Thin-section CT Research Articles

Preoperative evaluation of the segmental artery of left upper lobe by thin-section CT and 3d-CTA.

Understanding pulmonary artery (PA) branches and their variations is crucial for successful lung resection. We aimed to evaluate the segmental PA branching pattern of the left upper lobe (LUL) using thin-section computed tomography (TSCT) images and 3D-CT angiography (3D-CTA). This study included 108 patients who underwent CTA and left upper lobectomy. The segmental PA branching pattern of the LUL was meticulously identified by two thoracic radiologists using 3D-CTA and TSCT images. The lingular artery branches from the left PA (LPA) were classified into mediastinal type (pars mediastinalis: PM), interlobar type (pars interlobaris: PI), and PI originating from the lower portion (PI'), specifically from A8. The intraoperative findings of the PA branches of the LUL were compared with the preoperatively obtained 3D-CTA and TSCT images in each patient's case. The median (range) number of LPA branches of the LUL was 4.36 (3-8). The most common number of A1 + 2 branches was two, seen in 34 cases (31.5%). One or more branches of A1 + 2c directly originating from the LPA were found in 63 cases (58.3%). The number of branches of A3 was single in 85 cases and the most frequent (78.7%). Instances where one or more branches of A3a directly originated from the LPA were found in seven cases (6.5%). A1 + 2 and A3 origins were separate and independent in 40 cases (37.0%). As the branching pattern of the lingular artery, PI/PI' was most frequent (61.1%). PI´ was observed in 26 cases (24.1%). Inter-observer agreement for A1 + 2, A1 + 2c, A3, A3a, and lingular artery branching patterns was moderate to substantial (κ = 0.53-0.72). Preoperative 3D-CTA and TSCT images identified 99.8% of LPA branches compared to intraoperative findings, except one. The segmental PA branching pattern of the LUL can be evaluated using TSCT and 3D-CTA images, providing precise preoperative information.

Discriminating bronchiolar adenoma from peripheral lung cancer by thin-section computed tomography (CT): a 2-center study.

Bronchiolar adenoma (BA) is frequently misdiagnosed as peripheral lung cancer (PLC) because it resembles PLC. Computed tomography (CT) examination is an effective tool for detecting and diagnosing lung diseases. To date, there has been no comprehensive study on the differential diagnosis of BAs and PLCs using thin-section computed tomography (TSCT) based on a large sample, and the efficiency of CT in diagnosing BAs has not been verified. The goal of this study was to distinguish BA from PLC by summarizing their clinical and TSCT characteristics. A retrospective cross-sectional study on 71 cases with BAs and 218 matched controls with PLCs (from March 2020 to May 2023) within 2 centers (The First Affiliated Hospital of Chongqing Medical University and the Second Affiliated Hospital of Army Medical University) was conducted to investigate their clinical and radiological differences. The clinical characteristics and TSCT features of BAs and PLCs were summarized and compared. A multivariate logistic regression analysis was performed to reveal the key predictors of BAs. The BAs and PLCs exhibited significant differences in TSCT features. Multivariate analysis revealed that the lesion being located in basal segments [odds ratio (OR), 17.835; 95% confidence interval (CI): 6.977-45.588; P<0.001], irregular shape (OR, 4.765; 95% CI: 1.877-12.099; P=0.001), negative of spiculation sign (OR, 7.436; 95% CI: 2.063-26.809; P=0.002), central vessel sign with pulmonary artery (OR, 3.576; 95% CI: 1.557-8.211; P=0.003), peripheral vessel sign with pulmonary vein (OR, 12.444; 95% CI: 4.934-31.383; P<0.001), and distance from lesion edge to pleura (D-ETP) ≤5 mm (OR, 5.535; 95% CI: 2.346-13.057; P<0.001) were independent predictors of BAs, and the area under the curve (AUC) of this model was 0.935; 95% CI: 0.901-0.960 (sensitivity: 88.0%, specificity: 86.03%, P<0.001). Peripheral pulmonary nodules locating in the basal segment of lower lobe with irregular shape, central vessel sign with pulmonary artery, peripheral vessel sign with pulmonary vein and D-ETP ≤5 mm, but without spiculation sign, should be highly suspected of BAs.

Favorable Diagenesis and Pore Evolution of The Qi-2 Member Reservoir in the Hechuan-Tongnan Area

Abstract This article takes the Qi-2 member dolomite reservoir in the Hechuan-Tongnan area as the research object, and combines research methods such as drilling cores, thin section analysis, CT scanning, and geochemical analysis to deeply explore the pore structure characteristics, diagenetic characteristics, and influencing factors on pore evolution of the Qi-2 member dolomite reservoir. This results indicate that: ①The Qi-2 member should develop two types of reservoir spaces: pores and fractures. The reservoir space is mainly composed of intercrystalline dissolved pores (0.5∼5%), dissolved fractures and dissolved cavities (2∼4%). The intercrystalline pores are relatively evenly distributed, and the fractures improve the connectivity of pores. The reservoir space of the poor gas layer is mainly composed of biocast pores and intragranular dissolved pores (1∼2%), which are relatively isolated; ② The matrix pores of the Qi-2 member are relatively evenly distributed, with a radius of 200μm~600μm is the main focus. The throat radius is mainly 20μm~60μm fine throats.③According to the effect of diagenesis on pore evolution of dolomite reservoir in Qi-2 formation, it can be divided into constructive and destructive. The formation of reservoir space is mainly caused by quasi-syngenetic atmospheric fresh water erosion, and the matrix pores are mainly intercrystalline pores and intercrystalline dissolved pores. The early dolomitization is the key factor of pore preservation. The geological study of dolomite reservoir of the Qixia Formation in the Daqing exploration area has been enhanced, providing strong basis for reservoir prediction and deployment in the later stage.

Comparison of the Patient Demographics, High-Resolution Computed Tomography Features of the Pulmonary Ground-Glass Opacity and Its Diagnostic Value Analysis

Introduction Pulmonary ground-glass opacity (GGO) observed on computed tomography (CT) is widely regarded as a diagnostic feature of lung adenocarcinoma; however, a significant radiological predictive sign remains controversial. In this retrospective study, we conducted a comprehensive analysis of 206 patients with GGO to establish a correlation analysis model between CT images and diagnosis of GGO nodules. Methods Histopathologic specimens were obtained from 206 patients (130 women, 76 men; age range 24–77). The clinical data, pathologic findings, and thin-section CT features of solid, pure GGO (pGGO), and mixed GGO (mGGO) nodules were compared using rigorous statistical methods, such as t-test, Fisher’s exact test, or univariate logistic regression analysis. Results Significant differences were observed among the 3 groups in terms of patient demographics, including gender (P = 0.016), smoking history (P = 0.002), and nodule size (P = 0.002). Morphologic CT characteristics, such as the presence of spiculated sign, lobulated sign, vascular sign, bubble-lucency sign, or pleural retraction, exhibited significant differences among the solid nodule, pGGO, and mGGO groups. However, no significant differences were observed in terms of air-bronchogram sign. Notably, the incidence of malignancy was significantly higher in pGGO nodules (76.52%) than in solid (48.48%) and mGGO (73.86%) nodules. Patients with mGGO had up to 2.988 times higher hazard of malignant lesions than those with solid nodules [P = 0.036, hazard ratio (HR) = 2.988]. Similarly, the hazard of malignant lesions was 2.941 times higher in patients with pGGO than in those with solid nodules (P = 0.007, HR = 2.941). Conclusion Clinical, pathological, and thin-section CT features of solid, pGGO and mGGO nodules were found to be significantly different. Based on our comprehensive analysis, patients presenting with a mGGO or pGGO on CT scans were more likely to be diagnosed with lung cancer.

Hypersensitivity Pneumonitis on Thin-Section Chest CT Scans: Diagnostic Performance of the ATS/JRS/ALAT versus ACCP Imaging Guidelines.

Purpose To compare the diagnostic performance of the American Thoracic Society, Japanese Respiratory Society, and Asociación Latinoamericana del Tórax (ATS/JRS/ALAT) versus the American College of Chest Physicians (ACCP) imaging classifications for hypersensitivity pneumonitis (HP). Materials and Methods Patients in the institutional review board-approved Interstitial Lung Disease (ILD) registry referred for multidisciplinary discussion (MDD) at the authors' institution (January 1, 2006-April 1, 2021) were included in this retrospective study when ILD was diagnosed at MDD. MDD diagnoses included HP, connective tissue disease-ILD, and idiopathic pulmonary fibrosis. Retrospective review of thin-section CT images was performed in consensus by two cardiothoracic radiologists blinded to the diagnosis. Diagnostic patterns were determined for thin-section CT images using both classifications. Discordance rates were determined. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were assessed using MDD diagnosis as the reference standard. Results A total of 297 patients were included in the study: 200 (67%) with HP, 49 (16%) with connective tissue disease-ILD, and 48 (16%) with idiopathic pulmonary fibrosis at MDD. The discordance rate between the two classifications was 21%. Assuming low HP prevalence (10%), ATS/JRS/ALAT classification outperformed ACCP classification, with greater accuracy (92.3% vs 87.6%) and greater positive predictive value (60.7% vs 42.9%). Assuming high prevalence (50%), accuracy and negative predictive value were superior using ACCP classification (81.7% vs 79.7% and 77.7% vs 72.6%, respectively), and positive predictive value was superior using ATS/JRS/ALAT classification (93.3% vs 87.1%). Conclusion Accuracy of the ATS/JRS/ALAT and ACCP HP classifications was greater in settings with low and high HP prevalence, respectively. Diagnostic performance of both classifications was discordant in a minority of cases. Keywords: CT, Thorax, Hypersensitivity Pneumonitis, Interstitial Lung Disease Supplemental material is available for this article. © RSNA, 2024.

Application of Synthetic Time-Of-Flight Magnetic Resonance Angiography-Computed Tomography Fusion Imaging in Preoperative Planning for Aneurysm Clipping Surgery: A Comparative Study with Three-Dimensional Computed Tomography Angiography

Some patients with intracranial aneurysms (IAs) cannot undergo three-dimensional computed tomography angiography (3D-CTA) or digital subtraction angiography due to contraindications to contrast agents or radiation. Time-of-flight magnetic resonance angiography (TOF-MRA) offers a contrast-free alternative but lacks cranial bone detail critical for surgical planning. This study evaluates the feasibility of using 3D Slicer to fuse TOF-MRA with thin-section CT images to generate synthetic images resembling CTA for surgical clipping planning. This prospective study included 22 patients with unruptured IAs and 8 with ruptured IAs undergoing aneurysm clipping surgery (≥3mm). TOF-MRA and CT/3D-CTA scans were fused using 3D Slicer. Neuroradiologists and neurosurgeons independently assessed 3D-CTA and synthetic TOF-MRA-CT images for aneurysm detection rates, morphology, and dimensions. Evaluation metrics included dice similarity coefficient and 95% Hausdorff distance. Evaluation of aneurysm detection rates, morphology, and dimensions showed no significant differences between synthetic TOF-MRA-CT fusion images and 3D-CTA (all P > 0.05). Neuroradiologist assessments revealed strong concordance in aneurysm morphology between synthetic TOF-MRA-CT fusion images and 3D-CTA (κ= 0.867, P < 0.001). The dice similarity coefficient (0.937±0.012) and Hausdorff distance (4.54±0.26) indicated a high degree of image overlap between synthetic TOF-MRA-CT fusion images and 3D-CTA. Surgeons rated the consistency of aneurysm morphology between synthetic TOF-MRA-CT fusion images and intraoperative findings as strongly concordant (κ= 0.873, P < 0.001). Synthetic TOF-MRA-CT fusion images closely match 3D-CTA for ≥3mm aneurysms, demonstrating comparable diagnostic and surgical clipping planning effectiveness. They represent a promising alternative for personalized preoperative planning, particularly when contrast agents are contraindicated.

Dual-source dual-energy CT and deep learning for equivocal lymph nodes on CT images for thyroid cancer.

This study investigated the diagnostic performance of dual-energy computed tomography (CT) and deep learning for the preoperative classification of equivocal lymph nodes (LNs) on CT images in thyroid cancer patients. In this prospective study, from October 2020 to March 2021, 375 patients with thyroid disease underwent thin-section dual-energy thyroid CT at a small field of view (FOV) and thyroid surgery. The data of 183 patients with 281 LNs were analyzed. The targeted LNs were negative or equivocal on small FOV CT images. Six deep-learning models were used to classify the LNs on conventional CT images. The performance of all models was compared with pathology reports. Of the 281 LNs, 65.5% had a short diameter of less than 4 mm. Multiple quantitative dual-energy CT parameters significantly differed between benign and malignant LNs. Multivariable logistic regression analyses showed that the best combination of parameters had an area under the curve (AUC) of 0.857, with excellent consistency and discrimination, and its diagnostic accuracy and sensitivity were 74.4% and 84.2%, respectively (p < 0.001). The visual geometry group 16 (VGG16) based model achieved the best accuracy (86%) and sensitivity (88%) in differentiating between benign and malignant LNs, with an AUC of 0.89. The VGG16 model based on small FOV CT images showed better diagnostic accuracy and sensitivity than the spectral parameter model. Our study presents a noninvasive and convenient imaging biomarker to predict malignant LNs without suspicious CT features in thyroid cancer patients. Our study presents a deep-learning-based model to predict malignant lymph nodes in thyroid cancer without suspicious features on conventional CT images, which shows better diagnostic accuracy and sensitivity than the regression model based on spectral parameters. Many cervical lymph nodes (LNs) do not express suspicious features on conventional computed tomography (CT). Dual-energy CT parameters can distinguish between benign and malignant LNs. Visual geometry group 16 model shows superior diagnostic accuracy and sensitivity for malignant LNs.

Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial.

To investigate the feasibility of low-radiation dose and low iodinated contrast medium (ICM) dose protocol combining low-tube voltage and deep-learning reconstruction (DLR) algorithm in thin-slice abdominal CT. This prospective study included 148 patients who underwent contrast-enhanced abdominal CT with either 120-kVp (600 mgL/kg, n = 74) or 80-kVp protocol (360 mgL/kg, n = 74). The 120-kVp images were reconstructed using hybrid iterative reconstruction (HIR) (120-kVp-HIR), while 80-kVp images were reconstructed using HIR (80-kVp-HIR) and DLR (80-kVp-DLR) with 0.5 mm thickness. Size-specific dose estimate (SSDE) and iodine dose were compared between protocols. Image noise, CT attenuation, and contrast-to-noise ratio (CNR) were quantified. Noise power spectrum (NPS) and edge rise slope (ERS) were used to evaluate noise texture and edge sharpness, respectively. The subjective image quality was rated on a 4-point scale. SSDE and iodine doses of 80-kVp were 40.4% (8.1 ± 0.9 vs. 13.6 ± 2.7 mGy) and 36.3% (21.2 ± 3.9 vs. 33.3 ± 4.3 gL) lower, respectively, than those of 120-kVp (both, p < 0.001). CT attenuation of vessels and solid organs was higher in 80-kVp than in 120-kVp images (all, p < 0.001). Image noise of 80-kVp-HIR and 80-kVp-DLR was higher and lower, respectively than that of 120-kVp-HIR (both p < 0.001). The highest CNR and subjective scores were attained in 80-kVp-DLR (all, p < 0.001). There were no significant differences in average NPS frequency and ERS between 120-kVp-HIR and 80-kVp-DLR (p ≥ 0.38). Compared with the 120-kVp-HIR protocol, the combined use of 80-kVp and DLR techniques yielded superior subjective and objective image quality with reduced radiation and ICM doses at thin-section abdominal CT. Scanning at low-tube voltage (80-kVp) combined with the deep-learning reconstruction algorithm may enhance diagnostic efficiency and patient safety by improving image quality and reducing radiation and contrast doses of thin-slice abdominal CT. Reducing radiation and iodine doses is desirable; however, contrast and noise degradation can be detrimental. The 80-kVp scan with the deep-learning reconstruction technique provided better images with lower radiation and contrast doses. This technique may be efficient for improving diagnostic confidence and patient safety in thin-slice abdominal CT.

Prognostic implication of consolidation-to-tumor ratio in early lung adenocarcinoma: a retrospective cross-sectional study.

The threshold value of consolidation-to-tumor ratio (CTR) for distinguishing between ground-glass opacity (GGO)-predominant and solid-predominant ground-glass nodules (GGNs) needs to be clarified, as the lack of clarity has caused the prognostic implications to remain ambiguous. This study aimed to determine the threshold value of CTR for distinguishing between GGO-predominant GGNs and solid-predominant GGNs and elucidate the prognostic implications of the solid-predominant GGNs categorized by CTR on c-stage IA lung adenocarcinoma. Between January 2016 and October 2018, 764 c-stage IA lung adenocarcinoma cases were assembled from the First Affiliated Hospital of Chongqing Medical University. Of the 764 lesions, 515 (67.4%) were nodules with a GGO component, and 249 (32.6%) were solid nodules (SNs) on thin-section computed tomography (CT). We evaluated the correlation of the 3-dimensional (3D) consolidation component volume ratio with CTR based on the coefficient of determination, r. After receiver operating characteristic (ROC) analysis of 515 GGNs, we defined the nodule with CTR >0.750 as solid-predominant GGN and the nodule with CTR ≤0.750 as GGO-predominant GGN. Subsequently, the prognosis of 439 patients who had follow-up registration was evaluated. Survival curves were calculated using the Kaplan-Meier method, and the log-rank test was employed to compare survival rates among different groups. Cox proportional hazard regression models were applied to evaluate the independent risk factors for recurrence-free survival (RFS). Among 764 patients, 515 (67.4%) were nodules with a GGO component, and 249 (32.6%) were SNs on thin-section CT. For 515 GGNs, the 3D consolidation component volume ratio correlated well with CTR (r=0.888). CTR tended to be slightly larger than the 3D consolidation component volume ratio. A 3D consolidation component volume ratio >50% was best predicted by CTR >0.750, followed by CTR >0.549. CTR >0.750 and CTR >0.549 predicted 3D consolidation component volume ratio >50% with 85% and 99.2% sensitivity and 91.6% and 57.2% specificity, respectively. The 5-year RFS and overall survival (OS) of patients with 0.750< CTR <1 were worse than those of patients with 0≤ CTR ≤0.750 (P<0.001 and P<0.001, respectively) but better than those of patients with CTR =1 (P=0.002 and P=0.03, respectively). Carcinoembryonic antigen (CEA) >2.1 [hazard ratio (HR) =12.516, 95% confidence interval (CI): 1.729-90.598], CTR >0.750 (HR =13.934, 95% CI: 3.341-58.123), larger consolidation component size with diameter more than 20 mm (HR =1.855, 95% CI: 1.242-2.770), poorly differentiated (HR =1.622, 95% CI: 1.056-2.491), lymph node metastasis (HR =2.473, 95% CI: 1.601-3.821), and sublobar resection (HR =2.596, 95% CI: 1.701-3.962) could predict the poor prognosis. Patients with 0≤ CTR ≤0.750 receiving sublobar resection had prognoses comparable to those receiving lobar resection, whether the tumor size ≤2 cm or consolidation component size ≤3 cm. Lobar resection was superior to sublobar resection for non-small cell lung cancer (NSCLC) ≤2 cm with CTR >0.750. Compared to CTR =0.5, the 2-dimensional (2D) CTR =0.750 found using the 3D consolidation component volume ratio as the gold standard better differentiated between solid-predominant GGNs and GGO-predominant GGNs. CTR >0.750 was an independent risk factor associated with the poor prognosis of patients with c-stage IA lung adenocarcinoma. Sublobar resection should be cautiously adopted in GGNs with 0.750< CTR ≤1.

Cochlear implantation through retro-facial approach with congenital microtia malformation with facial nerve deformity: a case report

The difficulty of cochlear implantation in patients with congenital microtia is usually increased due to the vague anatomical marks and facial nerve malformation. The common types of facial nerve malformation include facial nerve bony cover loss, aberrant position, and bifurcation malformation. Bifurcation malformation may obscure the oval window, press against stapes, and bifurcate in the vestibular window while obscuring the round window. It is important to correctly identify the facial nerve and choose a reasonable surgical approach to avoid postoperative complications. This article describes a case of profound sensorineural hearing loss due to facial nerve malformation in our institution. The patient underwent cochlear implantation through the retro-facial approach. There was no facial nerve injury or dysfunction symptoms such as facial paralysis and hemifacial spasm 2 years after the operation, and the cochlear implant works well. The score of the categories of the auditory performance（CAP） questionnaire was 7, and the score of the speech intelligibility rating（SIR） questionnaire was 4. When the round window cannot be exposed through the facial recess approach during surgery, the retro-facial approach is a feasible method. To avoid facial nerve injury, a thin-section CT of the temporal bone should be performed before the middle and inner ear surgery for patients with facial nerve malformation, and the intraoperative facial nerve monitor should be used to clarify the course of the facial nerve to avoid injury.

Prediction of Pleural Lavage Cytology According to Thin-Section Computed Tomography in Non–Small-Cell Lung Cancer

BackgroundAlthough the positive rate of preresection pleural lavage cytology (PLC) is low, it is an important indicator of poor prognosis for non–small-cell lung cancer patients with frequent pleural dissemination (PD) recurrence. Thin-section computed tomography (TSCT) can reveal relationships between a primary tumor and the pleura at 1 to 2 mm intervals, and this is associated with visceral pleural invasion (VPI). However, its association with PLC remains unclear. Therefore, we aimed to improve PLC efficiency and predict PD recurrence by understanding the relationship between PLC and preoperative TSCT findings. Patients and MethodsBetween January 2014 and December 2018, we reviewed 978 patients with non–small-cell lung cancer who underwent PLC tests during complete resection surgery. Preoperative TSCT findings were evaluated, and factors with the highest specificity (proportion of patients with radiologically to pathologically diagnosed positive PLC) were investigated. We also evaluated their relationships with VPI and PD recurrence. ResultsPLC positive was identified in 55 (5.6%) of the 978 patients. The two TSCT findings predicting PLC results, “the absence of pleural findings,” ie, tumor not attached to pleura or without pleural tag, and “consolidation-to-tumor ratio ≤0.5”, had a specificity of 100% (95% confidence interval: 90.4%-100%); additionally, all cases with these findings were VPI negative and had no PD recurrence. And 24% of the cohort had either of these findings. ConclusionThe absence of pleural findings and/or consolidation-to-tumor ratio ≤0.5 of primary tumor on preoperative TSCT can predict PLC negativity with very high probability; therefore, PLC can be omitted for such patients.

Study on Electrical Characteristics and Formation Mechanism of Micro-Ion Capacitor in Shale Pore and Fracture Structure

Due to the micro/nanoscale and intricate pore structure, poor connectivity, complex pathways, the presence of microfractures, and the coexistence of organic and inorganic pores, shale and other tight reservoirs exhibit increasingly complex conductive characteristics. This paper mainly studied the electrical properties of shale based on experimental test data, including scanning electron microscope (SEM) thin-section and microtomography CT (μ-CT) images, and analyzed the shale pore type, pore structure characteristics, and development of fracture. Then, the distribution of pyrite, content, and graphitization of organics and their influence on the electrical properties are discussed. Furthermore, the double electrical layer and zeta potential in the shale zone were discussed in depth. The results revealed that, within the content of pyrite, organics, and its graphitization, the vitrinite maturity are inversely proportional to shale resistivity. It was also found that in the presence of an external electromagnetic field, the fluid in shale pores is subjected to the combined strength of pore pressure and external field potential difference. Thus, its response equation should be an improved Navier-Stokes equation, which considers pore pressure, zeta potential, and Coulomb force. When shale is subjected to an external electromagnetic field, due to the complex pores structure and organic and inorganic minerals, it will represent more of a dielectric-like property than electricity. So, it will form special microscopic ionic capacitors, which are different from common plate capacitors. There are three special kinds of microscopic ionic capacitors, they are (I) the intergranular pore microscopic ionic capacitor model, (II) the particle with isolated pore microscopic ionic capacitor model, and (III) the pyrite or graphite or other organics microscopic ionic capacitor model. Finally, the characteristics of microscopic ion capacitors are summarized: irregular polar area and varying distance between poles, varying charges with time, and salinity of the formation water.

Cone-Beam CT of the Extremities in Clinical Practice.

Cone-beam CT (CBCT) is a promising tool with increasing applications in musculoskeletal imaging due to its ability to provide thin-section CT images of the appendicular skeleton and introduce weight bearing, which accounts for loading forces that typically interact with and affect this anatomy. CBCT devices include an x-ray source directly opposite a digital silicon detector panel that performs a single rotation around an object of interest, obtaining thin-section images. Currently, the majority of research has been focused on the utility of CBCT with foot and ankle pathologic abnormalities, due to the complex architectural arrangement of the tarsal bones and weight-bearing nature of the lower extremities. Associated software can provide a variety of options for image reconstruction, including metal artifact reduction, three-dimensional biometric measurements, and digitally reconstructed radiographs. Advancements in this technology have allowed imaging of the knee, hip, hand, and elbow. As more data are published, it is becoming evident that CBCT provides many additional benefits, including fast imaging time, low radiation dose, lower cost, and small equipment footprint. These benefits allow placement of CBCT units outside of the traditional radiology department, including the orthopedic clinic setting. These technologic developments have motivated clinicians to define the scope of CBCT for diagnostics, surgical planning, and longitudinal imaging. As efforts are made to create standardized protocol and measurements, the current understanding and surgical approach for various orthopedic pathologic conditions will continue to shift, with the hope of improving outcomes. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Mammographically detected breast clustered microcalcifications localized by chest thin-section computed tomography

BackgroundTo explore the capability and clinical significance of chest thin-section computed tomography (CT) for localization of mammographically detected clustered microcalcifications.MethodsA total of 69 patients with 71 mammographically detected clustered microcalcifications received surgical biopsy under the guidance of mammography (MG), CT was used to localize calcifications combined with MG if calcifications can be seen on CT. Intraoperative mammography of the specimens were performed in all cases for identification of the resected microcalcifications. The clinical, imaging and pathological information of these patients were analyzed.ResultsA total of 42 (59.15%) cases of calcifications were localized by CT + MG, 29 (40.85%) cases were guided only by the mammography. All suspicious calcifications on the mammography were successfully removed. Pathological results showed 42 cases were cancer, 23 cases were benign, and 6 cases were atypical hyperplasia. The mean age in the CT + MG group was older than that of the MG group (54.12 vs. 49.27 years; P = 0.014). The maximum diameter of clusters of microcalcifications on mammography in the CT + MG group was larger than that of the MG group [(cranio-caudal view, 1.52 vs. 0.61 mm, P = 0.000; mediolateral oblique (MLO) view, 1.53 vs. 0.62 mm, P = 0.000)]. The gray value ratio (calcified area / paraglandular; MLO, P = 0.004) and the gray value difference (calcified area - paraglandular; MLO, P = 0.005) in the CT + MG group was higher than that of the MG group. Multivariate analysis showed that the max diameter of clusters of microcalcifications (MLO view) was a significant predictive factor of localization by CT in total patients (P = 0.001).ConclusionsAbout half of the mammographically detected clustered microcalcifications could be localized by thin-section CT. Maximum diameter of clusters of microcalcifications (MLO view) was a predictor of visibility of calcifications by CT. Chest thin-section CT may be useful for localization of calcifications in some patients, especially for calcifications that are only visible in one view on the mammography.

Thin-Section CT in the Categorization and Management of Pulmonary Fibrosis including Recently Defined Progressive Pulmonary Fibrosis.

While idiopathic pulmonary fibrosis (IPF) is the most common type of fibrotic lung disease, there are numerous other causes of pulmonary fibrosis that are often characterized by lung injury and inflammation. Although often gradually progressive and responsive to immune modulation, some cases may progress rapidly with reduced survival rates (similar to IPF) and with imaging features that overlap with IPF, including usual interstitial pneumonia (UIP)-pattern disease characterized by peripheral and basilar predominant reticulation, honeycombing, and traction bronchiectasis or bronchiolectasis. Recently, the term progressive pulmonary fibrosis has been used to describe non-IPF lung disease that over the course of a year demonstrates clinical, physiologic, and/or radiologic progression and may be treated with antifibrotic therapy. As such, appropriate categorization of the patient with fibrosis has implications for therapy and prognosis and may be facilitated by considering the following categories: (a) radiologic UIP pattern and IPF diagnosis, (b) radiologic UIP pattern and non-IPF diagnosis, and (c) radiologic non-UIP pattern and non-IPF diagnosis. By noting increasing fibrosis, the radiologist contributes to the selection of patients in which therapy with antifibrotics can improve survival. As the radiologist may be first to identify developing fibrosis and overall progression, this article reviews imaging features of pulmonary fibrosis and their significance in non-IPF-pattern fibrosis, progressive pulmonary fibrosis, and implications for therapy. Keywords: Idiopathic Pulmonary Fibrosis, Progressive Pulmonary Fibrosis, Thin-Section CT, Usual Interstitial Pneumonia © RSNA, 2024.

Evaluation of Prognosis in Patients with Lung Adenocarcinoma with Atypical Solid Nodules on Thin-Section CT Images.

Purpose To evaluate the clinicopathologic characteristics and prognosis of patients with clinical stage IA lung adenocarcinoma with atypical solid nodules (ASNs) on thin-section CT images. Materials and Methods Data from patients with clinical stage IA lung adenocarcinoma who underwent resection between January 2005 and December 2012 were retrospectively reviewed. According to their manifestations on thin-section CT images, nodules were classified as ASNs, subsolid nodules (SSNs), and typical solid nodules (TSNs). The clinicopathologic characteristics of the ASNs were investigated, and the differences across the three groups were analyzed. The Kaplan-Meier method and multivariable Cox analysis were used to evaluate survival differences among patients with ASNs, SSNs, and TSNs. Results Of the 254 patients (median age, 58 years [IQR, 53-66]; 152 women) evaluated, 49 had ASNs, 123 had SSNs, and 82 had TSNs. Compared with patients with SSNs, those with ASNs were more likely to have nonsmall adenocarcinoma (P < .001), advanced-stage adenocarcinoma (P = .004), nonlepidic growth adenocarcinoma (P < .001), and middle- or low-grade differentiation tumors (P < .001). Compared with patients with TSNs, those with ASNs were more likely to have no lymph node involvement (P = .009) and epidermal growth factor receptor mutation positivity (P = .018). Average disease-free survival in patients with ASNs was significantly longer than that in patients with TSNs (P < .001) but was not distinguishable from that in patients with SSNs (P = .051). Conclusion ASNs were associated with better clinical outcomes than TSNs in patients with clinical stage IA lung adenocarcinoma. Keywords: Adenocarcinoma, Atypical Solid Nodules, CT, Disease-free Survival, Lung, Prognosis, Pulmonary Supplemental material is available for this article. Published under a CC BY 4.0 license.

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in different body fields. However, there have been no reports regarding the utility of DLR for image quality and T-factor assessment improvements on T2-weighted imaging (T2WI), short inversion time (TI) inversion recovery (STIR) imaging, and unenhanced- and contrast-enhanced (CE) 3D fast spoiled gradient echo (GRE) imaging with and without CS in comparison with thin-section multidetector-row CT (MDCT) for non-small cell lung cancer (NSCLC) patients. The purpose of this study was to determine the utility of DLR for improving image quality and the appropriate sequence for T-category assessment for NSCLC patients. As subjects for this study, 213 pathologically diagnosed NSCLC patients who underwent thin-section MDCT and MR imaging as well as T-factor diagnosis were retrospectively enrolled. SNR of each tumor was calculated and compared by paired t-test for each sequence with and without DLR. T-factor for each patient was assessed with thin-section MDCT and all MR sequences, and the accuracy for T-factor diagnosis was compared among all sequences and thin-section CT by means of McNemar's test. SNRs of T2WI, STIR imaging, unenhanced thin-section Quick 3D imaging, and CE-thin-section Quick 3D imaging with DLR were significantly higher than SNRs of those without DLR (P < 0.05). Diagnostic accuracy of STIR imaging and CE-thick- or thin-section Quick 3D imaging was significantly higher than that of thin-section CT, T2WI, and unenhanced thick- or thin-section Quick 3D imaging (P < 0.05). DLR is thus considered useful for image quality improvement on MR imaging. STIR imaging and CE-Quick 3D imaging with or without CS were validated as appropriate MR sequences for T-factor evaluation in NSCLC patients.

Measurement of solid size in early-stage lung adenocarcinoma by virtual 3D thin-section CT applied artificial intelligence

An artificial intelligence (AI) system that reconstructs virtual 3D thin-section CT (TSCT) images from conventional CT images by applying deep learning was developed. The aim of this study was to investigate whether virtual and real TSCT could measure the solid size of early-stage lung adenocarcinoma. The pair of original thin-CT and simulated thick-CT from the training data with TSCT images (thickness, 0.5–1.0 mm) of 2700 pulmonary nodules were used to train the thin-CT generator in the generative adversarial network (GAN) framework and develop a virtual TSCT AI system. For validation, CT images of 93 stage 0–I lung adenocarcinomas were collected, and virtual TSCTs were reconstructed from conventional 5-mm thick-CT images using the AI system. Two radiologists measured and compared the solid size of tumors on conventional CT and virtual and real TSCT. The agreement between the two observers showed an almost perfect agreement on the virtual TSCT for solid size measurements (intraclass correlation coefficient = 0.967, P < 0.001, respectively). The virtual TSCT had a significantly stronger correlation than that of conventional CT (P = 0.003 and P = 0.001, respectively). The degree of agreement between the clinical T stage determined by virtual TSCT and the clinical T stage determined by real TSCT was excellent in both observers (k = 0.882 and k = 0.881, respectively). The AI system developed in this study was able to measure the solid size of early-stage lung adenocarcinoma on virtual TSCT as well as on real TSCT.

A spatio-temporal image analysis for growth of indeterminate pulmonary nodules detected by CT scan.

The objective is to evaluate the performance of computational image classification for indeterminate pulmonary nodules (IPN) chronologically detected by CT scan. Total 483 patients with 670 abnormal pulmonary nodules, who were taken chest thin-section CT (TSCT) images at least twice and resected as suspicious nodules in our hospital, were enrolled in this study. Nodular regions from the initial and the latest TSCT images were cut manually for each case, and approached by Python development environment, using the open-source cv2 library, to measure the nodular change rate (NCR). These NCRs were statistically compared with clinico-pathological factors, and then, this discriminator was evaluated for clinical performance. NCR showed significant differences among the nodular consistencies. In terms of histological subtypes, NCR of invasive adenocarcinoma (ADC) were significantly distinguishable from other lesions, but not from minimally invasive ADC. Only for cancers, NCR was significantly associated with loco-regional invasivity, p53-immunoreactivity, and Ki67-immunoreactivity. Regarding Epidermal Growth Factor Receptor gene mutation of ADC-related nodules, NCR showed a significant negative correlation. On staging of lung cancer cases, NCR was significantly increased with progression from pTis-stage 0 up to pT1b-stage IA2. For clinical shared decision-making (SDM) whether urgent resection or watchful-waiting, receiver operating characteristic (ROC) analysis showed that area under the ROC curve was 0.686. For small-sized IPN detected by CT scan, this approach shows promise as a potential navigator to improve work-up for life-threatening cancer screening and assist SDM before surgery.

Quantitative Analysis for Lung Disease on Thin-Section CT.

Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses.