CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint
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Abstract:
Dynamic magnetic resonance (MR) imaging has generated great research interest, as it can provide both spatial and temporal information for clinical diagnosis. However, slow imaging speed or long scanning time is still one of the challenges for dynamic MR imaging. Most existing methods reconstruct Dynamic MR images from incomplete k-space data under the guidance of compressed sensing (CS) or low rank theory, which suffer from long iterative reconstruction time. Recently, deep learning has shown great potential in accelerating dynamic MR. Our previous work proposed a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training. Nevertheless, there was still a certain degree of smooth in the reconstructed images at high acceleration factors. In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhance loss constraint, dubbed as CRDN. Specifically, the cascaded residual dense networks fully exploit the hierarchical features from all the convolutional layers with both local and global feature fusion. We further utilize the total variation (TV) loss function, which has the edge enhancement properties, for training the networks.Keywords:
Dynamic imaging
Dynamic Contrast-Enhanced MRI
Feature (linguistics)
Compressed sensing 은 기존의 Nyquist sampling 이론에 기반을 두었던 dynamic MRI 에서의 시·공간 해상도의 제한을 획기적으로 향상시킴으로써, 최근 몇 년 사이, MR reconstruction 분야에서 가장 큰 이슈가 되고 있는 연구주제이다. Dynamic MRI 는 대부분 시간방향의 redundancy 가 매우 크므로, 쉽게 sparse 변환이 가능하다. 따라서 sparsity를 기본 조건으로 하는 compressed sensing 은 거의 모든 dynamic MRI 에 대해 효과적으로 적용될 수 있다. 본 review 페이퍼에서는 최근 compressed sensing 에 기반을 두거나 영상의 sparsity를 이용하여 개발된 dynamic MR imaging algorithm 들을 간략히 소개하고, 비교·분석함으로써, compressed sensing과 같은 새로운 접근 방식의 dynamic MRI 가 실제 임상에서 가져다 줄 발전 가능성을 제시한다.
Dynamic Contrast-Enhanced MRI
Dynamic imaging
Real-time MRI
Nyquist–Shannon sampling theorem
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Compressed sensing (CS) is a data-reduction technique that has been applied to speed up the acquisition in MRI. In this work, the feasibility of the CS framework for accelerated dynamic MRI is assessed. The fundamental condition of sparsity required in the CS framework is exploited by applying a wavelet transform and a Fourier transform along spatial and temporal directions. The second condition for CS, random sampling, is done by randomly skipping spiral interleaves in each dynamic frame. The proposed approach was tested in simulated and in vivo cardiac MRI data. Results show that higher acceleration factors, with improved spatial and temporal quality, can be obtained with the proposed approach in comparison to the standard CS reconstruction.
Dynamic Contrast-Enhanced MRI
Real-time MRI
Reconstruction algorithm
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Compressed Sensing (CS) and parallel imaging are two promising techniques that accelerate the MRI acquisition process. Combining these two techniques is of great interest due to the complementary information used in each. In this study, we proposed a novel reconstruction framework that effectively combined compressed sensing and nonlinear parallel imaging technique for dynamic cardiac imaging. Specifically, the proposed method decouples the reconstruction process into two sequential steps: In the first step, a series of aliased dynamic images were reconstructed from the highly undersampled k-space data using compressed sensing; In the second step, nonlinear parallel imaging technique, i.e. nonlinear GRAPPA, was utilized to reconstruct the original dynamic images from the reconstructed k-space data obtained from the first step. In addition, we also proposed a tailored k-space down-sampling scheme that satisfies both the incoherent undersampling requirement for CS and the structured undersampling requirement for nonlinear parallel imaging. The proposed method was validated using four in vivo experiments of dynamic cardiac cine MRI with retrospective undersampling. Experimental results showed that the proposed method is superior at reducing aliasing artifacts and preserving the spatial details and temporal variations, compared with the competing k-t FOCUSS and k-t FOCUSS with sensitivity encoding methods, with the same numbers of measurements.
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This study compared dynamic MR imaging with fluoroscopic cystocolpoproctography for the detection and measurement of prolapse of pelvic organs.Ten patients underwent triphasic dynamic MR imaging and triphasic fluoroscopic cystocolpoproctography with identical amounts of contrast material to opacify the bladder, vagina, and rectum. The dynamic MR imaging procedure included cine-loop presentation. Each examination was analyzed to determine the presence and extent of prolapse of pelvic organs based on specific measurements.Both dynamic MR imaging and fluoroscopic cystocolpoproctography revealed 10 rectoceles (mean extents, 2.85 and 2.45 cm, respectively). Nine cystoceles were revealed by both dynamic MR imaging (mean extent, 4.05 cm) and fluoroscopy (mean extent, 4.55 cm). Seven enteroceles were revealed, one of which was initially not seen on dynamic MR imaging. Two sigmoidoceles were revealed, one of which was not seen on fluoroscopy. The mean extent of the enteroceles and sigmoidoceles on dynamic MR imaging was 3.50 cm, and the mean extent on fluoroscopy was 4.25 cm. Nine of the 10 patients were able to defecate in the supine position on the MR imaging table. Patients were divided equally in their preference for dynamic MR imaging or fluoroscopic cystocolpoproctography.Triphasic dynamic MR imaging and triphasic fluoroscopic cystocolpoproctograpy show similar detection rates for prolapse of pelvic organs. Although dynamic MR imaging underestimates the extent of cystoceles and enteroceles, it has the advantage of revealing all pelvic organs and the pelvic floor musculature in a multiplanar cine-loop presentation.
Supine position
Dynamic imaging
Dynamic Contrast-Enhanced MRI
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The recently developed sampling theory, is gathering huge interest in MR reconstruction area because of its feasibility of high spatio-temporal resolution of dynamic MRI which has been limited in conventional methods based on Nyquist sampling theory. Since dynamic MRI usually has high redundant information along temporal direction, this can be very sparsely represented in most of cases. Therefore, compressed sensing that exploits the sparsity of unknown images can be effectively applied in most of dynamic MRI. This review article briefly introduces currently proposed compressed sensing based dynamic MR imaging algorithms and other methods exploiting sparsity. By comparing them with conventional methods, you may have insight how the compressed sensing based methods can impact nearly every area of clinical dynamic MRI.
Dynamic Contrast-Enhanced MRI
Nyquist–Shannon sampling theorem
Dynamic imaging
Temporal resolution
Nyquist rate
Real-time MRI
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Dynamic Contrast-Enhanced MRI
Modality (human–computer interaction)
Dynamic imaging
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Dynamic imaging
Temporal resolution
Dynamic Contrast-Enhanced MRI
Dynamic contrast
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The value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in characterizing breast lesions on magnetic resonance imaging (MRI) was evaluated. Sixty-eight malignant and 34 benign lesions were included. In the scanning protocol, high temporal resolution imaging was combined with high spatial resolution imaging. The high temporal resolution images were recorded every 4.1 s during initial enhancement (fast dynamic analysis). The high spatial resolution images were recorded at a temporal resolution of 86 s (slow dynamic analysis). In the fast dynamic evaluation pharmacokinetic parameters (K(trans), V(e) and k(ep)) were evaluated. In the slow dynamic analysis, each lesion was scored according to the BI-RADS classification. Two readers evaluated all data prospectively. ROC and multivariate analysis were performed. The slow dynamic analysis resulted in an AUC of 0.85 and 0.83, respectively. The fast dynamic analysis resulted in an AUC of 0.83 in both readers. The combination of both the slow and fast dynamic analyses resulted in a significant improvement of diagnostic performance with an AUC of 0.93 and 0.90 (P = 0.02). The increased diagnostic performance found when combining both methods demonstrates the additional value of our method in further improving the diagnostic performance of breast MRI.
Dynamic contrast
Dynamic imaging
Dynamic Contrast-Enhanced MRI
Neuroradiology
Breast MRI
Temporal resolution
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Dynamic Contrast-Enhanced MRI
Dynamic imaging
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