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Motion estimation for cardiac functional analysis using two x-ray computed tomography scans.

ABSTRACT: This work concerns computed tomography (CT)-based cardiac functional analysis (CFA) with a reduced radiation dose. As CT-CFA requires images over the entire heartbeat, the scans are often performed at 10-20% of the tube current settings that are typically used for coronary CT angiography. A large image noise then degrades the accuracy of motion estimation. Moreover, even if the scan was performed during the sinus rhythm, the cardiac motion observed in CT images may not be cyclic with patients with atrial fibrillation. In this study, we propose to use two CT scan data, one for CT angiography at a quiescent phase at a standard dose and the other for CFA over the entire heart beat at a lower dose.We have made the following four modifications to an image-based cardiac motion estimation method we have previously developed for a full-dose retrospectively gated coronary CT angiography: (a) a full-dose prospectively gated coronary CT angiography image acquired at the least motion phase was used as the reference image; (b) a three-dimensional median filter was applied to lower-dose retrospectively gated cardiac images acquired at 20 phases over one heartbeat in order to reduce image noise; (c) the strength of the temporal regularization term was made adaptive; and (d) a one-dimensional temporal filter was applied to the estimated motion vector field in order to decrease jaggy motion patterns. We describe the conventional method iME1 and the proposed method iME2 in this article. Five observers assessed the accuracy of the estimated motion vector field of iME2 and iME1 using a 4-point scale. The observers repeated the assessment with data presented in a new random order 1 week after the first assessment session.The study confirmed that the proposed iME2 was robust against the mismatch of noise levels, contrast enhancement levels, and shapes of the chambers. There was a statistically significant difference between iME2 and iME1 (accuracy score, 2.08 ± 0.81 versus 2.77 ± 0.98, P < 0.01) and the improvement by the score of + 0.69 seemed clinically relevant. Inter-observer concordance was good: The inter-class correlation coefficient was 0.63 and Kendall's rank correlation coefficients were in the range of 0.41-0.67 (P < 0.01), respectively. Intra-observer reproducibility between sessions was good with the inter-class correlation coefficient of 0.76.We have proposed iME2 method for CT-CFA with two CT scans. The observer study verified the robustness and accuracy of iME2 method and its improved performance over iME1 method.

SUBMITTER: Fung GSK

PROVIDER: S-EPMC5603222 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Motion estimation for cardiac functional analysis using two x-ray computed tomography scans.

Fung George S K GSK Ciuffo Luisa L Ashikaga Hiroshi H Taguchi Katsuyuki K

Medical physics 20170801 9

<h4>Purpose</h4>This work concerns computed tomography (CT)-based cardiac functional analysis (CFA) with a reduced radiation dose. As CT-CFA requires images over the entire heartbeat, the scans are often performed at 10-20% of the tube current settings that are typically used for coronary CT angiography. A large image noise then degrades the accuracy of motion estimation. Moreover, even if the scan was performed during the sinus rhythm, the cardiac motion observed in CT images may not be cyclic ...[more]

PMID: 28639400

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Project description:A novel cardiac motion correction algorithm has been introduced recently. Unlike other segmentation-based approaches it is fully automatic and capable of correcting motion artifacts of myocardial wall and other moving structures as well as coronary arteries of the heart. In addition, it requires raw data of only less than a single rotation for motion estimation and correction, which is a significant advantage from the perspective of x-ray exposure and workflow. The aim of this study is to explore the capability of the proposed method through phantoms and in-vivo experiments. Motion correction of coronary arteries and other heart structures including myocardial wall is the main focus of the evaluation. First, we provide a brief introduction to the concept of the motion correction algorithm. Next we address the procedure of our studies using an XCAT phantom and commercially available physical phantoms. Results of XCAT phantom demonstrate that our solution significantly improves the structural similarity of coronary arteries compared to FBP (proposed: 0.94, FBP: 0.77, p<0.001). Besides, it provides significantly lower root mean square error (proposed: 20.27, FBP: 25.33, p = 0.01) of the whole heart image. Mocomo phantom study shows that the proposed method improves the visualization of coronary arteries estimated based on motion score (1: worst, 5: best) from two experienced radiologists (proposed: 3.5, FBP: 2.1, p<0.001). The results of these phantom studies reveal that the proposed has a great potential in handling motion artifacts of other heart structures as well as coronary arteries. Finally, we provide the results of in-vivo animal and human studies. The 3D and 4D heart images show a consistently superior performance in the visualization of coronary arteries along with myocardial wall and other cardiothoracic structures. Based on these findings of our studies, we are of the opinion that our solution has a considerable potential to improve temporal resolution of cardiac CT imaging. This would open the door to innovations in structural or functional diagnosis of the heart.

Project description:BackgroundPancreatic perfusion computed tomography (CT) imaging is increasingly used for neoplastic grading, predicting prognosis, and evaluating the response to therapy. To optimize the clinical pancreatic CT perfusion imaging methods, we evaluated 2 different CT scanning protocols concerning pancreas perfusion parameters.MethodsA retrospective study was conducted on 40 patients who underwent whole pancreas CT perfusion scanning in The First Affiliated Hospital of Zhengzhou University. Of these 40 patients, 20 patients in group A underwent continuous perfusion scanning, while 20 patients in group B underwent intermittent perfusion scanning. For group A, continuous axial scanning was performed 25 times, and the total scan time was 50 s. For group B, arterial phase helical perfusion scanning was performed 8 times, and then venous phase helical perfusion scanning was performed 15 times, with a total scan time of 64.6 to 70.0 s. A comprehensive list of perfusion parameters between different parts of the pancreas and the 2 groups were compared. The effective radiation dose for the 2 scanning methods was analyzed.ResultsThe parameter of the mean slope of increase (MSI) at different pancreatic parts in group A differed (P=0.028). The pancreas head had the lowest value, and the tail had the highest (about a 20% difference). In group A compared to group B, the blood volume of the pancreatic head was smaller (15.256±2.925 vs. 16.953±3.602), the positive enhanced integral was smaller (0.307±0.050 vs. 0.344±0.060) and the permeability surface was larger (34.205±9. 612 vs. 24.377±8.413); the blood volume of the pancreatic neck was smaller (13.940±2.691 vs. 17.173±3.918), the positive enhanced integral was smaller (0.304±0.088 vs. 0.361±0.051) and the permeability surface was larger (34.898±11.592 vs. 25. 794±8.149); the blood volume of the pancreatic body was smaller (16.142±4.006 vs. 18.401±2.513), the positive enhanced integral was smaller (0.305±0.093 vs. 0.342±0.048) and the permeability surface was larger (28.861±10.448 vs. 22.158±6. 017); the blood volume of the pancreatic tail was smaller (16.446±3.709 vs. 17.374±3.781), the positive enhanced integral was smaller (0.304±0.057 vs. 0.350±0.073) and the permeability surface was larger (27.823±8.228 vs. 21.509±7.768) (P<0.05). The effective radiation dose in the intermittent scan mode was slightly lower at 16.657±2.259 mSv than in the continuous scan mode (17.973±3.698 mSv).ConclusionsDifferent CT scanning intervals had a significant influence on whole pancreas blood volume, permeability surface, and positive enhanced integral. These demonstrate the high sensitivity of intermittent perfusion scanning for identifying perfusion abnormalities. Therefore, for the diagnosis of pancreatic diseases, intermittent pancreatic CT perfusion may be more advantageous.

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Project description:BackgroundLesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe a method for fully automated segmentation and quantification of pathological COVID-19 lung tissue on chest Computed Tomography (CT) scans without the need for manually segmented training data.MethodsWe trained a cycle-consistent generative adversarial network (CycleGAN) to convert images of COVID-19 scans into their generated healthy equivalents. Subtraction of the generated healthy images from their corresponding original CT scans yielded maps of pathological tissue, without background lung parenchyma, fissures, airways, or vessels. We then used these maps to construct three-dimensional lesion segmentations. Using a validation dataset, Dice scores were computed for our lesion segmentations and other published segmentation networks using ground truth segmentations reviewed by radiologists.ResultsThe COVID-to-Healthy generator eliminated high Hounsfield unit (HU) voxels within pulmonary lesions and replaced them with lower HU voxels. The generator did not distort normal anatomy such as vessels, airways, or fissures. The generated healthy images had higher gas content (2.45 ± 0.93 vs 3.01 ± 0.84 L, P < 0.001) and lower tissue density (1.27 ± 0.40 vs 0.73 ± 0.29 Kg, P < 0.001) than their corresponding original COVID-19 images, and they were not significantly different from those of the healthy images (P < 0.001). Using the validation dataset, lesion segmentations scored an average Dice score of 55.9, comparable to other weakly supervised networks that do require manual segmentations.ConclusionOur CycleGAN model successfully segmented pulmonary lesions in mild and severe COVID-19 cases. Our model's performance was comparable to other published models; however, our model is unique in its ability to segment lesions without the need for manual segmentations.

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Motion estimation for cardiac functional analysis using two x-ray computed tomography scans.

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Motion estimation for cardiac functional analysis using two x-ray computed tomography scans.

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