Emphysema Quantification Using Ultralow-Dose CT With Iterative Reconstruction and Filtered Back Projection

Nishio, Motohiro; Koyama, Hisanobu; Ohno, Yoshiharu; Negi, Noriyuki; Seki, Shu; Yoshikawa, Takeshi; Sugimura, Kazuro

doi:10.2214/ajr.15.15684

Cited by 30 publications

(33 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the subsequent emphysema scoring task, four quantitative measures based on lung intensity were evaluated/compared: RA is defined as the relative lung area falling below an HU threshold . In this paper, we evaluated RA970, RA950, RA930, and RA910, indicating the RA with lung intensity below −970, −950, −930, and −910 HU, respectively. PERC indicates the cutoff HU value below which a specified percentage of voxels within lungs are distributed.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A convolutional neural network for ultra‐low‐dose CT denoising and emphysema screening

2019

View full text Add to dashboard Cite

Purpose Reducing dose level to achieve ALARA is an important task in diagnostic and therapeutic applications of computed tomography (CT) imaging. Effective image quality enhancement strategies are crucial to compensate for the degradation caused by dose reduction. In the past few years, deep learning approaches have demonstrated promising denoising performance on natural/synthetic images. This study tailors a neural network model for (ultra‐)low‐dose CT denoising, and assesses its performance in enhancing CT image quality and emphysema quantification. Methods The noise statistics in low‐dose CT images has its unique characteristics and differs from that used in general denoising models. In this study, we first simulate the paired ultra‐low‐dose and targeted high‐quality image of reference, with a well‐validated pipeline. These paired images are used to train a denoising convolutional neural network (DnCNN) with residual mapping. The performance of the DnCNN tailored to CT denoising (DnCNN‐CT) is assessed over various dose reduction levels, with respect to both image quality and emphysema scoring quantification. The possible over‐smoothing behavior of DnCNN and its impact on different subcohort of patients are also investigated. Results Performance evaluation results showed that DnCNN‐CT provided significant image quality enhancement, especially for very‐low‐dose level. With DnCNN‐CT denoising on 3%‐dose cases, the peak signal‐to‐noise ratio improved by 8 dB and the structure similarity index increased by 0.15. This outperformed the original DnCNN and the state‐of‐the‐art nonlocal‐mean‐type denoising scheme. Emphysema mask was also investigated, where lung voxels of abnormally low attenuation coefficient were marked as potential emphysema. Emphysema mask generated after DnCNN‐CT denoising on 3%‐dose image was demonstrated to agree well with that from the full‐dose reference. Despite over‐smoothing in DnCNN denoising, which contributed to slight underestimation of emphysema score compared to the reference, such minor overcorrection did not affect clinical conclusions. The proposed method provided effective detection for cases with appreciable emphysema while serving as a reasonable correction for normal cases without emphysema. Conclusions This work provides a tailored DnCNN for (ultra‐)low‐dose CT denoising, and demonstrates significant improvement on both the image quality and the clinical emphysema quantification accuracy over various dose levels. The clinical conclusion of emphysema obtained from the denoised low‐dose images agrees well with that from the full‐dose ones.

show abstract

Section: Methodsmentioning

confidence: 99%

“…RA is defined as the relative lung area falling below an HU threshold. 22,23 In this paper, we evaluated RA970, RA950, RA930, and RA910, indicating the RA with lung intensity below À970, À950, À930, and À910 HU, respectively. 2.…”

Section: B Materialsmentioning

confidence: 99%

A convolutional neural network for ultra‐low‐dose CT denoising and emphysema screening

2019

View full text Add to dashboard Cite

show abstract

“…For patients with LAM, cyst score is a useful tool for initial assessment of the condition as well as to track disease progression [8][9][10]. The standard high-resolution CT chest scan has been the gold standard for cyst score measurements, which facilitated the introduction of other scan protocols that reduce radiation dose with advanced iterative image reconstruction [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Clinical CT underestimation of the percentage volume occupied by cysts in patients with lymphangioleiomyomatosis

et al. 2020

View full text Add to dashboard Cite

To evaluate the accuracy of cyst score measurements by standard high-resolution helical volume chest CT (HRCT) in patients with lymphangioleiomyomatosis (LAM), using a short z-length ultra-high resolution rescan (UH re-scan) as the reference. In cystic lung diseases, cyst score is derived from CT scans and defined as the percentage of the total lung parenchymal volume occupied by cysts, a biomarker which measures the severity of the disease. Methods: In a prospective study of 73 LAM patients, each patient received the standard HRCT chest scan and a short z-length UH re-scan. Cyst scores were acquired from both scans using a standard FDA-approved scoring software on the CT scanner. Results: The limited UH re-scan resolved small cysts that were not resolved in the HRCT. The HRCT-derived cyst scores were on average 59.6% of the reference values from the UH re-scan (p = 4.7e-25). The amount of underestimation by HRCT varied from patient to patient, with an inter-quartile range of 29.8% and standard deviation of 20.7%. The overall trend was more pronounced underestimation for patients with lower cyst scores. For patients whose reference cyst scores were below 15 (n = 29), the HRCT cyst scores were 46.9 ± 21.6% of reference values (p = 7.4e-12), while for the rest of the patients (n = 44) the HRCT cyst scores were 68.0 ± 15.3% of reference values (p = 1.2E-19). Reconstructing the HRCT images to the resolution of the UH re-scan further widened the spread of the discrepancy between HRCT and reference values due to increased image noise, and did not provide accurate cyst scores. Conclusion: Cyst scores derived from standard high-resolution helical volume chest CT significantly underestimates the percentage lung volume occupied by cysts. This inaccuracy needs to be taken into consideration when cyst score is used as part of the CT assessment of the patient's condition.

show abstract

Section: Introductionmentioning

confidence: 99%

“…To overcome this issue, ultra-low-dose CT (ULDCT) has been studied intensively [ 4 , 5 , 6 ]. Because images obtained by LDCT or ULDCT are severely affected by noise (e.g., streak artifact) and differentiation between normal/abnormal findings on the noisy CT images is difficult, image-processing techniques have been utilized to improve image quality of LDCT or ULDCT [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ]. While many methods have been proposed in previous studies, image-processing techniques of LDCT or ULDCT can be roughly divided into two categories: raw-data-based techniques [ 4 , 5 , 6 ] and post-processing techniques [ 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional auto-encoder for image denoising of ultra-low-dose CT

Nishio

Nagashima

Hirabayashi

et al. 2017

Heliyon

Self Cite

View full text Add to dashboard Cite

ObjectivesThe purpose of this study was to validate a patch-based image denoising method for ultra-low-dose CT images. Neural network with convolutional auto-encoder and pairs of standard-dose CT and ultra-low-dose CT image patches were used for image denoising. The performance of the proposed method was measured by using a chest phantom.Materials and methodsStandard-dose and ultra-low-dose CT images of the chest phantom were acquired. The tube currents for standard-dose and ultra-low-dose CT were 300 and 10 mA, respectively. Ultra-low-dose CT images were denoised with our proposed method using neural network, large-scale nonlocal mean, and block-matching and 3D filtering. Five radiologists and three technologists assessed the denoised ultra-low-dose CT images visually and recorded their subjective impressions of streak artifacts, noise other than streak artifacts, visualization of pulmonary vessels, and overall image quality.ResultsFor the streak artifacts, noise other than streak artifacts, and visualization of pulmonary vessels, the results of our proposed method were statistically better than those of block-matching and 3D filtering (p-values < 0.05). On the other hand, the difference in the overall image quality between our proposed method and block-matching and 3D filtering was not statistically significant (p-value = 0.07272). The p-values obtained between our proposed method and large-scale nonlocal mean were all less than 0.05.ConclusionNeural network with convolutional auto-encoder could be trained using pairs of standard-dose and ultra-low-dose CT image patches. According to the visual assessment by radiologists and technologists, the performance of our proposed method was superior to that of large-scale nonlocal mean and block-matching and 3D filtering.

show abstract

Emphysema Quantification Using Ultralow-Dose CT With Iterative Reconstruction and Filtered Back Projection

Abstract: ULDCT with and without IR can substitute for SDCT in emphysema quantification.

Cited by 30 publications

References 22 publications

A convolutional neural network for ultra‐low‐dose CT denoising and emphysema screening

A convolutional neural network for ultra‐low‐dose CT denoising and emphysema screening

Clinical CT underestimation of the percentage volume occupied by cysts in patients with lymphangioleiomyomatosis

Convolutional auto-encoder for image denoising of ultra-low-dose CT

Contact Info

Product

Resources

About