Journal of Computer Assisted Tomography最新文献

Objective: The purpose of this study was to evaluate the potential clinical value of spectral CT for image guidance during percutaneous thermal ablation, particularly investigating high monoenergetic (MonoE) for metal artifact reduction.

Materials and methods: A phantom experiment was performed in which an ablation probe was scanned in "continuous CT" (CCT) mode on a spectral CT scanner. ROI measurements quantified metal artifacts for conventional and MonoE CCT images. Subsequently, a retrospective reader study was conducted with 10 patient cases. Three radiologists reviewed cases to select the optimal MonoE setting and perform a Likert score rating (+2: MonoE preferred, -2: conventional preferred).

Results: Phantom measurements indicated a reduction in metal artifact for high MonoE (150 keV: -46 HU) relative to conventional CCT (-122 HU). The MonoE setting preference was 151±21 keV. The mean Likert score was 1.2±0.7. Radiologists indicated a perceived benefit of high MonoE CCT when probe placement was near a critical structure.

Conclusions: This study indicates a clinical preference for high MonoE CCT compared with conventional CCT. Insofar as spectral CCT can be offered at a clinically feasible time frame, our results support the assessment of 150 keV image to reduce metal artifact while maintaining satisfactory soft tissue contrast.

Photon-counting computed tomography (PCCT) is a new imaging technology that has advanced diagnostic imaging by offering improved spatial and contrast resolution as well as novel spectral imaging capabilities. Unlike conventional CT, which uses energy-integrating detectors, PCCT employs photon-counting detectors that directly measure individual photon energies, enabling applications such as virtual monochromatic imaging (VMI) and material decomposition. These innovations allow for artifact reduction, better visualization of fine anatomic structures, and improved diagnostic accuracy, all while reducing radiation dose. This review explores select applications of PCCT in neuroimaging, focusing on the brain, temporal bone, and spine. In the brain, we discuss how PCCT demonstrates superior performance for evaluating aneurysms, metallic prostheses, and vessel stenosis, offering enhanced visualization of vascular structures and minimizing artifacts. For temporal bone imaging, we review assessment of both complex anatomy and potentially subtle pathologies such as otosclerosis, as well as visualization of implants like cochlear devices and their intricate components. In spinal imaging, we explore how PCCT improves precise detection of causes of cerebrospinal fluid leaks, improves localization of tiny vessels such as the artery of Adamkiewicz and spinal dural arteriovenous fistulas, and reduces metal artifacts associated with postoperative hardware. PCCT addresses limitations of conventional CT while unlocking new diagnostic possibilities across neuroimaging applications. As clinical adoption of PCCT grows, ongoing research and development will refine imaging protocols and expand its utility. The accumulating evidence underscores PCCT's transformative potential to improve diagnostic confidence in neuroimaging and beyond.

Aim: This study aims to investigate whether preoperative contrast-enhanced laryngeal CT-derived histogram parameters and shape features can correlate with cervical lymph node metastasis, tumor differentiation (grade), lymphovascular invasion, and perineural invasion in laryngeal squamous cell carcinoma (LSCC).

Materials and methods: Ninety-one patients who underwent laryngectomy and simultaneous cervical lymph node dissection at our hospital between 2015 and 2021, with LSCC as the final pathological outcome, were included. Two radiologists independently performed segmentation using 3D Slicer Software, drawing regions of interest (ROIs) from the widest axial section of each tumor and volumes of interest (VOIs) to cover the entire visualizable tumor. Histogram parameters and shape features were extracted for each segmentation. Cut-off values were calculated, and intraclass correlation was used to measure interobserver agreement.

Results: ROI measurements showed significant differences only for lymphovascular invasion. VOI measurements revealed significant differences between lymphovascular and perineural invasion and cervical lymph node metastasis. Key parameters included entropy, mean absolute deviation, and uniformity. No significant differences were found for tumor grade. VOI-based measurements outperformed ROI-based measurements in terms of reproducibility and diagnostic breadth, showing higher intraclass correlation values and more significant associations with pathological features.

Conclusion: Preoperative contrast-enhanced CT histogram parameters and shape features may be useful in evaluating tumor characteristics in LSCC.

Aim: To investigate the predictive value of combining qualitative and quantitative parameters from dual-layer spectral detector CT (DLCT) in identifying intracranial hemorrhage (ICH) after mechanical thrombectomy (MT) in patients with acute ischemic stroke and large vessel occlusion (AIS-LVO).

Materials and methods: This retrospective study consecutively enrolled 120 patients with AIS-LVO who underwent MT, followed by DLCT performed 3 hours postprocedure. After applying the inclusion and exclusion criteria, 30 patients were included in the final analysis. Two radiologists independently assessed the presence of high-density areas (HDA) on noncontrast DLCT images. Qualitative imaging signs and quantitative parameters were subsequently obtained through observation and measurement of HDAs. Follow-up CT examinations conducted during hospitalization were reviewed for ICH development. The sensitivity and specificity of the DLCT parameters for early ICH diagnosis were calculated, and the diagnostic accuracy was evaluated using receiver operating characteristic (ROC) curve analysis.

Results: Fifty-five HDAs were detected on DLCT images from 30 patients. Follow-up noncontrast CT confirmed the development of ICH in 19/55 (34.5%) HDAs. Univariate analysis revealed significant differences in the mass effect, low-density edema zone, the median maximum CT value, the median cross-sectional area, the median maximum iodine concentration, the median relative iodine concentration, and the median Z-effective value between the ICH and non-ICH groups were significantly different ( P < 0.05). Multivariate logistic regression identified low-density edema zone and the relative iodine concentration as independent predictors, which were incorporated into a combined diagnostic model. ROC analysis revealed an area under the curve (AUC) of 0.901 (95% CI: 0.807-0.994) for ICH prediction, with a sensitivity of 89.5% and specificity of 80.6%.

Conclusions: The combination of qualitative and quantitative DLCT parameters demonstrated excellent predictive performance for identifying ICH after MT in patients with AIS-LVO.

Objective: To assess the performance of whole-abdomen diffusion-weighted imaging (DWI) combined with conventional MRI compared with CT in preoperative planning of the primary debulking surgery (PDS) in epithelial ovarian cancer (EOC) patients.

Methods: Fifty-seven patients with EOC who underwent preoperative whole-abdomen MRI and CT were recruited for this study. During a multidisciplinary treatment (MDT) discussion, anatomical sites were allocated to specialized surgeons and the surgical plan, including assistant and backup surgeons, was finalized. According to the surgical records and pathological diagnoses, the χ 2 test was used to compare the consistency rates of CT and MRI for planning PDS. Diagnostic efficiencies of MRI and CT for planning PDS were compared using a 2-tailed McNemar test.

Results: For the hepatobiliary sites, MRI demonstrated significantly higher sensitivity (0.947 vs. 0.632, P =0.022), specificity (0.842 vs. 0.684, P =0.039), and accuracy (0.912 vs. 0.649, P =0.041) than CT. For gastrointestinal sites, MRI showed a significantly higher sensitivity (0.750 vs. 0.450, P =0.021), specificity (0.941 vs. 0.882, P =0.031), and accuracy (0.807 vs. 0.579, P =0.027) than CT. No significant differences in sensitivity (0.769 vs. 0.385, P =0.289), specificity (0.909 vs. 0.909, P =0.453), or accuracy (0.877 vs. 0.789, P =0.227) were found between MRI and CT for the urological sites. In addition, MRI demonstrated a significantly higher consistency rate with the surgical plans and records (0.667 vs. 0.281, P <0.001) than CT.

Conclusions: MRI offers superior diagnostic efficiency over CT for evaluating sites relevant to hepatobiliary and gastrointestinal surgeons, enhancing the accuracy of surgical planning and potentially becoming the preferred imaging modality for preoperative PDS planning.

Objective: Parkinson disease (PD) is a progressive neurodegenerative disorder with highly variable progression rates. Accurate prediction of rapid disease progression is critical for patient management and clinical trial design. This study evaluates the use of machine learning (ML) models to predict rapid PD progression using integrated clinical and imaging biomarkers.

Methods: A retrospective analysis was conducted on 683 patients from the Parkinson's Progression Markers Initiative (PPMI). Mixed effects models for longitudinal data were used to determine PD progression rates, with rapid progression of PD (RPPD) defined as exceeding the cohort mean rate of change in either the Movement Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) total score or the lowest Putamen-Specific Binding ratio (SBR) on I-123 Ioflupane single photon emission tomography (SPECT). The minimum redundancy maximum relevance (MRMR) algorithm identified the most predictive features from a total of 123 features. Support vector machines (SVM) and decision tree (DT) models were trained and evaluated using 5-fold cross-validation and receiver operating characteristic area under the curve (ROC AUC) analysis.

Results: A total of 193 patients (135 males and 68 females) were included. The most predictive feature for RPPD was the lowest putamen SBR (MRMR score=0.44, P<0.001). The SVM model achieved an ROC AUC of 0.86 (95% CI: 0.78-0.91) using the 6 highest-ranked MRMR features, whereas the DT model, using only 2 clinically available features (UPDRS total score and lowest putamen SBR), achieved an ROC AUC of 0.81 (95% CI: 0.73-0.86).

Conclusions: Machine learning models, particularly SVM, demonstrate strong predictive performance in identifying rapid PD progression using multimodal clinical and imaging biomarkers. The integration of SPECT imaging with clinical assessments improves disease trajectory characterization, potentially supporting personalized treatment strategies and optimizing clinical trial design. Future studies should incorporate additional biomarkers and longer follow-up durations to enhance model generalizability.

Objective: Displaced fragments in meniscal flap tears may be challenging to detect radiologically but are clinically relevant for treatment planning. This study aimed to characterize fragment migration patterns on MRI and evaluate associated intra-articular pathologies.

Methods: In this retrospective analysis of 89 knee MRIs performed between January 2018 and May 2022, patients with confirmed meniscal flap tears were assessed for tear location, fragment displacement direction, and associated findings, including cartilage defects, ligament injuries, bone marrow edema, osteophytes, osteochondral lesions, and joint effusion. Statistical associations between tear features and accompanying pathologies were evaluated.

Results: Inferior coronary recess was the most frequent displacement site, especially in medial tears (68.8%, P=0.007). Medial tears more often had cartilage defects (66.3%, P=0.024) and osteochondral lesions (55.0%, P=0.015). Posterior horn involvement predominated, and ACL tears were strongly associated with intercondylar notch displacement (77.8%, P=0.001).

Conclusion: Meniscal flap tears are most commonly located in the posterior horn of the medial meniscus and tend to displace into the inferior coronary recess. Their frequent association with cartilage damage, osteochondral lesions, and ACL injuries underscores the importance of careful MRI evaluation to support surgical decision-making.

Purpose: To evaluate MRI features for differentiating pulmonary hamartomas from other nodule pathologies, including carcinoid tumors and non-small cell lung cancer (NSCLC).

Materials and methods: This retrospective study analyzed chest MRIs of 48 patients with pulmonary nodules from 2017 to 2025. Patients had either pathologic diagnosis or stable follow-up confirming hamartoma. Two blinded radiologists reviewed images and recorded enhancement pattern (solid, rim, or speckled/mesh). The signal intensity index (SII) was calculated using chemical shift imaging.

Results: Hamartomas (n=19, 40%), carcinoids (n=17, 35%), and adenocarcinomas (n=9, 19%) were the most common diagnoses. Rim and speckled/mesh enhancement patterns were nearly exclusive to hamartomas (22% and 39% vs. 0% and 4%, respectively, P<0.001). An SII >10% showed 61% sensitivity and 100% specificity for hamartomas. Combining rim or speckled/mesh enhancement patterns and SII >10% yielded 79% sensitivity and 96.6% specificity for hamartomas.

Conclusions: Although chemical shift imaging is diagnostic for hamartomas, signal dropout is present in just over half of cases. Rim or speckled/mesh enhancement patterns are also highly suggestive. Combining these MRI features significantly improves diagnostic accuracy for pulmonary hamartomas, potentially reducing further follow-up or invasive procedures for indeterminate nodules.

Carotid blowout is the rupture of a carotid artery or branch vessel, a dangerous complication in post-treatment head and neck cancer patients. The purpose of this retrospective case review is to identify imaging features suggestive of eminent carotid blowout on routine surveillance post-treatment neck imaging, so that a prompt neuroendovascular consult is obtained and the patient is preemptively treated, thus improving patient outcomes. It is important to carefully interrogate the carotid vessels with appropriate window/leveling for irregularity, adjacent tissue ulceration, exposed vessel to either air or fluid, surrounding tumor, and pseudoaneurysm.