Current Medical Imaging - Volume 20, Issue 1, 2024

Abdominal multi-slice helical computed tomography (CT) and contrast-enhanced scanning have been widely recognized clinically.

The impact of the deep learning image reconstruction (DLIR) on the quality of dynamic contrast-enhanced CT imaging of primary liver cancer lesions was evaluated through comparison with the filtered back projection (FBP) and the new generation of adaptive statistical iterative reconstruction-V (ASIR-V).

We evaluated the image noise of the lesion, fine structures inside the lesion, and diagnostic confidence in 48 liver cancer subjects. The CT values of the solid part of the lesion and the adjacent normal liver tissue and the systolic and diastolic blood pressure (SD) values of the right paravertebral muscle were measured. The muscle SD value was considered as the background noise of the image, and the signal noise ratio (SNR) and contrast signal-to-noise ratio (CNR) of the lesion and normal liver parenchyma were calculated.

High consistency in the evaluation of image noise (Kappa = 0.717). The Kappa values for margin/pseudocapsule, fine structure within the lesion, and diagnostic confidence were 0.463, 0.527, and 0.625, respectively. Besides, the differences in SD, SNR and CNR data of reconstructed lesion images among the six groups were statistically significant.

The contrast-enhanced CT image noise of DLIR-H in the portal venous phase is much lower than that of ASIR-V and FBP in primary liver cancer patients. In terms of the lesion structure display, the new reconstruction algorithm DLIR is superior.

To investigate CT, MRI, and PET/CT features with histopathological findings of primary thymic neuroendocrine tumor.

All 9 cases with pathologically proven primary thymic neuroendocrine tumors were reviewed retrospectively. Among them, 7 underwent enhanced CT, 1 with MRI (enhanced) and another with PET/CT scan. Multiple characters were examined, including tumor location, contour, CT attenuation, enhancement pattern, involvement of surrounding structure and lymphadenopathy.

Among 9 patients studied, 7 (77%) masses were located in the anterior superior mediastinum, 1 in the anterior superior-middle mediastinum, and 1 in the anterior and middle mediastinum. The maximum diameter (longitudinal) ranged from 4.2 to 23 cm (mean ± standard deviation, 9.5 cm ± 2.8). Four masses had irregular, 3 had lobulated, and 2 had smooth contours, while 8 masses had clear margins and 1 had an ill-defined margin. Six masses showed heterogeneous attenuation with necrotic/cystic component (n=5), calcification (n=2) and hemorrhage(n=1), and 3 showed homogeneous attenuation on the non-enhanced image. After contrast administration, 8 masses showed heterogeneous attenuation, and 1 showed homogeneous attenuation with tumor vessels visible in 4 masses. Among all, 8 masses showed strong enhancement, and 1 showed moderate enhancement in comparison to muscles in the anterior thoracic wall on enhanced images. Involvement of adjacent mediastinal structures was observed in 5 cases. Immunohistochemical analysis showed that the tumor cells were positive for CgA, Syn, CK, CD56 and EMA.

Primary NETs are large masses located anterior superior mediastinum, irregular in contour, showing heterogeneous attenuation with necrotic/cystic component and strong heterogeneous enhancement with tumor vessels, compressing local mediastinal structures. In addition, immunohis-tochemical examination is required in such a diagnosis.

With the rapid development in computed tomography (CT), the establishment of artificial intelligence (AI) technology and improved awareness of health in folks in the decades, it becomes easier to detect and predict pulmonary nodules with high accuracy. The accurate identification of benign and malignant pulmonary nodules has been challenging for radiologists and clinicians. Therefore, this study applied the unenhanced CT images-based radiomics to identify the benign or malignant pulmonary nodules.

One hundred and four cases of pulmonary nodules confirmed by clinicopathology were analyzed retrospectively, including 79 cases of malignant nodules and 25 cases of benign nodules. They were randomly divided into a training group (n = 74 cases) and test group (n = 30 cases) according to the ratio of 7:3. Using ITK-SNAP software to manually mark the region of interest (ROI), and using AK software (Analysis kit, Version 3.0.0.R, GE Healthcare, America) to extract image radiomics features, a total of 1316 radiomics features were extracted. Then, the minimum–redundancy–maximum–relevance (mRMR) algorithms were used to preliminarily reduce the dimension, and retain the 30 most meaningful features, and then the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the optimal subset of features, so as to establish the final model. The performance of the model was evaluated by using the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, sensitivity and specificity. Calibration refers to the agreement between observed endpoints and predictions, and the clinical benefit of the model to patients was evaluated by decision curve analysis (DCA).

The accuracy, sensitivity, and specificity of the training and testing groups were 81.0%, 77.7%, 82.1% and 76.6%, 85.7%, 73.9%, respectively, and the corresponding AUCs were of 0.83 in both groups.

CT image-based radiomics could differentiate benign from malignant pulmonary nodules, which might provide a new method for clinicians to detect benign and malignant pulmonary nodules.

This study aims to assess the accuracy of computed tomography (CT) in detecting recurrent laryngeal tumors after failed chemoradiation therapy (CRT).

Local recurrence of laryngeal tumors following CRT has been reported in approximately 25%, yet it is often difficult to detect.

Ten patients with laryngeal cancer who failed CRT and subsequently underwent salvage total laryngectomy were included. The laryngeal subsites involved in the tumor were identified based on postoperative pathology. The corresponding preoperative CT scans were selected for review by seven experts (head-and-neck surgeons or radiologists) who scored the extent of tumor spread on each scan on a 5-point scale, from no tumor detected to clearly visible tumor.

The rates of high tumor detectability (scores 4-5) varied according to laryngeal subsite, from 75% in the glottic region, to 45% in the subglottic region, and to 19% in the supraglottic region (P=0.01). The detectability rates were higher on scans performed 2 years or more after CRT.

The CT evaluation of laryngeal cancer after CRT has limited value, particularly in the epiglottis and subglottis.

Immunoglobulin G4-related disease (IgG4-RD) is a relatively rare immune-mediated chronic inflammatory disease with fibrosis newly defined in recent years. It can involve multiple systems and organs with complex clinical manifestations. Due to mass-like lesions, it is easily misdiagnosed as tumors.

Herein, we report a 57-year-old woman treated for submandibular mass and anosmia. The serum IgG4 level was increased. The biopsy of the submandibular gland indicated salivary gland tissue and hyperplasia of fibrous tissue and lymphoid tissue. Immunohistochemical examination showed a large number of IgG4-positive plasma cells. M protein was found in the patient's serum by immunofixation electrophoresis, and plasma cell diseases were excluded by bone marrow puncture. PET/CT examination showed that besides the submandibular glands, the parotid gland, common bile duct, the transitional part of the left renal pelvis and ureter, retroperitoneum in the lower abdomen, and multiple lymph nodes were also involved. The patient was diagnosed with IgG4-RD, and after treatment with glucocorticoid, the enlargement of submandibular glands and decreased olfactory function improved. After 14 weeks of treatment, the serological examinations, PET/CT, and ultrasound re-examination results showed significant improvement. So far, the patient has been followed up for 27 months and is in continuous remission.

This case report aims to raise awareness of IgG4-RD and explore the value of PET/CT in the diagnosis and efficacy monitoring of the disease.

The accurate diagnosis of superficial lymphadenopathy is challenging. We aim to explore a non-invasive and accurate machine-learning method for distinguishing benign lymph nodes, lymphoma, and metastatic lymph nodes.

The clinical data and ultrasound images of 160 patients with superficial lymphadenopathy (58 benign lymph nodes, 62 lymphoma, 40 metastatic lymph nodes) admitted to our hospital from January 2020 to November 2022 were retrospectively studied. Patients were randomly divided into a training set and test set according to the ratio of 6:4. Firstly, the radiomics features of each lymph node were extracted, and then a series of statistical methods were used to avoid over-fitting. Then, the gradient boosting machine(GBM) was used to build the model. The area under receiver(AUC) operating characteristic curve, precision, recall rate and F1 value were calculated to evaluate the effectiveness of the model.

Ten robust features were selected to build the model. The AUC values of benign lymph nodes, lymphoma and metastatic lymph nodes in the training set were 1.00, 0.98 and 0.99, and the AUC values of the test set were 0.96, 0.84 and 0.90, respectively.

It was a reliable and non-invasive method for the differential diagnosis of lymphadenopathy based on the model constructed by machine learning.

Medical image registration plays an important role in several applications. Existing approaches using unsupervised learning encounter issues due to the data imbalance problem, as their target is usually a continuous variable.

In this study, we introduce a novel approach known as Unsupervised Imbalanced Registration, to address the challenge of data imbalance and prevent overconfidence while increasing the accuracy and stability of 4D image registration.

Our approach involves performing unsupervised image mixtures to smooth the input space, followed by unsupervised image registration to learn the continual target. We evaluated our method on 4D-Lung using two widely used unsupervised methods, namely VoxelMorph and ViT-V-Net.

Our findings demonstrate that our proposed method significantly enhances the mean accuracy of registration by 3%-10% on a small dataset while also reducing the accuracy variance by 10%.

Unsupervised Imbalanced Registration is a promising approach that is compatible with current unsupervised image registration methods applied to 4D images.

Glaucoma is a significant cause of irreversible blindness worldwide, with symptoms often going undetected until the patient's visual field starts shrinking.

To develop an AI-based glaucoma detection method to reduce glaucoma-related blindness and offer more precise diagnosis.

Discusses various methods and technologies, including Heidelberg Retinal Tomography (HRT), Optical Coherence Tomography (OCT), and Fundus Photography, for obtaining relevant information about the presence of glaucoma in a patient. Additionally, it mentions the use of Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) for glaucoma detection. There are many limitations for existing methods as; Asymptomatic Progression, reliance on subjective feedback, multiple tests required, late detection, limited availability of preventive tests, influence of external factors.

Findings reveal promising outcomes in terms of glaucoma detection accuracy, particularly in the analysis of the RIM-ONE-r3 dataset. By scrutinizing 20 images from the Healthy, Glaucoma, and Suspects categories through fundus image recognition, our developed AI model consistently achieved high diagnostic accuracy rates.

Our study suggests that further enhancements in glaucoma detection accuracy are attainable by augmenting the dataset with additional labeled images. We emphasize the significance of considering various application parameters when discussing the integration of computer-aided decision/management systems into healthcare frameworks.

Artificial intelligence (AI) is rapidly evolving in healthcare, with transformative potential. AI revolutionizes medical imaging by enabling online self-diagnosis for patients and improving diagnostic accuracy for healthcare professionals. While valuable datasets aid machine learning in disease detection, challenges persist in diagnosing similar lung conditions from chest X-rays. Integrating AI into healthcare holds promise for enhanced outcomes and efficiency.

In this article, we aim to present a new AI model that solves this challenge by allowing the differentiation, diagnosis and classification of three distinct diseases, whose symptoms are very similar. The fundamental contribution is to reduce the number of parameters used while maintaining the same level of precision for use in embedded systems.

Our proposed model combines the power of the neural network using the SqueezeNet architecture with a set of machine learning algorithms as classifiers, including logistic regression, support vector machine (SVM), k-nearest neighbors (KNN), decision tree, and naive Bayes. The chest X-ray dataset used in the proposed model consists of CXR images that are classified into four categories: pneumonia, tuberculosis, COVID-19, and normal cases.

Our proposed model demonstrated remarkable accuracy (97,32%), precision (97,33), F1 score (97,31%), recall (97,30%), and AUC (99,40), which is close to the best model. Whereas, the number of parameters used by our model (4,6 M) is very small compared to the best model in the literature (47M).

The model demonstrated good classification accuracy. In addition, the proposed model has the ability to use fewer parameters, which means it requires less internal memory and computing resources.

Colon cancer with liver metastasis is a common occurrence in clinical practice. The presence of liver metastasis has a significant impact on the treatment strategy of patients, so the first step is to diagnose whether it is liver metastasis. Imaging is one of the auxiliary methods for diagnosing liver metastases, but due to the presence of different diseases with the same shadow, we need to be cautious when using imaging methods for the diagnosis of liver metastases.

We report a 53-year-old female patient with sigmoid colon cancer and perforation who underwent a surgical operation. Three years after the operation, reexamination of the liver through computed tomography and magnetic resonance imagery scanning revealed multiple progressive liver lesions. However, the liver biopsy did not show malignant changes. Repeated analysis of the patient's liver magnetic resonance imaging revealed that multiple liver nodules were significantly enhanced in the arterial phase and that the portal vein density/signal ratio was higher than that of the liver parenchyma. The coincidence of doughnut-shaped nodules and high signal in the hepatobiliary phase, combined with the results of pathological liver puncture examination, led to nodular regenerative hyperplasia being considered as a possible diagnosis.

A review of the relevant literature showed that following oxaliplatin chemotherapy for colorectal cancer, it is not uncommon for doughnut-shaped nodules with obvious enhancement in the middle hepatic artery phase and high signal intensity in the hepatobiliary phase to develop. Such changes should be paid sufficient attention by radiologists.

Extrahepatic cholangiocarcinoma (EHCC), an exceedingly malignant neoplasm, often eludes early detection, culminating in a dire prognosis. Accurate cancer staging systems and pathological differentiation are designed to guide adjuvant interventions and predict postoperative prognoses.

This study sought to investigate the predictive capacity of DW-MRI in discerning T stages, lymph node metastasis, and pathological differentiation grades in patients with EHCC.

Eighty-five patients were pathologically diagnosed with EHCC and underwent abdominal MRI within two weeks before surgery at our hospital from Aug 2011 to Aug 2021. Tumor axial maximum area (AMA) and apparent diffusion coefficient (ADC) values for diverse T stages, N stages, and differentiation grades were retrospectively analyzed.

The Mann-Whitney U test displayed significantly higher lesion AMA values (P =0.006) and lower tumor ADC values (P = 0.001) in the node-positive group (median ADC and AMA value: 1.220×10^-3 mm²/s, 82.231 mm²) than in the node-negative group (median ADC and AMA value: 1.316×10^-3 mm²/s, 51.174 mm²). A tumor ADC value<1.249×10^-3 mm²/s from the receiver operating characteristic curve (AUC=0.725, P=0.001) exhibited the capability to predict node-positive EHCC with a sensitivity of 64.29%, and specificity of 73.68%. Furthermore, a progressive decrease in the degree of EHCC differentiation was associated with a reduction in the tumor ADC value (P=0.000).

The N stage and differentiation of EHCC can be evaluated non-invasively using diffusion-weighted MRI.

Artificial intelligence (AI) in medical imaging rapidly expands regarding image processing and interpretation. Therefore, the aim was to explore radiographers’ and radiologists’ perceptions and attitudes towards AI use in medical imaging technologies in Saudi Arabia.

The survey was distributed online, and responses were collected from 173 participants nationwide. Data analysis was performed using SPSS Statistics (version 27).

The participants scored an average of 1.7, 1.6, and 1.8 on a scale of 1–3 for attitudinal perspectives on clinical application and the positive and negative impact of integrating AI technology in diagnostic radiology. Lack of knowledge (43.9%) and perceived cyber threats (37.7%) were the most cited factors hindering AI implementation in Saudi Arabia.

The radiographers and radiologists in this study had a favorable attitude toward AI integration in diagnostic radiology; nonetheless, concerns were raised about data protection, cyber security, AI-related errors, and decision-making challenges.

The classification of Congenital Uterine Malformations (CONUTA) relies on coronal imaging of the uterus using 3D TVUS and MRI. In everyday practice, radiologists and gynaecologists often struggle to confidently categorize CONUTA due to varying classification systems and the lack of worldwide consensus.

The aim of this study was to evaluate the diagnostic concordance and discrepancies between two imaging techniques within the context of the ASRM, ESHRE/ESGE, and CUME systems.

Ninety-four patients suspected of having CONUTA underwent evaluation: 67 underwent 3D TVUS, 53 had MRI scans, and 34 were examined using both imaging techniques.

An initial cross-listing table of ASRM, ESHRE/ESGE, and CUME was created, and a flowchart schema was used to define the type of congenital uterine anomaly for each system

The prevalence of anomalies in each system was calculated, and Fleiss’ Kappa was used to assess and determine the level of agreement.

Class VI arcuate uterus was the most common form in ASRM 2016 and 2021, while the partially septate uterus predominated in the CUME 2018 and ESHRE/ESGE 2016 classification systems.

There is no discordance between classification systems for all fusion defects and complete septate type of absorption defects. In the ESHRE/ESGE system, nearly half of the abnormal uteruses were categorized as partially septate. However, the CUME system proved less effective in distinguishing between normal and arcuate uteruses.

Type 2 diabetes mellitus (T2DM) and hypertension (HT) are the two most common underlying diseases worldwide, and they often coexist. The long-term existence of both may lead to left ventricular dysfunction. Therefore, evaluating the cardiac function of T2DM patients with HT is vital to guide treatment and improve prognosis. Left ventricular pressure strain loops (LVPSL) combine left ventricular strain and afterload, which can quantify left ventricular energy expenditure and detect left ventricular subclinical systolic dysfunction. Many studies have focused on myocardial work (MW) in uncomplicated T2DM patients or simple HT patients, but a few have focused on T2DM patients with HT.

The study aimed to evaluate the MW changes in T2DM patients with HT using LVPSL and to find independent related factors of MW parameters.

40 T2DM patients, 35 HT patients, 40 T2DM patients with HT (T2DM+HT group), and 35 controls were enrolled. The differences between clinical data, conventional ultrasound parameters, and MW parameters were analyzed among the four groups.

The global longitudinal strain (GLS) of the T2DM group, HT group, and T2DM+HT group was lower than the control group (P<0.05). The global work index (GWI) and global constructive work (GCW) in the T2DM group were lower than other groups (P<0.05). The GWI of the HT group was higher than other groups (P<0.05), while GCW was only higher than the T2DM group and T2DM+HT group (P<0.05). The GWI and GCW of the T2DM+HT group were higher than the T2DM group and were lower than the HT group(P<0.05), while there was no significant difference with the control group. HT group and T2DM+HT group had higher global work waste (GWW) (P<0.05). The global work efficiency (GWE) of the T2DM+HT group was lower than other groups (P<0.05). Systolic blood pressure (SBP) and glycosylated hemoglobin (HbA1c) were independent factors of each MW parameter.

LVPSL can recognize left ventricular subclinical systolic dysfunction early in patients with T2DM and HT. Compared to simple T2DM or HT, the combination of T2DM and HT had greater damage to left ventricular systolic function. SBP and HbA1c are two factors that have a considerable impact on MW parameters. The impact of afterload on MW parameters should be paid more attention to.

The most difficult aspect of diagnosing lung cancer is early diagnosis. According to the American Cancer Society, each year, there are around 11 million newly diagnosed instances of cancer worldwide. Radiologists often turn to Computed Tomography (CT) scans to diagnose respiratory conditions, which can reveal if lung tissue remains normal or abnormal. However, there is an increased chance of inaccuracy and delay; therefore, radiologists are concerned with the physical segmentation of nodules.

The objective of the research is to implement an advanced modified threshold segmentation and classification model for early and accurate detection of lung cancer from CT images.

Using the Support Vector Machines (SVM) classifier as well as the Artificial Neural Network (ANN) classifier, the authors propose using Modified adaptive threshold segmentation as a segmentation approach for cancer detection. Here, Lung Image Database Consortium (LIDC) datasets, a collection of CT scans, are used as the video frames in an investigation to authorize the recitation of the suggested technique.

Both quantitative as well as qualitative analyses are used to analyze the segmentation function of the anticipated algorithm. Both the ANN and SVM classifiers used in the suggested technique for lung cancer diagnosis achieve world-record levels of accuracy, with the former achieving a 96.3% detection rate and the latter a 97% rate of accuracy.

This innovation may have a major impact on the worldwide rate of lung cancer rate due to its ability to detect lung tumors in their earliest stages when they are most amenable to being avoided and treated. This method is useful because it provides more information and facilitates quick, precise decision-making for doctors diagnosing lung cancer in their patients.

Lymphaticovenous anastomosis (LVA) surgery is an effective treatment for lymphedema. Accurate evaluation and localization of the superficial lymphatic vessels before the operation is crucial for the success of the operation. Contrast-enhanced ultrasound (CEUS) is a new ultrasound technology, and its clinical application value in LVA surgery has not been established.

This study aimed to assess the efficacy of CEUS in LVA surgery and provide a novel approach for the clinical assessment and localization of superficial lymphatic vessels.

Retrospective analysis of imaging and surgical data was performed on 20 LVA patients. Among them, 10 cases underwent evaluation and localization using indocyanine green (ICG) lymphatic imaging (Group A), while 10 cases were evaluated and localized using CEUS (Group B). The differences in surgical data between the two groups were compared and analyzed.

All 20 patients were female (mean age, 57.7 years ± 6.3 [SD]). CEUS demonstrated superior visualization and localization of superficial lymphatic vessels. The average diameter of lymphatic vessels identified in the CEUS group was significantly greater than that in the ICG group (0.78±0.06 vs. 0.52±0.05mm; P<0.001). The duration of operation in group B was significantly shorter than that in group A (4.47±0.37 vs. 6.70±0.45mm; P<0.001). The number of anastomosed lymphatic vessels in group B was less than that in group A [5.0(4.0, 6.0) vs. 9.5 (9.0, 11.3); P<0.001].

CEUS can serve as a viable alternative to ICG lymphatic imaging, facilitating improved lymphatic venous anastomosis surgery.

Recurrence of uterine cervical cancer is common and often shows a dismal prognosis. Local recurrence usually manifests as solid soft tissue lesions and has rarely been reported to have cystic lesions.

Herein, we report a case of recurrent uterine cervical cancer with initial manifestation as a hemorrhagic cyst, assessed using strain sonoelastography, CT, and MRI.

Although cystic recurrence is uncommon, newly detected simple or complex cystic lesions should be closely monitored.

To explore the development of a mind map-based predictive nursing protocol and assess its impact on the quality of images in patients undergoing high-concentration contrast three-dimensional computed tomography (CT) imaging of liver blood vessels.

A total of 600 patients who were admitted to Beijing You an Hospital were chosen for this prospective study and underwent high-concentration contrast three-dimensional CT imaging of liver blood vessels between April 2021 and December 2021. The patients were divided into two groups using the digital table method, with 300 cases. The control group received conventional nursing intervention, while the research group was provided with a mind map-based predictive nursing protocol. We recorded the image quality of three-dimensional CT imaging of liver blood vessels, satisfaction scores regarding nurse examination guidance, and the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) in both groups.

The research group achieved a perfect rate of 100.00% for the high-quality three-dimensional CT imaging of liver blood vessels, which was noticeably higher compared to the rate of the control group of 98.67%. Patients in the research group expressed higher satisfaction levels regarding the guidance provided by nurses, including their attitude, timeliness, accuracy, and overall satisfaction, compared to the control group. Initially, the two groups had no notable differences in the SAS and SDS scores. However, after the intervention, both groups experienced a significant decrease in SAS and SDS scores, with the research group showing an even more substantial decline.

Through the creation of a mind map-based predictive nursing protocol and its implementation on patients undergoing high-concentration contrast three-dimensional CT imaging of liver blood vessels, it is possible to significantly enhance the quality of CT scans, alleviate feelings of anxiety and depression, increase patient satisfaction with examination guidance by nurses, and effectively decrease the occurrences of contrast agent leakage and allergic reactions to iodine.

Chest wall tuberculosis may develop if tuberculous (TB) lesions spread through the chest wall and invade the thoracic cavity. The presence of a mass on the patient's chest wall may be the first indication of TB, and a chest CT scan can help diagnose external penetrating chest wall TB, the incursion of tuberculosis from the lungs into the chest wall.

This study examines the safety and efficacy of thoracoscopic-assisted surgery for the treatment of penetrating chest wall tuberculosis as a means of exploring novel concepts of minimally invasive surgery.

Our hospital conducted a retrospective study of 25 patients with penetrating chest wall TB who underwent thoracoscopic surgery between January 2020 and June 2021. General demographics, CT scan data linked to surgery, and postoperative patient outcomes were compared between the two groups. The data was also evaluated to determine the range of operation time and the volume of bleeding from different foci in the thoracic cavity.

All procedures went well after patients took conventional antituberculosis medication for at least two weeks prior to surgery. CT scans showed that thoracoscopic surgery needed a smaller incision than traditional chest wall TB surgery, with no discernible increase in surgical time. Postoperative tube use, length of hospital stay, and blood loss were all significantly lower than they would have been with conventional surgery. In addition, thoracoscopy was associated with a significantly reduced rate of subsequent treatment. Fibrous plate development and calcification caused the longest operation times in the thoracoscopic surgery group, whereas multiple pleural tuberculomas generated the most hemorrhage. Thoracoscopic surgery usually reveals tuberculous foci hiding in the thoracic cavity.

Thethoracic surgery can eliminate the TB focus in the chest wall and intrathoracic while treating penetrating chest wall tuberculosis. The CT scan is a crucial part of the diagnostic process for these patients. Minor surgical trauma, low complication and recurrence rates, and good results. There is a greater distinction between the two surgical approaches for patients with penetrating chest wall TB as opposed to those with basic chest wall tuberculosis.

This study aims to investigate the value of gemstone spectral imaging (GSI) combined with an 80 mm wide-body detector in head-neck CTA.

Ninety patients with head-neck CTA were prospectively selected and randomly divided into a control group and a test group, with 45 patients in each group. The control group was scanned conventionally. With a tube voltage of 100 kVp and detector width of 40 mm, a 70 ml contrast agent was injected at a flow rate of 5.0 ml/s. The test group used GSI. With a tube current fixed of 445 mAs and a detector width of 80 mm, the contrast agent was injected at a flow rate of 3.5 ml/s and 0.6 ml/kg body weight, and the 55 keV virtual monoenergetic images (VMIs) were automatically reconstructed. Finally, the target vessel CT values, background noise (BN), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), subjective scores, contrast agent dose, CT dose index volume (CTDIvol), and dose length product (DLP) were recorded. The DLP was converted to the effective dose (ED).

The target vessel CT values, BN, SNR, CNR, and subjective scores of the two groups were not statistically significant (all P > 0.05), and the image quality of both groups was the same and met the diagnostic requirements. The contrast agent dose and effective dose (ED) in the test group were approximately 44% and 26% lower than that of the control group, respectively (all P < 0.05).

In head-neck CTA examination, the Revolution CT GSI combined with an 80 mm wide-body detector can reduce the contrast agent dose and radiation dose while ensuring image quality.