Visual scoring of osteoporosis on low-dose CT in lung cancer screening population

OBJECTIVES

The risk factors for lung cancer screening eligibility, age as well as smoking history, are also present for osteoporosis. This study aims to develop a visual scoring system to identify osteoporosis that can be applied to low-dose CT scans obtained for lung cancer screening.

MATERIALS AND METHODS

We retrospectively reviewed 1000 prospectively enrolled participants in the lung cancer screening program at the Mount Sinai Hospital. Optimal window width and level settings for the visual assessment were chosen based on a previously described approach. Visual scoring of osteoporosis and automated measurement using dedicated software were compared. Inter-reader agreement was conducted using six readers with different levels of experience who independently visually assessed 30 CT scans.

RESULTS

Based on previously validated formulas for choosing window and level settings, we chose osteoporosis settings of Width = 230 and Level = 80. Of the 1000 participants, automated measurement was successfully performed on 774 (77.4 %). Among these, 138 (17.8 %) had osteoporosis. There was a significant correlation between the automated measurement and the visual score categories for osteoporosis (Kendall's Tau = -0.64, p < 0.0001; Spearman's rho = -0.77, p < 0.0001). We also found substantial to excellent inter-reader agreement on the osteoporosis classification among the 6 radiologists (Fleiss κ = 0.91).

CONCLUSIONS

Our study shows that a simple approach of applying specific window width and level settings to already reconstructed sagittal images obtained in the context of low-dose CT screening for lung cancer is highly feasible and useful in identifying osteoporosis.

Demographics and socioeconomic determinants of health predict continued participation in a CT lung cancer screening program

PURPOSE

We developed machine learning (ML) models to assess demographic and socioeconomic status (SES) variables' value in predicting continued participation in a low-dose CT lung cancer screening (LCS) program.

MATERIALS AND METHODS

480 LCS subjects were retrospectively examined for the following outcomes: (#1) no follow-up (single LCS scan) vs. multiple follow-ups (220 and 260 subjects respectively) and (#2) absent or delayed (>1 month past the due date) follow-up vs timely follow-up (356 and 124 subjects respectively). We quantified the contributions of 14 socioeconomic, demographic, and clinical predictors to LCS adherence, and validated and compared prediction performances of multivariate logistic regression (MLR), support vector machine (SVM) and shallow neural network (NN) models.

RESULTS

For outcome #1, age, sex, race, insurance status, personal cancer history, and median household income were found to be associated with returning for follow-ups. For outcome #2, age, sex, race, and insurance status were significant predictor of absent/delayed LCS follow-up. Across 5-fold cross-validation, the MLR model achieved an average AUC of 0.732 (95% CI, 0.661-0.803) for outcome #1 and 0.633 (95% CI, 0.602-0.664) for outcome #2 and is the model with best predictive performance overall, whereas NN and SVM tended to overfit training data and fell short on testing data performance for either outcome.

CONCLUSIONS

We identified significant predictors of LCS adherence, and our ML models can predict which subjects are at higher risk of receiving no or delayed LCS follow-ups. Our results could inform data-driven interventions to engage vulnerable populations and extend the benefits of LCS.

Advances in Imaging (Intraop Cone-Beam Computed Tomography, Synthetic Computed Tomography, Bone Scan, Low-Dose Protocols)

Spine surgery has seen a rapid advance in the refinement and development of 3-dimensional and nuclear imaging modalities in recent years. Cone-beam CT has proven to be a valuable tool for improving the accuracy of pedicle screw placement. The use of synthetic CT and low-dose CT have also emerged as modalities which allow for little to no radiation while streamlining imaging workflows. Bone scans also serve to provide functional information about bone metabolism in both the preoperative and postoperative monitoring phases.

Artifact reduction in low and ultra-low dose chest computed tomography for patients with pacemaker: A phantom study

INTRODUCTION

Implanted pacemakers (PM) would decrease the detection of lung nodules in chest computed tomography (CT) due to the metal artifact. This study aimed to explore the computer-aided diagnosis (CAD) detectability of pulmonary nodules for the patients implanted with PMs in low- and ultra-low-dose chest CT screening.

METHODS

Four different sizes of artificial nodules were placed in an anthropomorphic chest phantom with two alternative diameters utilized. A commercially available PM was placed on the surface of the left chest wall of the phantom. The image acquisitions were performed with 120 kV and 150 kV with a dedicated selective photon shield made of tin filter (Sn150 kV) at low- and ultra-low- radiation doses (1.0 and 0.5 mGy of volume CT dose index), and reconstructed with and without Iterative Metal Artifact Reduction (iMAR, Siemens Healthineers, Erlangen, Germany). The relative artifact index (AIr) was calculated as an index of metal artifacts, and the nodule detectability was evaluated with a CAD system.

RESULTS

Sn150 kV reduced AIr in all acquisitions when comparing 120 kV and Sn150 kV. Although PM reduced the detectability of nodules, Sn150 kV showed higher detectability compared to 120 kV. The use of iMAR showed inconsistent results in nodule detectability.

CONCLUSION

Sn150 kV reduced PM-induced metal artifacts and improved nodule detectability with CAD compared to 120 kV acquisition in many conditions including low and ultra-low doses and large phantoms, but iMAR did not improve the detectability.

IMPLICATIONS FOR PRACTICE

Based on the results of the current phantom study, low and ultra-low dose with Sn150 kV acquisition reduced PM-induced metal artifacts and improved nodule detectability.

Patient-Provider Lung Cancer Screening Discussions: An Analysis of a National Survey

BACKGROUND

The US Preventative Service Task Force (USPSTF) updated lung cancer screening (LCS) recommendations with annual low-dose CT (LDCT) in 2021. We aimed to assess prevalence of patient-provider discussion about LCS and determine its associated factors.

MATERIALS AND METHODS

Using data from Health Information National Trends Survey (HINTS) 2022 cycle 6, 2 cohorts were evaluated: (1) potentially LCS-eligible, included participants at least 50 years old with a history of smoking and no prior history of lung cancer; (2) LCS-ineligible individuals based on age (eg, 18-49 years old), smoking history (eg, never smoked), or history of lung cancer. We assessed association of demographic, clinical, and social factors with LDCT discussion in a multivariable logistic regression model.

RESULTS

Among potentially LCS-eligible patients, 19% had never heard of LDCT and only 9.4% had discussed LCS with their provider within the past year. Those who accessed online patient portals were more likely to discuss LCS with their healthcare provider (OR, 4.25; 95% CI, 1.67, 10.81; P, .003), as were respondents with a history of current (vs. former) smoking (OR, 3.15; 95% CI, 1.21, 8.19; P, .019). Among LCS-ineligible, 1.9% discussed LCS with their providers. Individuals with a personal history of cancer (OR, 6.70; 95% CI, 1.65, 27.19; P, .009), and those who discussed colorectal cancer screening (OR, 5.74; 95% CI, 1.63, 20.14; P, .007) were more likely to discuss LCS with their provider.

CONCLUSION

Despite updated USPSTF recommendations, rates of patient-provider LCS remains low. Multi-level interventions to address barriers to LCS are needed.

Re-UNet: a novel multi-scale reverse U-shape network architecture for low-dose CT image reconstruction

In recent years, the growing awareness of public health has brought attention to low-dose computed tomography (LDCT) scans. However, the CT image generated in this way contains a lot of noise or artifacts, which make increasing researchers to investigate methods to enhance image quality. The advancement of deep learning technology has provided researchers with novel approaches to enhance the quality of LDCT images. In the past, numerous studies based on convolutional neural networks (CNN) have yielded remarkable results in LDCT image reconstruction. Nonetheless, they all tend to continue to design new networks based on the fixed network architecture of UNet shape, which also leads to more and more complex networks. In this paper, we proposed a novel network model with a reverse U-shape architecture for the noise reduction in the LDCT image reconstruction task. In the model, we further designed a novel multi-scale feature extractor and edge enhancement module that yields a positive impact on CT images to exhibit strong structural characteristics. Evaluated on a public dataset, the experimental results demonstrate that the proposed model outperforms the compared algorithms based on traditional U-shaped architecture in terms of preserving texture details and reducing noise, as demonstrated by achieving the highest PSNR, SSIM and RMSE value. This study may shed light on the reverse U-shaped network architecture for CT image reconstruction, and could investigate the potential on other medical image processing.

Deep learning-based algorithms for low-dose CT imaging: A review

The computed tomography (CT) technique is extensively employed as an imaging modality in clinical settings. The radiation dose of CT, however, is significantly high, thereby raising concerns regarding the potential radiation damage it may cause. The reduction of X-ray exposure dose in CT scanning may result in a significant decline in imaging quality, thereby elevating the risk of missed diagnosis and misdiagnosis. The reduction of CT radiation dose and acquisition of high-quality images to meet clinical diagnostic requirements have always been a critical research focus and challenge in the field of CT. Over the years, scholars have conducted extensive research on enhancing low-dose CT (LDCT) imaging algorithms, among which deep learning-based algorithms have demonstrated superior performance. In this review, we initially introduced the conventional algorithms for CT image reconstruction along with their respective advantages and disadvantages. Subsequently, we provided a detailed description of four aspects concerning the application of deep neural networks in LDCT imaging process: preprocessing in the projection domain, post-processing in the image domain, dual-domain processing imaging, and direct deep learning-based reconstruction (DLR). Furthermore, an analysis was conducted to evaluate the merits and demerits of each method. The commercial and clinical applications of the LDCT-DLR algorithm were also presented in an overview. Finally, we summarized the existing issues pertaining to LDCT-DLR and concluded the paper while outlining prospective trends for algorithmic advancement.

A Review of deep learning methods for denoising of medical low-dose CT images

To prevent patients from being exposed to excess of radiation in CT imaging, the most common solution is to decrease the radiation dose by reducing the X-ray, and thus the quality of the resulting low-dose CT images (LDCT) is degraded, as evidenced by more noise and streaking artifacts. Therefore, it is important to maintain high quality CT image while effectively reducing radiation dose. In recent years, with the rapid development of deep learning technology, deep learning-based LDCT denoising methods have become quite popular because of their data-driven and high-performance features to achieve excellent denoising results. However, to our knowledge, no relevant article has so far comprehensively introduced and reviewed advanced deep learning denoising methods such as Transformer structures in LDCT denoising tasks. Therefore, based on the literatures related to LDCT image denoising published from year 2016-2023, and in particular from 2020 to 2023, this study presents a systematic survey of current situation, and challenges and future research directions in LDCT image denoising field. Four types of denoising networks are classified according to the network structure: CNN-based, Encoder-Decoder-based, GAN-based, and Transformer-based denoising networks, and each type of denoising network is described and summarized from the perspectives of structural features and denoising performances. Representative deep-learning denoising methods for LDCT are experimentally compared and analyzed. The study results show that CNN-based denoising methods capture image details efficiently through multi-level convolution operation, demonstrating superior denoising effects and adaptivity. Encoder-decoder networks with MSE loss, achieve outstanding results in objective metrics. GANs based methods, employing innovative generators and discriminators, obtain denoised images that exhibit perceptually a closeness to NDCT. Transformer-based methods have potential for improving denoising performances due to their powerful capability in capturing global information. Challenges and opportunities for deep learning based LDCT denoising are analyzed, and future directions are also presented.

[Imaging in nephroureterolithasis]

In the acute diagnostics of a suspected nephroureterolithiasis, ultrasonography should be the examination modality of choice. In cases of suspected urolithiasis, unclear flank pain with fever or in cases of a solitary kidney, a noncontrast computed tomography (CT) scan should always subsequently be performed. If the sonography findings are inconclusive in pregnant women a magnetic resonance imaging (MRI) examination can be considered. If there are indications for urinary diversion, a retrograde imaging study should be performed as part of the urinary diversion. This or CT imaging is also suitable for preinterventional imaging before shock wave lithotripsy, percutaneous nephrolithotomy or ureteroscopy. Postinterventional imaging is not always necessary and sonography is often sufficient. In a conservative treatment approach an abdominal plain X‑ray can be used for follow-up assessment.

Proj2Proj: self-supervised low-dose CT reconstruction

In Computed Tomography (CT) imaging, one of the most serious concerns has always been ionizing radiation. Several approaches have been proposed to reduce the dose level without compromising the image quality. With the emergence of deep learning, thanks to the increasing availability of computational power and huge datasets, data-driven methods have recently received a lot of attention. Deep learning based methods have also been applied in various ways to address the low-dose CT reconstruction problem. However, the success of these methods largely depends on the availability of labeled data. On the other hand, recent studies showed that training can be done successfully without the need for labeled datasets. In this study, a training scheme was defined to use low-dose projections as their own training targets. The self-supervision principle was applied in the projection domain. The parameters of a denoiser neural network were optimized through self-supervised training. It was shown that our method outperformed both traditional and compressed sensing-based iterative methods, and deep learning based unsupervised methods, in the reconstruction of analytic CT phantoms and human CT images in low-dose CT imaging. Our method's reconstruction quality is also comparable to a well-known supervised method.

Joint denoising and interpolating network for low-dose cone-beam CT reconstruction under hybrid dose-reduction strategy

Cone-beam computed tomography (CBCT) is generally reconstructed with hundreds of two-dimensional X-Ray projections through the FDK algorithm, and its excessive ionizing radiation of X-Ray may impair patients' health. Two common dose-reduction strategies are to either lower the intensity of X-Ray, i.e., low-intensity CBCT, or reduce the number of projections, i.e., sparse-view CBCT. Existing efforts improve the low-dose CBCT images only under a single dose-reduction strategy. In this paper, we argue that applying the two strategies simultaneously can reduce dose in a gentle manner and avoid the extreme degradation of the projection data in a single dose-reduction strategy, especially under ultra-low-dose situations. Therefore, we develop a Joint Denoising and Interpolating Network (JDINet) in projection domain to improve the CBCT quality with the hybrid low-intensity and sparse-view projections. Specifically, JDINet mainly includes two important components, i.e., denoising module and interpolating module, to respectively suppress the noise caused by the low-intensity strategy and interpolate the missing projections caused by the sparse-view strategy. Because FDK actually utilizes the projection information after ramp-filtering, we develop a filtered structural similarity constraint to help JDINet focus on the reconstruction-required information. Afterward, we employ a Postprocessing Network (PostNet) in the reconstruction domain to refine the CBCT images that are reconstructed with denoised and interpolated projections. In general, a complete CBCT reconstruction framework is built with JDINet, FDK, and PostNet. Experiments demonstrate that our framework decreases RMSE by approximately 8 %, 15 %, and 17 %, respectively, on the 1/8, 1/16, and 1/32 dose data, compared to the latest methods. In conclusion, our learning-based framework can be deeply imbedded into the CBCT systems to promote the development of CBCT. Source code is available at https://github.com/LianyingChao/FusionLowDoseCBCT.

Eligibility for screening with low-dose CT in a real-world cohort of patients with lung cancer in Greece: A brief report

INTRODUCTION

NELSON and NLST prompted the implementation of lung cancer screening programs in the United States followed by several European countries. This study aimed to assess the sensitivity of different screening criteria among patients with lung cancer in Greece and investigate reasons for ineligibility.

METHODS

We performed a retrospective analysis on patients with lung cancer referred to the largest referral center in Athens, Greece, between June 2014 and May 2023. The proportion of patients who would meet the updated USPSTF and NLST criteria was compared to the corresponding proportion of the Greek population over 15 years of age.

RESULTS

Out of 2434 patients with lung cancer, 77.4 % (N = 1883) would meet the updated USPSTF criteria, and 58.9 % (N = 1439) would meet the NLST criteria at diagnosis; the corresponding proportions for the Greek population over 15 years would be 13.8 % and 8.2 %, respectively. Ineligible patients were more likely to be female, former or never-smokers, have adenocarcinoma histology, and have driver mutations (p < 0.001).

CONCLUSIONS

Although the updated USPSTF criteria demonstrated good sensitivity, a substantial proportion of patients with lung cancer would still not be eligible for screening. Future studies to shape a comprehensive screening strategy should focus on the incorporation of additional risk factors for lung cancer, including air pollution and individual genetic susceptibility.

Assessing, updating and utilising primary care smoking records for lung cancer screening

BACKGROUND

Lung cancer screening with low-dose computed tomography for high-risk populations is being implemented in the UK. However, inclusive identification and invitation of the high-risk population is a major challenge for equitable lung screening implementation. Primary care electronic health records (EHRs) can be used to identify lung screening-eligible individuals based on age and smoking history, but the quality of EHR smoking data is limited. This study piloted a novel strategy for ascertaining smoking status in primary care and tested EHR search combinations to identify those potentially eligible for lung cancer screening.

METHODS

Seven primary care General Practices in South Wales, UK were included. Practice-level data on missing tobacco codes in EHRs were obtained. To update patient EHRs with no tobacco code, we developed and tested an algorithm that sent a text message request to patients via their GP practice to update their smoking status. The patient's response automatically updated their EHR with the relevant tobacco code. Four search strategies using different combinations of tobacco codes for the age range 55-74+ 364 were tested to estimate the likely impact on the potential lung screening-eligible population in Wales. Search strategies included: BROAD (wide range of ever smoking codes); VOLUME (wide range of ever-smoking codes excluding "trivial" former smoking); FOCUSED (cigarette-related tobacco codes only), and RECENT (current smoking within the last 20 years).

RESULTS

Tobacco codes were not recorded for 3.3% of patients (n = 724/21,956). Of those with no tobacco code and a validated mobile telephone number (n = 333), 55% (n = 183) responded via text message with their smoking status. Of the 183 patients who responded, 43.2% (n = 79) had a history of smoking and were potentially eligible for lung cancer screening. Applying the BROAD search strategy was projected to result in an additional 148,522 patients eligible to receive an invitation for lung cancer screening when compared to the RECENT strategy.

CONCLUSION

An automated text message system could be used to improve the completeness of primary care EHR smoking data in preparation for rolling out a national lung cancer screening programme. Varying the search strategy for tobacco codes may have profound implications for the size of the population eligible for lung-screening invitation.

Convolutional neural network-based kidney volume estimation from low-dose unenhanced computed tomography scans

PURPOSE

Kidney volume is important in the management of renal diseases. Unfortunately, the currently available, semi-automated kidney volume determination is time-consuming and prone to errors. Recent advances in its automation are promising but mostly require contrast-enhanced computed tomography (CT) scans. This study aimed at establishing an automated estimation of kidney volume in non-contrast, low-dose CT scans of patients with suspected urolithiasis.

METHODS

The kidney segmentation process was automated with 2D Convolutional Neural Network (CNN) models trained on manually segmented 2D transverse images extracted from low-dose, unenhanced CT scans of 210 patients. The models' segmentation accuracy was assessed using Dice Similarity Coefficient (DSC), for the overlap with manually-generated masks on a set of images not used in the training. Next, the models were applied to 22 previously unseen cases to segment kidney regions. The volume of each kidney was calculated from the product of voxel number and their volume in each segmented mask. Kidney volume results were then validated against results semi-automatically obtained by radiologists.

RESULTS

The CNN-enabled kidney volume estimation took a mean of 32 s for both kidneys in a CT scan with an average of 1026 slices. The DSC was 0.91 and 0.86 and for left and right kidneys, respectively. Inter-rater variability had consistencies of ICC = 0.89 (right), 0.92 (left), and absolute agreements of ICC = 0.89 (right), 0.93 (left) between the CNN-enabled and semi-automated volume estimations.

CONCLUSION

In our work, we demonstrated that CNN-enabled kidney volume estimation is feasible and highly reproducible in low-dose, non-enhanced CT scans. Automatic segmentation can thereby quantitatively enhance radiological reports.

Lung Cancer Screening: The European Perspective

Several randomized and observational studies on lung cancer screening held in Europe significantly contributed to the knowledge on low-dose computed tomography screening targets in high-risk individuals with smoking history and older than 50 years. In particular, steps forward have been made in the field of risk modeling, screening interval, diagnostic protocol with volumetry, optimization, overdiagnosis estimation, oncological outcome, oncological risk due to radiation exposure, recruitment, and communication strategy.

Historical Perspective on Lung Cancer Screening

Lung cancer represents a large burden on society with a staggering incidence and mortality rate that has steadily increased until recently. The impetus to design an effective screening program for the deadliest cancer in the United States and worldwide began in 1950. It has taken more than 50 years of numerous clinical trials and continued persistence to arrive at the development of modern-day screening program. As the program continues to grow, it is important for clinicians to understand its evolution, track outcomes, and continually assess the impact and bias of screening on the medical, social, and economic systems.

Feasibility analysis of low-dose CT with asynchronous quantitative computed tomography to assess vBMD

BACKGROUND

To explore the feasibility of low-dose computed tomography (LDCT) with asynchronous quantitative computed tomography (asynchronous QCT) for assessing the volumetric bone mineral density (vBMD).

METHODS

416 women patients, categorized into 4 groups, were included and underwent chest CT examinations combined with asynchronous QCT, and CT scanning dose protocols (LDCT or CDCT) were self-determined by the participants. Radiation dose estimations were retrieved from patient protocols, including volume CT dose index (CTDIvol) and dose-length-product (DLP), and then calculated effective dose (ED). Delimiting ED by 1.0 mSv, chest CT examinations were categorized into 2 groups, LDCT group and CDCT group. vBMD of T12-L2 was obtained by transferring the LDCT and CDCT images to the QCT workstation, without extra radiation.

RESULTS

There was no difference of vBMD among 4 age groups in LDCT group (P = 0.965), and no difference in CDCT group (P = 0.988). In LDCT group and CDCT group, vBMD was not correlated to mAs, CTDIvol and DLP (P > 0.05), respectively. Between LDCT group and CDCT group, there was no difference of vBMD (P ≥ 0.480), while differences of mAs, CTDIvol and DLP.

CONCLUSION

There was no difference of vBMD between LDCT group and CDCT group and vBMD was not correlated to mAs. While screening for diseases such as lung cancer and mediastinal lesions, LDCT combined with asynchronous QCT can be also used to assess vBMD simultaneously with no extra imaging equipment, patient visit time, radiation dose and no additional economic cost.

Comparison of endoscopic versus CT assessment of stone-free status after percutaneous nephrolithotomy (PCNL)

This study is aimed to determine whether postoperative low dose computed tomography (LDCT) imaging is necessary after percutaneous nephrolithotomy (PCNL), or the surgeon's intraoperative assessment of residual fragments (RF) is sufficient and avoidance of postoperative imaging with reduction of radiation exposure can be achieved. Data of all 610 patients who underwent PCNL in prone position in our institution from February 2009 to September 2020 was collected. Parameters such as age, gender, BMI, ASA-Classification, stone related parameters and the surgeon's assessment of stone-free status were analyzed. The LDCT performed postoperatively was compared to the intraoperative assessment of the surgeon regarding RF. The mean age of patients was 52.82 years; the mean BMI was 28.18 kg/m2. In 418 cases, the surgeon made a clear statement about the presence of RF and postoperative LDCT was carried out. The discrepancy between the two methods (surgeon´s assessment vs. LDCT) was significant at p < 0.0001. The sensitivity, specificity, positive and negative predictive value of the surgeon when assessing RF were 24.05%, 99.45%, 98.28% and 50%. Stone free rate (SFR) after primary PCNL was 45.57%. The overall SFR at discharge was 96.23%. Although the surgeon´s assessment of RF was reliable, postoperative LDCT imaging should still be performed if endoscopic stone clearance is suspected due to the high false negative rate and the low negative predictive value. The optimal timing of postoperative imaging following PCNL remains unclear.

HCformer: Hybrid CNN-Transformer for LDCT Image Denoising

Low-dose computed tomography (LDCT) is an effective way to reduce radiation exposure for patients. However, it will increase the noise of reconstructed CT images and affect the precision of clinical diagnosis. The majority of the current deep learning-based denoising methods are built on convolutional neural networks (CNNs), which concentrate on local information and have little capacity for multiple structures modeling. Transformer structures are capable of computing each pixel's response on a global scale, but their extensive computation requirements prevent them from being widely used in medical image processing. To reduce the impact of LDCT scans on patients, this paper aims to develop an image post-processing method by combining CNN and Transformer structures. This method can obtain a high-quality images from LDCT. A hybrid CNN-Transformer (HCformer) codec network model is proposed for LDCT image denoising. A neighborhood feature enhancement (NEF) module is designed to introduce the local information into the Transformer's operation, and the representation of adjacent pixel information in the LDCT image denoising task is increased. The shifting window method is utilized to lower the computational complexity of the network model and overcome the problems that come with computing the MSA (Multi-head self-attention) process in a fixed window. Meanwhile, W/SW-MSA (Windows/Shifted window Multi-head self-attention) is alternately used in two layers of the Transformer to gain the information interaction between various Transformer layers. This approach can successfully decrease the Transformer's overall computational cost. The AAPM 2016 LDCT grand challenge dataset is employed for ablation and comparison experiments to demonstrate the viability of the proposed LDCT denoising method. Per the experimental findings, HCformer can increase the image quality metrics SSIM, HuRMSE and FSIM from 0.8017, 34.1898, and 0.6885 to 0.8507, 17.7213, and 0.7247, respectively. Additionally, the proposed HCformer algorithm will preserves image details while it reduces noise. In this paper, an HCformer structure is proposed based on deep learning and evaluated by using the AAPM LDCT dataset. Both the qualitative and quantitative comparison results confirm that the proposed HCformer outperforms other methods. The contribution of each component of the HCformer is also confirmed by the ablation experiments. HCformer can combine the advantages of CNN and Transformer, and it has great potential for LDCT image denoising and other tasks.

Computed tomography micromotion analysis in the follow-up of patients with surgically treated pelvic fractures: a prospective clinical study

PURPOSE

High-energy pelvic fractures are complex injuries often requiring surgical treatment. Different radiological methods exist to evaluate the reduction and healing process postoperatively but with certain limitations. The aim of this study was to evaluate Computed Tomography Micromotion Analysis (CTMA) in a clinical setting for follow-up of surgically treated pelvic fracture patients.

METHODS

10 patients surgically treated for a pelvic fracture were included and prospectively followed with Computed Tomography (CT) at 0, 6, 12 and 52 weeks postoperatively. CTMA was used to measure postoperative translation and rotation of the pelvic fracture during the 52 weeks follow-up. Clinical outcomes were collected through the questionnaires EQ-5D index score and Majeed score.

RESULTS

10 patients were included with mean age (± SD, min-max) 52 (16, 31-80) years and 70% (n = 7) were males. The median (IQR, min-max) global translation from 0 to 52 weeks was 6.0 (4.6, 1.4-12.6) millimeters and median global rotation was 2.6 (2.4, 0.7-4.7) degrees. The general trend was a larger translation between 0 and 6 weeks postoperatively compared to 6-12 and 12-52 weeks. For the clinical outcomes, the general trend was that all patients started from high scores which decreased in the first postoperative follow-up and recovered to different extent during the study period.

CONCLUSION

CTMA was successfully used in the follow-up of surgically treated pelvic fracture patients. Movement in the pelvic fractures after surgical fixation was largest between 0 and 6 weeks.

Utilization of Diagnostic Procedures After Lung Cancer Screening in the National Lung Screening Trial

OBJECTIVE

To examine utilization patterns of diagnostic procedures after lung cancer screening among participants enrolled in the National Lung Screening Trial.

METHODS

Using a sample of National Lung Screening Trial participants with abstracted medical records, we assessed utilization of imaging, invasive, and surgical procedures after lung cancer screening. Missing data were imputed using multiple imputation by chained equations. For each procedure type, we examined utilization within a year after the screening or until the next screen, whichever came first, across arms (low-dose CT [LDCT] versus chest X-ray [CXR]) and by screening results. We also explored factors associated with having these procedures using multivariable negative binomial regressions.

RESULTS

After baseline screening, our sample had 176.5 and 46.7 procedures per 100 person-years for those with a false-positive and negative result, respectively. Invasive and surgical procedures were relatively infrequent. Among those who screened positive, follow-up imaging and invasive procedures were 25% and 34% less frequent in those screened with LDCT, compared with CXR. Postscreening utilization of invasive and surgical procedures was 37% and 34% lower at the first incidence screen compared with baseline. Participants with positive results at baseline were six times more likely to undergo additional imaging than those with normal findings.

DISCUSSION

Use of imaging and invasive procedures to evaluate abnormal findings varied by screening modality, with a lower rate for LDCT than CXR. Invasive and surgical workup were less prevalent after subsequent screening examinations compared with baseline screening. Utilization was associated with older age but not gender, race or ethnicity, insurance status, or income.

Multi-Scale Feature Fusion Network for Low-Dose CT Denoising

Computed tomography (CT) is an imaging technique extensively used in medical treatment, but too much radiation dose in a CT scan will cause harm to the human body. Decreasing the dose of radiation will result in increased noise and artifacts in the reconstructed image, blurring the internal tissue and edge details. To get high-quality CT images, we present a multi-scale feature fusion network (MSFLNet) for low-dose CT (LDCT) denoising. In our MSFLNet, we combined multiple feature extraction modules, effective noise reduction modules, and fusion modules constructed using the attention mechanism to construct a horizontally connected multi-scale structure as the overall architecture of the network, which is used to construct different levels of feature maps at all scales. We innovatively define a composite loss function composed of pixel-level loss based on MS-SSIM-L1 and edge-based edge loss for LDCT denoising. In short, our approach learns a rich set of features that combine contextual information from multiple scales while maintaining the spatial details of denoised CT images. Our laboratory results indicate that compared with the existing methods, the peak signal-to-noise ratio (PSNR) value of CT images of the AAPM dataset processed by the new model is 33.6490, and the structural similarity (SSIM) value is 0.9174, which also achieves good results on the Piglet dataset with different doses. The results also show that the method removes noise and artifacts while effectively preserving CT images' architecture and grain information.

Dual-domain fusion deep convolutional neural network for low-dose CT denoising

BACKGROUND

In view of the underlying health risks posed by X-ray radiation, the main goal of the present research is to achieve high-quality CT images at the same time as reducing x-ray radiation. In recent years, convolutional neural network (CNN) has shown excellent performance in removing low-dose CT noise. However, previous work mainly focused on deepening and feature extraction work on CNN without considering fusion of features from frequency domain and image domain.

OBJECTIVE

To address this issue, we propose to develop and test a new LDCT image denoising method based on a dual-domain fusion deep convolutional neural network (DFCNN).

METHODS

This method deals with two domains, namely, the DCT domain and the image domain. In the DCT domain, we design a new residual CBAM network to enhance the internal and external relations of different channels while reducing noise to promote richer image structure information. For the image domain, we propose a top-down multi-scale codec network as a denoising network to obtain more acceptable edges and textures while obtaining multi-scale information. Then, the feature images of the two domains are fused by a combination network.

RESULTS

The proposed method was validated on the Mayo dataset and the Piglet dataset. The denoising algorithm is optimal in both subjective and objective evaluation indexes as compared to other state-of-the-art methods reported in previous studies.

CONCLUSIONS

The study results demonstrate that by applying the new fusion model denoising, denoising results in both image domain and DCT domain are better than other models developed using features extracted in the single image domain.

Axial Spondyloarthritis and Diagnostic Challenges: Over-diagnosis, Misdiagnosis, and Under-diagnosis

PURPOSE OF REVIEW

This article aims to review the challenges in axial spondyloarthritis diagnosis and identify the possible contributing factors.

RECENT FINDINGS

The inability to reach an accurate diagnosis in a timely fashion can lead to treatment delays and worse disease outcomes. The lack of validated diagnostic criteria and the misuse of the currently available classification criteria could be contributing. There is also significant inter-reader variability in interpreting images, and the radiologic definitions of axial spondyloarthritis continue to be re-defined to improve their positive predictive value. The role of inflammatory back pain features, serologic biomarkers, genetics, and their diagnostic contribution to axial spondyloarthritis continues to be investigated. There is still a significant amount of delay in the diagnosis of axial spondyloarthritis. Appreciating the factors that contribute to this delay is of utmost importance to close the gap. It is similarly important to recognize other conditions that may present with symptoms that mimic axial spondyloarthritis so that misdiagnosis and wrong treatment can be avoided.

AIGAN: Attention-encoding Integrated Generative Adversarial Network for the reconstruction of low-dose CT and low-dose PET images

X-ray computed tomography (CT) and positron emission tomography (PET) are two of the most commonly used medical imaging technologies for the evaluation of many diseases. Full-dose imaging for CT and PET ensures the image quality but usually raises concerns about the potential health risks of radiation exposure. The contradiction between reducing the radiation exposure and remaining diagnostic performance can be addressed effectively by reconstructing the low-dose CT (L-CT) and low-dose PET (L-PET) images to the same high-quality ones as full-dose (F-CT and F-PET). In this paper, we propose an Attention-encoding Integrated Generative Adversarial Network (AIGAN) to achieve efficient and universal full-dose reconstruction for L-CT and L-PET images. AIGAN consists of three modules: the cascade generator, the dual-scale discriminator and the multi-scale spatial fusion module (MSFM). A sequence of consecutive L-CT (L-PET) slices is first fed into the cascade generator that integrates with a generation-encoding-generation pipeline. The generator plays the zero-sum game with the dual-scale discriminator for two stages: the coarse and fine stages. In both stages, the generator generates the estimated F-CT (F-PET) images as like the original F-CT (F-PET) images as possible. After the fine stage, the estimated fine full-dose images are then fed into the MSFM, which fully explores the inter- and intra-slice structural information, to output the final generated full-dose images. Experimental results show that the proposed AIGAN achieves the state-of-the-art performances on commonly used metrics and satisfies the reconstruction needs for clinical standards.

CGP-Uformer: A low-dose CT image denoising Uformer based on channel graph perception

BACKGROUND

An effective method for achieving low-dose CT is to keep the number of projection angles constant while reducing radiation dose at each angle. However, this leads to high-intensity noise in the reconstructed image, adversely affecting subsequent image processing, analysis, and diagnosis.

OBJECTIVE

This paper proposes a novel Channel Graph Perception based U-shaped Transformer (CGP-Uformer) network, aiming to achieve high-performance denoising of low-dose CT images.

METHODS

The network consists of convolutional feed-forward Transformer (ConvF-Transformer) blocks, a channel graph perception block (CGPB), and spatial cross-attention (SC-Attention) blocks. The ConvF-Transformer blocks enhance the ability of feature representation and information transmission through the CNN-based feed-forward network. The CGPB introduces Graph Convolutional Network (GCN) for Channel-to-Channel feature extraction, promoting the propagation of information across distinct channels and enabling inter-channel information interchange. The SC-Attention blocks reduce the semantic difference in feature fusion between the encoder and decoder by computing spatial cross-attention.

RESULTS

By applying CGP-Uformer to process the 2016 NIH AAPM-Mayo LDCT challenge dataset, experiments show that the peak signal-to-noise ratio value is 35.56 and the structural similarity value is 0.9221.

CONCLUSIONS

Compared to the other four representative denoising networks currently, this new network demonstrates superior denoising performance and better preservation of image details.

X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels

In clinical applications, multi-dose scan protocols will cause the noise levels of computed tomography (CT) images to fluctuate widely. The popular low-dose CT (LDCT) denoising network outputs denoised images through an end-to-end mapping between an LDCT image and its corresponding ground truth. The limitation of this method is that the reduced noise level of the image may not meet the diagnostic needs of doctors. To establish a denoising model adapted to the multi-noise levels robustness, we proposed a novel and efficient modularized iterative network framework (MINF) to learn the feature of the original LDCT and the outputs of the previous modules, which can be reused in each following module. The proposed network can achieve the goal of gradual denoising, outputting clinical images with different denoising levels, and providing the reviewing physicians with increased confidence in their diagnosis. Moreover, a multi-scale convolutional neural network (MCNN) module is designed to extract as much feature information as possible during the network's training. Extensive experiments on public and private clinical datasets were carried out, and comparisons with several state-of-the-art methods show that the proposed method can achieve satisfactory results for noise suppression of LDCT images. In further comparisons with modularized adaptive processing neural network (MAP-NN), the proposed network shows superior step-by-step or gradual denoising performance. Considering the high quality of gradual denoising results, the proposed method can obtain satisfactory performance in terms of image contrast and detail protection as the level of denoising increases, which shows its potential to be suitable for a multi-dose levels denoising task.

Enhancement based convolutional dictionary network with adaptive window for low-dose CT denoising

BACKGROUND

Recently, one promising approach to suppress noise/artifacts in low-dose CT (LDCT) images is the CNN-based approach, which learns the mapping function from LDCT to normal-dose CT (NDCT). However, most CNN-based methods are purely data-driven, thus lacking sufficient interpretability and often losing details.

OBJECTIVE

To solve this problem, we propose a deep convolutional dictionary learning method for LDCT denoising, in which a novel convolutional dictionary learning model with adaptive window (CDL-AW) is designed, and a corresponding enhancement-based convolutional dictionary learning network (called ECDAW-Net) is constructed to unfold the CDL-AW model iteratively using the proximal gradient descent technique.

METHODS

In detail, the adaptive window-constrained convolutional dictionary atom is proposed to alleviate spectrum leakage caused by data truncation during convolution. Furthermore, in the ECDAW-Net, a multi-scale edge extraction module that consists of LoG and Sobel convolution layers is proposed in the unfolding iteration, to supplement lost textures and details. Additionally, to further improve the detail retention ability, the ECDAW-Net is trained by the compound loss function of the pixel-level MSE loss and the proposed patch-level loss, which can assist to retain richer structural information.

RESULTS

Applying ECDAW-Net to the Mayo dataset, we obtained the highest peak signal-to-noise ratio (33.94) and sub-optimal structural similarity (0.92).

CONCLUSIONS

Compared with some state-of-art methods, the interpretable ECDAW-Net performs well in suppressing noise/artifacts and preserving textures of tissue.

Prospective evaluation of low-dose multiphase hepatic computed tomography for detecting and characterizing hepatocellular carcinoma in patients with chronic liver disease

BACKGROUND

Knowing the lowest acceptable radiation dose of multiphase hepatic CT may allow us to reduce the radiation dose for detecting HCC.

PURPOSE

To prospectively assess the image quality and diagnostic performance of low-dose and ultra-low-dose multiphase hepatic computed tomography using a dual-source CT scanner.

METHODS

Three reconstructed different dose scan images (standard-dose, low-dose, and ultra-low-dose) of hepatic multiphase CT were obtained from 67 patients with a dual-source CT scanner. The image quality and the diagnostic performance of the three radiation dose CT scans of the hepatic focal lesion (≥ 0.5 cm) were analyzed by two independent readers using the Liver Imaging Reporting and Data System.

RESULTS

Qualitative image quality and signal-to-noise ratio were significantly different among the radiation doses (p < 0.001). In total, 154 lesions comprising 32 hepatocellular carcinomas (HCC) and 122 non-HCC were included. The sensitivities of SDCT, LDCT, and ULDCT were 90.6%(29/32), 81.3%(26/32), and 56.2%(18/32), respectively. The accuracies of SDCT, LDCT, and ULDCT were 98.1%(151/154), 96.1%(148/154), and 89.6%(138/154), respectively. On per-lesion analysis, SDCT and LDCT did not show significantly different sensitivity and accuracy in diagnosing HCC (p = 0.250 and 0.250).

CONCLUSIONS

The diagnostic performance of dynamic hepatic LDCT with 33% reduced radiation dose in comparison to SDCT would be acceptable even though its image quality was qualitatively and quantitatively inferior. However, few HCCs could be overlooked. Therefore, with caution, radiation dose reduction by one-third could be implemented for follow-up CT scans for patients suspected of having HCC with caution and further studies are needed in the future.

[Prevalence and risk factors of coronary artery calcification on lung cancer screening with low-dose CT]

Objective: To investigate the prevalence and risk factors of coronary artery calcification (CAC) on lung cancer screening with low-dose computed tomography (LDCT). Methods: A total of 4 989 asymptomatic subjects (2 542 males and 2 447 females) who underwent LDCT lung cancer screening were recruited at Cancer Hospital, Chinese Academy of Medical Sciences from 2014 to 2017. The visual scoring method was used to assess coronary artery calcification score. χ(2) test or independent t-test was used to compare the difference of CAC positive rate among different groups. Multivariate logistic regression was used to analyze risk factors associated with CAC in the study. Results: Of the 4 989 asymptomatic subjects, CAC occurred in 1 018 cases. The positive rate was 20.4%, of which mild, moderate and severe calcification accounted for 86.3%, 11.4% and 2.3%, respectively. Gender, age, BMI, education level, occupation, smoking history, diabetes, hypertension and hyperlipidemia had statistically significant differences in CAC positive rates among groups. Multivariate logistic regression analysis showed that gender, age, diabetes, hypertension, hyperlipidemia and smoking history were risk factors for CAC. Age, diabetes, hypertension and smoking history were statistically significant risk factors between the mild and moderate CAC group. A total of 1 730 coronary arteries in 1 018 CAC positive cases had calcification, CAC positive rate of left anterior descending was the highest(51.3%); 568 cases (55.8%) were single vessel calcification, 450 cases (44.2%) were multiple vessel calcification. Conclusions: LDCT can be used for the 'one-stop' early detection of lung cancer and coronary atherosclerosis. Gender, age, diabetes, hypertension, hyperlipidemia and smoking are related risk factors for coronary atherosclerosis.

MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging

BACKGROUND AND OBJECTIVE

Low-dose computed tomography (LDCT) has become increasingly important for alleviating X-ray radiation damage. However, reducing the administered radiation dose may lead to degraded CT images with amplified mottle noise and nonstationary streak artifacts. Previous studies have confirmed that deep learning (DL) is promising for improving LDCT imaging. However, most DL-based frameworks are built intuitively, lack interpretability, and suffer from image detail information loss, which has become a general challenging issue.

METHODS

A multiscale reweighted convolutional coding neural network (MRCON-Net) is developed to address the above problems. MRCON-Net is compact and more explainable than other networks. First, inspired by the learning-based reweighted iterative soft thresholding algorithm (ISTA), we extend traditional convolutional sparse coding (CSC) to its reweighted convolutional learning form. Second, we use dilated convolution to extract multiscale image features, allowing our single model to capture the correlations between features of different scales. Finally, to automatically adjust the elements in the feature code to correct the obtained solution, a channel attention (CA) mechanism is utilized to learn appropriate weights.

RESULTS

The visual results obtained based on the American Association of Physicians in Medicine (AAPM) Challenge and United Image Healthcare (UIH) clinical datasets confirm that the proposed model significantly reduces serious artifact noise while retaining the desired structures. Quantitative results show that the average structural similarity index measurement (SSIM) and peak signal-to-noise ratio (PSNR) achieved on the AAPM Challenge dataset are 0.9491 and 40.66, respectively, and the SSIM and PSNR achieved on the UIH clinical dataset are 0.915 and 42.44, respectively; these are promising quantitative results.

CONCLUSION

Compared with recent state-of-the-art methods, the proposed model achieves subtle structure-enhanced LDCT imaging. In addition, through ablation studies, the components of the proposed model are validated to achieve performance improvements.

Nationwide lung cancer screening with low-dose computed tomography: implementation and first results of the HUNCHEST screening program

OBJECTIVES

Lung cancer (LC) kills more people than any other cancer in Hungary. Hence, there is a clear rationale for considering a national screening program. The HUNCHEST pilot program primarily aimed to investigate the feasibility of a population-based LC screening in Hungary, and determine the incidence and LC probability of solitary pulmonary nodules.

METHODS

A total of 1890 participants were assigned to undergo low-dose CT (LDCT) screening, with intervals of 1 year between procedures. Depending on the volume, growth, and volume doubling time (VDT), screenings were defined as negative, indeterminate, or positive. Non-calcified lung nodules with a volume > 500 mm³ and/or a VDT < 400 days were considered positive. LC diagnosis was based on histology.

RESULTS

At baseline, the percentage of negative, indeterminate, and positive tests was 81.2%, 15.1%, and 3.7%, respectively. The frequency of positive and indeterminate LDCT results was significantly higher in current smokers (vs. non-smokers or former smokers; p < 0.0001) and in individuals with COPD (vs. those without COPD, p < 0.001). In the first screening round, 1.2% (n = 23) of the participants had a malignant lesion, whereas altogether 1.5% (n = 29) of the individuals were diagnosed with LC. The overall positive predictive value of the positive tests was 31.6%. Most lung malignancies were diagnosed at an early stage (86.2% of all cases).

CONCLUSIONS

In terms of key characteristics, our prospective cohort study appears consistent to that of comparable studies. Altogether, the results of the HUNCHEST pilot program suggest that LDCT screening may facilitate early diagnosis and thus curative-intent treatment in LC.

KEY POINTS

• The HUNCHEST pilot study is the first nationwide low-dose CT screening program in Hungary. • In the first screening round, 1.2% of the participants had a malignant lesion, whereas altogether 1.5% of the individuals were diagnosed with lung cancer. • The overall positive predictive value of the positive tests in the HUNCHEST screening program was 31.6%.

Management of Lung Cancer Screening Results Based on Individual Prediction of Current and Future Lung Cancer Risks

OBJECTIVES

We propose a risk-tailored approach for management of lung cancer screening results. This approach incorporates individual risk factors and low-dose computed tomography (LDCT) image features into calculations of immediate and next-screen (1-y) risks of lung cancer detection, which in turn can recommend short-interval imaging or 1-year or 2-year screening intervals.

METHODS

We first extended the "LCRAT+CT" individualized risk calculator to predict lung cancer risk after either a negative or abnormal LDCT screen result. To develop the abnormal screen portion, we analyzed 18,129 abnormal LDCT results in the National Lung Screening Trial (NLST), including lung cancers detected immediately (n = 649) or at the next screen (n = 235). We estimated the potential impact of this approach among NLST participants with any screen result (negative or abnormal).

RESULTS

Applying the draft National Health Service (NHS) England protocol for lung screening to NLST participants referred 76% of participants to a 2-year interval, but delayed diagnosis for 40% of detectable cancers. The Lung Cancer Risk Assessment Tool+Computed Tomography (LCRAT+CT) risk model, with a threshold of less than 0.95% cumulative lung cancer risk, would also refer 76% of participants to a 2-year interval, but would delay diagnosis for only 30% of cancers, a 25% reduction versus the NHS protocol. Alternatively, LCRAT+CT, with a threshold of less than 1.7% cumulative lung cancer risk, would also delay diagnosis for 40% of cancers, but would refer 85% of participants for a 2-year interval, a 38% further reduction in the number of required 1-year screens beyond the NHS protocol.

CONCLUSIONS

Using individualized risk models to determine management in lung cancer screening could substantially reduce the number of screens or increase early detection.

Strided Self-Supervised Low-Dose CT Denoising for Lung Nodule Classification

Lung nodule classification based on low-dose computed tomography (LDCT) images has attracted major attention thanks to the reduced radiation dose and its potential for early diagnosis of lung cancer from LDCT-based lung cancer screening. However, LDCT images suffer from severe noise, largely influencing the performance of lung nodule classification. Current methods combining denoising and classification tasks typically require the corresponding normal-dose CT (NDCT) images as the supervision for the denoising task, which is impractical in the context of clinical diagnosis using LDCT. To jointly train these two tasks in a unified framework without the NDCT images, this paper introduces a novel self-supervised method, termed strided Noise2Neighbors or SN2N, for blind medical image denoising and lung nodule classification, where the supervision is generated from noisy input images. More specifically, the proposed SN2N can construct the supervision information from its neighbors for LDCT denoising, which does not need NDCT images anymore. The proposed SN2N method enables joint training of LDCT denoising and lung nodule classification tasks by using self-supervised loss for denoising and cross-entropy loss for classification. Extensively experimental results on the Mayo LDCT dataset demonstrate that our SN2N achieves competitive performance compared with the supervised learning methods that have paired NDCT images as supervision. Moreover, our results on the LIDC-IDRI dataset show that the joint training of LDCT denoising and lung nodule classification significantly improves the performance of LDCT-based lung nodule classification.

Results of 10-year mobile low-dose computed tomography screenings for lung cancer in Shimane, Japan

BACKGROUND

Some randomized controlled trials have evaluated the effects of low-dose computed tomography (CT) screening on lung cancer mortality in heavy smokers. Based on the results of those trials, our CT screening program recommended screening for people aged ≥40 years with a history of smoking. This retrospective study aimed to verify the validity of our CT screening program and elucidate the current state of CT screening program.

METHODS

We retrospectively examined lung cancer detection in 25,189 participants who underwent chest CT screening by a mobile low-dose CT screening unit in the 10-year period from April 2009 to March 2019. Participants were recruited at Japan Agricultural Cooperatives (JA) Shimane Kouseiren. Participants requested CT screening for lung cancer. CT images were read by two pulmonologists.

RESULTS

Lung cancer was identified in 82 of the 25,189 participants over 10 years, an overall lung cancer detection rate (percentage of lung cancers detected among all participants) of 0.33%. Lung cancer among never smokers accounted for 54.9% of the detected cases. The lung cancer detection rate was similar for smokers versus never smokers. The stage IA detection rate (percentage of stage IA lung cancers among all lung cancers detected) was 62%, while the stage Ⅳ detection rate was 10%.

CONCLUSIONS

Chest CT detected lung cancer in never smokers as well as current or former smokers. Our CT screening program was not effective for never smokers; thus, further study of the effectiveness of CT screening in never smokers is needed.

Comparison of metabolic risk factors, physical performances, and prevalence of low back pain among categories determined by visceral adipose tissue and trunk skeletal muscle mass in middle-aged men

The present study compared metabolic risk factors, physical performances, and musculoskeletal impairment among categories determined by visceral adipose tissue (VAT) and trunk skeletal muscle (SM) mass in middle-aged Japanese men. In total, 1026 healthy Japanese males aged between 35 and 59 years were categorized into 4 groups according to the amount of VAT and SM in the trunk measured using low-dose computed tomography (LowVAT-HighSM, LowVAT-LowSM, HighVAT-HighSM, and HighVAT-LowSM). Height, body mass waist circumference, body fat, intramuscular adipose tissue (IntraMAT), subcutaneous adipose tissue, biochemical blood profiles (triglycerides, high-density lipoprotein cholesterol, fasting blood glucose, aspartate transaminase, alanine transaminase and γ-glutamyl trans peptidase), physical performances (trunk flexibility, the chair-stand test, two-step length and hand-grip strength), the prevalence of low back pain, and lifestyle habits for exercise, alcohol intake and smoking, were compared among the groups. The results showed that LowVAT-HighSM had significantly superior biochemical blood profiles and physical performances to the other groups. HighVAT-LowSM had significantly higher %IntraMAT and the prevalence of low back pain. The two-step length, which is an index of walking ability, significantly differed according to the four subject categories. These results indicate that metabolic risk factors, physical performances, and prevalence of low back pain in middle-aged Japanese men may differ among four categories determined by VAT and trunk SM.

CycleGAN denoising of extreme low-dose cardiac CT using wavelet-assisted noise disentanglement

In electrocardiography (ECG) gated cardiac CT angiography (CCTA), multiple images covering the entire cardiac cycle are taken continuously, so reduction of the accumulated radiation dose could be an important issue for patient safety. Although ECG-gated dose modulation (so-called ECG pulsing) is used to acquire many phases of CT images at a low dose, the reduction of the radiation dose introduces noise into the image reconstruction. To address this, we developed a high performance unsupervised deep learning method using noise disentanglement that can effectively learn the noise patterns even from extreme low dose CT images. For noise disentanglement, we use a wavelet transform to extract the high-frequency signals that contain the most noise. Since matched low-dose and high-dose cardiac CT data are impossible to obtain in practice, our neural network was trained in an unsupervised manner using cycleGAN for the extracted high frequency signals from the low-dose and unpaired high-dose CT images. Once the network is trained, denoised images are obtained by subtracting the estimated noise components from the input images. Image quality evaluation of the denoised images from only 4% dose CT images was performed by experienced radiologists for several anatomical structures. Visual grading analysis was conducted according to the sharpness level, noise level, and structural visibility. Also, the signal-to-noise ratio was calculated. The evaluation results showed that the quality of the images produced by the proposed method is much improved compared to low-dose CT images and to the baseline cycleGAN results. The proposed noise-disentangled cycleGAN with wavelet transform effectively removed noise from extreme low-dose CT images compared to the existing baseline algorithms. It can be an important denoising platform for low-dose CT.

Low-dose CT denoising using a Progressive Wasserstein generative adversarial network

Low-dose computed tomography (LDCT) imaging can greatly reduce the radiation dose imposed on the patient. However, image noise and visual artifacts are inevitable when the radiation dose is low, which has serious impact on the clinical medical diagnosis. Hence, it is important to address the problem of LDCT denoising. Image denoising technology based on Generative Adversarial Network (GAN) has shown promising results in LDCT denoising. Unfortunately, the structures and the corresponding learning algorithms are becoming more and more complex and diverse, making it tricky to analyze the contributions of various network modules when developing new networks. In this paper, we propose a progressive Wasserstein generative adversarial network to remove the noise of LDCT images, providing a more feasible and effective way for CT denoising. Specifically, a recursive computation is designed to reduce the network parameters. Moreover, we introduce a novel hybrid loss function for achieving improved results. The hybrid loss function aims to reduce artifacts while better retaining the details in the denoising results. Therefore, we propose a novel LDCT denoising model called progressive Wasserstein generative adversarial network with the weighted structurally-sensitive hybrid loss function (PWGAN-WSHL), which provides a better and simpler baseline by considering network architecture and loss functions. Extensive experiments on a publicly available database show that our proposal achieves better performance than the state-of-the-art methods.

Low-dose CT imaging via cascaded ResUnet with spectrum loss

The suppression of artifact noise in computed tomography (CT) with a low-dose scan protocol is challenging. Conventional statistical iterative algorithms can improve reconstruction but cannot substantially eliminate large streaks and strong noise elements. In this paper, we present a 3D cascaded ResUnet neural network (Ca-ResUnet) strategy with modified noise power spectrum loss for reducing artifact noise in low-dose CT imaging. The imaging workflow consists of four components. The first component is filtered backprojection (FBP) reconstruction via a domain transformation module for suppressing artifact noise. The second is a ResUnet neural network that operates on the CT image. The third is an image compensation module that compensates for the loss of tiny structures, and the last is a second ResUnet neural network with modified spectrum loss for fine-tuning the reconstructed image. Verification results based on American Association of Physicists in Medicine (AAPM) and United Image Healthcare (UIH) datasets confirm that the proposed strategy significantly reduces serious artifact noise while retaining desired structures.

Diagnostic accuracy of low-dose and ultra-low-dose CT in detection of chest pathology: a systematic review

PURPOSE

Studies have evaluated imaging modalities with a lower radiation dose than standard-dose CT (SD-CT) for chest examination. This systematic review aimed to summarize evidence on diagnostic accuracy of these modalities - low-dose and ultra-low-dose CT (LD- and ULD-CT) - for chest pathology.

METHOD

Ovid-MEDLINE, Ovid-EMBASE and the Cochrane Library were systematically searched April 29th-30th, 2019 and screened by two reviewers. Studies on diagnostic accuracy were included if they defined their index tests as 'LD-CT', 'Reduced-dose CT' or 'ULD-CT' and had SD-CT as reference standard. Risk of bias was evaluated on study level using the Quality Assessment of Diagnostic Accuracy Studies-2. A narrative synthesis was conducted to compare the diagnostic accuracy measurements.

RESULTS

Of the 4257 studies identified, 18 were eligible for inclusion. SD-CT (3.17 ± 1.47 mSv) was used as reference standard in all studies to evaluate diagnostic accuracy of LD- (1.22 ± 0.34 mSv) and ULD-CT (0.22 ± 0.05 mSv), respectively. LD-CT had high sensitivities for detection of bronchiectasis (82-96%), honeycomb (75-100%), and varying sensitivities for nodules (63-99%) and ground glass opacities (GGO) (77-91%). ULD-CT had high sensitivities for GGO (93-100%), pneumothorax (100%), consolidations (90-100%), and varying sensitivities for nodules (60-100%) and emphysema (65-90%).

CONCLUSION

The included studies found LD-CT to have high diagnostic accuracy in detection of honeycombing and bronchiectasis and ULD-CT to have high diagnostic accuracy for pneumothorax, consolidations and GGO. Summarizing evidence on diagnostic accuracy of LD- and ULD-CT for other chest pathology was not possible due to varying outcome measures, lack of precision estimates and heterogeneous study design and methodology.

Dual residual convolutional neural network (DRCNN) for low-dose CT imaging

The excessive radiation doses in the application of computed tomography (CT) technology pose a threat to the health of patients. However, applying a low radiation dose in CT can result in severe artifacts and noise in the captured images, thus affecting the diagnosis. Therefore, in this study, we investigate a dual residual convolution neural network (DRCNN) for low-dose CT (LDCT) imaging, whereby the CT images are reconstructed directly from the sinogram by integrating analytical domain transformations, thus reducing the loss of projection information. With this new framework, feature extraction is performed simultaneously on both the sinogram-domain sub-net and the image-domain sub-net, which utilize the residual shortcut networks and play a complementary role in suppressing the projection noise and reducing image error. This new DRCNN approach helps not only decrease the sinogram noise but also preserve significant structural information. The experimental results of simulated and real projection data demonstrate that our DRCNN achieve superior performance over other state-of-art methods in terms of visual inspection and quantitative metrics. For example, comparing with RED-CNN and DP-ResNet, the value of PSNR using our DRCNN is improved by nearly 3 dB and 1 dB, respectively.

Low-dose Computed Tomography (CT) for the Diagnosis of Congenital Heart Disease in Children: A Meta-analysis

BACKGROUND

Low dose CT has become a promising examination method for the diagnosis of Congenital heart disease (CHD) in children because it has a low radiation dose, but it has not been widely accepted as an alternative to standard-dose CT in clinical applications due to concerns about image quality. Therefore, we suggest that the diagnostic accuracy, image quality, and radiation dose of low-dose CT for CHD in children should be fully explored through a metaanalysis of existing studies.

METHODS

A comprehensive search was performed to identify relevant English and Chinese articles (from inception to May 2019). All selected studies concerned the diagnosis of CHD in children using low-dose CT. The accuracy of low-dose CT was determined by calculating pooled estimates of sensitivity, specificity, diagnostic odds ratio, and likelihood ratio. Pooling was conducted using a bivariate generalized linear mixed model. Forest plots and summary receiver operating characteristic (SROC) curves were generated.

RESULTS

Ten studies, accounting for 577 patients, met the eligibility criteria. The pooled sensitivity and specificity were 0.95 (95% confidence interval (CI) 0.92-0.97) and 1.00 (95% CI 1.00- 1.00), respectively. The pooled diagnostic odds ratio, positive likelihood ratio, and negative likelihood ratio of low-dose CT were 12705.53 (95% CI 5065.00-31871.73), 671.29 (95% CI 264.77- 1701.97), and 0.05 (95% CI 0.03-0.08), respectively. Additionally, the area under the SROC curve was 1.00 (95% CI 0.99-1.00), suggesting that low-dose CT is an excellent diagnostic tool for CHD in children.

CONCLUSION

Low-dose CT, especially with a prospective ECG-triggering mode, provides excellent imaging quality and high diagnostic accuracy for CHD in children.

Contrast-Medium Anisotropy-Aware Tensor Total Variation Model for Robust Cerebral Perfusion CT Reconstruction with Low-Dose Scans

Perfusion computed tomography (PCT) is critical in detecting cerebral ischemic lesions. PCT examination with low-dose scans can effectively reduce radiation exposure to patients at the cost of degraded images with severe noise and artifacts. Tensor total variation (TTV) models are powerful tools that can encode the regional continuous structures underlying a PCT object. In a TTV model, the sparsity structures of the contrast-medium concentration (CMC) across PCT frames are assumed to be isotropic with identical and independent distribution. However, this assumption is inconsistent with practical PCT tasks wherein the sparsity has evident variations and correlations. Such modeling deviation hampers the performance of TTV-based PCT reconstructions. To address this issue, we developed a novel contrast-medium anisotropy-aware tensor total variation (CMAA-TTV) model to describe the intrinsic anisotropy sparsity of the CMC in PCT imaging tasks. Instead of directly on the difference matrices, the CMAA-TTV model characterizes sparsity on a low-rank subspace of the difference matrices which are calculated from the input data adaptively, thus naturally encoding the intrinsic variant and correlated anisotropy sparsity structures of the CMC. We further proposed a robust and efficient PCT reconstruction algorithm to improve low-dose PCT reconstruction performance using the CMAA-TTV model. Experimental studies using a digital brain perfusion phantom, patient data with low-dose simulation and clinical patient data were performed to validate the effectiveness of the presented algorithm. The results demonstrate that the CMAA-TTV algorithm can achieve noticeable improvements over state-of-the-art methods in low-dose PCT reconstruction tasks.

A Comparison of Biannual Two-Phase Low-Dose Liver CT and US for HCC Surveillance in a Group at High Risk of HCC Development

BACKGROUND AND AIMS

Biannual ultrasonography (US) is a current recommendation for hepatocellular carcinoma (HCC) surveillance in a high-risk group. The sensitivity of US, however, has been low in patients with a high risk of developing HCC. We aimed to compare sensitivity for HCC of biannual US and two-phase low-dose computed tomography (LDCT) in patients with a high risk of HCC.

METHODS

In this prospective single-arm study, participants with an annual risk of HCC greater than 5% (based on a risk index of ≥2.33) and who did not have a history of HCC were enrolled from November 2014 to July 2016. Participants underwent paired biannual US and two-phase LDCT 1-3 times. Two-phase LDCT included arterial and 3-min delayed phases. The sensitivity, specificity, and positive predictive value of HCC detection using US and two-phase LDCT were compared using a composite algorithm as a standard of reference.

RESULTS

Of the 139 enrolled participants, 137 underwent both the biannual US and two-phase LDCT at least once and had follow-up images. Among them, 27 cases of HCC (mean size: 14 ± 4 mm) developed in 24 participants over 1.5 years. Two-phase LDCT showed a significantly higher sensitivity (83.3% [20/24] vs. 29.2% [7/24], p < 0.001) and specificity (95.6% [108/113] vs. 87.7% [99/113], p =0.03) than US. A false-positive result was reported in 14 participants at US and 5 participants at two-phase LDCT, resulting in a significantly higher positive predictive value of two-phase LDCT (33.3% [7/21] vs. 80% [20/25], p < 0.001).

CONCLUSIONS

Patients with a risk index ≥2.33 showed a high annual incidence of HCC development in our study, and two-phase LDCT showed significantly higher sensitivity and specificity for HCC detection than US.

[Risk factors for lung cancer based on low-dose computed tomography screening]

Objective: To analyze the risk factors related to lung cancer in participants with low-dose computed tomography (LDCT) screening, to provide data support for identifying high-risk groups of lung cancer and to improve the effectiveness of LDCT lung cancer screening. Methods: A total of 5 366 asymptomatic subjects (2 762 males and 2 604 females) who underwent LDCT lung cancer screening were recruited at Cancer Hospital, Chinese Academy of Medical Sciences from 2014 to 2017. The result of LDCT and the risk factors of participants were analyzed. The LDCT positive results were defined as solid or part-solid nodules≥5 mm and non-solid nodule≥8 mm. A total of 12 factors were included and multivariate logistic regression was used to analyze the risk factors associated with lung cancer in the study. Results: Of the 5 366 asymptomatic subjects, 389 were positive and 4 977 were negative for LDCT screening. Among them, 26 of 389 positive cases were confirmed as lung cancers pathologically, and the detection rate of stage I lung cancer was 92.3% (24/26). Multivariate logistic regression showed that age, smoking, low level of education were the relevant risk factors for lung cancer and positive nodules. A stratified analysis of age showed that no risk factors were detected in the 40-49 years old group, while age, smoking, low level of education (primary school and below) were recognized as risk factors in the ≥50 years old group. No statistically significant risk factor was detected between the lung cancer group and the positive nodules group. Conclusions: Age, smoking, and low level of education (primary school and below) are related risk factors for lung cancer and positive nodules. People aged 50 years or older, smoking, and low level of education may be a high risk group for lung cancer. LDCT can effectively detect early lung cancer.

Chest X-ray Dose Equivalent Low-dose CT with Tin Filtration: Potential Role for the Assessment of Pectus Excavatum

RATIONALE AND OBJECTIVES

To determine the value of chest CT with tin filtration applying a dose equivalent to chest x-ray for the assessment of the Haller index for evaluation of pectus excavatum.

MATERIALS AND METHODS

Two hundred seventy-two patients from a prospective single center study were included and underwent a clinical standard dose chest CT (effective dose 1.8 ± 0.7 mSv) followed by a low-dose CT (0.13 ± 0.01 mSv) in the same session. Two blinded readers independently evaluated all data sets. Image quality for bony chest wall assessment was noted. Radiologists further assessed (a) transverse thoracic diameter, (b) anteroposterior thoracic diameter, and calculated (c) Haller index by dividing transverse diameter by anteroposterior diameter. The agreement of both readers in standard dose and low-dose CT was assessed using Lin's concordance correlation coefficient (p_c).

RESULTS

Subjective image quality was lower for low dose compared to standard dose CT images by both readers (p < 0.001). In total, 99% (n = 540) of low-dose CT scans were rated as diagnostic for bony chest wall assessment by both readers. There was a high agreement for assessment of transverse diameter, anteroposterior diameter and Haller index comparing both readers in standard dose and low-dose CT with p_c values indicating substantial agreement (i.e., 0.95> and ≤0.99) in 12/18 (67%) and almost perfect agreement (i.e., >0.99) in 6/18 (33%).

CONCLUSION

Our study suggests that low-dose CT with tin filtration applying a radiation dose equivalent to a plain chest X-ray is excellent for assessing the Haller index.

Surgical Outcomes in a Lung Cancer-Screening Program Using Low Dose Computed Tomography

OBJECTIVE

Lung cancer (LC) is the leading cause of death from cancer worldwide. More than 27,000 LCs are diagnosed annually in Spain, and most are unresectable. Early detection and treatment reduce LC mortality. This study describes surgical outcomes in a longstanding LC screening cohort in Spain.

METHODS

We conducted a retrospective study of surgical outcomes in a LC screening (LCS) program using low dose computed tomography (LDCT) since the year 2000. A descriptive analysis of clinical and radiological parameters, presence or absence of a preoperative diagnosis, pathological staging, morbidity, mortality, and survival was performed.

RESULTS

Ninety-seven (2.5%) LC were diagnosed in 3825 screened. Twenty individuals with LC had no surgery due to advanced stage or small cell histology. Eighty-seven surgical procedures were carried out for suspected or biopsy proven LC, detected by LDCT. Most operated patients were male (57[85%]) aged 64±9.1 years. Nine patients underwent a second operation for a metachronous primary lung cancer. Mean tumor size was 15.2±7.6mm. Eight nodules were benign (9.2%). Lobectomy was performed in 56 cases (83.6%). Adenocarcinoma (n=39; 58.2%) was the most frequent histological type followed by squamous cell carcinoma (n=17; 25.4%). Fifty-nine (88%) tumors were in Stage I. Thirteen patients (15.4%) had 16 complications. The estimated survival rates at 5 and 10 years for stage I were 93% (95% CI: 79%-98%) and 83% (95% CI: 65%-92%), respectively.

CONCLUSION

Lung cancer screening was associated with excellent surgical outcomes with 5 and 10-year survival rates exceeding 90 and 80%, respectively.

Proposal of a low-dose, long-pitch, dual-source chest CT protocol on third-generation dual-source CT using a tin filter for spectral shaping at 100 kVp for CoronaVirus Disease 2019 (COVID-19) patients: a feasibility study

Aim

To subjectively and objectively evaluate the feasibility and diagnostic reliability of a low-dose, long-pitch dual-source chest CT protocol on third-generation dual-source CT (DSCT) with spectral shaping at 100Sn kVp for COVID-19 patients.

Materials and methods

Patients with COVID-19 and positive swab-test undergoing to a chest CT on third-generation DSCT were included. The imaging protocol included a dual-energy acquisition (HD-DECT, 90/150Sn kVp) and fast, low-dose, long-pitch CT, dual-source scan at 100Sn kVp (LDCT). Subjective (Likert Scales) and objective (signal-to-noise and contrast-to-noise ratios, SNR and CNR) analyses were performed; radiation dose and acquisition times were recorded. Nonparametric tests were used.

Results

The median radiation dose was lower for LDCT than HD-DECT (Effective dose, ED: 0.28 mSv vs. 3.28 mSv, p = 0.016). LDCT had median acquisition time of 0.62 s (vs 2.02 s, p = 0.016). SNR and CNR were significantly different in several thoracic structures between HD-DECT and LDCT, with exception of lung parenchyma. Qualitative analysis demonstrated significant reduction in motion artifacts (p = 0.031) with comparable diagnostic reliability between HD-DECT and LDCT.

Conclusions

Ultra-low-dose, dual-source, fast CT protocol provides highly diagnostic images for COVID-19 with potential for reduction in dose and motion artifacts.

Substantial radiation dose reduction with consistent image quality using a novel low-dose stone composition protocol

PURPOSE

To assess a novel low-dose CT-protocol, combining a 150 kV spectral filtration unenhanced protocol (Sn150 kVp) and a stone-targeted dual-energy CT (DECT) in patients with urolithiasis.

METHODS

232 (151 male, 49 ± 16.4 years) patients with urolithiasis received a low-dose non-contrast enhanced CT (NCCT) for suspected urinary stones either on a third-generation dual-source CT system (DSCT) using Sn150 kVp (n = 116, group 1), or on a second-generation DSCT (n = 116 group 2) using single energy (SE) 120 kVp. For group 1, a subsequent dual-energy CT (DECT) with a short stone-targeted scan range was performed. Objective and subjective image qualities were assessed. Radiation metrics were compared.

RESULTS

534 stones (group 1: n = 242 stones; group 2: n = 292 stones) were found. In group 1, all 215 stones within the stone-targeted DECT-scan range were identified. DE analysis was able to distinguish between UA and non-UA calculi in all collected stones. 11 calculi (5.12%) were labeled as uric acid (UA) while 204 (94.88%) were labeled as non-UA calculi. There was no significant difference in overall Signal-to-noise-ratio between group 1 and group 2 (p = 0.819). On subjective analysis both protocols achieved a median Likert rating of 2 (p = 0.171). Mean effective dose was significantly lower for combined Sn150 kVp and stone-targeted DECT (3.34 ± 1.84 mSv) compared to single energy 120 kVp NCCT (4.45 ± 2.89 mSv) (p < 0.001), equaling a 24.9% dose reduction.

CONCLUSION

The evaluated novel low-dose stone composition protocol allows substantial radiation dose reduction with consistent high diagnostic image quality.

Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction

Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences. Since breast is sensitive to x-ray radiation, dose reduction of breast CT is an important topic, and for this purpose, few-view scanning is a main approach. In this article, we propose a Deep Efficient End-to-end Reconstruction (DEER) network for few-view breast CT image reconstruction. The major merits of our network include high dose efficiency, excellent image quality, and low model complexity. By the design, the proposed network can learn the reconstruction process with as few as O ( N ) parameters, where N is the side length of an image to be reconstructed, which represents orders of magnitude improvements relative to the state-of-the-art deep-learning-based reconstruction methods that map raw data to tomographic images directly. Also, validated on a cone-beam breast CT dataset prepared by Koning Corporation on a commercial scanner, our method demonstrates a competitive performance over the state-of-the-art reconstruction networks in terms of image quality. The source code of this paper is available at: https://github.com/HuidongXie/DEER.