Comparison of ultrafast wave-controlled aliasing in parallel imaging (CAIPI) magnetization-prepared rapid acquisition gradient echo (MP-RAGE) and standard MP-RAGE in non-sedated children: initial clinical experience

Optimized flow compensation for contrast-enhanced T1-weighted Wave-CAIPI 3D MPRAGE imaging of the brain

Flow-related artifacts have been observed in highly accelerated T1-weighted contrast-enhanced wave-controlled aliasing in parallel imaging (CAIPI) magnetization-prepared rapid gradient-echo (MPRAGE) imaging and can lead to diagnostic uncertainty. We developed an optimized flow-mitigated Wave-CAIPI MPRAGE acquisition protocol to reduce these artifacts through testing in a custom-built flow phantom. In the phantom experiment, maximal flow artifact reduction was achieved with the combination of flow compensation gradients and radial reordered k-space acquisition and was included in the optimized sequence. Clinical evaluation of the optimized MPRAGE sequence was performed in 64 adult patients, who all underwent contrast-enhanced Wave-CAIPI MPRAGE imaging without flow-compensation and with optimized flow-compensation parameters. All images were evaluated for the presence of flow-related artifacts, signal-to-noise ratio (SNR), gray-white matter contrast, enhancing lesion contrast, and image sharpness on a 3-point Likert scale. In the 64 cases, the optimized flow mitigation protocol reduced flow-related artifacts in 89% and 94% of the cases for raters 1 and 2, respectively. SNR, gray-white matter contrast, enhancing lesion contrast, and image sharpness were rated as equivalent for standard and flow-mitigated Wave-CAIPI MPRAGE in all subjects. The optimized flow mitigation protocol successfully reduced the presence of flow-related artifacts in the majority of cases.Relevance statementAs accelerated MRI using novel encoding schemes become increasingly adopted in clinical practice, our work highlights the need to recognize and develop strategies to minimize the presence of unexpected artifacts and reduction in image quality as potential compromises to achieving short scan times.Key points• Flow-mitigation technique led to an 89-94% decrease in flow-related artifacts.• Image quality, signal-to-noise ratio, enhancing lesion conspicuity, and image sharpness were preserved with the flow mitigation technique.• Flow mitigation reduced diagnostic uncertainty in cases where flow-related artifacts mimicked enhancing lesions.

Diagnosis of intracranial lesions using accelerated 3D T1 MPRAGE with wave-CAIPI technique: comparison with conventional 3D T1 MPRAGE

We aimed to evaluate the agreement in the diagnosis of intracranial lesions between conventional pre-contrast 3D T1 magnetization-prepared rapid gradient echo (MPRAGE) and wave-CAIPI (wave-controlled aliasing in parallel imaging) MPRAGE. Institutional review board approval was obtained and informed consent was waived for this retrospective study. We included 149 consecutive patients who had undergone brain MR with both conventional MPRAGE (scan time: 5 min 42 s) and wave-CAIPI MPRAGE (scan time: 2 min 44 s) from February to June 2018. All images were independently reviewed by two radiologists for the diagnosis of intracranial lesion and scored image quality using visual analysis. One technician measured signal-to-noise ratio. The agreement for diagnosis of intracranial lesion was calculated, and the intra- and interobserver agreements were analyzed by using kappa value. For the diagnosis of intracranial lesion, the conventional and wave-CAIPI MPRAGE demonstrated 99.7% of agreement (297 of 298) in the pooled analysis with very good agreement (k = 0.994). Intra- and inter-observer agreement showed very good (k > 0.9 in all) and good (k > 0.75) agreement, respectively. In the quantitative analysis, the signal-to-noise ratio had no difference (P > 0.05 for all). The overall image quality was poorer in images of wave-CAIPI MPRAGE (P < 0.001), but motion artifact had no difference between two sequences (P = 0.06). Compared to conventional MPRAGE, pre-contrast 3D T1 wave-CAIPI MPRAGE achieved higher agreement for the diagnosis of intracranial lesions and reduced the scan time by approximately 50%.

Accelerated Nonenhanced 3D T1-MPRAGE Using Wave-Controlled Aliasing in Parallel Imaging for Infant Brain Imaging

BACKGROUND AND PURPOSE

MPRAGE is the most commonly used sequence for high-resolution 3D T1-weighted imaging in pediatric patients. However, its longer scan time is a major drawback because pediatric patients are prone to motion and frequently require sedation. This study compared nonenhanced accelerated MPRAGE using wave-controlled aliasing in parallel imaging (wave-T1-MPRAGE) with standard MPRAGE in infants.

MATERIALS AND METHODS

We retrospectively evaluated 68 infants (mean age, 1.78 [SD. 1.70] months) who underwent nonenhanced standard and wave-T1-MPRAGE. Two neuroradiologists independently assessed each image for image quality, artifacts, myelination degree, and anatomic delineation using the 4-point Likert scale. For diagnostic performance, both observers determined whether nonenhancing lesions were present in the brain parenchyma in 2 types of nonenhanced MPRAGE sequences.

RESULTS

Wave-T1-MPRAGE showed a significantly lower mean score and lower interobserver agreement for overall image quality and anatomic delineation than standard MPRAGE (P< .001 for each). However, there were no significant differences between the 2 types of MPRAGE sequences for motion artifacts (P = .90 for observer 1, P = .14 for observer 2) and degree of myelination (P = .16 for observer 1, P = .32 for observer 2). Among the nonenhancing pathologic lesions observed on standard MPRAGE by both observers, only 2 were missed on wave-T1-MPRAGE, and they were very tiny, faint, nonhemorrhagic WM injuries.

CONCLUSIONS

Although wave-T1-MPRAGE showed lower overall image quality than standard MPRAGE, the diagnostic performance for nonenhancing parenchymal lesions was comparable. Wave-T1-MPRAGE could be an alternative for diagnosing intracranial lesions in infants, with marked scan time reduction.

Pediatric magnetic resonance imaging: faster is better

Magnetic resonance imaging (MRI) has emerged as the preferred imaging modality for evaluating a wide range of pediatric medical conditions. Nevertheless, the long acquisition times associated with this technique can limit its widespread use in young children, resulting in motion-degraded or non-diagnostic studies. As a result, sedation or general anesthesia is often necessary to obtain diagnostic images, which has implications for the safety profile of MRI, the cost of the exam and the radiology department's clinical workflow. Over the last decade, several techniques have been developed to increase the speed of MRI, including parallel imaging, single-shot acquisition, controlled aliasing techniques, compressed sensing and artificial-intelligence-based reconstructions. These are advantageous because shorter examinations decrease the need for sedation and the severity of motion artifacts, increase scanner throughput, and improve system efficiency. In this review we discuss a framework for image acceleration in children that includes the synergistic use of state-of-the-art MRI hardware and optimized pulse sequences. The discussion is framed within the context of pediatric radiology and incorporates the authors' experience in deploying these techniques in routine clinical practice.

Pediatric Emergency MRI

There is an overall increase in the use of imaging in the pediatric emergency room setting, which is accompanied by a reduction in computed tomography examinations performed mainly due to the increased awareness of the risks of ionizing radiation. Advances in MRI technology have led to shortened scan time, decreased motion sensitivity, and improved spatial resolution. With increased access to MRI in the emergency room setting, the goal of this article is to review major applications of MR in pediatric emergency room patients.

Evaluation of highly accelerated wave controlled aliasing in parallel imaging (Wave-CAIPI) susceptibility-weighted imaging in the non-sedated pediatric setting: a pilot study

BACKGROUND

Susceptibility-weighted imaging (SWI) is highly sensitive for intracranial hemorrhagic and mineralized lesions but is associated with long scan times. Wave controlled aliasing in parallel imaging (Wave-CAIPI) enables greater acceleration factors and might facilitate broader application of SWI, especially in motion-prone populations.

OBJECTIVE

To compare highly accelerated Wave-CAIPI SWI to standard SWI in the non-sedated pediatric outpatient setting, with respect to the following variables: estimated scan time, image noise, artifacts, visualization of normal anatomy and visualization of pathology.

MATERIALS AND METHODS

Twenty-eight children (11 girls, 17 boys; mean age ± standard deviation [SD] = 128.3±62 months) underwent 3-tesla (T) brain MRI, including standard three-dimensional (3-D) SWI sequence followed by a highly accelerated Wave-CAIPI SWI sequence for each subject. We rated all studies using a predefined 5-point scale and used the Wilcoxon signed rank test to assess the difference for each variable between sequences.

RESULTS

Wave-CAIPI SWI provided a 78% and 67% reduction in estimated scan time using the 32- and 20-channel coils, respectively, corresponding to estimated scan time reductions of 3.5 min and 3 min, respectively. All 28 children were imaged without anesthesia. Inter-reader agreement ranged from fair to substantial (k=0.67 for evaluation of pathology, 0.55 for anatomical contrast, 0.3 for central noise, and 0.71 for artifacts). Image noise was rated higher in the central brain with wave SWI (P<0.01), but not in the peripheral brain. There was no significant difference in the visualization of normal anatomical structures and visualization of pathology between the standard and wave SWI sequences (P=0.77 and P=0.79, respectively).

CONCLUSION

Highly accelerated Wave-CAIPI SWI of the brain can provide similar image quality to standard SWI, with estimated scan time reduction of 3-3.5 min depending on the radiofrequency coil used, with fewer motion artifacts, at a cost of mild but perceptibly increased noise in the central brain.

Scan-specific artifact reduction in k-space (SPARK) neural networks synergize with physics-based reconstruction to accelerate MRI

PURPOSE

To develop a scan-specific model that estimates and corrects k-space errors made when reconstructing accelerated MRI data.

METHODS

Scan-specific artifact reduction in k-space (SPARK) trains a convolutional-neural-network to estimate and correct k-space errors made by an input reconstruction technique by back-propagating from the mean-squared-error loss between an auto-calibration signal (ACS) and the input technique's reconstructed ACS. First, SPARK is applied to generalized autocalibrating partially parallel acquisitions (GRAPPA) and demonstrates improved robustness over other scan-specific models, such as robust artificial-neural-networks for k-space interpolation (RAKI) and residual-RAKI. Subsequent experiments demonstrate that SPARK synergizes with residual-RAKI to improve reconstruction performance. SPARK also improves reconstruction quality when applied to advanced acquisition and reconstruction techniques like 2D virtual coil (VC-) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS region, and 2D/3D wave-encoded imaging.

RESULTS

SPARK yields SSIM improvement and 1.5 - 2× root mean squared error (RMSE) reduction when applied to GRAPPA and improves robustness to ACS size for various acceleration rates in comparison to other scan-specific techniques. When applied to advanced reconstruction techniques such as residual-RAKI, 2D VC-GRAPPA and LORAKS, SPARK achieves up to 20% RMSE improvement. SPARK with 3D GRAPPA also improves RMSE performance by ~2×, SSIM performance, and perceived image quality without a fully sampled ACS region. Finally, SPARK synergizes with non-Cartesian, 2D and 3D wave-encoding imaging by reducing RMSE between 20% and 25% and providing qualitative improvements.

CONCLUSION

SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space.