Journal of Computer Assisted Tomography最新文献

Objective: The purpose of this study was to evaluate the usefulness of the injection pressure-to-injection rate (IPIR) ratio for the early detection of contrast extravasation at the venipuncture site during contrast-enhanced computed tomography.

Methods: We retrospectively enrolled 57,528 patients who underwent contrast-enhanced computed tomography examinations in a single hospital. The power injector recorded the contrast injection pressure at 0.25-second intervals. We constructed logistic regression models using the IPIR ratio as the independent variable and extravasation occurrence as the dependent variable (IPIR ratio models) at 1, 2, 3, 4, 5, and 6 seconds after the start of contrast administration. Univariate logistic regression models in which injection pressure is used as an independent variable (injection pressure models) were also constructed as a reference baseline. The performance of the models was evaluated with the area under the receiver operating characteristic curves.

Results: Of the 57,528 cases, 46,022 were assigned to the training group and 11,506 were assigned to the test group, which included 112 extravasation cases (0.24%) in the training group and 28 (0.24%) in the test group. The area under the receiver operating characteristic curves for the IPIR ratio models and injection pressure models were 0.555 versus 0.563 at t = 1 ( P = 0.270), 0.712 versus 0.678 at t = 2 ( P = 0.305), 0.758 versus 0.693 at t = 3 ( P = 0.032), 0.776 versus 0.688 at t = 4 ( P = 0.005), 0.810 versus 0.699 at t = 5 ( P = 0.002), and 0.811 versus 0.706 at t = 6 ( P = 0.002).

Conclusions: The IPIR ratio models perform better in detecting contrast extravasation at 3 to 6 seconds after the start of contrast administration than injection pressure models.

Objective: The aim of the study is to assess the validity of a recently published consensus magnetic resonance imaging (MRI) diagnostic algorithm for differentiating degenerating leiomyomas from uterine sarcomas and other atypical appearing uterine malignancies.

Methods: Atypical uterine masses on pelvic MRI were identified using a radiology report search engine and teaching files with the keywords "atypical leiomyoma," "atypical fibroid," and "sarcoma." All cases were pathology-proven. Two radiologists blinded to clinical, surgical, and pathologic reports retrospectively and independently reviewed 40 pelvic MRI examinations dated 1/2007-9/2022 to determine whether the masses appeared benign or malignant, using the 2022 consensus atypical uterine mass flow chart. Imaging features assessed included intermediate/high signal intensity (SI) at T2-weighted imaging, high diffusion weighted imaging SI (equal or higher SI than endometrium or lymph nodes on high b value imaging), apparent diffusion coefficient (ADC) value ≤0.905 × 10 -3 mm 2 /s, peritoneal metastases, and abnormal lymph nodes.

Results: Among the 40 atypical uterine mass cases reviewed, 24 masses were benign (22 leiomyomas, 1 adenomyoma, and 1 borderline ovarian tumor) and 16 masses were malignant (6 leiomyosarcomas, 6 carcinosarcomas, 2 endometrial stromal sarcomas, 1 high-grade adenosarcoma, and 1 low-grade uterine sarcoma). Sensitivity, specificity, positive predictive value, and negative predictive value of whether a mass was benign or malignant were 75%, 95.8%, 92.3%, and 85% for reader 1, and 81.2%, 91.7%, 86.7%, and 88% for reader 2, respectively. Interrater agreement was strong, with a kappa statistic of 0.89. When excluding nonleiomyosarcoma uterine malignancies, sensitivity and negative predictive value improved to 100%.

Conclusions: The new consensus pelvic MRI algorithm for evaluating atypical uterine masses has good specificity, sensitivity, positive predictive value, and negative predictive value for determining malignancy, particularly for uterine sarcomas that are leiomyosarcomas. However, if ADC value is near but not below 0.905 × 10 -3 mm 2 /s, the mass may still be malignant, especially if a b value lower than 1000 is used. If the atypical uterine mass is predominantly endometrial, morphological features on T2 and postgadolinium sequences should guide suspicion, as some atypical appearing nonleiomyosarcoma uterine malignancies may have an ADC value greater than 0.905 × 10 -3 mm 2 /s.

Objective: The aim of this study was to assess the usefulness of picture archiving and communication system (PACS)-based quantitative grayscale ultrasonography (US) measurements in detecting allograft dysfunction in posttransplant patients.

Methods: In this retrospective study, 116 patients with liver transplantation who underwent biopsy for allograft evaluation were recruited from the database. All participants had US images prior to procedure. Normal, acute cellular rejection (ACR), recurrent hepatitis (Hep), or combined (ACR/Hep) groups were generated based on pathology results. Region of interests were drawn for liver and rectus abdominus muscle to perform quantitative US analysis. The liver/muscle mean ratio (L/M) and heterogeneity index (HI; liver standard deviation/liver mean) were obtained. The ratios of groups were compared, and receiver-operating-characteristic analysis was performed.

Results: There was a significant difference between normal (n = 16) and each of other groups (ACR, 39; Hep, 36; combined, 25) for L/M and HI ( P < 0.05). No significant difference was detected between ACR, Hep, and combined groups. The areas under the curve for L/M and HI were 0.755 (moderate) and 0.817 (good), respectively. To differentiate abnormal (ACR, Hep, and combined) from normal allografts sensitivity, specificity, PPV, and NPV were 50.0%, 87.5%, 96.2%, and 21.9% for cut point of L/M ≥1 and 84.0%, 68.8%, 94.4%, and 40.7% for cut point of HI ≥0.2 with odds ratios of 7.52 (for L/M ≥1) and 13.10 (for HI ≥0.2), respectively ( P < 0.01).

Conclusions: L/M has moderate and HI has good discrimination of normal from abnormal allograft in liver transplant patients. PACS-based quantitative US measurements is an objective, easy to use, noninvasive auxiliary tool to discriminate hepatic allograft dysfunction.

Objective: To evaluate the efficacy of an enhanced computed tomography (CT) radiomics nomogram in predicting preoperative lymphovascular invasion (LVI) or perineural invasion (PNI) in patients with advanced gastric cancer (GC).

Materials and methods: Data from 149 patients with GC from our hospital (January 2019 to December 2022) were analyzed. High throughput radiomics features were extracted from manually delineated volumes of interest on enhanced CT venous phase images. Optimal features were identified using intraclass correlation coefficient analysis and least absolute shrinkage and selection operator. Models were constructed using the radiomics score (Rad-score), the above features, and independent risk factors. Performance was assessed via the receiver operating characteristic, decision curve analysis and calibration curves.

Results: Eight radiomics features were deemed essential. Factors including history of alcohol consumption ( P = 0.029), peritumor fatty infiltration ( P = 0.046), degree of enhancement ( P = 0.012), and Rad-score ( P < 0.001) were significant predictors of LVI/PNI. The radiomics nomogram, which integrated these factors, showed superior prediction (the training group: area under the curve [AUC] = 0.917; the validation group: AUC = 0.925) compared with other models.

Conclusion: The enhanced CT radiomics nomogram offers robust preoperative prediction for LVI/PNI in patients with GC.

Objectives: In recent years, the use of shear-wave elastography (SWE) as a diagnostic tool for detecting malignant breast lesions has shown promising results. This study aims to determine the clinical diagnostic value of SWE in detecting malignant nipple retraction.

Methods: Both US and SWE (Philips EPIQ7 system) were performed for 41 consecutive patients with nipple retraction (56 nipples). The mean, median, and maximum tissue elasticity values (in kilopascals) were determined for each nipple by using SWE. The sensitivity, specificity, and overall accuracy of each measurement was determined by using the surgical pathology results or clinical diagnosis as the gold standard.

Results: Of the 56 retracted nipples, 32 were due to benign lesions, and 24 were due to malignant lesions. No significant differences in dimensions or echo features were found between the benign and malignant groups. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the color Doppler flow imaging (CDFI) pattern were 63.89% (23/36), 95% (19/20), 95.83 (23/24), 59.38 (19/32), and 75% (42/56), respectively; the corresponding values for median elasticity on SWE were 88.46% (23/26), 96.67% (29/30), 95.83 (23/24), 90.63 (29/32), and 92.85 (52/56), respectively.

Conclusions: The addition of SWE to conventional US could help differentiate benign from malignant lesions associated with nipple retraction.

Objective: Epicardial adipose tissue (EAT) is an important imaging indicator of cardiovascular risk. EAT volume is usually measured using electrocardiogram (ECG) gating. However, there are concerns regarding the influence of motion artifacts when measuring EAT volume on non-ECG-gated plain chest computed tomography (CT) images. Few studies have evaluated the EAT volume using non-ECG gating. This study aimed to validate the accuracy of EAT quantification using non-ECG-gated chest CT imaging.

Methods: We included 100 patients (64 males, 36 females) who underwent simultaneous coronary artery calcification score imaging (ECG gated) and plain chest CT imaging (non-ECG gated). Images taken using non-ECG gating were reconstructed using the same field of view and slice thickness as those obtained with ECG gating. The EAT capacity of each image was measured and compared. An AZE Virtual Place (Canon) was used for the measurements. The Mann-Whitney U test and intraclass correlation coefficient were used for statistical analyses. P values <0.05 were considered statistically significant. Concordance was evaluated using Bland-Altman analysis.

Results: The mean EAT volume measured by ECG-gated imaging was 156.5 ± 66.9 mL and 155.4 ± 67.9 mL by non-ECG-gated imaging, with no significant difference between the two groups ( P = 0.86). Furthermore, the EAT volumes measured using ECG-gated and non-ECG-gated imaging showed a strong correlation ( r = 0.95, P < 0.05). Bland-Altman analysis revealed that the mean error of the EAT volume (non-ECG-gated imaging - ECG-gated imaging) was -1.02 ± 2.95 mL (95% confidence interval, -6.49 to 4.76).

Conclusions: The EAT volume obtained using non-ECG-gated imaging was equivalent to that obtained using ECG-gated imaging.

Purpose: This study aimed to determine the optimal threshold iodine density to distinguish enhancing and nonenhancing renal masses with dual-layer dual-energy CT (dlDECT).

Methods: In this retrospective, HIPAA-compliant, institutional review board-approved study, 383 consecutive renal mass dlDECT studies from September 5, 2018, through December 15, 2022, were reviewed for enhancing solid renal masses with ≥∆20 HU. Studies with simple cysts in the same interval served as controls. Lesion ROI HU measurements on unenhanced and nephrographic phases and ROI iodine density measurements of each lesion and the abdominal aorta for normalization were recorded. The mean lesion HU values and absolute and normalized iodine densities were compared with enhancing and nonenhancing renal lesions using a two-sample t test. The diagnostic accuracy of iodine thresholds was assessed by calculating sensitivity and specificity, with receiver operating characteristic curve and AUC analysis.

Results: There were 38 enhancing and 39 nonenhancing renal lesions. The mean (standard deviation [SD]) ∆HU was 73.5 (38.7) and 3.9 (5.1) HU for enhancing and nonenhancing lesions, respectively. The mean absolute iodine density was significantly different for enhancing and nonenhancing lesions (3.2 [1.7] mg/mL and 0.20 [0.22] mg/mL, respectively; P < 0.0001). Normalized mean iodine density was significantly different for enhancing and nonenhancing lesions (0.62 [0.33] mg/mL and 0.04 [0.04] mg/mL, respectively; P < 0.0001). The optimal absolute iodine density threshold of 0.70 mg/mL (AUC, 0.999) and normalized iodine density threshold of 0.11 mg/mL (AUC, 0.999) were 100% sensitive and 97.4% specific for discriminating enhancing and nonenhancing renal lesions.

Conclusions: This study provides absolute and normalized iodine density thresholds to distinguish enhancing and nonenhancing renal lesions with high sensitivity and specificity using dlDECT.

Objectives: This study evaluated the feasibility of a model-based iterative reconstruction technique (MBIR) tuned for the myocardium on myocardial computed tomography late enhancement (CT-LE).

Methods: Twenty-eight patients who underwent myocardial CT-LE and late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) within 1 year were retrospectively enrolled. Myocardial CT-LE was performed using a 320-row CT with low tube voltage (80 kVp). Myocardial CT-LE images were scanned 7 min after CT angiography (CTA) without additional contrast medium. All myocardial CT-LE images were reconstructed with hybrid iterative reconstruction (HIR), conventional MBIR (MBIR_cardiac), and new MBIR tuned for the myocardium (MBIR_myo). Qualitative (5-grade scale) scores and quantitative parameters (signal-to-noise ratio [SNR] and contrast-to-noise ratio [CNR]) were assessed as image quality. The sensitivity, specificity, and accuracy of myocardial CT-LE were evaluated at the segment level using an American Heart Association (AHA) 16-segment model, with LGE-MRI as a reference standard. These results were compared among the different CT image reconstructions.

Results: In 28 patients with 448 segments, 160 segments were diagnosed with positive by LGE-MRI. In the qualitative assessment of myocardial CT-LE, the mean image quality scores were 2.9 ± 1.2 for HIR, 3.0 ± 1.1 for MBIR_cardiac, and 4.0 ± 1.0 for MBIR_myo. MBIR_myo showed a significantly higher score than HIR ( P < 0.001) and MBIR_cardiac ( P = 0.018). In the quantitative image quality assessment of myocardial CT-LE, the median image SNR was 10.3 (9.1-11.1) for HIR, 10.8 (9.8-12.1) for MBIR_cardiac, and 16.8 (15.7-18.4) for MBIR_myo. The median image CNR was 3.7 (3.0-4.6) for HIR, 3.8 (3.2-5.1) for MBIR_cardiac, and 6.4 (5.0-7.7) for MBIR_myo. MBIR_myo significantly improved the SNR and CNR of CT-LE compared to HIR and MBIR_cardiac ( P < 0.001). The sensitivity, specificity, and accuracy for the detection of myocardial CT-LE were 70%, 92%, and 84% for HIR; 71%, 92%, and 85% for MBIR_cardiac; and 84%, 92%, and 89% for MBIR_myo, respectively. MBIR_myo showed significantly higher image quality, sensitivity, and accuracy than the others ( P < 0.05).

Conclusions: MBIR tuned for myocardium improved image quality and diagnostic performance for myocardial CT-LE assessment.

Objectives: We investigated volumetric changes in buccal fat pad (BFP) in age groups and sexes by cranial or neck computed tomography (CT) or cranial CT angiography.

Methods: One hundred twenty patients underwent cranial or neck CT examinations or cranial CT angiography were retrospectively screened: 18-29 years old (group 1), 30-49 years old (group 2), and 50 years and older (group 3). Left buccal fat tissue measurements were performed in age groups, sexes, and body mass index (BMI) groups.

Results: Left buccal fat volume in the 30-49 age group and the ≥50 age group was significantly higher than that in the 18-29 age group ( P < 0.05). Across all groups and specifically within the 18-29 age group, females exhibited significantly lower buccal fat volume than males ( P < 0.05). The left buccal fat volume of individuals classified as overweight and obese was significantly higher than that of the underweight and normal weight groups. There was a negative relationship between buccal fat volume and fat density. Moreover, as age increased, within age groups 1 to 3, there was a notable increase in body weight, body length, BMI, and BMI groups (underweight and normal weight to obesity), accompanied by a significant rise in buccal fat volume. Conversely, fat density exhibited a significant decrease with advancing age.

Conclusions: Buccal fat volume, localized in the middle third of the face, increased with aging and increasing BMI values. Young females had lower buccal fat volume. Buccal fat tissue volume is important in facial rejuvenation procedures such as facial filler applications.

Objective: This study aimed to evaluate the feasibility of the recently commercialized reverse encoding distortion correction (RDC) method for echo-planar imaging (EPI) diffusion-weighted imaging (DWI) by applying clinical head MRI.

Methods: This study included 50 consecutive patients who underwent head MRI, including single-shot (SS) EPI DWI and RDC-EPI DWI. For evaluation of normal structures, qualitative scores for image distortion, Dice similarity coefficient (DSC) values, distortion ratios, and mean apparent diffusion coefficient (ADC) values were assessed in the pons, temporal lobe at the skull base, and frontal lobe at the level of the lateral ventricles in 30 patients. To evaluate pathologies, qualitative scores for image distortion were assessed for 25 intracranial and 21 extracranial pathologies identified in 32 patients.

Results: Qualitative scores for image distortion, DSC values, distortion ratios, and mean ADC values of the pons and temporal lobe were significantly different between SS-EPI DWI and RDC-EPI DWI, whereas those of the frontal lobe at the level of the lateral ventricles were not significantly different between the 2 DWIs. The qualitative scores for image distortion and mean ADC values of extracranial pathologies were significantly different between the DWIs, whereas those of intracranial pathologies were not significantly different.

Conclusions: RDC-EPI DWI significantly reduced image distortion and showed higher mean ADC values of the brain parenchyma in the skull base and extracranial pathologies.