Journal of Computer Assisted Tomography最新文献

Objective: Image quality evaluation in radiology is most relevant when reflects radiologists' performance. This study assessed how image quality measurement in terms of in vivo-characterized detectability index () for low-contrast liver lesion assessment in CT is correlated with radiologists' performance across 2 different CT reconstructions.

Methods: Fifty-one contrast-enhanced abdominal studies for investigating colorectal liver metastases were prospectively performed using 2 radiation dose exposures and reconstructed with Filtered back projection (FBP) and deep learning image reconstruction (DL) algorithms for a total of 161 noncalcified hypoattenuating lesions for 3 lesion size (D) subsets (<6 mm, 6 to 10 mm, and >10 mm). Images were assessed by expert radiologists for hepatic lesion detection task and likelihood of malignancy across the 2 imaging conditions. All cases were also evaluated automatically in terms of in vivo as a metric of task-based performance, both using a conventional technique and a new formalism of an added frequency term in the internal noise component of to accommodate the nonlinearity of the DL reconstruction (adj).

Results: The study found conventionally defined d' well-reflective of radiologists' evaluation of FBP images but not well-aligned with that of DL images. The new formalism provided more consistent reflection of performance across reconstruction techniques. In particular, in the lesion group D <=6 mm, the difference between radiologists' accuracy in images acquired with DL and images acquired with FBP was -26%, and the related adj difference was -9%, whereas the was 34%. Analogously, for the lesion group 6 mm < D <=10 mm, the differences were -15%, -13%, and 29%, respectively. Lastly, for the lesion group D>10 mm, radiologists showed the same accuracy in both FPB and DL images, difference in adj was -11%, and difference in was 31%.

Conclusion: The new formalism can robustly reflect CT systems clinical performance irrespective of reconstruction algorithm. The methodology can be more readily applied to assess the real-world performance of CT systems.

Objectives: To investigate the feasibility and image quality of artificial intelligence iterative reconstruction (AIIR) for computed tomography angiography (CTA) of the bronchial artery (BA) with a reduced radiation dose and contrast agent dosage.

Materials and methods: A total of 110 hemoptysis patients were prospectively enrolled for bronchial artery CTA (BA-CTA) and were randomly divided into 2 groups. Routine-dose group (group A, n=55) used a routine CTA protocol (tube voltage: 120 kVp; contrast dosage: 80 mL) with hybrid iterative reconstruction, while the low-dose group (group B, n=55) used the low-dose protocol (tube voltage: 100 kVp; contrast dosage: 50 mL) with AIIR. Attenuation values, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured for objective analysis. Subjective image quality was rated by 2 blinded radiologists using 5-point scales.

Results: No significant differences in demographic characteristics were observed between the 2 groups (all P>0.05). The radiation dose in group B was reduced by 73.8%, respectively, compared with group A. The mediastinal segment of BA was shown in both group images, while the hilar segment of BA was higher in group B than in group A (P<0.05). The mean subjective scores between the 2 groups showed no significant difference (all P>0.05), while SNR and CNR of group B were higher than those of group A (all P<0.0001).

Conclusions: The simultaneous reconstruction of BA-CTA images using the AIIR algorithm with reduced tube voltage and contrast agent dosage not only substantially reduces the radiation dose of preoperative BA-CTA for BAE but also achieves better image quality than routine-dose images.

Objective: To investigate the association of the pericoronary fat attenuation index (FAI) derived from coronary computed tomography angiography (CCTA) with target vessel revascularization (TVR) in symptomatic postprocedure patients.

Methods: A retrospective analysis was conducted, including 154 patients with 191 lesions scheduled for stenting who underwent invasive coronary angiography (ICA) after preinterventional CCTA. The proximal pericoronary FAI of the 3 major coronary arteries and lesion-specific pericoronary FAI were measured on preprocedure CCTA using semi-automated software. Lesions were randomly allocated to a training set (n=133) and a test set (n=58). Multivariate logistic regression analyses were performed to identify independent variables associated with TVR in the training cohort. Analyses were performed on the patient and vessel levels.

Results: A total of 154 patients (age 60.9±9.5 y, 68.8% male) with 191 lesions scheduled for stenting were included. On the basis of patient-level analysis, patients with TVR showed higher pericoronary FAI compared with patients without TVR. In vessel-level analysis, the regression model incorporating 1 cm 2 mm lesion-specific pericoronary FAI demonstrated superior diagnostic performance in both cohorts (training set AUC 0.814, 95% CI: 0.721-0.907; test set AUC 0.794, 95% CI: 0.659-0.928). The optimal cutoff value of -70.49 HU for the 1 cm 2 mm lesion-specific pericoronary FAI was determined by maximizing Youden's index, achieving a sensitivity of 75.0% and specificity of 63.0% in the test set. The model exhibited excellent calibration and clinical utility as confirmed by calibration curves and decision curve analysis (DCA). Multivariate logistic regression analysis showed that 1 cm 2 mm lesion-specific pericoronary FAI (OR 1.2, 95% CI: 1.01-1.42, P=0.036) was an independent predictor of TVR.

Conclusions: CCTA-derived pericoronary FAI is significantly associated with TVR in postprocedural patients. The 1 cm 2 mm lesion-specific pericoronary FAI, with an optimal cutoff of -70.49 HU, represents an effective tool for TVR risk stratification in this patient population.

Objective: To evaluate the impact of Super-Resolution Deep Learning Reconstruction (SR-DLR) (Canon Medical Systems Corporation) on image quality and myocardial hemodynamic parameters in dynamic myocardial computed tomography (CT) perfusion compared with filtered-back projection (FBP), hybrid iterative reconstruction (IR), and normal-resolution deep learning reconstruction (NR-DLR).

Methods: This prospective single-center study included 25 patients (mean age ± SD, 65 ± 10; 21 men) who underwent dynamic myocardial CT perfusion. For qualitative analysis, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were assessed, while qualitative analysis included overall image quality and lesion visibility. Myocardial blood flow (MBF) at rest and stress, as well as coronary flow reserve (CFR) were analyzed. Image quality and hemodynamic parameters were compared across 4 reconstruction methods.

Results: SR-DLR achieved the lowest image noise (20.33 ± 2.45 HU), significantly lower than FBP (145.20 ± 74.81 HU), hybrid IR (47.19 ± 10.02 HU), and NR-DLR (22.92 ± 2.63 HU) (P < 0.001). In rest imaging, SR-DLR showed significantly higher SNR (6.71 ± 1.88) and CNR (15.41 ± 5.48) compared with other reconstruction methods (P < 0.001). Similar improvements were observed in stress imaging, with SR-DLR providing significantly enhanced SNR and CNR compared with all other methods. The mean CFR was 2.75 ± 1.88 for SR-DLR, 2.75 ± 1.99 for NR-DLR, 2.74 ± 2.44 for hybrid IR, and 2.56 ± 3.17 for FBP, with no statistically significant differences observed in any pairwise comparisons. Qualitative analysis showed that SR-DLR achieved the highest overall image quality and lesion visibility, significantly outperforming FBP and comparable to hybrid IR and NR-DLR.

Conclusions: SR-DLR and NR-DLR significantly enhanced image quality by reducing noise and improving SNR and CNR while maintaining hemodynamic quantification.

Objective: This study aims to assess the diagnostic value of the coronary artery calcium score (CACS) to predict significant coronary artery stenosis (SCAS) in presolid organ transplant patients with a moderate and high risk of coronary artery disease.

Methods: In this retrospective HIPAA-compliant study, all pre-liver/kidney transplant patients with intermediate or high risk of coronary artery disease who underwent cardiac CT angiogram (CCTA) and CACS between January 1, 2018 and December 31, 2022 were reviewed. CACS was assessed according to the Agatston score. SCAS was defined as ≥50% diameter stenosis on CCTA. The potential to predict SCAS was assessed by computing the area under the receiver curve (AUC), and the sensitivity and specificity of CACS in diagnosing SCAS were calculated at various CACS thresholds.

Results: A total of 291 patients (81; 28% female) with a mean age of 57.7±9.9 years, were included. The median CACS was 772 (IQR: 320 to 1892) in patients with SCAS versus 23 (IQR: 0 to 187) in those without SCAS (P<0.01). CACS demonstrated efficacy in predicting SCAS, with an AUC of 0.88. In the subgroup analysis, mean CACS differed significantly between pre-liver and pre-kidney solid organ transplant patients, but the difference between AUC curves was not significant. With the use of traditional CACS thresholds, the sensitivity dropped below 95%, but with optimized thresholds of CACS ≤62 or ≥869 for 95% sensitivity and specificity, 63% of pretransplant patients could be attributed either to a low-risk or high-risk for SCAS, respectively.

Conclusions: Our study shows that CACS can accurately predict SCAS in a large proportion of pre-kidney and liver transplant patients with intermediate or high risk of coronary artery disease. CACS can be considered as a screening tool to identify patients with low likelihood of SCAS, who might not require CCTA, as well as those with a high likelihood in whom a proactive approach should be considered.

Background: Magnetic resonance cholangiopancreatography (MRCP) is used in the diagnosis and management of primary sclerosing cholangitis (PSC). However, MRCP is subjective, and guidelines state that there is insufficient evidence to recommend MRCP as a prognostic tool. EASL-PSC guidelines have identified quantitative MRCP (MRCP+) as having promising utility for risk assessment and prediction of clinical outcomes. We conducted a systematic review to determine the clinical utility of MRCP+ metrics for the management of patients with PSC.

Methods: We systematically searched PubMed and MEDLINE for cohort studies reporting on MRCP+ metrics associated with risk prediction, clinical outcomes, and disease progression published between January 2019 and May 2024. Studies reporting on adults with diagnosed PSC with paired MRCP were selected. There were no limitations on the type of study (retrospective/prospective). Studies reporting on only qualitative assessment of MRCP were excluded.

Results: Six manuscripts with 512 subjects met the study criteria. For risk prediction and disease progression, 4 articles reported 21 unique MRCP+ metrics, which were significantly associated with the Amsterdam-Oxford model and the MAYO risk score. MRCP+ metrics had AUC ranging 0.65 to 0.87 and hazard ratios ranging 0.96 to 17.79 for the prediction of adverse outcomes, including liver transplantation, death, hepatic decompensation, biliary complications, and cholangiocarcinoma. Associations between biochemical markers of liver function (alkaline phosphatase, bilirubin, aspartate aminotransferase, gamma glutamyl transferase, and albumin) ranged -0.42≤R≤0.48.

Conclusion: MRCP+ metrics have clinical utility to support patient management alongside addressing key gaps, including standardising MRCP assessment, early detection of disease, and quantification of risk.

Objective: To assess photon counting CT iodine density as a marker of histopathologic treatment response after neoadjuvant chemotherapy in patients with pancreatic ductal adenocarcinoma.

Materials and methods: A retrospective PACS search identified 21 pancreatic ductal adenocarcinoma patients [14 men; mean (SD) age: 64 (10) y] who underwent neoadjuvant chemotherapy and pancreatic photon counting CT 2 months before resection from April 11, 2022 to February 2, 2024. The histopathologic treatment response grade was the reference standard. Freehand regions-of-interest measurements were drawn independently by 2 radiologists as large as possible within the mass on pancreatic parenchymal phase images. Attenuation, iodine density, and iodine density normalized to the aorta were recorded. Mann-Whitney U test was used to compare attenuation, iodine density, and normalized iodine density for responders (pathologic grade 1, 2) versus nonresponders (grade 3). Receiver operating characteristic curves were created, and optimal thresholds were determined with Youden's index. A P <0.05 indicated statistical significance.

Results: Thirteen of 21 (61.9%) patients showed pathologic treatment response. Iodine density for nonresponders and responders was mean (SD) 0.47 (0.23) mg/mL and 1.20 (0.75) mg/mL, respectively ( P= 0.005). Normalized iodine density for nonresponders and responders was 7.6 (5.5)% and 22.5 (16.0)%, ( P= 0.006). Attenuation for nonresponders and responders was 56.5 (10.9) HU and 70.6 (17.7) HU, ( P =0.04). Upon receiver operating characteristic analysis, an iodine density threshold of 0.65 mg/mL had 77% sensitivity and 88% specificity (AUC=0.86), and a normalized iodine density threshold of 10.1% had 77% sensitivity and 88% specificity (AUC=0.86) for treatment response. A 61.8 HU threshold had 77% sensitivity and 75% specificity (AUC=0.78).

Conclusions: Elevated iodine density correlates with pancreatic ductal adenocarcinoma histopathologic treatment response with high specificity. Photon counting CT iodine density may be used as a marker of histopathologic treatment response.

Objective: To evaluate an edge-on-irradiated silicon-based photon-counting detector CT (Deep Si-PCD-CT) prototype for quantification of iodine concentration and stability of HU values, as well as detectability of subtle features in simulated kidney parenchyma.

Materials and methods: A phantom, simulating moderately and strongly enhancing kidney parenchyma (at 180 and 240 HU) inside a small, medium, and large patient (23, 30, 37 cm diameter, respectively), was scanned on a Deep Si-PCD-CT. Centered in the kidney parenchyma was a water-equivalent rod at 0 HU and a rod of 0.8 mg/mL iodine concentration to simulate a benign, mildly enhancing cystic renal lesion, as well as a rod with a 2 mm septum and 5 mm mural nodule. Accuracy and stability of HU values were evaluated with repeated ROI measurements across consecutive slices, while the septum and nodule were identified on standard polychromatic clinical images and iodine maps. Images were reconstructed with a soft tissue kernel at 0.417- and 0.625-mm slice-thickness without additional denoising.

Results: Deep Si-PCD-CT produced accurate HU value measurements for water, intralesional iodine content, and renal parenchymal enhancement. The HU values were similarly variable from the ground truth values as compared with measurements from a commercial energy-integrating detector CT. The nodule and septum inside the phantom were successfully identified using the new Deep Si-PCD-CT prototype, while they were difficult to identify using the standard EID-CT at clinical window-level settings. The iodine maps created from the photon-counting detector CT displayed both the nodule and the septum well, facilitating quick identification.

Conclusions: Deep Si-PCD-CT is a promising tool for the accurate measurement of HU values, as well as the detection of subtle features of complexity in cystic renal lesions. It has the potential to improve the diagnosis and management of cystic renal lesions.

Objective: To compare the predictive value of minimal ablative margin (MAM) quantification using tumor segmentation on intraprocedural contrast-enhanced hepatic arterial (HAP) versus portal venous phase (PVP) CT on local outcomes following percutaneous thermal ablation of colorectal liver metastases (CRLM).

Methods: This dual-center retrospective study included patients undergoing thermal ablation of CRLM with intraprocedural preablation and postablation contrast-enhanced CT imaging between 2009 and 2021. Tumors were segmented in both HAP and PVP CT phases using an artificial intelligence-based auto-segmentation model and reviewed by a trained radiologist. The MAM was quantified using a biomechanical deformable image registration process. The area under the receiver operating characteristic curve (AUROC) was used to compare the prognostic value for predicting local tumor progression (LTP).

Results: Among 81 patients (60 y±13, 53 men), 151 CRLMs were included. During 29.4 months of median follow-up, LTP was noted in 24/151 (15.9%). Median tumor volumes on HAP and PVP CT were 1.7 mL and 1.2 mL, respectively, with respective median MAMs of 2.3 and 4.0 mm (both P < 0.001). The AUROC for 1-year LTP prediction was 0.78 (95% CI: 0.70-0.85) on HAP and 0.84 (95% CI: 0.78-0.91) on PVP ( P = 0.002).

Conclusions: During CT-guided percutaneous thermal ablation, MAM measured based on tumors segmented on PVP images conferred a higher predictive accuracy of ablation outcomes among CRLM patients than those segmented on HAP images, supporting the use of PVP rather than HAP images for segmentation during ablation of CRLMs.

CT imaging has advanced significantly, with dual-energy CT (DECT) marking a milestone by using 2 energy spectra for enhanced tissue characterization. The latest innovation is photon-counting detectors (PCD), which offer superior spatial resolution, contrast-to-noise ratio (CNR), and potential for reduced radiation dose compared with traditional energy-integrating detectors (EID). Photon-counting CT (PCD-CT), which directly counts individual photons using semiconductors, has important implications for chest imaging, especially for complex disease processes that benefit from imaging at higher spatial resolution. PCD-CT achieves improved spatial resolution by eliminating the blurring effects associated with EID scintillators. Enhanced CNR is achieved through energy discrimination and selective use of photon energies, which also helps to minimize electronic noise. PCD-CT facilitates significant radiation dose reduction, particularly valuable for patients who receive regular follow-ups, like in lung cancer screening. In addition, PCD-CT provides spectral capabilities in every scan, unlike DECT, which requires preselecting a specific spectral scan mode. In chest imaging, PCD-CT shows promise in detecting and definitively characterizing infectious diseases, interstitial lung disease, malignancies, and vascular conditions at low radiation doses, offering higher diagnostic accuracy and patient safety. Despite these advancements, challenges remain in optimizing spectral imaging and integrating PCD-CT into routine clinical workflows, necessitating ongoing research and development.