Effectiveness of a single low-dose computed tomography screening for lung cancer: A population-based perspective cohort study in China.

The purpose of this perspective cohort study was to evaluate the effectiveness of low-dose computed tomography (LDCT) screening for lung cancer in China. This study was conducted under the China Urban Cancer Screening Program (CanSPUC). The analysis was based on participants aged 40 to 74 years from 2012 to 2019. A total of 255 569 eligible participants were recruited in the study. Among the 58 136 participants at high risk of lung cancer, 20 346 (35.00%) had a single LDCT scan (defined as the screened group) and 37 790 (65.00%) not (defined as the non-screened group). Overall, 1162 participants were diagnosed with lung cancer at median follow-up time of 5.25 years. The screened group had the highest cumulative incidence of lung cancer and the non-screened group had the highest cumulative lung cancer mortality and all-cause cumulative mortality. We performed inverse probability weighting (IPW) to account for potential imbalances, and Cox proportional hazards model to estimate the weighted association between mortality and LDCT scans. After IPW adjusted with baseline characteristics, the lung cancer incidence density was significantly increased (37.0% increase) (HR1.37 [95%CI 1.12-1.69]), lung cancer mortality was decreased (31.0% decrease) (HR0.69 [95%CI 0.49-0.97]), and the all-cause mortality was significantly decreased (23.0% lower) (HR0.77 [95% CI 0.68-0.87]) in the screened group. In summary, a single LDCT for lung cancer screening will reduce the mortality of lung cancer and all-cause mortality in China.

Ultra-low-dose vs. standard-of-care-dose CT of the chest in patients with post-COVID-19 conditions-a prospective intra-patient multi-reader study.

OBJECTIVES: To conduct an intrapatient comparison of ultra-low-dose computed tomography (ULDCT) and standard-of-care-dose CT (SDCT) of the chest in terms of the diagnostic accuracy of ULDCT and intrareader agreement in patients with post-COVID conditions. METHODS: We prospectively included 153 consecutive patients with post-COVID-19 conditions. All participants received an SDCT and an additional ULDCT scan of the chest. SDCTs were performed with standard imaging parameters and ULDCTs at a fixed tube voltage of 100 kVp (with tin filtration), 50 ref. mAs (dose modulation active), and iterative reconstruction algorithm level 5 of 5. All CT scans were separately evaluated by four radiologists for the presence of lung changes and their consistency with post-COVID lung abnormalities. Radiation dose parameters and the sensitivity, specificity, and accuracy of ULDCT were calculated. RESULTS: Of the 153 included patients (mean age 47.4 ± 15.3 years; 48.4% women), 45 (29.4%) showed post-COVID lung abnormalities. In those 45 patients, the most frequently detected CT patterns were ground-glass opacities (100.0%), reticulations (43.5%), and parenchymal bands (37.0%). The accuracy, sensitivity, and specificity of ULDCT compared to SDCT for the detection of post-COVID lung abnormalities were 92.6, 87.2, and 94.9%, respectively. The median total dose length product (DLP) of ULDCTs was less than one-tenth of the radiation dose of our SDCTs (12.6 mGy*cm [9.9; 15.5] vs. 132.1 mGy*cm [103.9; 160.2]; p < 0.001). CONCLUSION: ULDCT of the chest offers high accuracy in the detection of post-COVID lung abnormalities compared to an SDCT scan at less than one-tenth the radiation dose, corresponding to only twice the dose of a standard chest radiograph in two views. CLINICAL RELEVANCE STATEMENT: Ultra-low-dose CT of the chest may provide a favorable, radiation-saving alternative to standard-dose CT in the long-term follow-up of the large patient cohort of post-COVID-19 patients.

Photon-Counting Detector CT With Denoising for Imaging of the Osseous Pelvis at Low Radiation Doses: A Phantom Study.

BACKGROUND. Photon-counting detector (PCD) CT may allow lower radiation doses than used for conventional energy-integrating detector (EID) CT, with preserved image quality. OBJECTIVE. The purpose of this study was to compare PCD CT and EID CT, reconstructed with and without a denoising tool, in terms of image quality of the osseous pelvis in a phantom, with attention to low radiation doses. METHODS. A pelvic phantom comprising human bones in acrylic material mimicking soft tissue underwent PCD CT and EID CT at various tube potentials and radiation doses ranging from 0.05 to 5.00 mGy. Additional denoised reconstructions were generated using a commercial tool. Noise was measured in the acrylic material. Two readers performed independent qualitative assessments that entailed determining the denoised EID CT reconstruction with the lowest acceptable dose and then comparing this reference reconstruction with PCD CT reconstructions without and with denoising, using subjective Likert scales. RESULTS. Noise was lower for PCD CT than for EID CT. For instance, at 0.05 mGy and 100 kV with tin filter, noise was 38.4 HU for PCD CT versus 48.8 HU for EID CT. Denoising further reduced noise; for example, for PCD CT at 100 kV with tin filter at 0.25 mGy, noise was 19.9 HU without denoising versus 9.7 HU with denoising. For both readers, lowest acceptable dose for EID CT was 0.10 mGy (total score, 11 of 15 for both readers). Both readers somewhat agreed that PCD CT without denoising at 0.10 mGy (reflecting reference reconstruction dose) was relatively better than the reference reconstruction in terms of osseous structures, artifacts, and image quality. Both readers also somewhat agreed that denoised PCD CT reconstructions at 0.10 mGy and 0.05 mGy (reflecting matched and lower doses, respectively, with respect to reference reconstruction dose) were relatively better than the reference reconstruction for the image quality measures. CONCLUSION. PCD CT showed better-quality images than EID CT when performed at the lowest acceptable radiation dose for EID CT. PCD CT with denoising yielded better-quality images at a dose lower than lowest acceptable dose for EID CT. CLINICAL IMPACT. PCD CT with denoising could facilitate lower radiation doses for pelvic imaging.

Chest CT at X-Ray Dose Using a Noise-Mitigating Weighted Projection: The Thoracic Tomogram. Diagnostic Performance for Pneumonia Detection in Hemato-Oncology Patients.

PURPOSE: To compare the diagnostic performance of a thick-slab reconstruction obtained from an ultra-low-dose CT (termed thoracic tomogram) with standard-of-care low-dose CT (SOC-CT) for rapid interpretation and detection of pneumonia in hemato-oncology patients. METHODS: Hemato-oncology patients with a working diagnosis of pneumonia underwent an SOC-CT followed by an ultra-low-dose CT, from which the thoracic tomogram (TT) was reconstructed. Three radiologists evaluated the TT and SOC-CT in the following categories: (I) infectious/inflammatory opacities, (II) small airways infectious/inflammatory changes, (III) atelectasis, (IV) pleural effusions, and (V) interstitial abnormalities. The TT interpretation time and radiation dose were recorded. Sensitivity, specificity, diagnostic accuracy, ROC, and AUC were calculated with the corresponding power analyses. The agreement between TT and SOC-CT was calculated by Correlation Coefficient for Repeated Measures (CCRM), and the Shrout-Fleiss intra-class correlations test was used to calculate interrater agreement. RESULTS: Forty-seven patients (mean age 58.7 ± 14.9 years; 29 male) were prospectively enrolled. Sensitivity, specificity, accuracy, AUC, and Power for categories I/II/III/IV/V were: 94.9/99/97.9/0.971/100, 78/91.2/86.5/0.906/100, 88.6/100/97.2/0.941/100, 100/99.2/99.3/0.995/100, and 47.6/100/92.2/0.746/87.3. CCRM between TT and SOC-CT for the same categories were .97/.81/.92/.96/.62 with an interobserver agreement of .93/.88/.82/.96/.61. Mean interpretation time was 18.6 ± 5.4 seconds. The average effective radiation dose of TT was similar to a frontal and lateral chest X-ray (0.27 ± 0.08 vs 1.46 ± 0.64 mSv for SOC-CT; P < .01). CONCLUSION: Thoracic tomograms provide comparable diagnostic information to SOC-CT for the detection of pneumonia in immunocompromised patients at one-fifth of the radiation dose with high interobserver agreement.

APNet: Adaptive projection network for medical image denoising.

BACKGROUND: In clinical medicine, low-dose radiographic image noise reduces the quality of the detected image features and may have a negative impact on disease diagnosis. OBJECTIVE: In this study, Adaptive Projection Network (APNet) is proposed to reduce noise from low-dose medical images. METHODS: APNet is developed based on an architecture of the U-shaped network to capture multi-scale data and achieve end-to-end image denoising. To adaptively calibrate important features during information transmission, a residual block of the dual attention method throughout the encoding and decoding phases is integrated. A non-local attention module to separate the noise and texture of the image details by using image adaptive projection during the feature fusion. RESULTS: To verify the effectiveness of APNet, experiments on lung CT images with synthetic noise are performed, and the results demonstrate that the proposed approach outperforms recent methods in both quantitative index and visual quality. In addition, the denoising experiment on the dental CT image is also carried out and it verifies that the network has a certain generalization. CONCLUSIONS: The proposed APNet is an effective method that can reduce image noise and preserve the required image details in low-dose radiographic images.

Promoting lung cancer screening of high-risk patients by primary care providers.

BACKGROUND: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) of the chest of eligible patients remains low. Accordingly, augmentation of appropriate LCS referrals by primary care providers (PCPs) was sought. METHODS: The quality improvement (QI) project was performed between April 2021 and June 2022. It incorporated patient education, shared decision-making (SDM) with PCPs, and tracking of initial LDCT completion. In each case, lag time (LT) to LCS and pack-years (PYs) were calculated from initial LCS eligibility. The cohort's scores were compared to national scores. Patient zip codes were used to create a geographic map of our cohort for comparison with public health data. RESULTS: An immediate and sustained increase in weekly LCS referrals from PCPs was recorded. Of 337 initial referrals, 95% were men, consisting of 66.2% Black, 28.4% White, and 5.4% other. Mean PY was less for minorities (45.3 vs. 37.3 years; p = .0002) but mean LT was greater for Whites (7.9 vs. 6.2 years; p = .03). Twenty-five percent of veterans failed to report to their scheduled screening, and two declined referrals. Notably, most no-show patients lived in transit deserts. Furthermore, Lung-RADS scores 4B/4X were more than double the expected prevalence (p = .008). CONCLUSIONS: The PCPs in this study successfully augmented LCS referrals. A substantial proportion of these patients were no-shows, and our data suggest complex racial and socioeconomic factors as contributing variables. In addition, a higher-than-expected number of initial Lung-RADS scores 4B/4X were reported. A large, multisite QI project is warranted to address overcoming potential transportation barriers in high-risk patient populations.

Sequential low-dose CT thorax scans to determine invasive pulmonary fungal infection incidence after allogeneic hematopoietic cell transplantation.

Invasive fungal disease (IFD) during neutropenia goes along with a high mortality for patients after allogeneic hematopoietic cell transplantation (alloHCT). Low-dose computed tomography (CT) thorax shows good sensitivity for the diagnosis of IFD with low radiation exposure. The aim of our study was to evaluate sequential CT thorax scans at two time points as a new reliable method to detect IFD during neutropenia after alloHCT. We performed a retrospective single-center observational study in 265/354 screened patients admitted for alloHCT from June 2015 to August 2019. All were examined by a low-dose CT thorax scan at admission (CT t0) and after stable neutrophil recovery (CT t1) to determine the incidences of IFD. Furthermore, antifungal prophylaxis medications were recorded and cohorts were analyzed for statistical differences in IFD incidence using the sequential CT scans. In addition, IFD cases were classified according to EORTC 2008. At CT t0 in 9.6% of the patients, an IFD was detected and antifungal therapy initiated. The cumulative incidence of IFD in CT t1 in our department was 14%. The use of Aspergillus-effective prophylaxis through voriconazole or posaconazole decreased CT thorax t1 suggesting IFD is statistically significant compared to prophylaxis with fluconazole (5.6% asp-azol group vs 16.3% fluconazole group, p = 0.048). In 86%, CT t1 was negative for IFD. Low-dose sequential CT thorax scans are a valuable tool to detect pulmonary IFDs and guide antifungal prophylaxis and therapies. Furthermore, a negative CT t1 scan shows a benefit by allowing discontinuation of antifungal medication sparing patients from drug interactions and side effects.

Low-dose coronary artery calcium scoring compared to the standard protocol.

BACKGROUND: We aimed to compare coronary artery calcium scoring (CACS) with computed tomography (CT) with 80 and 120 kVp in a large patient population and to establish whether there is a difference in risk classification between the two scores. METHODS: Patients with suspected CAD undergoing MPS were included. All underwent standard CACS assessment with 120-kVp tube voltage and with 80 kVp. Two datasets (low-dose and standard) were generated and compared. Risk classes (0 to 25, 25 to 50, 50 to 75, 75 to 90, and > 90%) were recorded. RESULTS: 1511 patients were included (793 males, age 69 ± 9.1 years). There was a very good correlation between scores calculated with 120 and 80 kVp (R = 0.94, R2 = 0.88, P < .001), with Bland-Altman limits of agreement of - 563.5 to 871.9 and a bias of - 154.2. The proportion of patients assigned to the < 25% percentile class (P = .03) and with CACS = 0 differed between the two protocols (n = 264 vs 437, P < .001). CONCLUSION: In a large patient population, despite a good correlation between CACS calculated with standard and low-dose CT, there is a systematic underestimation of CACS with the low-dose protocol. This may have an impact especially on the prognostic value of the calcium score, and the established "power of zero" may no longer be warranted if CACS is assessed with low-dose CT.

U.S. Newspaper Coverage of Lung Cancer Screening From 2010 to 2022.

BACKGROUND. Newspapers are an important source of information for the public about low-dose CT (LDCT) lung cancer screening (LCS) and may influence public perception and knowledge of this important cancer screening service. OBJECTIVE. The purpose of this article was to evaluate the volume, content, and other characteristics of articles pertaining to LCS that have been published in U.S. newspapers. METHODS. The ProQuest U.S. Newsstream database was searched for U.S. newspaper articles referring to LCS published between January 1, 2010 (the year of publication of the National Lung Screening Trial results), and March 28, 2022. Search terms included "lung cancer screening(s)," "lung screening(s)," "low dose screening(s)," and "LDCT." Search results were reviewed to identify those articles mentioning LCS. Characteristics of included articles and originating newspapers were extracted. Articles were divided among nine readers, who independently assessed article sentiment regarding LCS and additional article content using a standardized form. RESULTS. The final analysis included 859 articles, comprising 816 nonsyndicated articles published in a single newspaper and 43 syndicated articles published in multiple newspapers. Sentiment regarding LCS was positive in 76% (651/859) of articles, neutral in 21% (184/859), and negative in 3% (24/859). Frequency of positive sentiment was lowest (61%) for articles published from 2010 to 2012; frequency of negative sentiment was highest (8%) for articles published in newspapers in the highest quartile for weekly circulation. LCS enrollment criteria were mentioned in 52% of articles, smoking cessation programs in 28%, need for annual CT in 27%, and shared decision-making in 4%. Cost or insurance coverage for LCS was mentioned in 33% in articles. A total of 64% of articles mentioned at least one benefit of LCS (most commonly early detection or possible cure of lung cancer), and 23% mentioned at least one harm (most commonly false-positives). A total of 9% of articles interviewed or mentioned a radiologist. CONCLUSION. The sentiment of U.S. newspaper articles covering LCS from 2010 to 2022 was overall positive. However, certain key elements of LCS were infrequently mentioned. CLINICAL IMPACT. The findings highlight areas for potential improvement of LCS media coverage; radiologists have an opportunity to take a more active role in this coverage.

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.

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.

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.

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.

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.

A deep learning algorithm for detecting lytic bone lesions of multiple myeloma on CT.

BACKGROUND: Whole-body low-dose CT is the recommended initial imaging modality to evaluate bone destruction as a result of multiple myeloma. Accurate interpretation of these scans to detect small lytic bone lesions is time intensive. A functional deep learning) algorithm to detect lytic lesions on CTs could improve the value of these CTs for myeloma imaging. Our objectives were to develop a DL algorithm and determine its performance at detecting lytic lesions of multiple myeloma. METHODS: Axial slices (2-mm section thickness) from whole-body low-dose CT scans of subjects with biochemically confirmed plasma cell dyscrasias were included in the study. Data were split into train and test sets at the patient level targeting a 90%/10% split. Two musculoskeletal radiologists annotated lytic lesions on the images with bounding boxes. Subsequently, we developed a two-step deep learning model comprising bone segmentation followed by lesion detection. Unet and "You Look Only Once" (YOLO) models were used as bone segmentation and lesion detection algorithms, respectively. Diagnostic performance was determined using the area under the receiver operating characteristic curve (AUROC). RESULTS: Forty whole-body low-dose CTs from 40 subjects yielded 2193 image slices. A total of 5640 lytic lesions were annotated. The two-step model achieved a sensitivity of 91.6% and a specificity of 84.6%. Lesion detection AUROC was 90.4%. CONCLUSION: We developed a deep learning model that detects lytic bone lesions of multiple myeloma on whole-body low-dose CTs with high performance. External validation is required prior to widespread adoption in clinical practice.

A Joint-Parameter Estimation and Bayesian Reconstruction Approach to Low-Dose CT.

Most penalized maximum likelihood methods for tomographic image reconstruction based on Bayes' law include a freely adjustable hyperparameter to balance the data fidelity term and the prior/penalty term for a specific noise-resolution tradeoff. The hyperparameter is determined empirically via a trial-and-error fashion in many applications, which then selects the optimal result from multiple iterative reconstructions. These penalized methods are not only time-consuming by their iterative nature, but also require manual adjustment. This study aims to investigate a theory-based strategy for Bayesian image reconstruction without a freely adjustable hyperparameter, to substantially save time and computational resources. The Bayesian image reconstruction problem is formulated by two probability density functions (PDFs), one for the data fidelity term and the other for the prior term. When formulating these PDFs, we introduce two parameters. While these two parameters ensure the PDFs completely describe the data and prior terms, they cannot be determined by the acquired data; thus, they are called complete but unobservable parameters. Estimating these two parameters becomes possible under the conditional expectation and maximization for the image reconstruction, given the acquired data and the PDFs. This leads to an iterative algorithm, which jointly estimates the two parameters and computes the to-be reconstructed image by maximizing a posteriori probability, denoted as joint-parameter-Bayes. In addition to the theoretical formulation, comprehensive simulation experiments are performed to analyze the stopping criterion of the iterative joint-parameter-Bayes method. Finally, given the data, an optimal reconstruction is obtained without any freely adjustable hyperparameter by satisfying the PDF condition for both the data likelihood and the prior probability, and by satisfying the stopping criterion. Moreover, the stability of joint-parameter-Bayes is investigated through factors such as initialization, the PDF specification, and renormalization in an iterative manner. Both phantom simulation and clinical patient data results show that joint-parameter-Bayes can provide comparable reconstructed image quality compared to the conventional methods, but with much less reconstruction time. To see the response of the algorithm to different types of noise, three common noise models are introduced to the simulation data, including white Gaussian noise to post-log sinogram data, Poisson-like signal-dependent noise to post-log sinogram data and Poisson noise to the pre-log transmission data. The experimental outcomes of the white Gaussian noise reveal that the two parameters estimated by the joint-parameter-Bayes method agree well with simulations. It is observed that the parameter introduced to satisfy the prior's PDF is more sensitive to stopping the iteration process for all three noise models. A stability investigation showed that the initial image by filtered back projection is very robust. Clinical patient data demonstrated the effectiveness of the proposed joint-parameter-Bayes and stopping criterion.

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.

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.

Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising.

Computer tomography (CT) has played an essential role in the field of medical diagnosis. Considering the potential risk of exposing patients to X-ray radiations, low-dose CT (LDCT) images have been widely applied in the medical imaging field. Since reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures, or false lesions derived from noise. To tackle these issues, we propose a novel degradation adaption local-to-global transformer (DALG-Transformer) for restoring the LDCT image. Specifically, the DALG-Transformer is built on self-attention modules which excel at modeling long-range information between image patch sequences. Meanwhile, an unsupervised degradation representation learning scheme is first developed in medical image processing to learn abstract degradation representations of the LDCT images, which can distinguish various degradations in the representation space rather than the pixel space. Then, we introduce a degradation-aware modulated convolution and gated mechanism into the building modules (i.e., multi-head attention and feed-forward network) of each Transformer block, which can bring in the complementary strength of convolution operation to emphasize on the spatially local context. The experimental results show that the DALG-Transformer can provide superior performance in noise removal, structure preservation, and false lesions elimination compared with five existing representative deep networks. The proposed networks may be readily applied to other image processing tasks including image reconstruction, image deblurring, and image super-resolution.

Combining the advantages of 3-D and 2-D templating of total hip arthroplasty using a new tin-filtered ultra-low-dose CT of the hip with comparable radiation dose to conventional radiographs.

BACKGROUND: Inaccurately scaled radiographs for total hip arthroplasty (THA) templating are a source of error not recognizable to the surgeon and may lead to inaccurate reconstruction and thus revision surgery or litigation. Planning based on computed tomography (CT) scans is more accurate but associated with higher radiation exposure. The aim of this study was (1) to retrospectively assess the scaling deviation of pelvic radiographs; (2) to prospectively assess the feasibility and the radiation dose of THA templating on radiograph-like images reconstructed from a tin-filtered ultra-low-dose CT dataset. METHODS: 120 consecutive patients were retrospectively analyzed to assess the magnification error of our current THA templates. 27 consecutive patients were prospectively enrolled and a radiographic work-up in the supine position including a new tin-filtered ultra-low-dose CT scan protocol was obtained. THA was templated on both images. Radiation dose was calculated. RESULTS: Scaling deviations between preoperative radiographs and CT of ≥ 5% were seen in 25% of the 120 retrospectively analyzed patients. Between the two templates trochanter tip distance differed significantly (Δ2.4 mm, 0-7 mm, p = 0.035)), predicted femoral shaft size/cup size was the same in 45%/41%. The radiation dose of the CT (0.58 mSv, range 0.53-0.64) was remarkably low. CONCLUSION: Scaling deviations of pelvic radiographs for templating THA may lead to planning errors of ≥ 3 mm in 25% and ≥ 6 mm in 2% of the patients. 2-D templating on radiograph-like images based on tin-filtered ultra-low-dose CT eliminates this source of error without increased radiation dose. LEVEL OF EVIDENCE: Retrospective and prospective comparative study, Level III.