Purpose: To unwrap multi-echo phase images for quantitative susceptibility mapping (QSM) in a rapid and robust manner without using complicated search algorithms. Background: Since QSM requires unaliased phase images as input, a reliable 3D phase unwrapping step is essential to reconstruct susceptibility maps. However, this is usually one of the most time-consuming steps in QSM, especially for multi-echo data acquisition. Methods: Strategically acquired gradient echo (STAGE) data are used to provide six flow compensated images with echo times of 2.5 ms, 7.5 ms, 8.75 ms, 12.5 ms, 17.5 ms and 18.75 ms. An unaliased phase image with an effective echo time of 1.25 ms can be created by a complex division between 7.5 ms and 8.75 ms. Using this short pseudo-echo data along with the acquired 2.5 ms data, all other echoes can be unwrapped using a bootstrapping approach. Results: The six echoes (384 × 288 × 64 × 6 voxels) acquired using STAGE data acquisition were unwrapped successfully. This resulted in reliable self-consistent QSM images in only 1 second compared to the quality guided 3DSRNCP algorithm, which took 137 seconds, and the Laplacian based algorithm, which took 23 seconds on the same computer. Conclusions: The proposed bootstrapping multi-echo unwrapping method provides a rapid, robust phase unwrapping method on a voxel-by-voxel basis for online QSM reconstruction.
Since performance and clinical utility of current computer-aided detection (CAD) schemes of detecting and classifying soft tissue lesions (e.g., breast masses and lung nodules) is not satisfactory, many researchers in CAD field call for new CAD research ideas and approaches. The purpose of presenting this opinion paper is to share our vision and stimulate more discussions of how to overcome or compensate the limitation of current lesion-detection based CAD schemes in the CAD research community. Since based on our observation that analyzing global image information plays an important role in radiologists’ decision making, we hypothesized that using the targeted quantitative image features computed from global images could also provide highly discriminatory power, which are supplementary to the lesion-based information. To test our hypothesis, we recently performed a number of independent studies. Based on our published preliminary study results, we demonstrated that global mammographic image features and background parenchymal enhancement of breast MR images carried useful information to (1) predict near-term breast cancer risk based on negative screening mammograms, (2) distinguish between true- and false-positive recalls in mammography screening examinations, and (3) classify between malignant and benign breast MR examinations. The global case-based CAD scheme only warns a risk level of the cases without cueing a large number of false-positive lesions. It can also be applied to guide lesion-based CAD cueing to reduce false-positives but enhance clinically relevant true-positive cueing. However, before such a new CAD approach is clinically acceptable, more work is needed to optimize not only the scheme performance but also how to integrate with lesion-based CAD schemes in the clinical practice.
Due to the promotion of lung cancer screening, more Stage I non-small-cell lung cancers (NSCLC) are currently detected, which usually have favorable prognosis. However, a high percentage of the patients have cancer recurrence after surgery, which reduces overall survival rate. To achieve optimal efficacy of treating and managing Stage I NSCLC patients, it is important to develop more accurate and reliable biomarkers or tools to predict cancer prognosis. The purpose of this study is to investigate a new quantitative image analysis method to predict the risk of lung cancer recurrence of Stage I NSCLC patients after the lung cancer surgery using the conventional chest computed tomography (CT) images and compare the prediction result with a popular genetic biomarker namely, protein expression of the excision repair cross-complementing 1 (ERCC1) genes. In this study, we developed and tested a new computer-aided detection (CAD) scheme to segment lung tumors and initially compute 35 tumor-related morphologic and texture features from CT images. By applying a machine learning based feature selection method, we identified a set of 8 effective and non-redundant image features. Using these features we trained a naïve Bayesian network based classifier to predict the risk of cancer recurrence. When applying to a test dataset with 79 Stage I NSCLC cases, the computed areas under ROC curves were 0.77±0.06 and 0.63±0.07 when using the quantitative image based classifier and ERCC1, respectively. The study results demonstrated the feasibility of improving accuracy of predicting cancer prognosis or recurrence risk using a CAD-based quantitative image analysis method.
Stage I non-small-cell lung cancers (NSCLC) usually have favorable prognosis. However, high percentage of NSCLC patients have cancer relapse after surgery. Accurately predicting cancer prognosis is important to optimally treat and manage the patients to minimize the risk of cancer relapse. Studies have shown that an excision repair crosscomplementing 1 (ERCC1) gene was a potentially useful genetic biomarker to predict prognosis of NSCLC patients. Meanwhile, studies also found that chronic obstructive pulmonary disease (COPD) was highly associated with lung cancer prognosis. In this study, we investigated and evaluated the correlations between COPD image features and ERCC1 gene expression. A database involving 106 NSCLC patients was used. Each patient had a thoracic CT examination and ERCC1 genetic test. We applied a computer-aided detection scheme to segment and quantify COPD image features. A logistic regression method and a multilayer perceptron network were applied to analyze the correlation between the computed COPD image features and ERCC1 protein expression. A multilayer perceptron network (MPN) was also developed to test performance of using COPD-related image features to predict ERCC1 protein expression. A nine feature based logistic regression analysis showed the average COPD feature values in the low and high ERCC1 protein expression groups are significantly different (p < 0.01). Using a five-fold cross validation method, the MPN yielded an area under ROC curve (AUC = 0.669±0.053) in classifying between the low and high ERCC1 expression cases. The study indicates that CT phenotype features are associated with the genetic tests, which may provide supplementary information to help improve accuracy in assessing prognosis of NSCLC patients.
For computer-aided diagnosis of cardiovascular diseases, accurately extracted centerlines of coronary arteries are important. However, centerlines extracted from incorrectly segmented vessels are usually unsatisfactory. For this reason, we propose two automatic centerline correction methods in this paper. First, a method based on the local volume comparison and the morphological comparison is presented to remove false centerlines from over-segmented tissues. Second, another method based on the judgment of vessel identity and the gradient-SDF (source distance field) calculation is presented to add missing centerlines of under-segmented vessels. We have validated the proposed centerline correction methods on real CT angiographic datasets of coronary arteries. The quantitative evaluation results show that the proposed methods can effectively correct centerline errors arising from erroneous vessel segmentation in most cases.
Reconstruction algorithms based on PI-line or Chord are active subject in CBCT. Among them back-projection filtered (BPF) reconstruction algorithm has obvious influence for its exact reconstruction results and less computations especially in selected volume of interesting (VOI) regions. However, the selecting and sampling method of PI-line segment can directly affect the quality of reconstructed images. In this paper, we proposed a general PI-line selecting scheme to reconstruct VOI regions by using BPF algorithm, which mainly based on the relationship between
reconstructed coordinate and PI-line coordinate. The proposed scheme is applicable for GPU accelerated back-projection filtered reconstruction.
Extracting coronary artery is one of the vital steps in the analysis process based on the modality of computed tomography angiography (CTA), the aim of which is to recognize coronary artery from 3D volume data, and then provide evidences of analysis and quantitative measurement information for coronary artery computer aided detection.
According to the structure features of coronary artery angiography scanned by multiple slices computed tomography (MSCT), an automatic segmentation algorithm is proposed. Firstly, detect and recognize the multiple seed points of the coronary artery in the scale space automatically from the 3D complex cardiac image datasets. Secondly, an improved layer region growing algorithm oriented to 3D tubular structure tissues is proposed to segment the coronary artery.
Experiments show that the algorithm can extract coronary artery vessels effectively, which can improve the automation of coronary artery analysis, thus improve physicians' work efficiency.
Purpose:
We developed an automatic method for measurement of vertebral bone density based on QCT with the use of internal references(muscle and subcutaneous fat) instead of traditional external phantom.
Methods:
The automatic multistep approach starts with segmentation of periosteal and endosteal surfaces of spine to define ellipse
ROI in cancellous bone followed by segmentation of muscle and subcutaneous fat in the spine image and a subsequent calculation of bone mineral density in ellipse ROI and segmentation trabecular and cortical bone ROI using muscle and subcutaneous fat as internal references. The segmentation approach used a hybrid region-growing method which used local adaptive threshold and morphological operation.
Results:
We conducted with-phantom and without-phantom measurements by using 94 clinical cases. The doctor manually defined the ellipse ROI in the with-phantom measurement. As for the without-phantom measurement, we use our method to automatically gain the BMD. The Interaclass Correlation Coefficient (ICC) is 0.93. We removed the points whose muscle and fat values are 2 times deviated from the standard deviation. And the calibrated ICC value is 0.999.
Conclusion:
The without-phantom measurement method is not fit for the patients whose muscle and fat are seriously deviated from the average value. The without-phantom measurement method proposed in this paper can automatically measure the BMD of spine. By accurately segmenting cortical bone and trabecular bone, determining ROI and removing
inappropriate data, it is proved that the BMD measurement result by this method is highly consistent with that by with-phantom method.
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