were consistently higher than those of both BB-LGE and conventional-LGE.
GB-LGE imaging improves the ability to identify and localize myocardial scarring compared to both BB-LGE and conventional LGE. Further studies are warranted to histologically validate GB-LGE.
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