Feature-based Non-rigid Registration between Pre- and Post-Contrast Lung CT Images

In this paper, a feature-based registration technique is proposed for pre-contrast and post-contrast lung CT images. It utilizes three dimensional(3-D) features with their descriptors and estimates feature correspondences by nearest neighborhood matching in the feature space. We design a transformation model between the input image pairs using a free form deformation(FFD) which is based on B-splines. Registration is achieved by minimizing an energy function incorporating the smoothness of FFD and the correspondence information through a non-linear gradient conjugate method. To deal with outliers in feature matching, our energy model integrates a robust estimator which discards outliers effectively by iteratively reducing a radius of confidence in the minimization process. Performance evaluation was carried out in terms of accuracy and efficiency using seven pairs of lung CT images of clinical practice. For a quantitative assessment, a radiologist specialized in thorax manually placed landmarks on each CT image pair. In comparative evaluation to a conventional feature-based registration method, our algorithm showed improved performances in both accuracy and efficiencyope

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.ope

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. © 2023 The Author(s)ope

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.ope

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.restrictio

Tracking of mitochondrial transports using a particle filtering method with a spatial constraint

This paper describes a tracking method to trace the movements of fluorescently labeled mitochondria in time-lapse image sequences. It is based on particle filtering, which is a state-of-the-art tracking method, and is enhanced with a spatial constraint to improve robustness. Since mitochondria move only through axons, the spatial constraint is generated by axon segmentation on a single frame, which is the average of all the frames. The spatial constraint limits the search space of the state vector and, consequently, lowers the chance for the tracking to get lost. Using a background subtraction algorithm, the proposed method is also equipped with automatic detection of starting points, thus minimizing the requirement for user input. Implementation of the proposed method for tracking of fluorescently labeled mitochondria in time-lapse images showed substantially improved robustness and speed compared to a conventional method. With these improvements, this new particle tracking method is expected to increase the throughput of fluorescently labeled mitochondrial transport experiments, which are required for neuroscience research.ope

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.restrictio

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.ope

適應的 追跡과 始作点 檢出에 基盤한 管形體의 領域化에 대한 硏究

학위논문(박사)--서울대학교 대학원 :전기공학부,2007.In this dissertation, methods to construct vascular structures in computed tomography angiography (CTA) are proposed. The construction is useful in numerous applications, including vascular surgery, interventional radiology (IR), and medical education. The construction commences with tracking of the central axis of a vessel segment. In this dissertation two adaptive tracking methods are proposed to extract a cerebral arterial segment in CTA. Segmentation of cerebral arteries in CTA is very challenging mainly due to bone contact and vein contamination. Both the two proposed tracking methods iteratively accumulate elliptical cross-sections from the given initial seed. The first method is very intuitive. The boundary of each cross-section is modelled as an ellipse with border points detected by gradient thresholds. The thresholds are established adaptively to bone contact and vein contamination. This adaptiveness improves robustness of the tracking compared to a conventional method. The other tracking method is augmented by rigorous mathematical framework of a particle filter (PF). Its measurement model is defined as the border points on the plane normal to the axis as the observation. The border points are detected using the radial component of the gradient and the adaptive thresholds in the same way as the intuitive tracking. The outliers in the measurement vector are discarded in the computation of the distance between each particle and the actual observation. The robustness of the PF tracking method is confirmed by intensive experiments on both clinical and synthesized vessel data. Since vascular structures are composed a number of vessel segments, tracking of a vessel segment need to be supplemented by any methods to extract many vessel segments. Specifically to cerebral arteries in CTA, the head CT volume is partitioned into two sub-volumes according to the amount of contact between bone and arteries. In the lower sub-volume, as the arteries are often adjacent to bone or veins, they are extracted by the above-mentioned tracking methods. In the upper sub-volume where bone and vessels are not adjacent to each other, vessels can be segmented well by intensity thresholds and connected component analysis (CCA). Separate application of different algorithms to lower and upper sub-volumes leads to discontinuity in the partitioning slice and inter-partition tracking algorithm is also proposed to resolve this discontinuity problem. The other method to construct vascular structure is based on detection of new seeds and can be generalized to other vascular structures, i.e. pulmonary vasculature, cardio-vasculature or hepatic vasculature. New seed is detected by searching for tubular object near the vessel segment already extracted. Objects are classified into tubular objects or non-tubular objects according to the eigenvalues of the covariance matrix. Construction results of the two methods are compared to Registration-Subtraction results of pre-contrast and post-contrast scanned volumes and are evaluated to show comparable performance by a clinical expert. Finally, as an application of the proposed methods, coronary arteries are extracted, which is not much different from extraction of cerebral arteries. It can be estimated as easier, since the main two problems of bone contact and vein contamination need not be considered. However, limited contrast resolution may cause leakage of the tracking. As multi-detector row CT (MDCT) technology develops, non-invasive image of coronary arteries becomes realizable and their extraction becomes more important.학위논문(박사)-

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.restrictio