Total variation minimization filter for DBT imaging

Mota, Ana M.; Matela, Nuno; Oliveira, N.; Almeida, Pedro

doi:10.1118/1.4919680

Cited by 13 publications

(20 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…was solved in this work using the backward‐forward splitting method together with variable splitting methods to convert the 2D TV denoising problem into a denoising problem with a generalized shrinkage operator as a solution. However, there are many other ways to solve the same optimization problem including other strategies to leverage the elegance of a variety of other variable splitting method and corresponding ADMM update strategies or to incorporate the benefits of TV regularization …”

Section: Discussionmentioning

confidence: 99%

Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction

Garrett

et al. 2018

Medical Physics

View full text Add to dashboard Cite

Statistical image reconstruction enabled a significant reduction of both the through-plane artifacts level and anatomical clutter in the DBT reconstructions. The β value was found to be β≈2.14 with the SIR method. This value stays in the middle between the β≈1.8 for cone beam CT and β≈3.2 for mammography. In contrast, the measured β value in the clinical reconstructions (β≈3.17) remains close to that of mammography.

show abstract

Section: Discussionmentioning

confidence: 99%

Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction

Garrett

et al. 2018

Medical Physics

View full text Add to dashboard Cite

show abstract

“…In that plane, a region of interest (ROI) containing the 0.5 mm disk and some background (BG) was extracted. To reduce the noise variation, a TV minimization filter was applied to this region [33]. In order to achieve the minimum value of TV, several values of Lagrange parameter were applied to the longitudinal direction.…”

Section: B Psf Estimation In Z-directionmentioning

confidence: 99%

“…TV is a quantity that characterizes how smoothly the intensity of an image is changing and it increases significantly in the presence of noise. Studies applying TV minimization to DBT data have grown significantly [29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

“…Using the method described in Refs. [36] and [37] and noise regularization based on minimization of TV [33], a first estimate for the PSF is obtained. In addition, since the out-ofplane artifact is caused by features of high attenuation, representing sudden intensity variations, information about gradient magnitude is also taken into account during the deconvolution.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Enhanced Visualization of DBT Imaging Using Blind Deconvolution and Total Variation Minimization Regularization

Mota

Clarkson

Almeida

et al. 2020

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

Digital Breast Tomosynthesis (DBT) presents out-of-plane artifacts caused by features of high intensity. Given observed data and knowledge about the point spread function (PSF), deconvolution techniques recover data from a blurred version. However, a correct PSF is difficult to achieve and these methods amplify noise. When no information is available about the PSF, blind deconvolution can be used. Additionally, Total Variation (TV) minimization algorithms have achieved great success due to its virtue of preserving edges while reducing image noise. This work presents a novel approach in DBT through the study of outof-plane artifacts using blind deconvolution and noise regularization based on TV minimization. Gradient information was also included. The methodology was tested using real phantom data and one clinical data set. The results were investigated using conventional 2D sliceby-slice visualization and 3D volume rendering. For the 2D analysis, the artifact spread function (ASF) and Full Width at Half Maximum (FWHMMASF) of the ASF were considered. The 3D quantitative analysis was based on the FWHM of disks profiles at 90º, noise and signal to noise ratio (SNR) at 0º and 90º. A marked visual decrease of the artifact with reductions of FWHMASF (2D) and FWHM90º (volume rendering) of 23.8% and 23.6%, respectively, was observed. Although there was an expected increase in noise level, SNR values were preserved after deconvolution. Regardless of the methodology and visualization approach, the objective of reducing the out-of-plane artifact was accomplished. Both for the phantom and clinical case, the artifact reduction in the z was markedly visible.

show abstract

“…For the IR algorithm, a certain objective function containing the data fidelity term and the regularization term needs to be well‐designed and optimized. It has been demonstrated that the regularizers such as the total variation (TV), 10 total

p

variation (TpV), 11 selective‐diffusion regularization, 12 curvelet sparse regularization, 13 can be used to reduce the DBT image artifacts.…”

Section: Introductionmentioning

confidence: 99%

DIR‐DBTnet: Deep iterative reconstruction network for three‐dimensional digital breast tomosynthesis imaging

Deng

Yang

et al. 2021

Medical Physics

View full text Add to dashboard Cite

The goal of this study is to develop a three-dimensional (3D) iterative reconstruction framework based on the deep learning (DL) technique to improve the digital breast tomosynthesis (DBT) imaging performance. Methods: In this work, the DIR-DBTnet is developed for DBT image reconstruction by mapping the conventional iterative reconstruction (IR) algorithm to the deep neural network. By design, the DIR-DBTnet learns and optimizes the regularizer and the iteration parameters automatically during the network training with a large amount of simulated DBT data. Numerical, experimental, and clinical data are used to evaluate its performance. Quantitative metrics such as the artifact spread function (ASF), breast density, and the signal difference to noise ratio (SDNR) are measured to assess the image quality. Results: Results show that the proposed DIR-DBTnet is able to reduce the in-plane shadow artifacts and the out-of-plane signal leaking artifacts compared to the filtered backprojection (FBP) and the total variation (TV)-based IR methods. Quantitatively, the full width half maximum (FWHM) of the measured ASF from the clinical data is 27.1% and 23.0% smaller than those obtained with the FBP and TV methods, while the SDNR is increased by 194.5% and 21.8%, respectively. In addition, the breast density obtained from the DIR-DBTnet network is more accurate and consistent with the ground truth. Conclusions: In conclusion, a deep iterative reconstruction network, DIR-DBTnet, has been proposed for 3D DBT image reconstruction. Both qualitative and quantitative analyses of the numerical, experimental, and clinical results demonstrate that the DIR-DBTnet has superior DBT imaging performance than the conventional algorithms.

show abstract

Total variation minimization filter for DBT imaging

Cited by 13 publications

References 28 publications

Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction

Reduced anatomical clutter in digital breast tomosynthesis with statistical iterative reconstruction

An Enhanced Visualization of DBT Imaging Using Blind Deconvolution and Total Variation Minimization Regularization

DIR‐DBTnet: Deep iterative reconstruction network for three‐dimensional digital breast tomosynthesis imaging

Contact Info

Product

Resources

About