Background
There are concerns over gadolinium deposition from gadolinium‐based contrast agents (GBCA) administration.
Purpose
To reduce gadolinium dose in contrast‐enhanced brain MRI using a deep ...learning method.
Study type
Retrospective, crossover.
Population
Sixty patients receiving clinically indicated contrast‐enhanced brain MRI.
Sequence
3D T1‐weighted inversion‐recovery prepped fast‐spoiled‐gradient‐echo (IR‐FSPGR) imaging was acquired at both 1.5T and 3T. In 60 brain MRI exams, the IR‐FSPGR sequence was obtained under three conditions: precontrast, postcontrast images with 10% low‐dose (0.01mmol/kg) and 100% full‐dose (0.1 mmol/kg) of gadobenate dimeglumine. We trained a deep learning model using the first 10 cases (with mixed indications) to approximate full‐dose images from the precontrast and low‐dose images. Synthesized full‐dose images were created using the trained model in two test sets: 20 patients with mixed indications and 30 patients with glioma.
Assessment
For both test sets, low‐dose, true full‐dose, and the synthesized full‐dose postcontrast image sets were compared quantitatively using peak‐signal‐to‐noise‐ratios (PSNR) and structural‐similarity‐index (SSIM). For the test set comprised of 20 patients with mixed indications, two neuroradiologists scored blindly and independently for the three postcontrast image sets, evaluating image quality, motion‐artifact suppression, and contrast enhancement compared with precontrast images.
Statistical Analysis
Results were assessed using paired t‐tests and noninferiority tests.
Results
The proposed deep learning method yielded significant (n = 50, P < 0.001) improvements over the low‐dose images (>5 dB PSNR gains and >11.0% SSIM). Ratings on image quality (n = 20, P = 0.003) and contrast enhancement (n = 20, P < 0.001) were significantly increased. Compared to true full‐dose images, the synthesized full‐dose images have a slight but not significant reduction in image quality (n = 20, P = 0.083) and contrast enhancement (n = 20, P = 0.068). Slightly better (n = 20, P = 0.039) motion‐artifact suppression was noted in the synthesized images. The noninferiority test rejects the inferiority of the synthesized to true full‐dose images for image quality (95% CI: –14–9%), artifacts suppression (95% CI: –5–20%), and contrast enhancement (95% CI: –13–6%).
Data Conclusion
With the proposed deep learning method, gadolinium dose can be reduced 10‐fold while preserving contrast information and avoiding significant image quality degradation.
Level of Evidence: 3
Technical Efficacy: Stage 5
J. MAGN. RESON. IMAGING 2018;48:330–340.
Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In ...addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative adversarial networks (GAN) to model the (low-dimensional) manifold of high-quality MR images. Leveraging a mixture of least-squares (LS) GANs and pixel-wise ℓ 1 /ℓ 2 cost, a deep residual network with skip connections is trained as the generator that learns to remove the aliasing artifacts by projecting onto the image manifold. The LSGAN learns the texture details, while the ℓ 1 /ℓ 2 cost suppresses high-frequency noise. A discriminator network, which is a multilayer convolutional neural network (CNN), plays the role of a perceptual cost that is then jointly trained based on high-quality MR images to score the quality of retrieved images. In the operational phase, an initial aliased estimate (e.g., simply obtained by zero-filling) is propagated into the trained generator to output the desired reconstruction. This demands a very low computational overhead. Extensive evaluations are performed on a large contrast-enhanced MR dataset of pediatric patients. Images rated by expert radiologists corroborate that GANCS retrieves higher quality images with improved fine texture details compared with conventional Wavelet-based and dictionary-learning-based CS schemes as well as with deep-learning-based schemes using pixel-wise training. In addition, it offers reconstruction times of under a few milliseconds, which are two orders of magnitude faster than the current state-of-the-art CS-MRI schemes.
Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training ...for reconstruction networks, where the inputs are undersampled k-space and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset. The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality. The generator is an unrolled neural network - a cascade of convolutional and data consistency layers. The discriminator is also a multilayer CNN that plays the role of a critic scoring the quality of reconstructed images based on the Wasserstein distance. Our experiments with knee MRI datasets demonstrate that the proposed unpaired training enables diagnostic-quality reconstruction when high-quality image labels are not available for the input types of interest, or when the amount of labels is small. In addition, our adversarial training scheme can achieve better image quality (as rated by expert radiologists) compared with the paired training schemes with pixel-wise loss.
Metal-induced artifacts in MRI Hargreaves, Brian A; Worters, Pauline W; Pauly, Kim Butts ...
American journal of roentgenology,
09/2011, Letnik:
197, Številka:
3
Journal Article
Recenzirano
Odprti dostop
The purpose of this article is to review some of the basic principles of imaging and how metal-induced susceptibility artifacts originate in MR images. We will describe common ways to reduce or ...modify artifacts using readily available imaging techniques, and we will discuss some advanced methods to correct readout-direction and slice-direction artifacts.
The presence of metallic implants in MRI can cause substantial image artifacts, including signal loss, failure of fat suppression, geometric distortion, and bright pile-up artifacts. These cause large resonant frequency changes and failure of many MRI mechanisms. Careful parameter and pulse sequence selections can avoid or reduce artifacts, although more advanced imaging methods offer further imaging improvements.
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