Application of T1-weighted BLADE sequence to abdominal magnetic resonance imaging of young children: a comparison with turbo spin echo sequence

Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI

INTRODUCTION

Deep learning-based MRI reconstruction has recently been introduced to improve image quality. This study aimed to evaluate the performance of deep learning reconstruction in pediatric brain MRI.

METHODS

A total of 107 consecutive children who underwent 3.0 T brain MRI were included in this study. T2-weighted brain MRI was reconstructed using the three different reconstruction modes: deep learning reconstruction, conventional reconstruction with an intensity filter, and original T2 image without a filter. Two pediatric radiologists independently evaluated the following image quality parameters of three reconstructed images on a 5-point scale: overall image quality, image noisiness, sharpness of gray-white matter differentiation, truncation artifact, motion artifact, cerebrospinal fluid and vascular pulsation artifacts, and lesion conspicuity. The subjective image quality parameters were compared among the three reconstruction modes. Quantitative analysis of the signal uniformity using the coefficient of variation was performed for each reconstruction.

RESULTS

The overall image quality, noisiness, and gray-white matter sharpness were significantly better with deep learning reconstruction than with conventional or original reconstruction (all P < 0.001). Deep learning reconstruction had significantly fewer truncation artifacts than the other two reconstructions (all P < 0.001). Motion and pulsation artifacts showed no significant differences among the three reconstruction modes. For 36 lesions in 107 patients, lesion conspicuity was better with deep learning reconstruction than original reconstruction. Deep learning reconstruction showed lower signal variation compared to conventional and original reconstructions.

CONCLUSION

Deep learning reconstruction can reduce noise and truncation artifacts and improve lesion conspicuity and overall image quality in pediatric T2-weighted brain MRI.

State-of-the-art magnetic resonance imaging sequences for pediatric body imaging

Longer examination time, need for anesthesia in smaller children and the inability of most children to hold their breath are major limitations of MRI in pediatric body imaging. Fortunately, with technical advances, many new and upcoming MRI sequences are overcoming these limitations. Advances in data acquisition and k-space sampling methods have enabled sequences with improved temporal and spatial resolution, and minimal artifacts. Sequences to minimize movement artifacts mainly utilize radial k-space filling, and examples include the stack-of-stars method for T1-weighted imaging and the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER)/BLADE method for T2-weighted imaging. Similarly, the sequences with improved temporal resolution and the ability to obtain multiple phases in a single breath-hold in dynamic imaging mainly use some form of partial k-space filling method. New sequences use a variable combination of data sampling methods like compressed sensing, golden-angle radial k-space filling, parallel imaging and partial k-space filling to achieve free-breathing, faster sequences that could be useful for pediatric abdominal and thoracic imaging. Simultaneous multi-slice method has improved diffusion-weighted imaging (DWI) with reduction in scan time and artifacts. In this review, we provide an overview of data sampling methods like parallel imaging, compressed sensing, radial k-space sampling, partial k-space sampling and simultaneous multi-slice. This is followed by newer available and upcoming sequences for T1-, T2- and DWI based on these other advances. We also discuss the Dixon method and newer approaches to reducing metal artifacts.

Free-breathing magnetic resonance imaging with radial k-space sampling for neonates and infants to reduce anesthesia

BACKGROUND

Conventional chest and abdominal MRI require breath-holds to reduce motion artifacts. Neonates and infants require general anesthesia with intubation to enable breath-held acquisitions.

OBJECTIVE

We aimed to validate a free-breathing approach to reduce general anesthesia using a motion-insensitive radial acquisition with respiratory gating.

MATERIALS AND METHODS

We retrospectively enrolled children <3 years old who were referred for MRI of the chest or abdomen. They were divided into two groups according to MRI protocol: (1) breath-held scans under general anesthesia with T2-weighted single-shot fast spin-echo (SSFSE) and contrast-enhanced T1-weighted modified Dixon, and (2) free-breathing scans using radial sequences (T2-W MultiVane XD and contrast-enhanced T1-W three-dimensional [3-D] Vane XD). Two readers graded image quality and motion artifacts.

RESULTS

We included 23 studies in the free-breathing cohort and 22 in the breath-hold cohort. The overall imaging scores for the free-breathing radial T2-W sequence were similar to the scores for the breath-held T2-W SSFSE sequence (chest, 3.6 vs. 3.2, P=0.07; abdomen, 3.9 vs. 3.7, P=0.66). The free-breathing 3-D radial T1-W sequence also had image quality scores that were similar to the breath-held T1-W sequence (chest, 4.0 vs. 3.0, P=0.06; abdomen, 3.7 vs. 3.9, P=0.15). Increased motion was seen in the abdomen on the radial T2-W sequence (P<0.001), but increased motion was not different in the chest (P=0.73) or in contrast-enhanced T1-W sequences (chest, P=0.39; abdomen, P=0.15). The mean total sequence time was longer in free-breathing compared to breath-held exams (P<0.01); however, this did not translate to longer overall exam times (P=0.94).

CONCLUSION

Motion-insensitive radial sequences used for infants and neonates were of similar image quality to breath-held sequences and had decreased sedation and intubation.

Improved Image Quality for Static BLADE Magnetic Resonance Imaging Using the Total-Variation Regularized Least Absolute Deviation Solver

In order to improve the image quality of BLADE magnetic resonance imaging (MRI) using the index tensor solvers and to evaluate MRI image quality in a clinical setting, we implemented BLADE MRI reconstructions using two tensor solvers (the least-squares solver and the L1 total-variation regularized least absolute deviation (L1TV-LAD) solver) on a graphics processing unit (GPU). The BLADE raw data were prospectively acquired and presented in random order before being assessed by two independent radiologists. Evaluation scores were examined for consistency and then by repeated measures analysis of variance (ANOVA) to identify the superior algorithm. The simulation showed the structural similarity index (SSIM) of various tensor solvers ranged between 0.995 and 0.999. Inter-reader reliability was high (Intraclass correlation coefficient (ICC) = 0.845, 95% confidence interval: 0.817, 0.87). The image score of L1TV-LAD was significantly higher than that of vendor-provided image and the least-squares method. The image score of the least-squares method was significantly lower than that of the vendor-provided image. No significance was identified in L1TV-LAD with a regularization strength of λ= 0.4–1.0. The L1TV-LAD with a regularization strength of λ= 0.4–0.7 was found consistently better than least-squares and vendor-provided reconstruction in BLADE MRI with a SENSitivity Encoding (SENSE) factor of 2. This warrants further development of the integrated computing system with the scanner.