Journal of Computer Assisted Tomography最新文献

Objectives: This study aims to evaluate various large language models (LLMs) for their effectiveness in answering Japan Radiology Board Examination (JRBE).

Materials and methods: A total of 315 examination questions from 2021 to 2023 JRBE were administered to 14 LLMs, comprising 7 open-source and 7 closed-source models. Each model processed the questions in their original Japanese language and after translating them into English by LLMs themselves. Performance metrics, including median scores, interquartile ranges (IQR), P -values, and correlation coefficients, were analyzed using Python.

Results: Closed-source models achieved a higher median correct response rate of 44.28% (IQR: 32.38%-53.02%) compared with open-source models at 29.52% (IQR: 24.76%-36.67%), exhibiting a 50.2% improvement ( P < 0.001). Translating questions to English improved performance, with median scores increasing from 33.02% (IQR: 27.86%-43.65%) to 40.00% (IQR: 27.54%-46.27%), representing a 21.1% increase ( P = 0.005). A positive correlation was observed between the cost per 1M tokens and accuracy (Japanese: r = 0.623, P = 0.017; English: r = 0.613, P = 0.020). No significant correlation was found between model release dates and performance. Only the high-cost closed-source models, GPT-4 and Claude-3-Opus, surpassed the 60% passing threshold by Japanese Medical Specialty Board when using English translation. Among open-source models, LLama-3-70B demonstrated notable performance improvements compared with its predecessors.

Conclusions: Closed-source, high-end LLMs exhibit superior performance in JRBE, and translating questions into English by themselves further enhances their accuracy. There is a significant positive correlation between the cost of LLMs and their performance, whereas the release date does not significantly influence their performance.

Objective: To investigate the potential feasibility of ultra-low-dose (ULD) liver CT with the artificial intelligence iterative reconstruction (AIIR).

Methods: Sixty-five patients who underwent triphasic contrast-enhanced liver CT were prospectively enrolled. Low tube voltage (80/100 kV) and tube current (35 to 78 mAs) were set in both portal venous phase (PVP) and delayed phase (DP). For each phase, an ULD acquisition (1.11 to 2.50 mGy) was taken followed immediately by a routine-dose (RD) acquisition (11.71 to 19.73 mGy). RD images were reconstructed with a hybrid iterative reconstruction algorithm (RD-HIR), while ULD images were reconstructed with both HIR (ULD-HIR) and AIIR (ULD-AIIR). The noise power spectrum (NPS) noise magnitude, average NPS spatial frequency, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for the quantitative assessment. Qualitative assessment was performed by 2 radiologists who independently scored the images for diagnostic acceptance. In addition, the radiologists identified focal lesions and characterized noncystic lesions as benign or malignant with both RD and ULD liver CT.

Results: Among the enrolled patients (mean age: 58.6±12.9 y, 35 men), 234 lesions with a mean size of 1.27±1.56 cm were identified. In both phases, ULD-AIIR showed comparable NPS noise magnitude with RD-HIR (all P >0.017), and lower NPS noise than ULD-HIR (all P <0.001). Average NPS spatial frequency, SNR, and CNR were highest with ULD-AIIR, followed by RD-HIR and ULD-HIR (all P <0.001). ULD-AIIR showed comparable diagnostic acceptance scores with RD-HIR, while ULD-HIR failed to meet the diagnostic acceptance requirements. RD-HIR and ULD-AIIR achieved comparable detection rate (99.6% vs. 99.1%) and area under curve (AUC) of the receiver operating characteristic curve (ROC) in classifying benign (n=46) and malignant (n=58) noncystic lesions (0.98 vs. 0.97, P =0.3).

Conclusions: With AIIR, it is potentially feasible to achieve ULD liver CT (60% dose reduction) while preserving the image and diagnostic quality.

Objectives: To explore the diagnostic accuracy and robustness of artificial intelligence (AI)-based fully automated CT-derived fractional flow reserve (CT-FFR) in detecting significant coronary artery disease (CAD) in patients with transcatheter aortic valve replacement (TAVR).

Methods: This single-center retrospective study included consecutive patients who underwent TAVR between January 2020 and June 2023. All patients received preoperative coronary CT angiography (CCTA) and invasive coronary angiography (ICA). CT-FFR was evaluated with a fully automated AI-based software. The diagnostic performance of CCTA and CT-FFR for the identification of significant CAD was compared using ICA (≥70% diameter stenosis) as the reference standard. Patients who underwent post-TAVR CCTA within 3 months were used to calculate CT-FFR values. The post-TAVR CT-FFR calculations were compared with pre-TAVR CT-FFR to evaluate the robustness of the AI-based software.

Results: A total of 77 pre-TAVR patients and 164 vessels were included. Significant CAD was identified by ICA in 18 patients (23.4%). In per-patient analysis, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 44.4%, 91.5%, 61.5%, 84.4%, and 80.5% for CCTA and 94.4%, 83.1%, 64.0%, 98.0%, and 85.7% for CT-FFR. The area under the receiver operating characteristic curve of CT-FFR was superior to CCTA (0.83 vs. 0.63, P = 0.001). Thirty-five (45.5%) patients underwent CT-FFR calculations before and after TAVR. There was good agreement between pre- and post-TAVR of CT-FFR values (intraclass correlation coefficient 0.85).

Conclusions: AI-based fully automated CT-FFR enables to improve the diagnostic performance of CCTA for the detection of significant CAD pre-TAVR and demonstrates robust stability post-TAVR.

Objective: Pulmonary air trapping is critical for diagnosing and prognostication of various lung diseases. Expiratory CT imaging serves as an accessible method to assess air trapping, which correlates with small airway disease outcomes. Air trapping manifests as mosaic attenuation on inspiratory chest CT that is difficult for visual estimation. The primary aim of this study was to develop an automated tool to quantify mosaic attenuation on inspiratory CT and air trapping on paired expiratory CT. Secondary aims included comparing CT-derived parameters with PFT measurements and dyspnea scores.

Methods: This retrospective analysis of noncontrast chest CTs from 2 academic hospitals was conducted between January 1, 2018, and December 31, 2019. Patients with paired inspiratory and expiratory CT chest scans and PFTs performed on the same day were included. A chest radiologist manually annotated lung parenchyma in a reference cohort. Several histogram-based metrics were computed from lung parenchymal CT values, with the maximum peak position showing the strongest correlation with manually determined thresholds. This threshold, derived from the histogram peak, was applied in the adaptive thresholding process to quantify mosaic attenuation and air trapping.

Results: We analyzed 267 patients (65.5% female, median age 68). Most exhibited normal physiological patterns (44.0%). Patients with elevated residual volume (RV) by PFTs (28.1%) had significantly higher inspiratory CT mosaic attenuation (1629.6 vs. 1311.5 mL, P <0.01) and expiratory CT air trapping volumes (1413.7 vs. 886.2 mL, P <0.01). Correlation analyses demonstrated strong relationships between CT-derived mosaic attenuation and air trapping measures and RV. The correlation with PFT parameters was even stronger in subgroup analyses in patients with obstructive PFT patterns. These models had good predictive ability for an abnormal RV (AUC of 0.92, sensitivity of 72.4%, and specificity of 92.0%) and clinical utility based on good correlation with the mMRC dyspnea score ( r =0.71; 95% CI: 0.65-0.77).

Conclusions: This automated adaptive thresholding on inspiratory and expiratory chest CT scans showed a high correlation of lung volume and air trapping parameters with PFTs, revealing that measures of lung function have a complex interplay with air trapping.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide, necessitating accurate and early diagnosis to guide therapy, along with assessment of treatment response. Response assessment criteria have evolved from traditional morphologic approaches, such as WHO criteria and Response Evaluation Criteria in Solid Tumors (RECIST), to more recent methods focused on evaluating viable tumor burden, including European Association for Study of Liver (EASL) criteria, modified RECIST (mRECIST) and Liver Imaging Reporting and Data System (LI-RADS) Treatment Response (LI-TR) algorithm. This shift reflects the complex and evolving landscape of HCC treatment in the context of emerging systemic and locoregional therapies. Each of these criteria have their own nuanced strengths and limitations in capturing the detailed characteristics of HCC treatment and response assessment. The emergence of functional imaging techniques, including dual-energy CT, perfusion imaging, and rising use of radiomics, are enhancing the capabilities of response assessment. Growth in the realm of artificial intelligence and machine learning models provides an opportunity to refine the precision of response assessment by facilitating analysis of complex imaging data patterns. This review article provides a comprehensive overview of existing criteria, discusses functional and emerging imaging techniques, and outlines future directions for advancing HCC tumor response assessment.

Objective: The second-generation motion correction algorithm, Snapshot Freeze 2.0 (SSF2), is designed to suppress coronary artery motion in cardiac CT angiography. This study aimed to evaluate whether SSF2 improves unenhanced CT images and to compare the coronary artery calcium score (CACS) values reconstructed with and without SSF2.

Methods: One hundred nineteen patients with coronary artery calcium (CACS >0) were enrolled in this study. Unenhanced CT for CACS was performed with a phase window limited to 75% of the R-R interval, using 120 kVp and automatic tube current modulation. CACS values were measured on images with and without SSF2, and absolute differences were calculated. Two radiologists assessed the overall image quality, focusing on coronary artery motion, using a 4-point scale (1=uninterpretable, 4=no motion artifacts).

Results: The absolute differences in CACS for patients with heart rates of 60-95 bpm (n=85) were larger than those with heart rates of up to 59 bpm (n=21) or above 95 bpm (n=13) (median: 10.6, range: 0.1 to 171.2; median: 9.3, range: 0.8 to 31.8; median: 6.0, range: 1.6 to 43.4, respectively). In patients with heart rates of 60 to 95 bpm, SSF2 improved image quality scores ( P <0.001); however, for heart rates of up to 59 bpm or above 95 bpm, the improvements were not significant ( P =0.18 and 0.10, respectively).

Conclusions: SSF2 reduces motion artifacts in the coronary arteries on unenhanced CT and significantly alters the CACS values. A more accurate calcification assessment is anticipated with SSF2, especially in patients with heart rates of 60 to 95 bpm.

Objective: Primary hyperparathyroidism (PHPT) is commonly caused by parathyroid adenomas (PAs), and four-dimensional computed tomography (4DCT) is increasingly used for localising PAs due to its high sensitivity and specificity. However, the relative high radiation dose of 4DCT may limit its widespread use as first line imaging in some settings. A reduced phase protocol and enhancement maps, which highlight relative enhancement differences between the nonenhanced and arterial phases, have been proposed as ways to reduce radiation exposure without compromising diagnostic accuracy. This study aims to assess whether reduced 4DCT protocols can maintain diagnostic performance and if the enhancement map can further assist in adenoma localisation.

Methods: This retrospective study included 27 PHPT patients, with both single and double adenomas, and some ectopic cases and 3 secondary HPT patients. Five-phase combinations derived from our institution's 4-phase protocol were evaluated using a multireader, multicase approach involving experienced neuroradiologists and general radiologists. The phases tested included combinations of nonenhanced, arterial, venous, and delayed venous phases. An enhancement map was introduced as one of the phases. Readers were asked to identify adenomas and assign confidence levels. Performance metrics, including sensitivity, specificity, and area under the curve (AUC), were calculated, and noninferiority tests compared the results to the current 4-phase protocol.

Results: Sensitivity of the total group was between 0.64 and 0.70 with a specificity between 0.94 and 0.97. AUC were between 0.80 and 0.84. All reduced phase combinations were noninferior to the 4-phase protocol. Neuroradiologists achieved noninferior results with 1-phase to 3-phase protocols, while general radiologists required at least 3-phases. The enhancement map did not improve sensitivity or specificity, although readers found it useful as a supplementary tool for identifying lesions. Artefacts, especially in ectopic locations, reduced its effectiveness.

Conclusions: This study supports the use of reduced 4DCT protocols for PHPT. A 1-phase or 2-phase protocol is recommended for experienced radiologists, while a 3-phase protocol is suitable for less experienced radiologists.

Claustrophobia during MRI is a well-documented challenge that affects diagnostic accuracy and patient care. Each year, nearly 2 million MRI scans are disrupted due to anxiety, thus leading to early termination of the scan, image degradation from motion, and increasing healthcare costs. This review examines the prevalence of MRI-related claustrophobia, along with the financial and operational burdens. This review also highlights the latest strategies to improve patient tolerance, which range from technological advancements, behavioral techniques and pharmacological interventions, all of which show promise in reducing scan-related distress. Ultimately, a holistic patient-centered approach is key to optimizing both imaging efficiency and the overall MRI experience.

Objective: To assess the agreement between cardiac computed tomography (CCT) and cardiac magnetic resonance (CMR) for measuring left ventricular (LV) and left atrial (LA) function, and to evaluate the influence of segmentation approach and volume calculation method.

Methods: This retrospective study included 24 patients (mean age 74.2±10.9 y; 54% male) who underwent CMR and multiphase CCT within 14 days. LV and LA end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and LV mass were measured using CCT voxel-based volumetry with papillary-inclusion (PIS) and exclusion (PES) segmentations and then compared with CMR. Simulations of Simpson's Area-Length (AL) and Disk-Summation (DS) techniques were performed on reformatted CCT images using increasing slice spacings (1 to 32 mm). Agreement was evaluated using correlation coefficients, intraclass correlation coefficients (ICC), percent error, and Bland-Altman analysis.

Results: CCT demonstrated excellent correlation with CMR for LV-EDV, ESV, EF, and mass (r=0.82 to 0.98; ICC=0.72 to 0.94). PES yielded no EF bias, while PIS overestimated EF by 7.0%. LV mass was consistently overestimated by 39 to 52 g (P<0.01). LA volumes showed moderate-to-strong correlation (r=0.70 to 0.90), but poor-to-moderate agreement (ICC=0.21 to 0.55). Simulated LA Simpson-DS measurements with slice spacings ≤8 mm preserved agreement with voxel-based values (ICC ≥0.99).

Conclusions: CCT can accurately assess LV function when compared with CMR when using voxel-based methods and consistent papillary segmentation approaches. Biplane and measurements with wide slice spacings reduce agreement, warranting standardization for clinical interchangeability.

Objective: The effect of hepatic steatosis on liver 18F-FDG uptake remains unclear, as previous PET/CT-based studies have reported inconsistent findings. This study aimed to investigate the impact of hepatic steatosis on 18F-FDG uptake and to identify the factors influencing the liver standardized uptake value (SUV) using PET/MRI.

Methods: A total of 188 participants who underwent PET/MRI for cancer screening between January 2017 and December 2021 were evaluated. The liver fat fraction was quantified using MRI proton density fat fraction (PDFF). Participants were classified into 3 groups based on PDFF thresholds: normal (<6%), mild steatosis (6% to 17%), and moderate-to-severe steatosis (>17%). Liver SUVmax and SUVmean were measured and analyzed for correlations with age, body mass index (BMI), serum lipids, PDFF, and iron deposition. Multivariate and segmented regression analyses were performed to identify independent predictors of liver SUV.

Results: Liver SUVmax and SUVmean increased with mild steatosis but decreased with moderate-to-severe steatosis (P<0.001). Triglyceride (β=0.068, P=0.002), high-density lipoprotein cholesterol (β=-0.295, P=0.001), body mass index (β=0.018, P=0 .037), and age (β=0.007, P=0.007) independently predicted liver SUVmean. PDFF was positively associated with SUVmean in normal livers but negatively associated with fatty livers.

Conclusions: PET/MRI demonstrated that hepatic steatosis, as assessed by PDFF, significantly influenced liver 18F-FDG uptake in a biphasic manner. These findings underscore the importance of integrating fat quantification into PET interpretation to improve the accuracy of oncologic imaging.