Journal of Computer Assisted Tomography最新文献

Objective: This study aims to identify factors associated with the detectability of the right adrenal vein (RAV) on preoperative contrast-enhanced CT scans of adrenal venous sampling (AVS) in the era of high-resolution CT (HRCT).

Materials and methods: In this retrospective study, 36 patients (15 men and 21 women; mean age, 56 y) who underwent preoperative contrast-enhanced CT [11 patients in HRCT with 0.25 mm detector matrix (Cannon Medical Systems) and 25 patients in conventional multidetector CT with 0.5 mm matrix] were included. A contrast agent dose of 600 mgI/kg was injected, and CT images were acquired at a fixed scan delay of 50 and 80 seconds. Adrenal venography and venous sampling were performed for the diagnosis of suspected primary hyperaldosteronism. The qualitative detectability of RAV on preoperative CT was assessed with adrenal venography as a reference. Clinical and imaging factors associated with a good detectability of RAV were analyzed via regression analysis. Optimal acquisition timing was assessed by analyzing the time-intensity curve and contrast enhancement pattern of the inferior vena cava using CT data from a separate cohort (n=5).

Results: The qualitative detectability of RAV was deemed good in 15 patients and poor in 21 patients. Regression analysis revealed that only heterogeneous enhancement of inferior vena cava with bolus high attenuation, corresponding to an optimal acquisition timing from time-intensity curve analysis, was associated with a good detectability of RAV (odds ratio, 5.06). The use of HRCT was not statistically significant.

Conclusions: Optimal acquisition timing is a crucial factor for the detectability of RAV in preprocedural CT for AVS, while high-resolution 0.25 detector CT appears to have limited significance.

Objective: Early diagnosis of primary and metastatic lung nodules is critical for effective therapeutic planning. Manual delineation of lung nodules is not time-efficient and is prone to human error as well as interobserver and intraobserver variability. This study aimed to address the unmet need for an open-source computer-aided detection (CAD) system for 3D segmentation of lung and metastatic lung nodules along with radiomic feature extraction.

Methods: The proposed adaptive region-growing-based lung nodule segmentation (RGLNS) tool was developed in-house, requiring only manual input to select a seed point within the nodule on computed tomography (CT) images. A total of 230 CT scans from 100 patients with sarcomas were screened. Lung nodules were present in 200 CT scans, which were further analyzed. The accuracy of the lung and nodule segmentation was evaluated qualitatively using a 5-point Likert scale (uninterpretable: 1; poor: 2; fair: 3; good: 4; excellent: 5) and quantitatively using the Dice coefficient and Jaccard index.

Results: A total of 200 CT scans comprising 12,000 CT slices were analyzed, among which 786 lung nodules were identified. Quantitative lung segmentation accuracies (n=2400 slices) yielded a Dice coefficient of 0.92±0.06 and a Jaccard index of 0.85±0.05. Qualitative scores (n=9600 slices) for lung boundary correction (4.56±1.18) and inclusion of pulmonary vessels (4.75±0.72) were rated as good to excellent. Quantitative nodule segmentation (n=142 nodules) accuracies were as follows: dice coefficient=0.92±0.03, 0.88±0.04, 0.86±0.03, 0.85±0.03, 084±0.04 and Jaccard index=0.84±0.03, 0.81±0.04, 0.78±0.04, 0.78±0.02, 0.76±0.04 for solitary (n=73), juxtapleural (n=32), juxtavascular (n=28), fissure-attached (n=6), and ground-glass (n=6) nodules, respectively. Qualitative scores (n=644 nodules) for nodule-boundary were good to excellent [solitary (n=342): 4.97±0.15; juxtapleural (n=155): 4.45±0.60; juxtavascular (n=127): 4.40±0.65; fissure-attached (n=9): 4.40±0.70; ground-glass (n=11): 4.25±0.75] and for exclusion of pulmonary vessels/pleura from nodules were good [juxtapleural (n=155): 4.10±0.66; juxtavascular (n=127): 4.08±0.64; fissure-attached (n=9): 4.30±0.67].

Conclusions: The proposed semiautomated CAD system, RGLNS, requiring minimal manual input, demonstrated robust, and promising segmentation results for the whole lung and various types of metastatic lung nodules.

Background: With the widespread use of lumbar pedicle screws for internal fixation, the morphology of the screws and the surrounding tissues should be evaluated. The metal artifact reduction (MAR) technique can reduce the artifacts caused by pedicle screws, improve the quality of computed tomography (CT) images after pedicle fixation, and provide more imaging information to the clinic.

Purpose: To explore whether the MAR+ method, a projection-based algorithm for correcting metal artifacts through multiple iterative operations, can reduce metal artifacts and have an impact on the structure of the surrounding metal.

Materials and methods: A total of 57 patients who underwent lumbar spine CT examination after lumbar internal fixation from January to December 2023 in our hospital were retrospectively enrolled. The CT images were reconstructed using MAR+ and non-MAR+ techniques and were subdivided into MAR+ and non-MAR+ groups. The CT number (in Hounsfield units) and the SD noise values of the spinal canal, vertebral body, psoas major muscle, and adjacent fat were measured in the 2 groups of CT images, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The subjective score was evaluated by two diagnostic radiologists using a double-blind method for image quality evaluation of the MAR+ group and the non-MAR+ group, and the image quality was classified on a 5-point scale. The rank-sum test was utilized to compare the subjective and objective scores of the 2 groups.

Results: The SD values of the spinal canal (Z=-4.12, P<0.01), vertebral body (Z=-3.81, P<0.01), and psoas major muscle (Z=-3.87, P<0.01) in the MAR+ group were significantly lower than those in the non-MAR+ group (P<0.05). However, the SD values of the adjacent fat (Z=-2.03, P=0.42) in the MAR+ group, although smaller than those in the non-MAR+ group, were not statistically significant. The CNR values of vertebral canal (Z=-2.67, P=0.008) and fat (Z=-2.60, P=0.009) were higher in the MAR+ group than in the non-MAR+ group, whereas the CNR values of the vertebral body (Z=-6.74, P<0.01) in the MAR+ group were smaller than those in the non-MAR+ group, and the difference of all of them was statistically significant (P<0.05). Furthermore, for both CT and SNR values, the MAR group's values were all less than those of the non-MAR group and were statistically significant (P<0.05). The subjective scores of the measurement points were all higher in the MAR+ group than in the non-MAR+ group.

Conclusions: The MAR+ technique has a noise reduction effect on different tissues and artifacts are significantly reduced. Although the artifacts caused by metal screws were not completely eliminated, the MAR+ technique was able to reduce the interference of artifacts in the diagnosis of CT images, thus improving their diagnostic quality.

Objective: To confirm that saline sealing of the needle trace after computed tomography (CT)-guided lung biopsy reduces the incidence of pneumothorax and chest tube insertion, and to observe its effects on pulmonary hemorrhage and hemoptysis.

Materials and methods: Patients who underwent CT-guided lung biopsy at our hospital between January 2018 and January 2024 were included in the study. Patients were divided into 2 groups according to whether the needle trace was sealed with saline after tissue sampling. Patient baseline characteristics, lung lesion factors, procedural factors, pneumothorax rates, chest tube insertion rates, pulmonary hemorrhage rates, and hemoptysis rates were recorded.

Results: The incidence of pneumothorax was 28.9% (38/132) and 15.8% (15/95) in groups A (control) and B (with sealed traces), respectively (P=0.002). The incidence of pneumothorax requiring chest tube insertion was significantly lower in group B than in group A (1.1% vs. 6.8%; P=0.048). The incidence of pulmonary hemorrhage was similar between the 2 groups (38.6% vs. 42.1%; P=0.599). No significant difference was observed in the hemoptysis of patients in groups A and B (6.8% vs. 10.5%; P=0.320). In the binary logistic regression analysis, significant risk factors for pneumothorax included lack of saline sealing, smaller lesion size, multiple passes through the pleura, and the lateral decubitus position. Smaller lesions and longer biopsy trace lengths were independent risk factors for hemoptysis.

Conclusions: Sealing the needle trace with saline significantly reduced the incidences of pneumothorax and chest tube insertion due to pneumothorax. Moreover, it did not significantly increase the incidence of pulmonary hemorrhage or hemoptysis. This technique is recommended for use in CT-guided lung biopsies.

Objective: To explore the application of low-energy image in dual-energy spectral CT (DEsCT) combined with deep learning image reconstruction (DLIR) to improve inferior vena cava imaging.

Materials and methods: Thirty patients with inferior vena cava syndrome underwent contrast-enhanced upper abdominal CT with routine dose, and the 40, 50, 60, 70, and 80 keV images in the delayed phase were first reconstructed with the ASiR-V40% algorithm. Image quality was evaluated both quantitatively [CT value, SD, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for inferior vena cava] and qualitatively to select an optimal energy level with the best image quality. Then, the optimal-energy images were reconstructed again using deep learning image reconstruction medium strength (DLIR-M) and DLIR-H (high strength) algorithms and compared with that of ASiR-V40%.

Results: The objective CT value, SD, SNR, and CNR increased with the decrease in energy level, with statistically significant differences (all P<0.05). The 40 keV images had the highest CT values, SNR, and CNR and good diagnostic acceptability, and 40 keV was selected as the best energy level. Compared with ASiR-V40% and DLIR-M, DLIR-H had the lowest SD, highest SNR and CNR, and subjective score (all P<0.001) with good consistencies between the 2 physicians (all k ≥0.75). The 40 keV images with DLIR-H had the highest overall image quality, showing sharper edges of inferior vena cava vessels and clearer lumen in patients with Budd-Chiari syndrome.

Conclusions: Compared with the ASiR-V algorithm, DLIR-H significantly reduces image noise and provides the highest CNR and best diagnostic image quality for the 40 keV DEsCT images in imaging inferior vena cava.

Objective: This study evaluated the performance of a deep learning-based vertebral compression fracture (VCF) detection tool in patients with incidental VCF. The purpose of this study was to validate this tool across multiple sites and multiple vendors.

Methods: This was a retrospective, multicenter, multinational blinded study using anonymized chest and abdominal CT scans performed for indications other than VCF in patients ≥50 years old. Images were obtained from 2 teleradiology companies in France and United States and were processed by CINA-VCF v1.0, a deep learning algorithm designed for VCF detection. Ground truth was established by majority consensus across 3 board-certified radiologists. Overall performance of CINA-VCF was evaluated, as well as subset analyses based on imaging acquisition parameters, baseline patient characteristics, and VCF severity. A subgroup was also analyzed and compared with available clinical radiology reports.

Results: Four hundred seventy-four CT scans were included in this study, comprising 166 (35.0%) positive and 308 (65.0%) negative VCF cases. CINA-VCF demonstrated an area under the curve (AUC) of 0.97 (95% CI: 0.96-0.99), accuracy of 93.7% (95% CI: 91.1%-95.7%), sensitivity of 95.2% (95% CI: 90.7%-97.9%), and specificity of 92.9% (95% CI: 89.4%-96.5%). Subset analysis based on VCF severity resulted in a specificity of 94.2% (95% CI: 90.9%-96.6%) for grade 0 negative cases and a specificity of 64.3% (95% CI: 35.1%-87.2%) for grade 1 negative cases. For grades 2 and 3 positive cases, sensitivity was 89.7% (95% CI: 79.9%-95.8%) and 99.0% (95% CI: 94.4%-100.0%), respectively.

Conclusions: CINA-VCF successfully detected incidental VCF and even outperformed clinical reports. The performance was consistent among all subgroups analyzed. Limitations of the tool included various confounding pathologies such as Schmorl's nodes and borderline cases. Despite these limitations, this study validates the applicability and generalizability of the tool in the clinical setting.

Objective: To develop a machine learning model that integrates clinical features and multisequence MRI radiomics for noninvasively predicting the expression status of prognostic-related factors cyclin D1 and TGF-β1 in breast cancer, providing additional information for the clinical development of personalized treatment plans.

Methods: A total of 123 breast cancer patients confirmed by surgical pathology were retrospectively enrolled in our Hospital from January 2016 to July 2022. The patients were randomly divided into a training group (87 cases) and a validation group (36 cases). Preoperative routine and dynamic contrast-enhanced magnetic resonance imaging scans of the breast were performed for treatment subjects. The region of interest was manually outlined, and texture features were extracted using AK software. Subsequently, the LASSO algorithm was employed for dimensionality reduction and feature selection to establish the MRI radiomics labels. The diagnostic efficacy and clinical value were assessed through receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA).

Results: In the cyclin D1 cohort, the area under the receiver operating characteristic (ROC) curve in the clinical prediction model training and validation groups was 0.738 and 0.656, respectively. The multisequence MRI radiomics prediction model achieved an AUC of 0.874 and 0.753 in these respective groups, while the combined prediction model yielded an AUC of 0.892 and 0.785. In the TGF-β1 cohort, the ROC AUC for the clinical prediction model was found to be 0.693 and 0.645 in the training and validation groups, respectively. For the multiseries MRI radiomics prediction model, it achieved an AUC of 0.875 and 0.760 in these respective groups; whereas for the combined prediction model, it reached an AUC of 0.904 and 0.833. Decision curve analysis (DCA) demonstrated that both cohorts indicated a higher clinical application value for the combined prediction model compared with both individual models-clinical prediction model alone or radiomics model.

Conclusion: The integration of clinical features and multisequence MRI radiomics in a combined modeling approach holds significant predictive value for the expression status of cyclin D1 and TGF-β1. The model provides a noninvasive, dynamic evaluation method that provides effective guidance for clinical treatment.

Objective: Patient positioning during clinical practice can be challenging, and mispositioning leads to a change in CT number. CT number fluctuation was assessed in single-energy (SE) EID, dual-energy (DE) EID, and deep silicon photon-counting detector (PCD) CT over water-equivalent diameter (WED) with different mispositions.

Methods: A phantom containing five clinically relevant inserts (Mercury Phantom, Gammex) was scanned on a clinical EID CT and a deep silicon PCD CT prototype at vertical positions of 0, 4, 8, and 12 cm. EID scans used 120 kV and rapid kV-switching DE techniques. CT number was calculated for air, water, polystyrene, iodine 10 mg/mL, and bone. Ideal CT numbers were calculated using the NIST XCOM database toolkit. Comparison of measured to ideal CT number utilized relative root mean square error (RMSE). Trends in CT number versus WED were compared using linear regression and statistical comparisons to test for differences in slope.

Results: No statistical difference of CT number with mispositioning was seen between acquisition modes. CT number fluctuation was larger due to WED than mispositioning for all material inserts. Water, iodine, and bone, for deep silicon PCD CT had statistically significant (P < 0.05) smaller slopes compared to EIDof CT number over WED for all tested mispositions. The accuracy of deep silicon PCD CT was higher than either SE or DE EID CT for all materials at all mispositions except for polystyrene.

Conclusions: WED (ie, patient size) contributes to CT number fluctuation more than mispositioning. The change in CT number was significantly smaller, and CT number accuracy was higher for deep silicon PCD CT in this phantom study.

Purpose: The aim of this study was to investigate the utility of native T 1 and T 2 mapping in the bowel to evaluate disease activity in Crohn disease (CD) using endoscopy as the reference standard.

Methods: This was a prospective study. Magnetic resonance imaging was performed by using a 1.5-T Philips scanner. We used a modified look-locker inversion recovery and a multiecho gradient-spin-echo sequences for single breath-hold native T 1 and T 2 maps, respectively, for the short-axis image of the intestine, and the measurement at the most severe site was compared with partial Simple Endoscopic Score for Crohn's Disease (pSES-CD, assessed by an expert endoscopist). A pSES-CD ≥ 4 indicated active disease. Statistical analyses were performed using the Student t test, Spearman correlation, and receiver operating characteristic curve analysis.

Results: A total of 27 patients (mean age ± standard deviation, 37 ± 18 years; 20 men, 7 women) were included in this study. The native T 1 value of active disease was significantly higher than that of inactive disease (1170.8 ± 100.5 milliseconds vs 924.5 ± 95.3 milliseconds; P = 0.018), but the T 2 value was not significantly different between active and inactive disease (76.1 ± 7.8 milliseconds vs 69.3 ± 10.9 milliseconds; P = 0.424). A good correlation was found between native T 1 value and pSES-CD (ρ = 0.71; P < 0.001) but not between T 2 value and pSES-CD (ρ = 0.06; P = 0.790). The area under the receiver operating characteristic curve for differentiating the disease activity was 0.96 (95% confidence interval [CI]: 0.90-1.00) for T 1 values and 0.68 (95% confidence interval: 0.41-0.96) for T 2 values.

Conclusions: Native T 1 mapping could be potentially used as a noninvasive method to differentiate disease activity in patients with CD and may be superior to T 2 mapping for this purpose.

Objective: Our aim was to investigate the effectiveness of the dual-energy computed tomography (DECT) virtual noncalcium (VNCa) technique in avascular necrosis (AVN) for detecting bone marrow edema (BME) and staging.

Methods: This prospective study included adult patients diagnosed with unilateral or bilateral femoral head AVN between January 2023 and December 2023, who had magnetic resonance imaging (MRI) and DECT. Two participants were excluded from the study due to undergoing surgical procedures during the period between the scans. Two reviewers, blinded to MRI images and clinical data, visually examined color-coded VNCa pictures to assess BME using a binary classification (0 = normal bone marrow, 1 = BME). Same 2 reviewers also used color-coded and nonmapped images to stage AVN in accordance to the "Association for Research on Osseous Circulation" (ARCO) staging system. Interobserver agreements for the visual evaluation and staging were calculated with κ coefficient. Following a visual assessment of BME and the staging of AVN, same 2 reviewers conducted CT density measurements on regions of BME regions utilizing DECT noncalcium images. An independent third investigator (reference standard) utilized MRI, x-ray, and clinical data to confirm the definitive diagnosis and staging of AVN. A P value less than 0.05 was considered statistically significant.

Results: Fifty patients (28 men, 22 women, mean age: 44.2 ± 13.1 years, range: 25-75 years) were included in the final analysis. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the VNCa technique in detecting BME were 96.0%, 94.4%, 97.9%, 89.4%, and 95.6%, respectively, for reviewer 1; and 96.0%, 88.9%, 96.0%, 88.9%, and 94.1%, respectively, for reviewer 2. Interobserver agreement was almost perfect ( κ = 0.84). Both reviewer 1 and reviewer 2 accurately classified 92.7% of the AVNs. The density measurements showed a statistically significant difference ( P = 0.001) between the edema regions and the normal marrow regions. No statistically significant difference was observed in the density measurements of edema regions at different stages ( P = 0.13).

Conclusions: DECT VNCa technique exhibits excellent performance in detecting BME in hip AVN cases, as well as accurately determining the stage of AVN.