Study of Non-Newtonian blood flow - heat transfer characteristics in the human coronary system with an external magnetic field.

This paper proposes a novel mathematical model of non-Newtonian blood flow and heat transfer in the human coronary system with an external magnetic field. As the blood viscosity is assumed to depend not only on shear rate but also on temperature and magnet strength, the modified Carreau-Yasuda viscosity model is formulated. The computational domain includes the base of the aorta, the right coronary artery, and the left coronary artery, with the left circumflex and left anterior descending arteries. The element-based finite volume method is derived for the solution of the proposed model. Numerical simulations are carried out to investigate the magnetic field effect on the blood flow-heat transfer characteristic in the human coronary system. It is found that the magnetic field has a significant impact on fluid viscosity, leading to enhanced fluid velocity.

A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre- and post-operative abdominal aortic aneurysm.

Abdominal aortic aneurysm (AAA) is one of the most common diseases worldwide. 3D segmentation of AAA provides useful information for surgical decisions and follow-up treatment. However, existing segmentation methods are time consuming and not practical in routine use. In this article, the segmentation task will be addressed automatically using a deep learning based approach which has been proved to successfully solve several medical imaging problems with excellent performances. This article therefore proposes a new solution of AAA segmentation using deep learning in a type of 3D convolutional neural network (CNN) architecture that also incorporates coordinate information. The tested CNNs are UNet, AG-DSV-UNet, VNet, ResNetMed and DenseVoxNet. The 3D-CNNs are trained with a dataset of high resolution (256 × 256) non-contrast and post-contrast CT images containing 64 slices from each of 200 patients. The dataset consists of contiguous CT slices without augmentation and no post-processing step. The experiments show that incorporation of coordinate information improves the segmentation results. The best accuracies on non-contrast and contrast-enhanced images have average dice scores of 97.13% and 96.74%, respectively. Transfer learning from a pre-trained network of a pre-operative dataset to post-operative endovascular aneurysm repair (EVAR) was also performed. The segmentation accuracy of post-operative EVAR using transfer learning on non-contrast and contrast-enhanced CT datasets achieved the best dice scores of 94.90% and 95.66%, respectively.

Multi-level pooling encoder-decoder convolution neural network for MRI reconstruction.

MRI reconstruction is one of the critical processes of MRI machines, along with the acquisition. Due to a slow processing time of signal acquiring, parallel imaging and reconstruction techniques are applied for acceleration. To accelerate the acquisition process, fewer raw data are sampled simultaneously with all RF coils acquisition. Then, the reconstruction uses under-sampled data from all RF coils to restore the final MR image that resembles the fully sampled MR image. These processes have been a traditional procedure inside the MRI system since the invention of the multi-coils MRI machine. This paper proposes the deep learning technique with a lightweight network. The deep neural network is capable of generating the high-quality reconstructed MR image with a high peak signal-to-noise ratio (PSNR). This also opens a high acceleration factor for MR data acquisition. The lightweight network is called Multi-Level Pooling Encoder-Decoder Net (MLPED Net). The proposed network outperforms the traditional encoder-decoder networks on 4-fold acceleration with a significant margin on every evaluation metric. The network can be trained end-to-end, and it is a lightweight structure that can reduce training time significantly. Experimental results are based on a publicly available MRI Knee dataset from the fastMRI competition.

COVID-19 detection and heatmap generation in chest x-ray images.

Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images. Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of 1500 × 1500 x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result. Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively. Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages.

A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images.

Epicardial fat (ECF) is localized fat surrounding the heart muscle or myocardium and enclosed by the thin-layer pericardium membrane. Segmenting the ECF is one of the most difficult medical image segmentation tasks. Since the epicardial fat is infiltrated into the groove between cardiac chambers and is contiguous with cardiac muscle, segmentation requires location and voxel intensity. Recently, deep learning methods have been effectively used to solve medical image segmentation problems in several domains with state-of-the-art performance. This paper presents a novel approach to 3D segmentation of ECF by integrating attention gates and deep supervision into the 3D U-Net deep learning architecture. The proposed method shows significant improvement of the segmentation performance, when compared with standard 3D U-Net. The experiments show excellent performance on non-contrast CT datasets with average Dice scores of 90.06%. Transfer learning from a pre-trained model of a non-contrast CT to contrast-enhanced CT dataset was also performed. The segmentation accuracy on the contrast-enhanced CT dataset achieved a Dice score of 88.16%.

Pyramid graph cut: Integrating intensity and gradient information for grayscale medical image segmentation.

Segmentation of grayscale medical images is challenging because of the similarity of pixel intensities and poor gradient strength between adjacent regions. The existing image segmentation approaches based on either intensity or gradient information alone often fail to produce accurate segmentation results. Previous approaches in the literature have approached the problem by embedded or sequential integration of different information types to improve the performance of the image segmentation on specific tasks. However, an effective combination or integration of such information is difficult to implement and not sufficiently generic for closely related tasks. Integration of the two information sources in a single graph structure is a potentially more effective way to solve the problem. In this paper we introduce a novel technique for grayscale medical image segmentation called pyramid graph cut, which combines intensity and gradient sources of information in a pyramid-shaped graph structure using a single source node and multiple sink nodes. The source node, which is the top of the pyramid graph, embeds intensity information into its linked edges. The sink nodes, which are the base of the pyramid graph, embed gradient information into their linked edges. The min-cut uses intensity information and gradient information, depending on which one is more useful or has a higher influence in each cutting location of each iteration. The experimental results demonstrate the effectiveness of the proposed method over intensity-based segmentation alone (i.e. Gaussian mixture model) and gradient-based segmentation alone (i.e. distance regularized level set evolution) on grayscale medical image datasets, including the public 3DIRCADb-01 dataset. The proposed method archives excellent segmentation results on the sample CT of abdominal aortic aneurysm, MRI of liver tumor and US of liver tumor, with dice scores of 90.49±5.23%, 88.86±11.77%, 90.68±2.45%, respectively.

3D segmentation of exterior wall surface of abdominal aortic aneurysm from CT images using variable neighborhood search.

A 3D model of abdominal aortic aneurysm (AAA) can provide useful anatomical information for clinical management and simulation. Thin-slice contiguous computed tomographic (CT) angiography is the best source of medical images for construction of 3D models, which requires segmentation of AAA in the images. Existing methods for segmentation of AAA rely on either manual process or 2D segmentation in each 2D CT slide. However, a traditional manual segmentation is a time consuming process which is not practical for routine use. The construction of a 3D model from 2D segmentation of each CT slice is not a fully satisfactory solution due to rough contours that can occur because of lack of constraints among segmented slices, as well as missed segmentation slices. To overcome such challenges, this paper proposes the 3D segmentation of AAA using the concept of variable neighborhood search by iteratively alternating between two different segmentation techniques in the two different 3D search spaces of voxel intensity and voxel gradient. The segmentation output of each method is used as the initial contour to the other method in each iteration. By alternating between search spaces, the technique can escape local minima that naturally occur in each search space. Also, the 3D search spaces provide more constraints across CT slices, when compared with the 2D search spaces in individual CT slices. The proposed method is evaluated with 10 easy and 10 difficult cases of AAA. The results show that the proposed 3D segmentation technique achieves the outstanding segmentation accuracy with an average dice similarity value (DSC) of 91.88%, when compared to the other methods using the same dataset, which are the 2D proposed method, classical graph cut, distance regularized level set evolution, and registration based geometric active contour with the DSCs of 87.57 ± 4.52%, 72.47 ± 8.11%, 58.50 ± 8.86% and 76.21 ± 10.49%, respectively.

Outer Wall Segmentation of Abdominal Aortic Aneurysm by Variable Neighborhood Search Through Intensity and Gradient Spaces.

Aortic aneurysm segmentation remains a challenge. Manual segmentation is a time-consuming process which is not practical for routine use. To address this limitation, several automated segmentation techniques for aortic aneurysm have been developed, such as edge detection-based methods, partial differential equation methods, and graph partitioning methods. However, automatic segmentation of aortic aneurysm is difficult due to high pixel similarity to adjacent tissue and a lack of color information in the medical image, preventing previous work from being applicable to difficult cases. This paper uses uses a variable neighborhood search that alternates between intensity-based and gradient-based segmentation techniques. By alternating between intensity and gradient spaces, the search can escape from local optima of each space. The experimental results demonstrate that the proposed method outperforms the other existing segmentation methods in the literature, based on measurements of dice similarity coefficient and jaccard similarity coefficient at the pixel level. In addition, it is shown to perform well for cases that are difficult to segment.

The Outcome of Radiofrequency Ablation of Metastatic Liver Tumors.

OBJECTIVE: To determine outcome of radiofrequency ablation (RFA) in metastatic liver tumor and to evaluate related factors of residual or local tumor recurrences. MATERIAL AND METHOD: With Institutional Review Board approval, we retrospectively reviewed RFA procedure between June 2006 and September 2013. Fifty-seven metastatic nodules in 36 patients were treated. The primary tumors were colorectal carcinoma (n = 30), neuroendocrine tumor (n = 2), gallbladder carcinoma (n = 1), adenocarcinoma of head of pancreas (n = 1), and gastrointestinal stromal tumor (n = 2). Tumor characteristics, RFA techniques, success rate, complication, and follow-up imaging were reviewed and recorded Clinical outcome and overall survival were analyzed. RESULTS: Complete ablation were found in 48/57 nodules (84.2%). The mean follow-up time was 17.9 ± 13.1 months (range, 1 to 47 months). Local tumor recurrence were noted in 12/57 nodules (21.1%), which mean time to recurrence was 8.3 ± 3.8 months (2 to 15 months). Residual tumor was associate with tumor larger than 3 cm (p = 0.009). The 1-, 3-, and 5-year overall survival rates were 93.6%, 56.2%, and 20%, respectively. Median overall survival was 37.8 ± 10.9 months. Major complication rate occurred about 5.3%. CONCLUSION: Radiofrequency ablation is effective and feasible method to treat small metastatic liver tumor Tumor size larger than 3 cm is significant risk factor of residual tumor. Tumor in high-risk location is not associated either incomplete ablation or local tumor recurrence.

Growth Rate of Aortic Diameter in Post Treatment of Aortic Dissection.

OBJECTIVE: To evaluate growth rate of aortic diameter true lumen, and false lumen in post endovascular aortic repair of aortic dissection. MATERIAL AND METHOD: Between January 2005 and September 2010, 48 consecutive patients with aortic dissection were followed with computed tomographic angiography (CTA), including 18 post-surgical repaired type A patients and 30 type B (14 conservative treatment and 16 post stent-grafting therapy). CTA were performed with a mean of 5.2 scans per patient, and a meanfollow-up of 37.08 months. The diameter ofthe aorta, true lumen, andfalse lumen were measured by axial scan images and multi-planar reformatting (MPR). RESULTS: In medically managed patients with type B aortic dissection, the aortic diameter increased over time at mean rate 1.49 mm/year at proximal descending aorta with greater increased in size ofthe false lumen diameters than the true lumen diameter. In post-surgical repaired type A and post stent-grafting therapy type B aortic dissection, aortic diameter did not change over time. In addition, 12 of 16 patients (75%)patients with post stent-grafting therapy type B dissection hadpartial thrombosed in false lumen and 4 of 16 patients (25%) had complete resolution offalse lumen. CONCLUSION: In post-surgical repaired type A and post stent-grafting therapy type B aortic dissection, aortic diameter did not change over time. Type B aortic dissection with medical treatment had minimal increased in aortic diameter over time. The results suggested that uncomplicated type B aortic dissection requires on-going medical treatment.

Incidence of complication and tumor recurrence after radiofrequency ablation in high-risk location of hepatocellular carcinoma patients.

OBJECTIVE: To evaluate complication, rate of residual, and tumor recurrence in high-risk location compared to non-high-risk location in hepatocellular carcinoma patients. MATERIAL AND METHOD: Radiofrequency ablation was performed on 409 tumors in Siriraj Hospital between October 2009 and May 2012. Eighty-eight nodules in 78 atients were treated by RF ablation, which divided into high risk and non-high-risk location. Complete ablation rate, residual tumor recurrent tumor, and complication were retrospectively reviewed. RESULTS: HCC nodules were in non-high-risk location 34 nodules (38.6%) and in high-risk location 54 nodules (61.4%). Complete tumor ablations were done in 34 nodules (100%) of non-high-risk location group and 50 nodules (92.6%) of high-risk location group. All residual tumors were four nodules (7.2%), which located in subcapsular location. Recurrent tumors were found in six nodules (6.8%), and mean time to recurrence were 210.2 days. Early complication was 10.2% and late complication was 4.5%. The recurrent tumor and complication were not significantly different between two groups. CONCLUSION: Radiofrequency ablation is effective treatment of hepatocellular carcinoma in high-risk location tumor There is no significant difference in complication and tumor recurrent rate between high-risk and non-high-risk group. However incidence of residual tumor is significantly increased in subcapsular location tumor

Radiofrequency ablation alone versus radiofrequency ablation combined with chemoembolization in unresectable hepatocellular carcinoma.

OBJECTIVE: To determine the effectiveness of the treatment in unresectable hepatocellular carcinoma between radiofrequency ablation (RFA) alone and combination of RFA and transcatheter arterial chemoembolization (TACE). MATERIAL AND METHOD: Forty-six patients with 57 hepatic nodules smaller than 5 cm in maximum diameter were treated with RFA alone in 37 nodules and combined RFA with TACE in 20 nodules. RFA electrode size was varying from 2 to 5 cm diameter. The chemotherapeutic drugs in TACE were mixture of flurouracil with lipiodol and mitomycin-C with lipiodol. The residual tumor local recurrent, and tumor progression was evaluated by dynamic enhanced CT or MRI study of the liver after treatment. RESULTS: Local response in RFA alone and combined treatment were 97.3% and 70%, respectively. Recurrence rate in RFA alone and combined treatment were 6.9% and 20%, respectively. The average ablative margin visualized on post RFA images were 0.7 cm and 0.4 cm in RFA alone and combined treatment, respectively. Complication rate was 0.07% and all of complications were minor complication. CONCLUSION: For small unrespectable HCC nodules, RFA ablation alone is the effective treatment. Additional TACE may not be necessary if RFA is performed completely under controlling the important factors, especially ablated margin.

Effect of branchings on blood flow in the system of human coronary arteries.

In this work, we investigate the behavior of the pulsatile blood flow in the system of human coronary arteries. Blood is modeled as an incompressible non-Newtonian fluid. The transient phenomena of blood flow through the coronary system are simulated by solving the three dimensional unsteady state Navier-Stokes equations and continuity equation. Distributions of velocity, pressure and wall shear stresses are determined in the system under pulsatile conditions on the boundaries. Effect of branching vessel on the flow problem is investigated. The numerical results show that blood pressure in the system with branching vessels of coronary arteries is lower than the one in the system with no branch. The magnitude of wall shear stresses rises at the bifurcation.

64-slice MDCT angiography of upper extremity in assessment of native hemodialysis access.

OBJECTIVE: To compare multidetector row computed tomographic (MDCT) angiography with conventional digital subtraction angiography (DSA) in the evaluation of vascular access stenoses in hemodialysis patients. MATERIALS AND METHODS: Twenty-one consecutive patients were imaged with MDCT angiography and subsequent DSA. The superficial vein of leg was used as the route for intravenous administration. The vascular stenosis was assessed in not significant (<50% stenosis), moderate stenosis (50%-74% stenosis), severe stenosis (75%-99%), and total occlusion (100%). The accuracy, sensitivity, specificity, positive, and negative predictive values were calculated for significant vascular stenosis using DSA as the standard reference. RESULTS: The sensitivity and specificity of MDCT angiography for the detection of significant hemodialysis vascular access were 100% (95% CI, 89.3%-100%) and 94.8% (95% CI, 89.1%-97.6%), respectively. The positive and negative predictive values were 84.2% (95% CI, 68.1%-93.4%) and 100% (95% CI, 95.8%-100%), respectively. The accuracy of MDCT angiography for detection of significant stenoses was 95.9% (95% CI, 91.4%-97.0%). CONCLUSIONS: MDCT angiography provides excellent correlation in vascular stenosis as compared with DSA in hemodialysis access. Complete assessment of entire vascular segments could be performing with MDCT angiography in planning before endovascular intervention or surgical correction.

Computed tomography of aortic intramural hematoma and thrombosed dissection.

A retrospective study was undertaken to evaluate the appearance of the aortic wall on computed tomography for the purpose of developing criteria for differentiating acute aortic intramural hematoma from thrombosed false lumen seen in aortic dissection. Computed tomography angiography findings of the thoracoabdominal aorta in 23 patients with suspected intramural hematoma and 25 with thrombosed false lumen were reviewed. The more common features of an intramural hematoma were hyperattenuation of the aortic wall, wall thickness less than a quarter of the aortic diameter, intrinsic wall calcification, a lesion extending around the entire aortic circumference, and ulcer-like projections that may be precursors of intramural hematoma. Wall thickness less than a quarter of the aortic diameter, lesion extending around the entire aortic circumference, and ulcer-like projections were the most useful indicators for distinguishing intramural hematoma from the thrombosed false lumen in aortic dissection.

CT angiography evaluation of endoleak after thoracic endovascularaortic repair in thoracic aortic aneurysm.

OBJECTIVE: Analyze the incidence and findings of endoleak after thoracic endovascular aortic repair by using CT angiography MATERIAL AND METHOD: Between August 2006 and December 2008, 68 patients diagnosed with thoracic aortic aneurysm underwent thoracic endovascular aortic repair and were included in the present study. The patients were 47 men and 21 women, with a mean age of 69 +/- 9.4 years old. Thoraco-abdominal CT angiographic images (64-slice MDCT) after operation of 68 patients were retrospectively reviewed to evaluate incidence of endoleak and classify findings of endoleak. RESULTS: Endoleaks were detected in 26 patients (38.2%). There were type I endoleaks in three cases (11.5%), type II endoleaks in 22 cases (84.6%), and type III endoleaks in one case (3.9%). Type II endoleaks were detected as peritubular collection, mostly located at periphery of the aneurysm. Eleven cases (50%) of type II endoleaks were supplied by left subclavian artery. Twenty patients who had completed 1, 3, and 6 months follow-up CT angiography were selected for further evaluation of changing in size of aneurysm. The measurement of the thoracic aneurysm showed no decreasing of the maximum length of diameter and volume of the aneurysmal sac in endoleak group. CONCLUSION: Follow-up CT angiography is useful for detection and characterization of endoleak after endovascular aortic repair of thoracic aneurysm. Most of type II endoleaks show peritubular (collection) shape and locate at the periphery. Patients with endoleak after thoracic endovascular aortic repair tend to continue to have sac expansion.

Optimization of the table speed of lower extremity CT angiography protocols in different patient age groups.

BACKGROUND: Scanning with 64-slice multidetector row CT (MDCT) is usually faster than blood flow in peripheral arteries of the lower extremities, and the distal arteries of lower extremities are difficult to visualize, particularly in elderly patients. Thus, the optimal table speed for CT angiography (CTA) studies should be adjusted for appropriate patient age groups. OBJECTIVE: We evaluated the relative efficacy of different table speeds in several age groups of patients with suspected peripheral arterial occlusive disease (PAOD) undergoing CTA of lower extremity arteries, as a guideline for routine use. METHODS: This retrospective study reviewed routine CTA of the lower extremity arteries of 107 patients with suspected PAOD to evaluate vascular opacification in each vascular segment of 5 age groups: < or =40 years (group 1), 41-60 years (group 2), 61-70 years (group 3), 71-75 years (group 4), and > or =76 years (group 5). Adequate vascular opacification was measured for attenuation in the suprarenal and infrarenal abdominal aorta and in the arteries of the lower extremity. Venous contamination was also measured. RESULTS: Adequate vascular opacification from the suprarenal aorta to the level of the mid-popliteal artery was shown in all patients. Arterial opacification at the dorsalis pedis or plantar arteries was visualized in 85.7%-91.7% of patients and at the plantar arch arteries in 84.1%-91.7%. Minimal venous contamination was also shown adjacent to arterial enhancement, ranging from 0% to 28.6%. CONCLUSION: Performing CTA of the lower extremities with 64-slice MDCT could reduce the table speed to allow adequate arterial opacification and minimal venous contamination.

Uncorrected pink tetralogy of Fallot in an adult patient: incidental CT findings.

Tetralogy of Fallot (TOF), one of the most common congenital heart diseases, has four major components: right ventricular hypertrophy, overriding aorta, membranous ventricular septal defect, and right ventricular outflow tract obstruction. If not already present at birth, cyanosis develops in the first year of life. Survival of the patient depends on the degree of pulmonary obstruction and the pulmonary blood supply. Patients rarely survive after the fourth decade of life. Limitation of blood to the lungs combined with ventricular septal defect results in supply of oxygen-poor blood to the body, causing cyanosis (blue coloration) in the patient. If the pulmonary stenosis is mild and ventricular septal defect is in balance, however, the noncyanotic patient is referred as having "pink tetralogy of Fallot."

Preoperative localization of Adamkiewicz arteries and their origins by using MDCT angiography.

OBJECTIVE: To evaluate the ability of thoraco-abdominal MDCT angiography to visualize Adamkiewicz arteries for preoperative planning in patients diagnosed with aortic disease. MATERIAL AND METHOD: The present study retrospectively reviewed clinical data from 73 patients who underwent a thoraco-abdominal 64-slice MDCT angiography. The Adamkiewicz artery was evaluated on multiplanar reformation images in each case. The visualization of the Adamkiewicz artery, level of origin, side of origin and continuation from an intercostal artery was investigated. RESULTS: The Adamkiewicz arteries were visualized in 52 of the 73 patients (71.2%), and the total number of the delineated Adamkiewicz arteries was 64. Two Adamkiewicz arteries were found in nine patients (17.3%). Four Adamkiewicz arteries were found in one patient (1.9%). Most of the delineated arteries arose from the T9-L2 levels (89.1%). A left side of origin was found in 41 of 64 arteries (64.1%), and a right side of origin was found in 23 of 64 arteries (35.9%). Only 12 of 64 delineated arteries (18.8%) showed continuity from their origins to the anterior radiculomedullary artery. CONCLUSION: The preoperative detection rate of the Adamkiewicz artery with the routine technique of 64-slice MDCT angiography was 71.2%. The preoperative location of the Adamkiewicz artery may help to reduce the risk of perioperative ischemic changes in the spinal cord.

Influence of heart rate on image quality to identify the best cardiac phase in 16-slice coronary CT angiography.

OBJECTIVE: To identify the best cardiac phase in different patient's heart rate on 16-slice coronary CT angiography. MATERIAL AND METHOD: The patients who had undergone coronary CT angiography with 16 multi-detector rows CT at Siriraj Hospital between September 2003 and August 2004. For each patient, the image reconstruction based on relative timing was performed placed at center of 35% to 85% of the R-R interval with step increments 10%. The authors created six data sets (35%, 45%, 55%, 65%, 75%, and 85% of R-R interval) throughout the cardiac cycle. The coronary arteries were reviewed based on cross-sectional images and reformat images. The authors inspected all data sets and selected the cardiac phase that contained the best image quality for each coronary artery. RESULTS: Five hundred sixty four vessels were evaluated in the 141 patients (83 men, 58 women). The mean patient age was 63.3 +/- 16.7 years (range 4-89 years). Mean patient heart rate was 65.7 +/- 16.5 beats per minute (bpm), range 46-104 bpm. The most coronary arteries were well demonstrated at center of 75% of R-R interval (66.8%). Ninety-eight patients (69.5%) required one phase that provided best overall image quality and motion free delineation for four coronary arteries. Forty-three patients (30.5%) required combination of data from each phase to achieve motion free images. CONCLUSION: The best cardiac phase of evaluate coronary artery in 16 slices coronary CT angiography in the patient's heart rate below 70 bpm is 75% of cardiac cycle (mid to late diastole). In patients with a heart rate 71-80 bpm, the authors required a combination of images from 45% and 75% of cardiac cycle to completely evaluate all coronary arteries. In patients with a heart rate above 80 bpm, 45% of cardiac cycle (end-systole) is the best phase.