Dynamic CT Imaging Research Articles

Opportunities for Time-resolved Dynamic CT Imaging in the Laboratory

Opportunities for Time-resolved Dynamic CT Imaging in the Laboratory

DOZ047.108: Utilizing dynamic CT imaging to map tissue regeneration in a porcine model of esophageal loss

Abstract Esophageal atresia (EA) is a congenital defect in which the esophagus is not fully connected. Current treatments to bring together long gaps between the esophagus have very high complication rates and the replacement tissue does not possess the correct structure or physiologic properties. Tissue engineering and regenerative medicine have been suggested as a new patient-specific treatment for EA utilizing the patient's own cells and a tubular scaffold. Our group recently published on the successful regeneration of an esophagus using a retrievable scaffold seeded with amniotic derived MSCsFurther evaluation with dynamic computed tomography has suggested that the regenerative process entails ingrowth of native esophageal cells from both proximal and distal ends including robust vascular ingrowth. This confirms our gene expression findings and lends support to our hypothesis that the regenerative process is driven by vascular ingrowth. The role of the cells seeded on esophageal scaffolds and how this affects the regenerative process is the focus of current investigation with animal studies that are ongoing.

Patch-based lung ventilation estimation using multi-layer supervoxels

Patch-based approaches have received substantial attention over the recent years in medical imaging. One of their potential applications may be to provide more anatomically consistent ventilation maps estimated on dynamic lung CT. An assessment of regional lung function may act as a guide for radiotherapy, ensuring a more accurate treatment plan. This in turn, could spare well-functioning parts of the lungs. We present a novel method for lung ventilation estimation from dynamic lung CT imaging, combining a supervoxel-based image representation with deformations estimated during deformable image registration, performed between peak breathing phases. For this we propose a method that tracks changes of the intensity of previously extracted supervoxels. For the evaluation of the method we calculate correlation of the estimated ventilation maps with static ventilation images acquired from hyperpolarized Xenon129 MRI. We also investigate the influence of different image registration methods used to estimate deformations between the peak breathing phases in the dynamic CT imaging. We show that our method performs favorably to other ventilation estimation methods commonly used in the field, independently of the image registration method applied to dynamic CT. Due to the patch-based approach of our method, it may be physiologically more consistent with lung anatomy than previous methods relying on voxel-wise relationships. In our method the ventilation is estimated for supervoxels, which tend to group spatially close voxels with similar intensity values. The proposed method was evaluated on a dataset consisting of three lung cancer patients undergoing radiotherapy treatment, and this resulted in a correlation of 0.485 with XeMRI ventilation images, compared with 0.393 for the intensity-based approach, 0.231 for the Jacobian-based method and 0.386 for the Hounsfield units averaging method, on average. Within the limitation of the small number of cases analyzed, results suggest that the presented technique may be advantageous for CT-based ventilation estimation. The results showing higher values of correlation of the proposed method demonstrate the potential of our method to more accurately mimic the lung physiology.

Deformable multimodal registration for navigation in beating-heart cardiac surgery.

Minimally invasive beating-heart surgery is currently performed using endoscopes and without navigation. Registration of intraoperative ultrasound to a preoperative cardiac CT scan is a valuable step toward image-guided navigation. The registration was achieved by first extracting a representative point set from each ultrasound image in the sequence using a deformable registration. A template shape representing the cardiac chambers was deformed through a hierarchy of affine transformations to match each ultrasound image using a generalized expectation maximization algorithm. These extracted point sets were matched to the CT by exhaustively searching over a large number of precomputed slices of 3D geometry. The result is a similarity transformation mapping the intraoperative ultrasound to preoperative CT. Complete data sets were acquired for four patients. Transesophageal echocardiography ultrasound sequences were deformably registered to a model of oriented points with a mean error of 2.3mm. Ultrasound and CT scans were registered to a mean of 3mm, which is comparable to the error of 2.8mm expected by merging ultrasound registration with uncertainty of cardiac CT. The proposed algorithm registered 3D CT with dynamic 2D intraoperative imaging. The algorithm aligned the images in both space and time, needing neither dynamic CT imaging nor intraoperative electrocardiograms. The accuracy was sufficient for navigation in thoracoscopically guided beating-heart surgery.

Groupwise image registration based on a total correlation dissimilarity measure for quantitative MRI and dynamic imaging data

The most widespread technique used to register sets of medical images consists of selecting one image as fixed reference, to which all remaining images are successively registered. This pairwise scheme requires one optimization procedure per pair of images to register. Pairwise mutual information is a common dissimilarity measure applied to a large variety of datasets. Alternative methods, called groupwise registrations, have been presented to register two or more images in a single optimization procedure, without the need of a reference image. Given the success of mutual information in pairwise registration, we adapt one of its multivariate versions, called total correlation, in a groupwise context. We justify the choice of total correlation among other multivariate versions of mutual information, and provide full implementation details. The resulting total correlation measure is remarkably close to measures previously proposed by Huizinga et al. based on principal component analysis. Our experiments, performed on five quantitative imaging datasets and on a dynamic CT imaging dataset, show that total correlation yields registration results that are comparable to Huizinga’s methods. Total correlation has the advantage of being theoretically justified, while the measures of Huizinga et al. were designed empirically. Additionally, total correlation offers an alternative to pairwise mutual information on quantitative imaging datasets.

Utility of the portal venous phase for diagnosing pancreatic necrosis in acute pancreatitis using the CT severity index.

The purpose of the study was to evaluate the value of portal venous phase (PVP) images in the diagnosis of pancreatic necrosis in patients with acute pancreatitis using computed tomography severity index (CTSI). This retrospective study was approved by our Institutional Review Board, and written informed consent was waived. Dynamic contrast-enhanced CT images, with the pancreatic parenchymal phase (PPP) and the PVP, were obtained from 56 consecutive patients with acute pancreatitis. Two radiologists reviewed two sets of images, namely PPP images alone (image set A) and combined PPP and PVP images (image set B) to evaluate the CTSI. Cases were categorized as necrotizing pancreatitis if ensuing walled-off necrosis formation was identified 4weeks after onset of symptoms. The relationship between pancreatic necrosis and CTSI was compared between image sets A and B. Logistic regression analysis was performed to evaluate the significance of clinical and radiological factors associated with the diagnosis of pancreatic necrosis. Pancreatic necrosis was confirmed in 14 out of 56 (25%) patients. The area under the receiver-operating-characteristic curve (AUC) for the diagnosis of pancreatic necrosis was 0.70 and 0.78 for image sets A and B, respectively. The AUC for image set B was significantly greater than that for image set A (P = 0.0002). Logistic regression analysis demonstrated that among clinical and radiological factors tested, CTSI for image set B was independently correlated with pancreatic necrosis (P = 0.025). Combined PPP and PVP images significantly improved the diagnostic accuracy of pancreatic necrosis following acute pancreatitis.

Development of Audio Indicator System for Respiratory Dynamic CT Imaging

We created the device, which can conduct a radiological technologist's voice to a subject during CT scanning. For 149 lung cancer, dynamic respiratory CT were performed. 92 cases were performed using this device, the others were without this device. The respiratory cycle and respiratory amplitude were analyzed from the lung density. A stable respirating cycle was obtained by using the audio indicator system. The audio indicator system is useful for respiratory dynamic CT.

Accuracy of the raw-data-based effective atomic numbers and monochromatic CT numbers for contrast medium with a dual-energy CT technique.

To evaluate the accuracy of raw-data-based effective atomic number (Zeff) values and monochromatic CT numbers for contrast material of varying iodine concentrations, obtained using dual-energy CT. We used a tissue characterization phantom and varying concentrations of iodinated contrast medium. A comparison between the theoretical values of Zeff and that provided by the manufacturer was performed. The measured and theoretical monochromatic CT numbers at 40-130 keV were compared. The average difference between the Zeff values of lung (inhale) inserts in the tissue characterization phantom was 81.3% and the average Zeff difference was within 8.4%. The average difference between the Zeff values of the varying concentrations of iodinated contrast medium was within 11.2%. For the varying concentrations of iodinated contrast medium, the differences between the measured and theoretical monochromatic CT values increased with decreasing monochromatic energy. The Zeff and monochromatic CT numbers in the tissue characterization phantom were reasonably accurate. The accuracy of the raw-data-based Zeff values was higher than that of image-based Zeff values in the tissue-equivalent phantom. The accuracy of Zeff values in the contrast medium was in good agreement within the maximum SD found in the iodine concentration range of clinical dynamic CT imaging. Moreover, the optimum monochromatic energy for human tissue and iodinated contrast medium was found to be 70 keV. Advances in knowledge: The accuracy of the Zeff values and monochromatic CT numbers of the contrast medium created by raw-data-based, dual-energy CT could be sufficient in clinical conditions.

A semi-automatic method for the extraction of the portal venous input function in quantitative dynamic contrast-enhanced CT of the liver.

To aid the extraction of the portal venous input function (PVIF) from axial dynamic contrast-enhanced CT images of the liver, eliminating the need for full manual outlining of the vessel across time points. A cohort of 20 patients undergoing perfusion CT imaging of the liver was examined. Dynamic images of the liver were reformatted into contiguous thin slices. A region of interest was defined within a transverse section of the portal vein on a single contrast-enhanced image. This region of interest was then computationally projected across all thin slices for all time points to yield a semi-automated PVIF curve. This was compared against the "gold-standard" PVIF curve obtained by conventional manual outlining. Bland-Altman plots of curve characteristics indicated no substantial difference between automated and manual PVIF curves [concordance correlation coefficient in the range (0.66, 0.98)]. No substantial differences were shown by Bland-Altman plots of derived pharmacokinetic parameters when a suitable kinetic model was applied in each case [concordance correlation coefficient in range (0.92, 0.95)]. This semi-automated method of extracting the PVIF performed equivalently to a "gold-standard" manual method for assessing liver function. Advances in knowledge: This technique provides a quick, simple and effective solution to the problems incurred by respiration motion and partial volume factors in the determination of the PVIF in liver dynamic contrast-enhanced CT.

Comparative evaluation of the algorithms for parametric mapping of the novel myocardial PET imaging agent 18F-FPTP

Objective(18F-fluoropentyl)triphenylphosphonium salt (18F-FPTP) is a new promising myocardial PET imaging tracer. It shows high accumulation in cardiomyocytes and rapid clearance from liver. We performed compartmental analysis of 18F-FPTP PET images in rat and evaluated two linear analyses: linear least-squares (LLS) and a basis function method (BFM) for generating parametric images. The minimum dynamic scan duration for kinetic analysis was also investigated and computer simulation undertaken.Methods18F-FPTP dynamic PET (18 min) and CT images were acquired from rats with myocardial infarction (MI) (n = 12). Regions of interest (ROIs) were on the left ventricle, normal myocardium, and MI region. Two-compartment (K1 and k2; 2C2P) and three-compartment (K1–k3; 3C3P) models with irreversible uptake were compared for goodness-of-fit. Partial volume and spillover correction terms (Va and α = 1 − Va) were also incorporated. LLS and BFM were applied to ROI- and voxel-based kinetic parameter estimations. Results were compared with the standard ROI-based nonlinear least-squares (NLS) results of the corresponding compartment model. A simulation explored statistical properties of the estimation methods.ResultsThe 2C2P model was most suitable for describing 18F-FPTP kinetics. Average K1, k2, and Va values were, respectively, 6.8 (ml/min/g), 1.1 (min−1), and 0.44 in normal myocardium and 1.4 (ml/min/g), 1.1 (min−1), and 0.32, in MI tissue. Ten minutes of data was sufficient for the estimation. LLS and BFM estimations correlated well with NLS values for the ROI level (K1: y = 1.06x + 0.13, r2 = 0.96 and y = 1.13x + 0.08, r2 = 0.97) and voxel level (K1: y = 1.22x − 0.30, r2 = 0.90 and y = 1.26x + 0.00, r2 = 0.92). Regional distribution of kinetic parametric images (αK1, K1, k2, Va) was physiologically relevant. LLS and BFM showed more robust characteristics than NLS in the simulation.ConclusionsFast kinetics and highly specific uptake of 18F-FPTP by myocardium enabled quantitative analysis with the 2C2P model using only the initial 10 min of data. LLS and BFM were feasible for estimating voxel-wise parameters. These two methods will be useful for quantitative evaluation of 18F-FPTP distribution in myocardium and in further studies with different conditions, disease models, and species.

Low-dose dynamic myocardial perfusion CT image reconstruction using pre-contrast normal-dose CT scan induced structure tensor total variation regularization

Dynamic myocardial perfusion CT (DMP-CT) imaging provides quantitative functional information for diagnosis and risk stratification of coronary artery disease by calculating myocardial perfusion hemodynamic parameter (MPHP) maps. However, the level of radiation delivered by dynamic sequential scan protocol can be potentially high. The purpose of this work is to develop a pre-contrast normal-dose scan induced structure tensor total variation regularization based on the penalized weighted least-squares (PWLS) criteria to improve the image quality of DMP-CT with a low-mAs CT acquisition. For simplicity, the present approach was termed as ‘PWLS-ndiSTV’. Specifically, the ndiSTV regularization takes into account the spatial-temporal structure information of DMP-CT data and further exploits the higher order derivatives of the objective images to enhance denoising performance. Subsequently, an effective optimization algorithm based on the split-Bregman approach was adopted to minimize the associative objective function. Evaluations with modified dynamic XCAT phantom and preclinical porcine datasets have demonstrated that the proposed PWLS-ndiSTV approach can achieve promising gains over other existing approaches in terms of noise-induced artifacts mitigation, edge details preservation, and accurate MPHP maps calculation.

Demystifying Tibial-Tuberosity Trochlear Groove Distance, the J-Sign and Trochlear Dysplasia: An Imaging Encomium

Aims and objective: The purpose of our study was to examine the association of the clinical J-sign with trochlear dysplasia and TTTG distance in patients with symptomatic patellofemoral instability. Material and methods: After obtaining approval from the institutional review board (class: 8.1-16/47-2, Nr.: 02/21AG, April 11, 2017), in our retrospective cross-sectional review study, 2 raters mutually assessed the type of trochlear dysplasia and separately measured the TTTG distance at 4-week intervals using 2 methods (standard and by Nizi#) on static and dynamic CT-images of 55 knees (46 patients) with clinical record of J-sign and anterior knee pain. Statistical analysis: All reported P values were 2-tailed with a level of <0.05 indicating statistical significance. The associations between variables were analyzed with appropriate correlation coefficients (Pearson, rp, pointbiserial, rpb, and phi, r#). The Wilcoxon signed-rank test for paired samples was used to evaluate the differences between static and dynamic tibial tuberosity-trochlear groove. Conclusion: The clinical manifestation of the J-sign, considered a dynamic marker of patellofemoral instability, has been frequently associated with numerous conditions, such as trochlear dysplasia, higher values of the Q-angle, and lateralized tibial tuberosity, yet its fundamental cause remains unknown, and the diagnostic value uncertain.

Dynamic CT imaging of volumetric changes in pulmonary nodules correlates with physical measurements of stiffness

Background and purposeA major challenge in CT screening for lung cancer is limited specificity when distinguishing between malignant and non-malignant pulmonary nodules (PN). Malignant nodules have different mechanical properties and tissue characteristics (‘stiffness’) from non-malignant nodules. This study seeks to improve CT specificity by demonstrating in rats that measurements of volumetric ratios in PNs with varying composition can be determined by respiratory-gated dynamic CT imaging and that these ratios correlate with direct physical measurements of PN stiffness. Methods and materialsRespiratory-gated MicroCT images acquired at extreme tidal volumes of 9 rats with PNs from talc, matrigel and A549 human lung carcinoma were analyzed and their volumetric ratios (δ) derived. PN stiffness was determined by measuring the Young’s modulus using atomic force microscopy (AFM) for each nodule excised immediately after MicroCT imaging. ResultsThere was significant correlation (p=0.0002) between PN volumetric ratios determined by respiratory-gated CT imaging and the physical stiffness of the PNs determined from AFM measurements. ConclusionWe demonstrated proof of concept that PN volume changes measured non-invasively correlate with direct physical measurements of stiffness. These results may translate clinically into a means of improving the specificity of CT screening for lung cancer and/or improving individual prognostic assessments based on lung tumor stiffness.

Robust low-dose dynamic cerebral perfusion CT image restoration via coupled dictionary learning scheme.

Dynamic cerebral perfusion x-ray computed tomography (PCT) imaging has been advocated to quantitatively and qualitatively assess hemodynamic parameters in the diagnosis of acute stroke or chronic cerebrovascular diseases. However, the associated radiation dose is a significant concern to patients due to its dynamic scan protocol. To address this issue, in this paper we propose an image restoration method by utilizing coupled dictionary learning (CDL) scheme to yield clinically acceptable PCT images with low-dose data acquisition. Specifically, in the present CDL scheme, the 2D background information from the average of the baseline time frames of low-dose unenhanced CT images and the 3D enhancement information from normal-dose sequential cerebral PCT images are exploited to train the dictionary atoms respectively. After getting the two trained dictionaries, we couple them to represent the desired PCT images as spatio-temporal prior in objective function construction. Finally, the low-dose dynamic cerebral PCT images are restored by using a general DL image processing. To get a robust solution, the objective function is solved by using a modified dictionary learning based image restoration algorithm. The experimental results on clinical data show that the present method can yield more accurate kinetic enhanced details and diagnostic hemodynamic parameter maps than the state-of-the-art methods.

Assessment of Microvascular Density in the Neuroendocrine Tumors of the Pancreas: a Correlation with Multidetector Computed Tomography (MDCT) Features and Tumor Grade

Introduction/ Background The biologic course of pancreatic neuroendocrine tumors (NET) can be accurately predicted on the basis of pathology parameters which, unfortunately, cannot be fully assessed until the entire tumor has been resected. When obvious landmarks of malignancy (metastases, vascular invasion) are lacking, tumour size is the only reliable preoperative parameter used to differentiate between presumably benign and malignant pancreatic NETs. It is generally accepted that pancreatic NETs exceeding 2 cm in their diameter may have borderline pathology or even malignancy. Anyway, up to 10% of tumors with diameter less than 2 cm are although malignant. That is why there is need in studies focusing on additional imaging characteristics, such as contrast-enhanced MDCT, useful for the aggressiveness assessment. Aims To determine whether it is possible to predict pancreatic NET Grade and microvascular density (MVD) according to contrast-enhanced MDCT findings. Methods A retrospective study was conducted and 70 patients with pancreatic NETs were retrieved from the institutional archives. Twenty eight of them had met the including criteria (a - patients underwent surgery in our Institution and b - patients underwent preoperative dynamic abdominal CT within 30 days prior surgery) and comprised the study population. Thus, research included 15 patients with functioning and non-functioning pancreatic NETs Grade 1 and 13 of patients with Grade 2 (according to the WHO classification of 2010). Radiology All preoperative CT examinations were performed by a 256-slice CT scanner. Dynamic CT images, including non-enhanced, arterial, portal venous and delay phase images, were obtained for all patients. For contrast-enhanced CT, 90–120 mL of high concentration contrast mediaiomeprol 400 (Iomeron 400; Bracco Imaging SpA, Milan, Italy). CT images were reconstructed with a section thickness of 1-2 mm. Pathology For identification of vessels, regardless lymphatic or venous, immunohistochemistry with CD34 (clone QBEnd/10, CellMarque) was performed on formalin-fixed paraffin-embedded (FFPE) tumor blocks. Image analysis Density of vessels was defined on 3 fields at x 200 (~ 1,8 mm2). The choice of fields of vision was carried out among sites with visually greatest density of vessels (“hot spots”). Determined total number of the pixels belonging to vessels by the Color Threshold tool (ImageJ) and divided this number into total of pixels in the image. Results The median of density of vessels at patients with NET Grade 1 made 8% ± 2% whereas in group of patients with NET Grade 2 this indicator made 5 ± 4%. The difference was statistically significant (p <0,05). Mean arterial enhancement ratio was 1.66(0.42) in Grade 1 and 1.04(0.39) in Grade 2 pancreatic NET (p<0.01); and correlated with intratumoral MVD (r=0.61, p<0.005) and tumor grade (p<0.01). Vascular invasion, metastases and pancreatic duct dilatation were observed in two cases, both in G2 tumors. Arterial enhancement ratio <1.1, size≥20 mm, ill-defined borders and tumor non-homogeneity showed 83%, 74%, 70% and 56% accuracy in diagnosing G2 tumor respectively, while the accuracy of two of these criteria used in combination was 91%. Contrast-enhanced MDCT-features of pancreatic NET correlate with MVD and can predict tumor grade during preoperative staging.

Extracting moving boundaries from dynamic, multislice CT images for fluid simulation

A method for extracting solid boundary motion from dynamic CT images of a human oropharyngeal swallow is presented. The data-set has high spatial resolution () but low temporal resolution (10 Hz). The low temporal resolution results in motion artefacts (i.e. blurring, ghosting) due to the fast moving solid structures. Automated segmentation and registration methods were not successful, so a tool for manual 3D deformable registration was created as a plug-in for the open-source modelling software, Blender. The tool gives users a high level of control to sculpt a mesh to match 3D volumetric image data. The resulting series of meshes, each corresponding to an instance in time, can be used in a fluid simulation using a technique such as smoothed-particle hydrodynamics.

Characterization of patellar maltracking using dynamic kinematic CT imaging in patients with patellar instability.

Little has been reported on the relationship between patellar maltracking and instability. Patellar maltracking has been subjectively described with the "J sign" but is difficult to assess objectively using traditional imaging. Dynamic kinematic computed tomography (DKCT) allows dynamic assessment of the patellofemoral joint. DKCT was used to visualize and quantify patellar maltracking patterns, and severity of maltracking was correlated with the presence or absence of patellar instability symptoms. Seventy-six knees in 38 patients were analysed using DKCT. Maltracking was defined as deviation of the patella from the trajectory of the trochlear groove and was characterized by patellar bisect offset, which was measured at 10° intervals of knee flexion during active flexion and extension. Bisect offset measurements were grouped by number of quadrants of maximum lateral patellar motion, with one, two, and three quadrants corresponding to 75-99, 100-125, and >125%, respectively. Patellar instability symptoms were correlated with maltracking severity. Two knees were excluded because of poor imaging quality. Fifty of 74 knees had patellar instability, and 13 patients had bilateral symptoms. Of these, four (8%) had normal tracking patterns; 41 (82%) had increased lateral translation in extension, which we termed the J-sign pattern; 4 (8%) had persistent lateralization of the patella throughout range of motion; and 1 had increased lateral translation in flexion. In knees with the J-sign pattern, degree of maltracking was graded by severity: J1 (n=24), J2 (n=19), and J3 (n=15). The sensitivities of J-sign grades in predicting patellar instability symptoms were 50% (J1), 80% (J2), and 93% (J3) (p<0.01). There were significant differences in sensitivity between knees with no J sign or J1 versus J2 or J3 (p=0.02). DKCT showed several patellar maltracking patterns in patients with patellar instability. A J-sign pattern with more than two quadrants of lateral translation correlated with the presence of patellar instability symptoms. Incorporation of this approach of objectively quantifying maltracking patterns is recommended in the evaluation of patellofemoral instability. IV.

Mixed Confidence Estimation for Iterative CT Reconstruction.

Dynamic (4D) CT imaging is used in a variety of applications, but the two major drawbacks of the technique are its increased radiation dose and longer reconstruction time. Here we present a statistical analysis of our previously proposed Mixed Confidence Estimation (MCE) method that addresses both these issues. This method, where framed iterative reconstruction is only performed on the dynamic regions of each frame while static regions are fixed across frames to a composite image, was proposed to reduce computation time. In this work, we generalize the previous method to describe any application where a portion of the image is known with higher confidence (static, composite, lower-frequency content, etc.) and a portion of the image is known with lower confidence (dynamic, targeted, etc). We show that by splitting the image space into higher and lower confidence components, MCE can lower the estimator variance in both regions compared to conventional reconstruction. We present a theoretical argument for this reduction in estimator variance and verify this argument with proof-of-principle simulations. We also propose a fast approximation of the variance of images reconstructed with MCE and confirm that this approximation is accurate compared to analytic calculations of and multi-realization image variance. This MCE method requires less computation time and provides reduced image variance for imaging scenarios where portions of the image are known with more certainty than others allowing for potentially reduced radiation dose and/or improved dynamic imaging.

Modeling the Nonlinear Motion of the Rat Central Airways.

Advances in volumetric medical imaging techniques allowed the subject-specific modeling of the bronchial flow through the first few generations of the central airways using computational fluid dynamics (CFD). However, a reliable CFD prediction of the bronchial flow requires modeling of the inhomogeneous deformation of the central airways during breathing. This paper addresses this issue by introducing two models of the central airways motion. The first model utilizes a node-to-node mapping between the discretized geometries of the central airways generated from a number of successive computed tomography (CT) images acquired dynamically (without breath hold) over the breathing cycle of two Sprague-Dawley rats. The second model uses a node-to-node mapping between only two discretized airway geometries generated from the CT images acquired at end-exhale and at end-inhale along with the ventilator measurement of the lung volume change. The advantage of this second model is that it uses just one pair of CT images, which more readily complies with the radiation dosage restrictions for humans. Three-dimensional computer aided design geometries of the central airways generated from the dynamic-CT images were used as benchmarks to validate the output from the two models at sampled time-points over the breathing cycle. The central airway geometries deformed by the first model showed good agreement to the benchmark geometries within a tolerance of 4%. The central airway geometry deformed by the second model better approximated the benchmark geometries than previous approaches that used a linear or harmonic motion model.

Correlation between dynamic contrast-enhanced CT and MRI features of intrahepatic mass-forming cholangiocarcinoma and pathologic differentiation

Objective To investigate the correlation between dynamic contrast-enhanced CT and MRI image findings of intrahepatic mass-forming cholangiocarcinoma(IMCC) and pathologic differentiation. Methods The CT and (or) MRI features of ninety-five patients with IMCC proved by pathologic examination were retrospectively reviewed. Sixty-five patients underwent multiphasic dynamic contrast-enhanced CT scan, twenty-nine underwent multiphasic dynamic contrast-enhanced MRI scan and ten patients underwent multiphasic dynamic contrast-enhanced CT and MRI examination simultaneously. According to the case history and laboratory examination, patients were divided into groups: with chronic hepatitis and without chronic hepatitis and according to the maximum diameter of the tumor, they were divided into 6 cm groups. The imaging features of tumor enhancement were reviewed among groups. The pathologic results and imaging features were compared and patients were divided into well differentiated, moderately differentiated, and poorly differentiated groups. Analyzed by Chi-square test, the difference of tumor enhancement between groups were analyzed. Results The patterns of multiphasic dynamic contrast-enhanced CT and MRI in IMCC patients had five types: type1∶60 cases, peripheral rim and (or) separating enhancement during artery phase, followed by centripetal enhancement progressively and(or) separating enhancement in the equilibrium phase; type 2∶11 cases, peripheral rim enhancement in the artery phase and hypointensity (hypoattenuating) during equilibrium phase with central nodular enhancement; type 3∶15 cases, no enhancement in the arterial phase and internal heterogeneous enhancement during equilibrium phase; type 4∶6 cases, heterogeneous enhancement in the early phase and wash-out during equilibrium phase; type 5∶3 cases, no enhancement throughout dynamic enhancement.The results showed that in hepatitis group type1 to 5 had 31, 7, 12, 5 and 1 cases and without hepatitis group had 29, 4, 3, 1 and 2 cases. There was no statistical significance between groups(χ2=3.567,P=0.059). The maximum diameter 6 cm group(27 cases) had 24, 1, 2, 0 and 0 cases. There was significant difference in groups(χ2=19.582,P<0.01).Pathological well differentiated and moderately differentiated groups(40 cases) had 24, 7,6, 2 and 1 cases presented enhancement type 1 to 5 and poorly differentiated group(55 cases) had 36, 4, 9, 3 and 3 cases. There was no statistical difference in three differentiated groups(χ2=0.296,P=0.586). Conclusions CT and MRI enhancement patterns of IMCC are presented with diversity. The imaging features are associated with focus size and had no relationship with pathological differentiated degree. Key words: Liver neoplasms; Bile duct neoplasms; Tomography, X-ray computed; Magnetic resonance imaging