Improved stent sharpness evaluation with Super-Resolution deep learning reconstruction in coronary computed tomography angiography.

OBJECTIVES: This study aimed to assess the impact of super-resolution deep learning reconstruction (SR-DLR) on coronary computed tomography angiography (CCTA) image quality and blooming artifacts from coronary artery stents in comparison to conventional methods, including hybrid iterative reconstruction (HIR) and deep learning-based reconstruction (DLR). METHODS: A retrospective analysis included sixty-six CCTA patients from July to November 2022. Major coronary arteries were evaluated for image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Stent sharpness was quantified using 10-90% edge rise slope (ERS) and 10-90% edge rise distance (ERD). Qualitative analysis employed a 5-point scoring system to assess overall image quality, image noise, vessel wall, and stent structure. RESULTS: SR-DLR demonstrated significantly lower image noise compared to HIR and DLR. SNR and CNR were notably higher in SR-DLR. Stent ERS was significantly improved in SR-DLR, with mean ERD values of 0.70 ± 0.20 mm for SR-DLR, 1.13 ± 0.28 mm for HIR, and 0.85 ± 0.26 mm for DLR. Qualitatively, SR-DLR scored higher in all categories. CONCLUSIONS: SR-DLR produces images with lower image noise, leading to improved overall image quality, compared with HIR and DLR. SR-DLR is a valuable image reconstruction algorithm for enhancing the spatial resolution and sharpness of coronary artery stents without being constrained by hardware limitations. ADVANCED IN KNOWLEDGE: The overall image quality was significantly higher in SR-DLR, resulting in sharper coronary artery stents compared to HIR and DLR.

Injection rate, scanning position, and values of parameters on pulmonary computed tomography perfusion in normal Beagles.

OBJECTIVE: This study evaluated the effects of scanning position and contrast medium injection rate on pulmonary CT perfusion (CTP) images in healthy dogs. ANIMALS: 7 healthy Beagles. METHODS: Experiments involved 4 conditions: dorsal and sternal recumbency at 2.5 mL/s (first) and sternal recumbency with additional rates of 1.5 and 3.5 mL/s (second). Various parameters, including the initial time of venous enhancement (Tv), peak time of arterial enhancement (PTa), and peak enhancement values of the artery, were measured. The PTa to Tv interval was calculated. Perfusion mapping parameters (pulmonary blood flow, pulmonary blood volume, mean transit time, time to maximum, and time to peak) were determined in different lung regions (left and right dorsal, middle, and ventral). RESULTS: There are significant variations in most perfusion mapping parameters based on the pulmonary parenchymal location. Dorsal recumbency had a lower peak value of arterial enhancement than sternal recumbency. Pulmonary blood flow in the dorsal region and mean transit time and time to maximum in all regions showed no significant differences based on position. Pulmonary blood volume and time to peak varied with scanning position. The PTa to Tv interval did not differ based on the injection rate, but the injection time at 1.5 mL/s was longer than at other rates. All perfusion mapping parameters of the ventral region increased with higher injection rates. CLINICAL RELEVANCE: The recommended CTP imaging approach in dogs is a low injection rate of 1.5 mL/s in the sternal recumbency. This study provides reference ranges for perfusion parameters based on the pulmonary parenchymal location, contributing to the acquisition and application of pulmonary CTP images for differential diagnosis in small-breed dogs.

Assessment of Image Quality of Coronary CT Angiography Using Deep Learning-Based CT Reconstruction: Phantom and Patient Studies.

BACKGROUND: In coronary computed tomography angiography (CCTA), the main issue of image quality is noise in obese patients, blooming artifacts due to calcium and stents, high-risk coronary plaques, and radiation exposure to patients. OBJECTIVE: To compare the CCTA image quality of deep learning-based reconstruction (DLR) with that of filtered back projection (FBP) and iterative reconstruction (IR). METHODS: This was a phantom study of 90 patients who underwent CCTA. CCTA images were acquired using FBP, IR, and DLR. In the phantom study, the aortic root and the left main coronary artery in the chest phantom were simulated using a needleless syringe. The patients were classified into three groups according to their body mass index. Noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured for image quantification. A subjective analysis was also performed for FBP, IR, and DLR. RESULTS: According to the phantom study, DLR reduced noise by 59.8% compared to FBP and increased SNR and CNR by 121.4% and 123.6%, respectively. In a patient study, DLR reduced noise compared to FBP and IR. Furthermore, DLR increased the SNR and CNR more than FBP and IR. In terms of subjective scores, DLR was higher than FBP and IR. CONCLUSION: In both phantom and patient studies, DLR effectively reduced image noise and improved SNR and CNR. Therefore, the DLR may be useful for CCTA examinations.

Coronary artery calcium quantification: comparison between filtered-back projection, hybrid iterative reconstruction, and deep learning reconstruction techniques.

BACKGROUND: The reference protocol for the quantification of coronary artery calcium (CAC) should be updated to meet the standards of modern imaging techniques. PURPOSE: To assess the influence of filtered-back projection (FBP), hybrid iterative reconstruction (IR), and three levels of deep learning reconstruction (DLR) on CAC quantification on both in vitro and in vivo studies. MATERIAL AND METHODS: In vitro study was performed with a multipurpose anthropomorphic chest phantom and small pieces of bones. The real volume of each piece was measured using the water displacement method. In the in vivo study, 100 patients (84 men; mean age = 71.2 ± 8.7 years) underwent CAC scoring with a tube voltage of 120 kVp and image thickness of 3 mm. The image reconstruction was done with FBP, hybrid IR, and three levels of DLR including mild (DLRmild), standard (DLRstd), and strong (DLRstr). RESULTS: In the in vitro study, the calcium volume was equivalent (P = 0.949) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. In the in vivo study, the image noise was significantly lower in images that used DLRstr-based reconstruction, when compared images other reconstructions (P < 0.001). There were no significant differences in the calcium volume (P = 0.987) and Agatston score (P = 0.991) among FBP, hybrid IR, DLRmild, DLRstd, and DLRstr. The highest overall agreement of Agatston scores was found in the DLR groups (98%) and hybrid IR (95%) when compared to standard FBP reconstruction. CONCLUSION: The DLRstr presented the lowest bias of agreement in the Agatston scores and is recommended for the accurate quantification of CAC.

The effectiveness of post-processing head and neck CT angiography using contrast enhancement boost technique.

BACKGROUND AND PURPOSE: This study aimed to investigate the potential of contrast enhancement (CE)-boost technique in the head and neck computed tomography (CT) angiography in terms of the objective and subjective image quality. MATERIALS AND METHODS: Consecutive patients who underwent head and neck CT angiography between May 2022 and July 2022 were included. The CE-boost images were generated by combining the subtracted iodinated image and contrast-enhanced image. The objective image analysis was compared for each image with and without CE-boost technique using the CT attenuation, image noise, signal-to-noise-ratio (SNR), contrast-to-noise-ratio (CNR), and image sharpness (full width at half width maximum, FWHM). The subjective image analysis was evaluated by two independent experienced radiologists in the following aspects: the overall image quality, motion artifact, vascular delineation, and vessel sharpness. RESULTS: A total of 65 patients (mean age, 59.48 ± 13.71 years; range, 24-87 years; 36 women) were included. The CT attenuation of the vertebrobasilar arteries was significantly (p < 0.001) higher in the images obtained using CE-boost technique than in conventional images. Image noise was significantly (p < 0.001) lower for CE-boost images (6.09 ± 1.93) than for conventional images (7.79 ± 1.73). Moreover, CE-boost technique yielded higher SNR (64.43 ± 17.17 vs. 121.37 ± 38.77, p < 0.001) and CNR (56.90 ± 18.79 vs. 116.65 ± 57.44, p < 0.001) than conventional images. CE-boost resulted in shorter FWHM than conventional images (p < 0.001). Higher subjective image quality scores were also demonstrated by the CE-boost than images without CE-boost technique. CONCLUSIONS: In both objective and subjective image analysis, the CE-boost technique provided higher image quality without increasing the flow rate and concentration of contrast media in the head and neck CT angiography. Furthermore, the vessel completeness and delineation were superior in CE-boost images than in conventional images.

Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising.

BACKGROUND: Most computed tomography (CT) denoising algorithms have been evaluated using image quality analysis (IQA) methods developed for natural image, which do not adequately capture the texture details in medical imaging. Radiomics is an emerging image analysis technique that extracts texture information to provide a more objective basis for medical imaging diagnostics, overcoming the subjective nature of traditional methods. By utilizing the difficulty of reproducing radiomics features under different imaging protocols, we can more accurately evaluate the performance of CT denoising algorithms. METHOD: We introduced radiomic feature reproducibility analysis as an evaluation metric for a denoising algorithm. Also, we proposed a low-dose CT denoising method based on a generative adversarial network (GAN), which outperformed well-known CT denoising methods. RESULTS: Although the proposed model produced excellent results visually, the traditional image assessment metrics such as peak signal-to-noise ratio and structural similarity failed to show distinctive performance differences between the proposed method and the conventional ones. However, radiomic feature reproducibility analysis provided a distinctive assessment of the CT denoising performance. Furthermore, radiomic feature reproducibility analysis allowed fine-tuning of the hyper-parameters of the GAN. CONCLUSION: We demonstrated that the well-tuned GAN architecture outperforms the well-known CT denoising methods. Our study is the first to introduce radiomics reproducibility analysis as an evaluation metric for CT denoising. We look forward that the study may bridge the gap between traditional objective and subjective evaluations in the clinical medical imaging field.

Deep learning reconstruction allows for usage of contrast agent of lower concentration for coronary CTA than filtered back projection and hybrid iterative reconstruction.

BACKGROUND: The demand for homogeneous and higher vascular contrast enhancement is critical to provide an appropriate interpretation of abnormal vascular findings in coronary computed tomography angiography (CTA). PURPOSE: To evaluate the effect of various contrast media concentrations (Iohexol-370, Iohexol-300, Iohexol-240) and image reconstructions (filtered back projection [FBP], hybrid iterative reconstruction [IR], and deep learning reconstruction [DLR]) on coronary CTA. MATERIAL AND METHODS: A total of 63 patients referred for coronary CTA between July and October 2021 were enrolled in this prospective study, and they randomly received one of three contrast media. CTA images were reconstructed with FBP, hybrid IR, and DLR. The CT attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated for all three images. The images were subjectively evaluated by two radiologists in terms of overall image quality, artifacts, image noise, and vessel wall delineation on a 5-point Likert scale. RESULTS: The application of DLR resulted in significantly lower image noise; higher CT attenuation, SNR, and CNR; and better subjective analysis among the three different concentrations of contrast media groups (P < 0.001). There was no significant difference in the CT attenuation of the left ventricle (P = 0.089) and coronary arteries (P = 0.072) between hybrid IR at Iohexol-300 and DLR at Iohexol-240. Furthermore, application of DLR to the Iohexol-240 significantly improved SNR and CNR; it achieved higher subjective scores compared with hybrid IR at Iohexol-300 (P < 0.001). CONCLUSION: We suggest that using DLR with Iohexol-240 contrast media is preferable to hybrid IR with Iohexol-300 contrast media in coronary CTA.

Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction.

OBJECTIVE: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods. MATERIALS AND METHODS: CCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.8 years, 36 male) who underwent examination at a single institution were reconstructed using DLR, FBP, and hybrid IR methods and reviewed. CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and stent evaluation, including 10%-90% edge rise slope (ERS) and 10%-90% edge rise distance (ERD), were measured. Quantitative data are summarized as the mean ± SD. The subjective visual scores (1 for worst -5 for best) of the images were obtained for the following: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (annulus, leaflets, papillary muscles, and chordae tendineae). These parameters were compared between the DLR, FBP, and hybrid IR methods. RESULTS: DLR provided higher Hounsfield unit (HU) values in the aorta and similar attenuation in the fat and muscle compared with FBP and hybrid IR. The image noise in HU was significantly lower in DLR (12.6 ± 2.2) than in hybrid IR (24.2 ± 3.0) and FBP (54.2 ± 9.5) (p < 0.001). The SNR and CNR were significantly higher in the DLR group than in the FBP and hybrid IR groups (p < 0.001). In the coronary stent, the mean value of ERS was significantly higher in DLR (1260.4 ± 242.5 HU/mm) than that of FBP (801.9 ± 170.7 HU/mm) and hybrid IR (641.9 ± 112.0 HU/mm). The mean value of ERD was measured as 0.8 ± 0.1 mm for DLR while it was 1.1 ± 0.2 mm for FBP and 1.1 ± 0.2 mm for hybrid IR. The subjective visual scores were higher in the DLR than in the images reconstructed with FBP and hybrid IR. CONCLUSION: DLR reconstruction provided better images than FBP and hybrid IR reconstruction.

A Novel Computed Tomography Image Reconstruction for Improving Visualization of Pulmonary Vasculature: Comparison Between Preprocessing and Postprocessing Images Using a Contrast Enhancement Boost Technique.

OBJECTIVE: This study aimed to evaluate chest computed tomography (CT) angiography image quality using the contrast enhancement (CE)-boost technique compared with conventional images. METHODS: Forty patients who underwent contrast-enhanced chest CT were included. Combined CT angiography images of the iodinated image obtained from the subtraction of nonenhanced CT images and CT angiography images were used to generate CE-boost images. Computed tomography attenuation, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for the right and left pulmonary arteries as the central and subsegmental arteries as peripheral vessels were assessed. Subjective image quality was rated on a 5-point scale by 2 radiologists. Image quality was assessed using a paired t test. RESULTS: Computed tomography attenuation in the main pulmonary artery was significantly higher for the CE-boost images (311.05 ± 91.94) than for the conventional images (221.25 ± 61.21, P < 0.001). Similarly, the CE-boost images resulted in significantly higher CT attenuation in the subsegmental arteries (right, 305.34 ± 90.13; left, 313.05 ± 97.21) than in the conventional images (right, 218.45 ± 63.16; left, 223.89 ± 74.27). The CE-boost technique demonstrated marked improvement in the visualization of the peripheral pulmonary artery without the administration of a higher iodine delivery rate. The mean SNR and CNR were also significantly higher in the central and peripheral vessels in the CE-boost images than in the conventional images (P < 0.001). In the subjective analysis, the image contrast and vascular contrast edge were significantly higher for the CE-boost images than for conventional images (P < 0.001). CONCLUSIONS: The CE-boost technique increases not only the visualization of peripheral arteries by improving vascular attenuation but also the SNR and CNR.

Reconnection of fragmented parts of coronary arteries using local geometric features in X-ray angiography images.

The segmentation of coronary arteries in X-ray images is essential for image-based guiding procedures and the diagnosis of cardiovascular disease. However, owing to the complex and thin structures of the coronary arteries, it is challenging to accurately segment arteries in X-ray images using only a single neural network model. Consequently, coronary artery images obtained by segmentation with a single model are often fragmented, with parts of the arteries missing. Sophisticated post-processing is then required to identify and reconnect the fragmented regions. In this paper, we propose a method to reconstruct the missing regions of coronary arteries using X-ray angiography images. METHOD: We apply an independent convolutional neural network model considering local details, as well as a local geometric prior, for reconnecting the disconnected fragments. We implemented and compared the proposed method with several convolutional neural networks with customized encoding backbones as baseline models. RESULTS: When integrated with our method, existing models improved considerably in terms of similarity with ground truth, with a mean increase of 0.330 of the Dice similarity coefficient in local regions of disconnected arteries. The method is efficient and is able to recover missing fragments in a short number of iterations. CONCLUSION AND SIGNIFICANCE: Owing to the restoration of missing fragments of coronary arteries, the proposed method enables a significant enhancement of clinical impact. The method is general and can simply be integrated into other existing methods for coronary artery segmentation.

Assessment of Image Quality for Selective Intracoronary Contrast-Injected CT Angiography in a Hybrid Angio-CT System: A Feasibility Study in Swine.

PURPOSE: To compare image quality in selective intracoronary contrast-injected computed tomography angiography (Selective-CTA) with that in conventional intravenous contrast-injected CTA (IV-CTA). MATERIALS AND METHODS: Six pigs (35 to 40 kg) underwent both IV-CTA using an intravenous injection (60 mL) and Selective-CTA using an intracoronary injection (20 mL) through a guide-wire during/after percutaneous coronary intervention. Images of the common coronary artery were acquired. Scans were performed using a combined machine comprising an invasive coronary angiography suite and a 320-channel multi-slice CT scanner. Quantitative image quality parameters of CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), mean lumen diameter (MLD), and mean lumen area (MLA) were measured and compared. Qualitative analysis was performed using intraclass correlation coefficient (ICC), which was calculated for analysis of interobserver agreement. RESULTS: Quantitative image quality, determined by assessing the uniformity of CT attenuation (399.06 vs. 330.21, p<0.001), image noise (24.93 vs. 18.43, p<0.001), SNR (16.43 vs. 18.52, p=0.005), and CNR (11.56 vs. 13.46, p=0.002), differed significantly between IV-CTA and Selective-CTA. MLD and MLA showed no significant difference overall (2.38 vs. 2.44, p=0.068, 4.72 vs. 4.95, p=0.078). The density of contrast agent was significantly lower for selective-CTA (13.13 mg/mL) than for IV-CTA (400 mg/mL). Agreement between observers was acceptable (ICC=0.79±0.08). CONCLUSION: Our feasibility study in swine showed that compared to IV-CTA, Selective-CTA provides better image quality and requires less iodine contrast medium.

Bayesian Estimation of Geometric Morphometric Landmarks for Simultaneous Localization of Multiple Anatomies in Cardiac CT Images.

We propose a robust method to simultaneously localize multiple objects in cardiac computed tomography angiography (CTA) images. The relative prior distributions of the multiple objects in the three-dimensional (3D) space can be obtained through integrating the geometric morphological relationship of each target object to some reference objects. In cardiac CTA images, the cross-sections of ascending and descending aorta can play the role of the reference objects. We employed the maximum a posteriori (MAP) estimator that utilizes anatomic prior knowledge to address this problem of localizing multiple objects. We propose a new feature for each pixel using the relative distances, which can define any objects that have unclear boundaries. Our experimental results targeting four pulmonary veins (PVs) and the left atrial appendage (LAA) in cardiac CTA images demonstrate the robustness of the proposed method. The method could also be extended to localize other multiple objects in different applications.

Improvement of depiction of the intracranial arteries on brain CT angiography using deep learning reconstruction.

To evaluate the ability of a commercialized deep learning reconstruction technique to depict intracranial vessels on the brain computed tomography angiography and compare the image quality with filtered-back-projection and hybrid iterative reconstruction in terms of objective and subjective measures. Forty-three patients underwent brain computed tomography angiography, and images were reconstructed using three algorithms: filtered-back-projection, hybrid iterative reconstruction, and deep learning reconstruction. The image noise, computed tomography attenuation value, signal-to-noise ratio, and contrast-to-noise ratio were measured in the bilateral cavernous segment of the internal carotid artery, vertebral artery, basilar apex, horizontal segment of the middle cerebral artery and used for the objective assessment of the image quality among the three different reconstructions. The subjective image quality score was significantly higher for the deep learning reconstruction than hybrid iterative reconstruction and filtered-back-projection images. The deep learning reconstruction markedly improved the reduction of blooming artifacts in surgical clips and coiled aneurysms. The deep learning reconstruction method generally improves the image quality of brain computed tomography angiography in terms of objective measurement and subjective grading compared with filtered-back-projection and hybrid iterative reconstruction. Especially, deep learning reconstruction is deemed advantageous for better depiction of small vessels compared to filtered-back projection and hybrid iterative reconstruction.

Clinical feasibility of catheter-directed selective intracoronary computed tomography angiography using an extremely low dose of iodine in patients with coronary artery disease.

OBJECTIVE: This study aimed to evaluate the clinical feasibility of catheter-directed selective computed tomography angiography (S-CTA) in patients with coronary artery disease (CAD). METHODS: We prospectively enrolled 65 patients diagnosed with CAD who underwent conventional computed tomography angiography (C-CTA). C-CTA was performed with 60-90 mL of contrast medium (370 mg iodine/mL), whereas S-CTA was performed with 15 mL of contrast medium and 17.19 mg iodine/mL. Luminal enhancement range, homogeneity of luminal enhancement, image quality, plaque volume (PV), and percent aggregate plaque volume (%APV) were measured. Paired Student's t test, Wilcoxon rank-sum test, and Pearson's correlation coefficient were used to compare two methods. RESULTS: Luminal enhancement was significantly higher on S-CTA than on C-CTA (324.4 ± 8.0 Hounsfield unit (HU) vs. 312.0 ± 8.0 HU, p < 0.0001 in the per-vessel analysis). Transluminal attenuation gradient showed a significantly slower reduction pattern on S-CTA than on C-CTA (-0.65 HU/10 mm vs. -0.89 HU/10 mm, p < 0.0001 in the per-vessel analysis). Image noise was significantly lower on S-CTA than on C-CTA (39.6 ± 10.0 HU vs. 43.9 ± 9.4 HU, p < 0.0001). There was excellent correlation between S-CTA and C-CTA with respect to PV and %APV (r = 0.99, r = 0.98, respectively). CONCLUSIONS: S-CTA might be useful in facilitating atherosclerotic plaque analysis and providing guidance for complex lesions such as chronic total occlusion, particularly in cases in which on-site procedure planning is required. KEY POINTS: • Selective computed tomography angiography (S-CTA) can serve as an intraprocedural computed tomography angiography protocol. • S-CTA was performed with low dose of iodine compared with conventional computed tomography angiography. • S-CTA enables on-site atherosclerotic plaque analysis.

Intra-articular Injection of Mesenchymal Stem Cells for the Treatment of Osteoarthritis of the Knee: A 2-Year Follow-up Study.

BACKGROUND: The intra-articular injection of mesenchymal stem cells (MSCs) into the knee has shown a potential for the treatment of generalized cartilage loss in osteoarthritis (OA). However, there have been few midterm reports with clinical and structural outcomes. PURPOSE: To assess the midterm safety and efficacy of an intra-articular injection of autologous adipose tissue-derived (AD) MSCs for knee OA at 2-year follow-up. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Eighteen patients with OA of the knee were enrolled (3 male, 15 female; mean age, 61.8 ± 6.6 years [range, 52-72 years]). Patients in the low-, medium-, and high-dose groups received an intra-articular injection of 1.0 × 107, 5.0 × 107, and 1.0 × 108 AD MSCs into the knee, respectively. Clinical and structural evaluations were performed with widely used methodologies including the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and measurements of the size and depth of the cartilage defect, signal intensity of regenerated cartilage, and cartilage volume using magnetic resonance imaging (MRI). RESULTS: There were no treatment-related adverse events during the 2-year period. An intra-articular injection of autologous AD MSCs improved knee function, as measured with the WOMAC, Knee Society clinical rating system (KSS), and Knee injury and Osteoarthritis Outcome Score (KOOS), and reduced knee pain, as measured with the visual analog scale (VAS), for up to 2 years regardless of the cell dosage. However, statistical significance was found mainly in the high-dose group. Clinical outcomes tended to deteriorate after 1 year in the low- and medium-dose groups, whereas those in the high-dose group plateaued until 2 years. The structural outcomes evaluated with MRI also showed similar trends. CONCLUSION: This study identified the safety and efficacy of an intra-articular injection of AD MSCs into the OA knee over 2 years, encouraging a larger randomized clinical trial. However, this study also showed potential concerns about the durability of clinical and structural outcomes, suggesting the need for further studies. CLINICAL TRIAL REGISTRATION: NCT01300598.

A semi-automated volumetric software for segmentation and perfusion parameter quantification of brain tumors using 320-row multidetector computed tomography: a validation study.

PURPOSE: We developed a semi-automated volumetric software, NPerfusion, to segment brain tumors and quantify perfusion parameters on whole-brain CT perfusion (WBCTP) images. The purpose of this study was to assess the feasibility of the software and to validate its performance compared with manual segmentation. METHODS: Twenty-nine patients with pathologically proven brain tumors who underwent preoperative WBCTP between August 2012 and February 2015 were included. Three perfusion parameters, arterial flow (AF), equivalent blood volume (EBV), and Patlak flow (PF, which is a measure of permeability of capillaries), of brain tumors were generated by a commercial software and then quantified volumetrically by NPerfusion, which also semi-automatically segmented tumor boundaries. The quantification was validated by comparison with that of manual segmentation in terms of the concordance correlation coefficient and Bland-Altman analysis. RESULTS: With NPerfusion, we successfully performed segmentation and quantified whole volumetric perfusion parameters of all 29 brain tumors that showed consistent perfusion trends with previous studies. The validation of the perfusion parameter quantification exhibited almost perfect agreement with manual segmentation, with Lin concordance correlation coefficients (ρ c) for AF, EBV, and PF of 0.9988, 0.9994, and 0.9976, respectively. On Bland-Altman analysis, most differences between this software and manual segmentation on the commercial software were within the limit of agreement. CONCLUSIONS: NPerfusion successfully performs segmentation of brain tumors and calculates perfusion parameters of brain tumors. We validated this semi-automated segmentation software by comparing it with manual segmentation. NPerfusion can be used to calculate volumetric perfusion parameters of brain tumors from WBCTP.

Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography.

We propose a Bayesian tracking and segmentation method of coronary arteries on coronary computed tomographic angiography (CCTA). The geometry of coronary arteries including lumen boundary is estimated in Maximum A Posteriori (MAP) framework. Three consecutive sphere based filtering is combined with a stochastic process that is based on the similarity of the consecutive local neighborhood voxels and the geometric constraint of a vessel. It is also founded on the prior knowledge that an artery can be seen locally disconnected and consist of branches which may be seemingly disconnected due to plaque build up. For such problem, an active search method is proposed to find branches and seemingly disconnected but actually connected vessel segments. Several new measures have been developed for branch detection, disconnection check and planar vesselness measure. Using public domain Rotterdam CT dataset, the accuracy of extracted centerline is demonstrated and automatic reconstruction of coronary artery mesh is shown.

Geodesic Distance Algorithm for Extracting the Ascending Aorta from 3D CT Images.

This paper presents a method for the automatic 3D segmentation of the ascending aorta from coronary computed tomography angiography (CCTA). The segmentation is performed in three steps. First, the initial seed points are selected by minimizing a newly proposed energy function across the Hough circles. Second, the ascending aorta is segmented by geodesic distance transformation. Third, the seed points are effectively transferred through the next axial slice by a novel transfer function. Experiments are performed using a database composed of 10 patients' CCTA images. For the experiment, the ground truths are annotated manually on the axial image slices by a medical expert. A comparative evaluation with state-of-the-art commercial aorta segmentation algorithms shows that our approach is computationally more efficient and accurate under the DSC (Dice Similarity Coefficient) measurements.

Intensity-vesselness Gaussian mixture model (IVGMM) for 2D + t segmentation of coronary arteries for X-ray angiography image sequences.

OBJECTIVE: This study aimed to propose an intensity-vesselness Gaussian mixture model (IVGMM) tracking for 2D + t segmentation of coronary arteries for X-ray angiography (XA) image sequences. METHODS: We compose a two dimensional (2D) feature vector of intensity and vesselness to characterize the Gaussian mixture models. In our IVGMM tracking, vessel segmentation is performed for each image frame based on these vessel and background IVGMMs and then the segmentation results of the current image frame is used to update these IVGMMs. The 2D + t segmentation of coronary arteries over the 2D XA image sequence is solved by means of iterating two processes, i.e., segmentation of coronary arteries and update of the IVGMMs. RESULTS: The performance of the proposed IVGMM tracking was evaluated using clinical 2D XA datasets. We evaluated the segmentation accuracy of the IVGMM tracking by comparing with two previous 2D vessel segmentation methods and seven background subtraction (BGS) methods. Of the ten segmentation methods, IVGMM tracking shows the highest similarity to the manual segmentation in terms of precision, recall, Jaccard index (JI), F1 score, and peak signal-to-noise ratio (PSNR). CONCLUSIONS: It is concluded that the IVGMM tracking could obtain reasonable segmentation accuracy outperforming conventional vessel enhancement methods and object tracking methods.

Viability assessment after conventional coronary angiography using a novel cardiovascular interventional therapeutic CT system: Comparison with gross morphology in a subacute infarct swine model.

BACKGROUND: Given the lack of promptness and inevitable use of additional contrast agents, the myocardial viability imaging procedures have not been used widely for determining the need to performing revascularization. OBJECTIVE: This study is aimed to evaluate the feasibility of myocardial viability assessment, consecutively with diagnostic invasive coronary angiography (ICA) without use of additional contrast agent, using a novel hybrid system comprising ICA and multislice CT (MSCT). METHODS: In all, 14 Yucatan miniature swine models (female; age, 3 months; weight, 28-30 kg) were subjected to ICA followed by balloon occlusion (90 minutes) and reperfusion of the left anterior descending coronary artery. Two weeks after induction of myocardial infarction, delayed hyperenhancement (DHE) images were obtained, using a novel combined machine comprising ICA and 320-channel MSCT scanner (Aquilion ONE, Toshiba), after 2, 5, 7, 10, 15, and 20 minutes after conventional ICA. The heart was sliced in 10-mm consecutive sections in the short-axis plane and was embedded in a solution of 1% triphenyltetrazolium chloride (TTC). Infarct size was determined as TTC-negative areas as a percentage of total left ventricular area. On MSCT images, infarct size per slice was calculated by dividing the DHE area by the total slice area (%) and compared with histochemical analyses. RESULTS: Serial MSCT scans revealed a peak CT attenuation of the infarct area (222.5 ± 36.5 Hounsfield units) with a maximum mean difference in CT attenuation between the infarct areas and normal myocardium of at 2 minutes after contrast injection (106.4; P for difference = 0.002). Furthermore, the percentage difference of infarct size by MSCT vs histopathologic specimen was significantly lower at 2 (8.5% ± 1.8%) and 5 minutes (9.5% ± 1.9%) than those after 7 minutes. Direct comparisons of slice-matched DHE area by MSCT demonstrated excellent correlation with TTC-derived infarct size (r = 0.952; P < .001). Bland-Altman plots of the differences between DHE by MSCT and TTC-derived infarct measurements plotted against their means showed good agreement between the 2 methods. CONCLUSION: The feasibility of myocardial viability assessment by DHE using MSCT after conventional ICA was proven in experimental models, and the optimal viability images were obtained after 2 to 5 minutes after the final intracoronary injection of contrast agent for conventional ICA.