An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization

Tourbier, Sébastien; Bresson, Xavier; Hagmann, Patric; Thiran, Jean‐Philippe; Meuli, Reto; Cuadra, Meritxell Bach

doi:10.1016/j.neuroimage.2015.06.018

Cited by 127 publications

(106 citation statements)

References 39 publications

(2 reference statements)

Supporting

Mentioning

106

Contrasting

Order By: Relevance

“…Equation (10) suggests that the denoising resultĨðhÞ ¼ I → I →Ī as h ¼ 0 →ĥ → ∞. There is an incremental noise reduction but no tissue boundary losses at h ¼ 0 →ĥ.…”

Section: Appendix: Theoretical Analysis Of the Dosd Imagesmentioning

confidence: 99%

“…Simple methods include low-pass filtering, median filtering, moving window averaging, and total variation optimization. [10][11][12] These simple methods generally reduce the soft tissue contrast and the effective spatial resolution. Prior to developing the proposed adaptive anatomical preservation optimal denoising (AAPOD) algorithm, we evaluated multiple advanced denoising algorithms that were developed to preserve the image gradient: anisotropic diffusion filters, 13,14 wavelet-based filters, [15][16][17] principal component analysis (PCA)-based; 18,19 and nonlocal means (NLM)-based filters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI

et al. 2017

View full text Add to dashboard Cite

, "Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI," J. Med. Imag. 4(3), 034004 (2017), doi: 10.1117/1.JMI.4.3.034004. Abstract. Low-field magnetic resonance imaging (MRI) has recently been integrated with radiation therapy systems to provide image guidance for daily cancer radiation treatments. The main benefit of the low-field strength is minimal electron return effects. The main disadvantage of low-field strength is increased image noise compared to diagnostic MRIs conducted at 1.5 T or higher. The increased image noise affects both the discernibility of soft tissues and the accuracy of further image processing tasks for both clinical and research applications, such as tumor tracking, feature analysis, image segmentation, and image registration. An innovative method, adaptive anatomical preservation optimal denoising (AAPOD), was developed for optimal image denoising, i.e., to maximally reduce noise while preserving the tissue boundaries. AAPOD employs a series of adaptive nonlocal mean (ANLM) denoising trials with increasing denoising filter strength (i.e., the block similarity filtering parameter in the ANLM algorithm), and then detects the tissue boundary losses on the differences of sequentially denoised images using a zero-crossing edge detection method. The optimal denoising filter strength per voxel is determined by identifying the denoising filter strength value at which boundary losses start to appear around the voxel. The final denoising result is generated by applying the ANLM denoising method with the optimal per-voxel denoising filter strengths. The experimental results demonstrated that AAPOD was capable of reducing noise adaptively and optimally while avoiding tissue boundary losses. AAPOD is useful for improving the quality of MRIs with low-contrast-to-noise ratios and could be applied to other medical imaging modalities, e.g., computed tomography.

show abstract

“…Equation (10) suggests that the denoising resultĨðhÞ ¼ I → I →Ī as h ¼ 0 →ĥ → ∞. There is an incremental noise reduction but no tissue boundary losses at h ¼ 0 →ĥ.…”

Section: Appendix: Theoretical Analysis Of the Dosd Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Later, Tourbier et al [34] introduced an adaptive regularization by applying novel fast convex optimization techniques to design an efficient optimization algorithm for the super-resolution problem using edge-preserving TV regularization.…”

Section: Introductionmentioning

confidence: 99%

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

Alansary

Rajchl

McDonagh

et al. 2017

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

In this paper, we present a novel method for the correction of motion artifacts that are present in fetal magnetic resonance imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patchwise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units, enabling its use in the clinical practice. We evaluate PVR’s computational overhead compared with standard methods and observe improved reconstruction accuracy in the presence of affine motion artifacts compared with conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio, structural similarity index, and cross correlation with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. We further evaluate the distance error for selected anatomical landmarks in the fetal head, as well as calculating the mean and maximum displacements resulting from automatic non-rigid registration to a motion-free ground truth image. These experiments demonstrate a successful application of PVR motion compensation to the whole fetal body, uterus, and placenta.

show abstract

“…In [2, 3], the first reconstruction techniques based on slice-to-volume registration and scattered data interpolation were introduced. Later, super-resolution (SR) techniques [4– 10] have boosted the quality of the reconstructed image by modeling an inverse problem for fetal image reconstruction. By providing finer details of the fetal brain, such techniques have enabled the neuroscience community to perform new research on early human brain development [11–18].…”

Section: Introductionmentioning

confidence: 99%

“…In general, algorithms [2–10, 19] rely only on brain tissue-relevant voxels of low-resolution (LR) images to warrant the assumption of motion rigidity used in rigid motion correction. This is a crucial step of the reconstruction algorithms.…”

Section: Introductionmentioning

confidence: 99%

Automated template-based brain localization and extraction for fetal brain MRI reconstruction

et al. 2017

Self Cite

View full text Add to dashboard Cite

Most fetal brain MRI reconstruction algorithms rely only on brain tissue-relevant voxels of low-resolution (LR) images to enhance the quality of inter-slice motion correction and image reconstruction. Consequently the fetal brain needs to be localized and extracted as a first step, which is usually a laborious and time consuming manual or semi-automatic task. We have proposed in this work to use age-matched template images as prior knowledge to automatize brain localization and extraction. This has been achieved through a novel automatic brain localization and extraction method based on robust template-to-slice block matching and deformable slice-to-template registration. Our template-based approach has also enabled the reconstruction of fetal brain images in standard radiological anatomical planes in a common coordinate space. We have integrated this approach into our new reconstruction pipeline that involves intensity normalization, inter-slice motion correction, and super-resolution (SR) reconstruction. To this end we have adopted a novel approach based on projection of every slice of the LR brain masks into the template space using a fusion strategy. This has enabled the refinement of brain masks in the LR images at each motion correction iteration. The overall brain localization and extraction algorithm has shown to produce brain masks that are very close to manually drawn brain masks, showing an average Dice overlap measure of 94.5%. We have also demonstrated that adopting a slice-to-template registration and propagation of the brain mask slice-by-slice leads to a significant improvement in brain extraction performance compared to global rigid brain extraction and consequently in the quality of the final reconstructed images. Ratings performed by two expert observers show that the proposed pipeline can achieve similar reconstruction quality to reference reconstruction based on manual slice-by-slice brain extraction. The proposed brain mask refinement and reconstruction method has shown to provide promising results in automatic fetal brain MRI segmentation and volumetry in 26 fetuses with gestational age range of 23 to 38 weeks.

show abstract

An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization

Cited by 127 publications

References 39 publications

Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI

Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

Automated template-based brain localization and extraction for fetal brain MRI reconstruction

Contact Info

Product

Resources

About