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General General

Adaptive-weighted high order TV algorithm for sparse-view CT reconstruction.

In Medical physics ; h5-index 59.0

BACKGROUND : With the development of low-dose computed tomography (CT), incomplete data reconstruction has been widely concerned. The total variation (TV) minimization algorithm can accurately reconstruct images from sparse or noisy data.

PURPOSE : However, the traditional TV algorithm ignores the direction of structures in images, leading to the loss of edge information and block artifacts when the object is not piecewise constant. Since the anisotropic information can facilitate preserving the edge and detail information in images, we aims to improve the TV algorithm in terms of reconstruction accuracy via this approach.

METHODS : In this paper, we propose an adaptive-weighted high order total variation (awHOTV) algorithm. We construct the second order TV-norm using the second order gradient, adapt the anisotropic edge property between neighboring image pixels, adjust the local image-intensity gradient to keep edge information, and design the corresponding Chambolle-Pock (CP) solving algorithm. Implementing the proposed algorithm, comprehensive studies are conducted in the ideal projection data experiment where the SSIM, RMSE, CNR, and MTF curves are utilized to evaluate the quality of reconstructed images in statism, structure, spatial resolution, and contrast, respectively. In the noisy data experiment, we further use the NPS curve to evaluate the reconstructed images and compare it with other three algorithms.

RESULTS : We use the 2D slice in the XCAT phantom, 2D slice in TCIA Challenge data and FORBILD phantom as simulation phantoms and use real bird data for real verification. The results show that, compared with the traditional TV and FBP algorithms, the awHOTV has better performance in terms of RMSE, SSIM, and PCC under the projected data with different sparsity. In addition, the awHOTV algorithm is robust against the noise of different intensities.

CONCLUSIONS : The proposed awHOTV method can reconstruct the images with high accuracy under sparse or noisy data. The awHOTV solves the strip artifacts caused by sparse data in the FBP method. Compared with the TV method, the awHOTV can effectively suppress block artifacts and has good detail protection ability.

Xi Yarui, Zhou Pengwu, Yu Haijun, Zhang Tao, Zhang Lingli, Qiao Zhiwei, Liu Fenglin

2023-Mar-19

adaptive-weighted high order total variation, chambolle-pock algorithm, compressed sensing, image reconstruction, iterative algorithm

General General

Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field Information.

In Advanced materials (Deerfield Beach, Fla.)

Solving materials engineering tasks is often hindered by limited information, such as in inverse problems with only boundary data information or design tasks with a simple objective but a vast search space. To address these challenges, we leverage multiple deep learning (DL) architectures to predict missing mechanical information given limited known data in part of the domain, and further characterize the composite geometries from the recovered mechanical fields for 2D and 3D complex microstructures. In 2D, we utilize a conditional generative adversarial network (GAN) to complete partially masked field maps and predict the composite geometry with convolutional models with great accuracy and generality by making precise predictions on field data with mixed stress/strain components, hierarchical geometries, distinct materials properties and various types of microstructures including ill-posed inverse problems. In 3D, a Transformer-based architecture is implemented to predict complete 3D mechanical fields from input field snapshots. The model manifests excellent performance regardless of microstructural complexity and recovers the entire bulk field even from a single surface field image, allowing internal structural characterization from only boundary measurements. The whole frameworks provide efficient ways for analysis and design with incomplete information and allow the direct inverse translation from properties back to materials structures. This article is protected by copyright. All rights reserved.

Yang Zhenze, Buehler Markus J

2023-Mar-19

Crystalline solids, defects, graph, machine learning, materials, nanomechanics, simulation

Surgery Surgery

Prediction of orthognathic surgery plan from 3D cephalometric analysis via deep learning.

In BMC oral health ; h5-index 40.0

BACKGROUND : Preoperative planning of orthognathic surgery is indispensable for achieving ideal surgical outcome regarding the occlusion and jaws' position. However, orthognathic surgery planning is sophisticated and highly experience-dependent, which requires comprehensive consideration of facial morphology and occlusal function. This study aimed to investigate a robust and automatic method based on deep learning to predict reposition vectors of jawbones in orthognathic surgery plan.

METHODS : A regression neural network named VSP transformer was developed based on Transformer architecture. Firstly, 3D cephalometric analysis was employed to quantify skeletal-facial morphology as input features. Next, input features were weighted using pretrained results to minimize bias resulted from multicollinearity. Through encoder-decoder blocks, ten landmark-based reposition vectors of jawbones were predicted. Permutation importance (PI) method was used to calculate contributions of each feature to final prediction to reveal interpretability of the proposed model.

RESULTS : VSP transformer model was developed with 383 samples and clinically tested with 49 prospectively collected samples. Our proposed model outperformed other four classic regression models in prediction accuracy. Mean absolute errors (MAE) of prediction were 1.41 mm in validation set and 1.34 mm in clinical test set. The interpretability results of the model were highly consistent with clinical knowledge and experience.

CONCLUSIONS : The developed model can predict reposition vectors of orthognathic surgery plan with high accuracy and good clinically practical-effectiveness. Moreover, the model was proved reliable because of its good interpretability.

Cheng Mengjia, Zhang Xu, Wang Jun, Yang Yang, Li Meng, Zhao Hanjiang, Huang Jingyang, Zhang Chenglong, Qian Dahong, Yu Hongbo

2023-Mar-18

Deep learning, Dento-maxillofacial deformity, Orthognathic surgery, Regression prediction, Transformer, Virtual surgical planning

Radiology Radiology

Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study.

In European radiology ; h5-index 62.0

OBJECTIVE : To investigate the image quality and lesion conspicuity of a deep-learning-based contrast-boosting (DL-CB) algorithm on double-low-dose (DLD) CT of simultaneous reduction of radiation and contrast doses in participants at high-risk for hepatocellular carcinoma (HCC).

METHODS : Participants were recruited and underwent four-phase dynamic CT (NCT04722120). They were randomly assigned to either standard-dose (SD) or DLD protocol. All CT images were initially reconstructed using iterative reconstruction, and the images of the DLD protocol were further processed using the DL-CB algorithm (DLD-DL). The primary endpoint was the contrast-to-noise ratio (CNR), the secondary endpoint was qualitative image quality (noise, hepatic lesion, and vessel conspicuity), and the tertiary endpoint was lesion detection rate. The t-test or repeated measures analysis of variance was used for analysis.

RESULTS : Sixty-eight participants with 57 focal liver lesions were enrolled (20 with HCC and 37 with benign findings). The DLD protocol had a 19.8% lower radiation dose (DLP, 855.1 ± 254.8 mGy·cm vs. 713.3 ± 94.6 mGy·cm, p = .003) and 27% lower contrast dose (106.9 ± 15.0 mL vs. 77.9 ± 9.4 mL, p < .001) than the SD protocol. The comparative analysis demonstrated that CNR (p < .001) and portal vein conspicuity (p = .002) were significantly higher in the DLD-DL than in the SD protocol. There was no significant difference in lesion detection rate for all lesions (82.7% vs. 73.3%, p = .140) and HCCs (75.7% vs. 70.4%, p = .644) between the SD protocol and DLD-DL.

CONCLUSIONS : DL-CB on double-low-dose CT provided improved CNR of the aorta and portal vein without significant impairment of the detection rate of HCC compared to the standard-dose acquisition, even in participants at high risk for HCC.

KEY POINTS : • Deep-learning-based contrast-boosting algorithm on double-low-dose CT provided an improved contrast-to-noise ratio compared to standard-dose CT. • The detection rate of focal liver lesions was not significantly differed between standard-dose CT and a deep-learning-based contrast-boosting algorithm on double-low-dose CT. • Double-low-dose CT without a deep-learning algorithm presented lower CNR and worse image quality.

Kang Hyo-Jin, Lee Jeong Min, Ahn Chulkyun, Bae Jae Seok, Han Seungchul, Kim Se Woo, Yoon Jeong Hee, Han Joon Koo

2023-Mar-18

Contrast media, Deep learning, Hepatocellular carcinoma, Prospective study, Tomography, X-ray computed

General General

Plasma extracellular vesicles and cell-free mitochondrial DNA are associated with cognitive dysfunction in treated older adults with HIV.

In Journal of neurovirology

Extracellular vesicles (EVs) are nanoparticles with a role in intercellular communication. Cell-free mitochondrial DNA (cf-mtDNA) has been associated with cognitive dysfunction in people with HIV (PWH). We conducted a nested case-control study to test the hypothesis that plasma EVs are associated with cf-mtDNA and cognitive dysfunction in older PWH. A machine learning-based model identified total EVs, including select EV subpopulations, as well as urine cf-mtDNA and 4-meter walk time carry power to predict the neurocognitive impairment. These features resulted in an AUC-ROC of 0.845 + / - 0.109 (0.615, 1.00).

Johnston Carrie D, de Menezes Erika G Marques, Bowler Scott, Siegler Eugenia L, Friday Courtney, Norris Philip J, Rice Michelle C, Choi Mary E, Glesby Marshall J, Ndhlovu Lishomwa C

2023-Mar-18

Aging, Cognition, Extracellular vesicles, Frailty, HIV, Inflammation, Mitochondria

Radiology Radiology

Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics.

In Journal of digital imaging

The purpose of this study is to test the feasibility for deep CNN-based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature-based machine learning methods were also implemented in this study as baseline and for comparison study. In this retrospective study, 596 patients with breast mass that underwent mammography from 4 hospitals were enrolled from January 2012 to October 2019. Margin and shape of each mass were annotated according to BI-RADS by 2 experienced radiologists. Deep CNN-based AI was implemented for the classification task based on Resnet50. Balanced sampler and CBAM were also used to improve the performance of the Deep CNNs. As comparison, image texture features were extracted and then dimensionality reduction methods (such as PCA, ICA) and classical classifiers (such as SVM, DT, KNN) were used for classification task. Based on Python programming software, accuracy (ACC) was used to evaluate the performance of the model, and the model with the highest ACC value was selected. Deep CNN based on Resnet50 with balanced sampler and CBAM achieved the best performance for both margin and shape classification, with ACC of 0.838 and 0.874, respectively. For the radiomics-based machine learning, the best performance for margin was achieved as 0.676 by the combination of FA + RF, while the best performance for shape was 0.802 by the combination of PCA + MLP. The feasibility for automatic classification with coarse labeling of the mass shape and margin was testified with the deep CNN-based AI methods, while radiomic feature-based machine learning methods achieved inferior classification results.

Qi Longxiu, Lu Xing, Shen Hailin, Gao Qilei, Han Zhigang, Zhu Jianguo, Meng You, Wang Linhua, Chen Shuangqing, Li Yonggang

2023-Mar-17

Breast mass, Deep learning, Machine learning, Margin, Shape