Automated size-specific dose estimates framework in thoracic CT using convolutional neural network based on U-Net model

PURPOSE

This study aimed to develop an automated method that uses a convolutional neural network (CNN) for calculating size-specific dose estimates (SSDEs) based on the corrected effective diameter (Deff corr ) in thoracic computed tomography (CT).

METHODS

Transaxial images obtained from 108 adult patients who underwent non-contrast thoracic CT scans were analyzed. To calculate the Deff corr according to Mihailidis et al., the average relative electron densities for lung, bone, and other tissues were used to correct the lateral and anterior-posterior dimensions. The CNN architecture based on the U-Net algorithm was used for automated segmentation of three classes of tissues and the background region to calculate dimensions and Deff corr values. Then, 108 thoracic CT images and generated segmentation masks were used for network training. The water-equivalent diameter (Dw ) was determined according to the American Association of Physicists in Medicine Task Group 220. Linear regression and Bland-Altman analysis were performed to determine the correlations between SSDEDeff corr(automated) , SSDEDeff corr(manual) , and SSDEDw .

RESULTS

High agreement was obtained between the manual and automated methods for calculating the Deff corr SSDE. The mean values for the SSDEDeff corr(manual) , SSDEDw , and SSDEDeff corr(automated) were 14.3 ± 2.1 mGy, 14.6 ± 2.2 mGy, and 14.5 ± 2.4 mGy, respectively. The U-Net model was successfully trained and used to accurately predict SSDEs, with results comparable to manual-labeling results.

CONCLUSION

The proposed automated framework using a CNN offers a reliable and efficient solution for determining the Deff corr SSDE in thoracic CT.

Value of Thoracic CT in Blunt Trauma Patients With High Glasgow Coma Scale and Low Injury Severity Scale

OBJECTIVE

To determine the value of ordering a routine chest CT (CCT) in patients with blunt trauma presenting to the emergency department with a high GCS and low ISS, we retrospectively collected patient data including CT scan results, when physical examination and initial chest X-ray were normal in the trauma bay area.

METHODS

A retrospective data collection of 901 consecutive blunt trauma patients seen in the ED between 2017 and 2019 was analyzed. Data included physical examination, age, gender, current use of anticoagulation therapy, comorbid conditions, as well as the result of radiologic images, hospital length of stay, surgical intervention, and mortality. The patients were divided into two groups: group one (patients with negative physical examination; chest x-ray and CT) and group 2 (negative physical examination, positive or negative chest x-ray, and positive CT). Statistical analysis was performed using student's t-test and chi-square test.

RESULTS

Of the 901 patients there were 489 (54%) males and 412 (46%) females with a mean age of 56 years. There were 461 patients who had a physical examination, chest x-ray, abdominal and CCT done. Group one included 442 (96%) patients, with negative physical examination, negative chest X-ray and CT scan. In group 2, 19 (4%) patients who had positive CT and or chest x-ray. Both groups were similar in GCS and ISS. Of the 19 patients, sixteen patients had a positive CCT, and thirteen of those had a positive chest x-ray. In the three patients who had negative physical examination and chest x-ray, the CT findings included one with a nondisplaced 10th rib fracture and two patients with osteoporotic compression fractures of dorsal vertebrae. The rate of both chest x-ray and CCT being positive among a group of screened patients was 16% (3/19) and the rate of a negative chest x-ray but positive CT was 16% (3/19). The odds ratio between the two outcomes was one.

CONCLUSION

In blunt trauma patients presenting to the ED with a high GCS and low ISS score, when initial physical examination and chest x-ray are negative, routine CCT is of little value.

Evaluation of silicone rubber-lead shield's effectiveness in protecting the breast during thoracic CT

Radiation of thoracic computed tomography (CT) involves the breast although it is not considered an organ of interest. According to the International Commission on Radiological Protection (ICRP) No. 103, the breast is an organ with a high level of sensitivity when interacting with x-rays, increasing the potential risk of breast cancer. Therefore, the radiation dose must be optimized while maintaining image quality. The dose optimization can be accomplished using a radiation shield. This study aims to determine the effect of silicone rubber (SR)-lead (Pb) in various thicknesses as an alternative protective material limiting dose and preserving the image quality of the breast in thoracic CT. SR-Pb was made from SR and Pb by a simple method. The SR-Pb had thicknesses of 3, 6, 9, and 12 mm. The breast dose was measured using a CT dose profiler on the surface of the breast phantom. The CT number and the noise level of the resulting image were determined quantitatively. The dose without the radiation shield was 5.4 mGy. The doses measured using shielding with thicknesses of 3, 6, 9, and 12 mm were 5.2, 4.5, 4.3, and 3.3 mGy, respectively. Radiation shielding with a thickness of 12 mm reduced breast surface dose by up to 38%. The CT numbers and noise levels for the left and right breast phantom images were almost the same as those ​​without radiation shields indicating there were only slight artifacts in the image. Therefore, SR-Pb is considered a good shielding material which can be pplied in a clinical setting by placing it directly on the breast surface for dose optimization.

Quantitative and Morphological Assessment of Computed Tomography-depicted Gynecomastia in Spinal and Bulbar Muscular Atrophy

RATIONALE AND OBJECTIVES

To evaluate the prevalence, size, and characteristics of gynecomastia on thoracic computed tomography (CT) in patients with spinal and bulbar muscular atrophy (SBMA) or amyotrophic lateral sclerosis (ALS), compared to those of patients with myasthenia gravis (as controls).

MATERIALS AND METHODS

A total of 189 male patients (SBMA [n = 15]; ALS [n = 76]; control [n = 98]) who underwent thoracic computed tomography were included. The size of breast glandular tissue diameters, and characteristic of CT-depicted gynecomastia were compared.

RESULTS

On multivariate logistic regression analysis, mean breast glandular tissue diameter (adjusted odds ratio [aOR] 1.13, 95% confidence interval [CI] 1.08-1.19), maximum breast glandular tissue diameter (aOR 1.14, 95% CI 1.08-1.20), prevalence of CT-depicted gynecomastia (aOR 21.71, 95% CI 5.39-87.38), dendritic or diffuse pattern of gynecomastia (aOR 35.30, 95% CI 8.02-155.40), and bilateral gynecomastia (aOR 41.96, 95% CI 10.20-172.69) were positively associated with SBMA, but not ALS. On receiver operating characteristic (ROC) analysis, the area under the curve of the mean breast tissue diameter for predicting SBMA was 0.92 with the optimal cutoff value of 16.5 mm. The ROC analysis showed that a maximum breast tissue diameter of 18.6 mm can also effectively distinguish SBMA from controls.

CONCLUSION

These findings suggest that the evaluation of breast glandular tissue on thoracic CT could be a screening examination to distinguish SBMA patients and assist in its differential diagnosis.

Improving motion-mask segmentation in thoracic CT with multiplanar U-nets

PURPOSE

Motion-mask segmentation from thoracic computed tomography (CT) images is the process of extracting the region that encompasses lungs and viscera, where large displacements occur during breathing. It has been shown to help image registration between different respiratory phases. This registration step is, for example, useful for radiotherapy planning or calculating local lung ventilation. Knowing the location of motion discontinuity, that is, sliding motion near the pleura, allows a better control of the registration preventing unrealistic estimates. Nevertheless, existing methods for motion-mask segmentation are not robust enough to be used in clinical routine. This article shows that it is feasible to overcome this lack of robustness by using a lightweight deep-learning approach usable on a standard computer, and this even without data augmentation or advanced model design.

METHODS

A convolutional neural-network architecture with three 2D U-nets for the three main orientations (sagittal, coronal, axial) was proposed. Predictions generated by the three U-nets were combined by majority voting to provide a single 3D segmentation of the motion mask. The networks were trained on a database of nonsmall cell lung cancer 4D CT images of 43 patients. Training and evaluation were done with a K-fold cross-validation strategy. Evaluation was based on a visual grading by two experts according to the appropriateness of the segmented motion mask for the registration task, and on a comparison with motion masks obtained by a baseline method using level sets. A second database (76 CT images of patients with early-stage COVID-19), unseen during training, was used to assess the generalizability of the trained neural network.

RESULTS

The proposed approach outperformed the baseline method in terms of quality and robustness: the success rate increased from 53 % to 79 % without producing any failure. It also achieved a speed-up factor of 60 with GPU, or 17 with CPU. The memory footprint was low: less than 5 GB GPU RAM for training and less than 1 GB GPU RAM for inference. When evaluated on a dataset with images differing by several characteristics (CT device, pathology, and field of view), the proposed method improved the success rate from 53 % to 83 % .

CONCLUSION

With 5-s processing time on a mid-range GPU and success rates around 80 % , the proposed approach seems fast and robust enough to be routinely used in clinical practice. The success rate can be further improved by incorporating more diversity in training data via data augmentation and additional annotated images from different scanners and diseases. The code and trained model are publicly available.

Thoracic manifestations of inhalational injury caused by the Whakaari/White Island eruption

INTRODUCTION

Thoracic imaging of people who have survived exposure to a volcanic pyroclastic flow has not been described. In December 2019, an active volcano in New Zealand erupted with loss of life and severe burns to groups of people who were within one kilometre of a new fissure. Our aim was to describe the range of pulmonary abnormality in patients admitted to the Burns unit at Middlemore Hospital.

METHODS

We describe the initial radiographic and computed tomography (CT) appearance of lung injuries in 14 people close to this fissure who were transported to our national burns centre in Middlemore hospital. We compared these appearances with bronchoscopy findings and A-a gradients as a measure of oxygen utilisation.

RESULTS

All patients had chest radiographs and eight had CT scans within two days after admission. Nine had bronchoscopies within the first week. Two were repatriated to Australia, one of whom did not survive. Two died within 3 days after admission, and the remaining ten patients survived the first week. Eight patients required ongoing ventilation, seven of whom had abnormal CXRs or CT scans on admission. Two of these patients developed an ARDS pattern of oedema reflecting lung injury from the toxic surge but they recovered. In the five patients who survived the first week with relatively minor evidence of lung injury, bibasal atelectasis was the most common finding.

CONCLUSION

Pyroclastic flow effect caused a variety of lung abnormalities most likely due to toxic gas emissions. Upper airway burns were seen at bronchoscopy in only 5 patients. An ARDS response in the lungs of two patients improved within three months.

Trauma-Associated Pulmonary Laceration in Dogs-A Cross Sectional Study of 364 Dogs

In this study, we describe the computed tomography (CT) features of pulmonary laceration in a study population, which included 364 client-owned dogs that underwent CT examination for thoracic trauma, and compared the characteristics and outcomes of dogs with and without CT evidence of pulmonary laceration. Lung laceration occurred in 46/364 dogs with thoracic trauma (prevalence 12.6%). Dogs with lung laceration were significantly younger than dogs in the control group (median 42 months (interquartile range (IQR) 52.3) and 62 months (IQR 86.1), respectively; p = 0.02). Dogs with lung laceration were significantly heavier than dogs without laceration (median 20.8 kg (IQR 23.3) and median 8.7 kg (IQR 12.4 kg), respectively p < 0.0001). When comparing groups of dogs with thoracic trauma with and without lung laceration, the frequency of high-energy motor vehicle accident trauma was more elevated in dogs with lung laceration than in the control group. No significant differences were observed between groups regarding tge frequency and length of hospitalization and 30-day mortality. Similar to the human classification scheme, four CT patterns are described in dogs in this study: Type 1, large pulmonary laceration located deeply in the pulmonary parenchyma or around an interlobar fissure; Type 2, laceration occurring in the paraspinal lung parenchyma, not associated with vertebral fracture; Type 3, subpleural lung laceration intimately associated with an adjacent rib or vertebral fracture; Type 4, subpleural lesions not associated with rib fractures. Complications were seen in 2/46 dogs and included lung abscess and collapse.

[Secondary syphilis with pulmonary involvement]

We present the case of a 62-year-old woman who consulted for fever (38°), stabbing thoracic pain (on one side), and pruritic skin lesions. She underwent peripheral blood tests, chest X-rays and CT. Her symptoms were interpreted as severe communityacquired pneumonia. After a treatment with antibiotics, her skin lesions persisted, and other symptoms were only partially relieved. A skin biopsy was performed, which revealed Treponema pallidum. Such finding was confirmed through positive serum VDRL and FTA-ABS tests. The patient received 4 doses of benzathine penicillin G with favorable evolution of skin lesions and improvement of radiological images.

Evaluation of a projection-domain lung nodule insertion technique in thoracic CT

Task-based assessment of computed tomography (CT) image quality requires a large number of cases with ground truth. Inserting lesions into existing cases to simulate positive cases is a promising alternative approach. The aim of this study was to evaluate a recently-developed raw-data based lesion insertion technique in thoracic CT. Lung lesions were segmented from patient CT images, forward projected, and reinserted into the same patient CT projection data. In total, 32 nodules of various attenuations were segmented from 21 CT cases. Two experienced radiologists and 2 residents blinded to the process independently evaluated these inserted nodules in two sub-studies. First, the 32 inserted and the 32 original nodules were presented in a randomized order and each received a rating score from 1 to 10 (1=absolutely artificial to 10=absolutely realistic). Second, the inserted and the corresponding original lesions were presented side-by-side to each reader, who identified the inserted lesion and provided a confidence score (1=no confidence to 5=completely certain). For the randomized evaluation, discrimination of real versus artificial nodules was poor with areas under the receiver operative characteristic curves being 0.69 (95% CI: 0.58-0.78), 0.57 (95% CI: 0.46-0.68), and 0.62 (95% CI: 0.54-0.69) for the 2 radiologists, 2 residents, and all 4 readers, respectively. For the side-by-side evaluation, although all 4 readers correctly identified inserted lesions in 103/128 pairs, the confidence score was moderate (2.6). Our projection-domain based lung nodule insertion technique provides a robust method to artificially generate clinical cases that prove to be difficult to differentiate from real cases.

Are combined CT scans of the thorax being overused?

PURPOSE

A news article in June 2011 reported that Medicare claims showed considerable overuse of "double" CT scans of the thorax (ie, combined scans without contrast followed by with contrast) at a number of hospitals. Most radiologists agree that they should be done only on rare occasions. The aim of this study was to determine what proportion of all thoracic CT scans are combined scans in the Medicare population.

METHODS

The data sources were the Medicare Part B Physician/Supplier Procedure Summary Master Files for 2001 to 2011. The 3 Current Procedural Terminology codes for thoracic CT (with contrast, without contrast, and without plus with contrast) were selected. Utilization rates per 1,000 beneficiaries and the percentage that were combined scans were calculated.

RESULTS

The utilization rate of combined scans increased from 2001 through 2006, remained steady in 2007, but then decreased sharply thereafter. The compound annual rate of change from 2007 to 2011 was -10.4%. From 2001 through 2006, combined thoracic CT scans constituted 6.0% to 6.1% of all thoracic CT scans. However, from 2006 to 2011, this percentage progressively declined, reaching a low of 4.2% in 2011.

CONCLUSIONS

Despite the 2011 news report, only a very small percentage of thoracic CT scans nationwide are done both without and with contrast. Moreover, that percentage dropped by almost one-third from 2006 to 2011, suggesting that the practice is declining. The figure of 4.2% can be used as a benchmark against which to judge radiology facilities in the future.

A review of computer-aided diagnosis in thoracic and colonic imaging

Medical imaging has been indispensable in medicine since the discovery of x-rays. Medical imaging offers useful information on patients' medical conditions and on the causes of their symptoms and diseases. As imaging technologies advance, a large number of medical images are produced which physicians/radiologists must interpret. Thus, computer aids are demanded and become indispensable in physicians' decision making based on medical images. Consequently, computer-aided detection and diagnosis (CAD) has been investigated and has been an active research area in medical imaging. CAD is defined as detection and/or diagnosis made by a radiologist/physician who takes into account the computer output as a "second opinion". In CAD research, detection and diagnosis of lung and colorectal cancer in thoracic and colonic imaging constitute major areas, because lung and colorectal cancers are the leading and second leading causes, respectively, of cancer deaths in the U.S. and also in other countries. In this review, CAD of the thorax and colon, including CAD for detection and diagnosis of lung nodules in thoracic CT, and that for detection of polyps in CT colonography, are reviewed.