Model-based adaptive filter for a dedicated cardiovascular CT scanner: Assessment of image noise, sharpness and quality

Vecsey-Nagy, Milán; Jermendy, Ádám L.; Suhai, Ferenc Imre; Panajotu, Alexisz; Csőre, Judit; Borzsák, Sarolta; Fontanini, Daniele Mariastefano; Kolossváry, Márton; Vattay, Borbála; Boussoussou, Melinda; Csobay-Novák, Csaba; Merkely, Béla; Maurovich-Horvat, Pál; Szilveszter, Bálint

doi:10.1016/j.ejrad.2021.110032

Cited by 9 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even so, in our results, the proposed prediction approach of CTRs reached ICCs of 0.943 (IN) and 0.904 (EX), possibly because the LoG filter for all treatment planning CT images enhanced the edges and reduced image noise. Nevertheless, the image quality of the treatment planning CT images should be improved continuously using other filters, which could reduce motion artifacts [44].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Consolidation Tumor Ratio on Planning CT Images of Lung Cancer Patients Treated with Radiotherapy Based on Deep Learning

Tong,

Arimura,

Yoshitake

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This study aimed to propose an automated prediction approach of the consolidation tumor ratios (CTRs) of part-solid tumors of patients treated with radiotherapy on treatment planning computed tomography images using deep learning segmentation (DLS) models. For training the DLS model for cancer regions, a total of 115 patients with non-small cell lung cancer (NSCLC) who underwent stereotactic body radiation therapy were selected as the training dataset, including solid, part-solid, and ground-glass opacity tumors. For testing the automated prediction approach of CTRs based on segmented tumor regions, 38 patients with part-solid tumors were selected as an internal test dataset A (IN) from a same institute as the training dataset, and 49 patients as an external test dataset (EX) from a public database. The CTRs for part-solid tumors were predicted as ratios of the maximum diameters of solid components to those of whole tumors. Pearson correlations between reference and predicted CTRs for the two test datasets were 0.953 (IN) and 0.926 (EX) for one of the DLS models (p < 0.01). Intraclass correlation coefficients between reference and predicted CTRs for the two test datasets were 0.943 (IN) and 0.904 (EX) for the same DLS models. The findings suggest that the automated prediction approach could be robust in calculating the CTRs of part-solid tumors.

show abstract

Section: Discussionmentioning

confidence: 99%

Prediction of Consolidation Tumor Ratio on Planning CT Images of Lung Cancer Patients Treated with Radiotherapy Based on Deep Learning

Tong,

Arimura,

Yoshitake

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…They claimed that image sharpness is an important index in CCTA for the assessment of coronary artery disease (CAD). 5 For the dental radiology, including the maxillofacial region, image sharpening is of enormous importance in diagnosis. 6 Image sharpening applied to different types of dental radiology images will signi cantly improve the level of diagnosis and appropriate treatment.…”

Section: Introductionmentioning

confidence: 99%

A Residual Based Adaptive Image Sharpening Scheme

Lu¹,

Chen²

2022

Preprint

View full text Add to dashboard Cite

The sharpening is a scheme applied to highlight the intensity transitions in an image. This enhancement increases edge acutance and significantly improves the overall sharpness of processed images. Image sharpening is of enormous importance in medicine for diagnosis. Noise overshoot and over-sharpening effects occur in classical sharpening algorithms. The adaptive approach skips the noise producing a better sharpening scheme. Abrupt pixel value changes, if adequately emphasized, can be utilized effectively in the sharpening process. Since the residual image contains noise and edges, the bigger the filter size the greater the number of edges left in the residual image. Two residual images are produced first with different filter sizes. There is a relationship between these two residuals. A simple regression and standard deviation (±σ) is measured using these residuals. This linear equation is an expectation and the standard deviation is the difference between two residuals. One deviation (±σ) is proposed as the threshold. The points contained edges if the residual value is measured using a larger sized filter off ± σ than those residual using a smaller sized filter. A sensitive filter is adaptively applied to the image on those locations. It has been demonstrated that the proposed approach yields better results than the global filter as well as previous work on the P calculation results. A sharpening scheme without latent image noise amplification is useful for pattern recognition and machine-learning. In the future this scheme will be used to pre-process image segmentation.

show abstract

Dual‐Low Technology in Coronary and Abdominal CT Angiography: A Comparative Study of Deep Learning Image Reconstruction and Adaptive Statistic Iterative Reconstruction‐Veo

Meng,

Xiang,

Cao

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

To investigate the application advantages of dual‐low technology (low radiation dose and low contrast agent dose) in deep learning image reconstruction (DLIR) compared to the adaptive statistical iterative reconstruction‐Veo (ASIR‐V) standard protocol when combing coronary computed tomography angiography (CCTA) and abdominal computed tomography angiography (ACTA). Sixty patients who underwent CCTA and ACTA were recruited. Thirty patients with low body mass index (BMI) (< 24 kg/m2, Group A, standard protocol) were reconstructed using 60% ASIR‐V, and 30 patients with high BMI (> 24 kg/m2, Group B, dual‐low protocol) were reconstructed using DLIR at high strength (DLIR‐H). The effective dose and contrast agent dose were recorded. The CT values, standard deviations, signal‐to‐noise ratio (SNR), and contrast‐to‐noise ratio (CNR) were measured. The subjective evaluation criteria were scored by two radiologists using a blind Likert 5‐point scale. The general data, objective evaluation, and subjective scores between both groups were compared using corresponding test methods. The consistency of objective and subjective evaluations between the two radiologists were analyzed using Kappa tests. Group B showed a remarkable 44.6% reduction in mean effective dose (p < 0.01) and a 20.3% decrease in contrast agent dose compared to Group A (p < 0.01). The DLIR‐H demonstrated the smallest standard deviations and highest SNR and CNR values (p < 0.01). The subjective score of DLIR‐H was the highest (p < 0.01), and there was good consistency between the two radiologists in the subjective scoring of CCTA and ACTA image quality (κ = 0.751 ~ 0.919, p < 0.01). In combined CCTA and ACTA protocols, DLIR can significantly reduce the effective dose and contrast agent dose compared to ASIR‐V while maintaining good image quality.

show abstract

Model-based adaptive filter for a dedicated cardiovascular CT scanner: Assessment of image noise, sharpness and quality

Cited by 9 publications

References 30 publications

Prediction of Consolidation Tumor Ratio on Planning CT Images of Lung Cancer Patients Treated with Radiotherapy Based on Deep Learning

Prediction of Consolidation Tumor Ratio on Planning CT Images of Lung Cancer Patients Treated with Radiotherapy Based on Deep Learning

A Residual Based Adaptive Image Sharpening Scheme

Dual‐Low Technology in Coronary and Abdominal CT Angiography: A Comparative Study of Deep Learning Image Reconstruction and Adaptive Statistic Iterative Reconstruction‐Veo

Contact Info

Product

Resources

About