Abstract. Although computed tomography (CT) perfusion (CTP) imaging enables rapid diagnosis and prognosis of ischemic stroke, current CTP analysis methods have several shortcomings. We propose a fast nonlinear regression method with a box-shaped model (boxNLR) that has important advantages over the current state-of-the-art method, block-circulant singular value decomposition (bSVD). These advantages include improved robustness to attenuation curve truncation, extensibility, and unified estimation of perfusion parameters. The method is compared with bSVD and with a commercial SVD-based method. The three methods were quantitatively evaluated by means of a digital perfusion phantom, described by Kudo et al. and qualitatively with the aid of 50 clinical CTP scans. All three methods yielded high Pearson correlation coefficients (>0.9) with the ground truth in the phantom. The boxNLR perfusion maps of the clinical scans showed higher correlation with bSVD than the perfusion maps from the commercial method. Furthermore, it was shown that boxNLR estimates are robust to noise, truncation, and tracer delay. The proposed method provides a fast and reliable way of estimating perfusion parameters from CTP scans. This suggests it could be a viable alternative to current commercial and academic methods.
ObjectiveStaging of laryngeal cancer largely depends on cartilage invasion. Presence of cartilage invasion affects treatment choice and prognosis. On MRI and contrast‐enhanced CT (CECT) it may be challenging to differentiate cartilage invasion from inflammation. The purpose of this study is to compare the diagnostic properties of dynamic contrast‐enhanced CT (DCECT) and CECT for visual detection of cartilage invasion in laryngeal cancer.Study DesignProspective cohort study.MethodsPatients with T3 or T4 laryngeal squamous cell carcinoma treated with total laryngectomy were evaluated using 0.625 mm slice CT. DCECT derived permeability and blood volume maps and CECT images were visually evaluated for the presence of invasion of the cartilaginous T‐stage subsites of laryngeal cancer, by detecting continuity with the tumor‐bulk of increased permeability, increased blood volume, and enhancement. Histological evaluation of the surgical total laryngectomy specimen served as the gold standard. Sensitivity, specificity, negative predictive value, and positive predictive value were calculated and compared using the McNemar and Chi‐squared test.ResultsFrom 14 included patients, a total of 462 subsites were available for T‐stage analysis, of which 84 were cartilage. The median time between CT imaging and total laryngectomy was 1 day (range 1–34 days). There was no significant difference in the detection of cartilage invasion between DCECT and CECT. The sensitivity of CECT was better for all subsites combined (0.85 vs. 0.75; p < 0.01).ConclusionDCECT does not improve visual detection of cartilage invasion in T3 and T4 laryngeal cancer compared to CECT.Level of Evidence2b, individual cohort study.
Dynamic contrast enhanced CT (DCE-CT) can be used to estimate blood perfusion and vessel permeability in tumors. Tumor induced angiogenesis is generally associated with disorganized microvasculature with increased permeability or leakage. Estimated vascular leakage (K(trans)) values and their reliability greatly depend on the perfusion model used. To identify the preferred model for larynx tumor analysis, several perfusion models frequently used for estimating permeability were compared in this study. DCE-CT scans were acquired for 16 larynx cancer patients. Larynx tumors were delineated based on whole-mount histopathology after laryngectomy. DCE-CT data within these delineated volumes were analyzed using the Patlak and Logan plots, the Extended Tofts Model (ETM), the Adiabatic Approximation to the Tissue Homogeneity model (AATH) and a variant of AATH with fixed transit time (AATHFT). Akaike's Information Criterion (AIC) was used to identify the best fitting model. K(trans) values from all models were compared with this best fitting model. Correlation strength was tested with two-tailed Spearman's rank correlation and further examined using Bland-Altman plots. AATHFT was found to be the best fitting model. The overall median of individual patient medians K(trans) estimates were 14.3, 15.1, 16.1, 2.6 and 22.5 mL/100 g min( - 1) for AATH, AATHFT, ETM, Patlak and Logan, respectively. K(trans) estimates for all models except Patlak were strongly correlated (P < 0.001). Bland-Altman plots show large biases but no significant deviating trend for any model other than Patlak. AATHFT was found to be the preferred model among those tested for estimation of K(trans) in larynx tumors.
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