Peng Wang, Dajiang Zhu, Xiang Li, Hanbo Chen, Xi Jiang, Li Sun, Q. Cao, L. An, Tianming Liu, Yufeng Wang
{"title":"Identifying functional connectomics abnormality in attention deficit hyperactivity disorder","authors":"Peng Wang, Dajiang Zhu, Xiang Li, Hanbo Chen, Xi Jiang, Li Sun, Q. Cao, L. An, Tianming Liu, Yufeng Wang","doi":"10.1109/ISBI.2013.6556532","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556532","url":null,"abstract":"Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric, neurodevelopmental and neurobehavioral disorders occurring in the childhood of human. The typical symptoms are characterized as excessive inattention, hyperactivity/impulsiveness or their combination. Traditionally, it has been thought to be a partial dysfunction caused by prefrontal-striatal circuits. Recent studies, however, indicate the involvement of other brain regions, including the occipital cortex and temporal cortex. Though researchers have already realized the importance of evaluation for the whole brain and multiple structural/functional networks, it is still very challenging to achieve consistent and comparable results across different labs. In the present paper, through the predefined cortical landmarks which possess group-wise structural consistency and intrinsic correspondence, we have the opportunity to access the whole brain and to reveal large-scale structural/functional connectomics abnormalities in ADHD. Our results not only confirmed that the major white matter (WM) alterations occurred at the anterior and posterior regions, but also indicate that hyper-interactions mainly exist between the emotion network and memory related networks. Our results also showed that hypo-interactions are found between the emotion and execution/attention networks. Hence, we hypothesize that the abnormal interactions associated with emotion network contribute to the dysfunction within the ADHD brain.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129909325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Model-based alignment of Look-Locker MRI sequences for calibrated myocardical scar tissue quantification","authors":"M. V. D. Giessen, Q. Tao, R. Geest, B. Lelieveldt","doi":"10.1109/ISBI.2013.6556655","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556655","url":null,"abstract":"The characterization of myocardial scar tissue in Late Gadolinium Enhancement (LGE) MRI volumes is hampered by the nonquantitative nature of MRI image intensities. Using the widely available Look-Locker (LL) sequence that images the heart using different inversion times a T1 map can be created per patient to calibrate the LGE datasets. However, due to the nature of the LL acquisition, the myocardium is imaged at different phases of the cardiac cycle, resulting in deformations between slices of the LL stack and preventing accurate T1 map estimates. In this paper a method is proposed for the non-rigid alignment of the LL stack that uses a model of the exponential contrast development throughout the LL stack to concurrently align all LL stack slices. The model based alignment is shown to be more robust than a pairwise mutual information based alignment. More importantly, correlations between the relaxivity (R1) map and the LGE intensities (needed for the LGE calibration) are higher using the proposed alignment than when using manual annotations. The model based alignment thereby allows the use of the LL sequence for LGE calibration without manually annotating the (typically) 33 slices in this sequence. After alignment, the myocardium only needs to be annotated in the LGE slice. The latter is also needed for non-calibrated scar quantification and thus requires no additional user effort.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127209388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Consistent hemodynamic response function estimation in functional MRI by first order differencing","authors":"A. Seghouane, Adnan Shah","doi":"10.1109/ISBI.2013.6556467","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556467","url":null,"abstract":"Non-parametric hemodynamic response function (HRF) estimation in noisy functional Magnetic Resonance Imaging (fMRI) plays an important role when investigating the temporal dynamic of a brain region response during activations. Assuming the drift Lipschitz continuous; a new algorithm for non-parametric HRF estimation is derived in this paper. The proposed algorithm estimates the HRF by applying a first order differencing to the fMRI time series samples. It is shown that the proposed HRF estimator is √(N) consistent. Its performance is assessed using both simulated and a real fMRI data sets obtained from an event-related fMRI experiment. The application results reveal that the proposed HRF estimation method is efficient both computationally and in term of accuracy.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130683495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sure-based parameter selection for parallel MRI reconstruction using GRAPPA and sparsity","authors":"D. Weller, S. Ramani, J. Nielsen, J. Fessler","doi":"10.1109/ISBI.2013.6556634","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556634","url":null,"abstract":"New methods have been developed for parallel MRI reconstruction combining GRAPPA and sparsity. One impediment to the practical application of such methods is selecting a regularization parameter that acceptably balances the contributions of GRAPPA and sparsity. We propose a broadly applicable Monte-Carlo-based approximation to Stein's unbiased risk estimate (SURE) for a suitable weighted mean-squared error (WMSE) metric. Applying this approximation to predict the WMSE-optimal tuning parameter for sparsity-based reconstruction, we are able to tune our parameter to achieve nearly MSE-optimal performance. In our simulations, we vary the noise level in the simulated data and use our Monte-Carlo method to tune the reconstruction to the noise level automatically.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130722712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Contrast-to-tissue ratio improvement by transmitted optimized binary signal in ultrasound pulse inversion imaging","authors":"S. Ménigot, J. Girault","doi":"10.1109/ISBI.2013.6556590","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556590","url":null,"abstract":"Ultrasound contrast imaging has provided more accurate medical diagnoses. One of the most used techniques is the pulse inversion imaging improving the contrast-to-tissue ratio (CTR) by extracting nonlinearities of contrast agents. The usual transmitted signal is at a fixed frequency. However, an optimal choice requires information about the transducer and the medium. This information is experimentally inaccessible. Moreover, the digital electronic setup can limit the solution. Our goal was to seek the binary command which maximized the CTR. A genetic algorithm sought the vector of input binary samples. By adding a closed loop, the system automatically proposed the optimal binary command without any a priori information about the system or the medium explored and without hypothesis on binary samples. In simulation, the gain compared with a transmitted signal at the optimal frequency can reach about 3 dB and 0.5 dB in comparison with a transmitted signal at the two-thirds of the central frequency of the transducer.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130757086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using the Infocus-Breakpoint to estimate the scale of neoplasia in colonoscopy","authors":"F. Chadebecq, C. Tilmant, A. Bartoli","doi":"10.1109/ISBI.2013.6556485","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556485","url":null,"abstract":"Colonoscopy is the reference medical examination for early diagnosis and treatment of colonic diseases. This minimally invasive technique allows gastroenterologists to explore the colon and remove neoplasias - abnormal growth of tissue - such as polyps which may transform into malignant tumors. Shape, texture and size of polyps are of particular interest for determining their nature. However, the size of neoplasias is difficult to estimate because the absolute scale of the observed tissue is not directly conveyed in the 2D endoscopic images. We here improve our Infocus-Breakpoint (IB) technique, which estimates an imagewise scale by detecting the blur/ unblur breakpoint in a video sequence. We simultaneously track a neoplasia with a 2D affine transformation and estimate the amount of defocus blur. This leads to an areawise scale estimate with better accuracy.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130469825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Sparsifying transform learning for Compressed Sensing MRI","authors":"S. Ravishankar, Y. Bresler","doi":"10.1109/ISBI.2013.6556401","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556401","url":null,"abstract":"Compressed Sensing (CS) enables magnetic resonance imaging (MRI) at high undersampling by exploiting the sparsity of MR images in a certain transform domain or dictionary. Recent approaches adapt such dictionaries to data. While adaptive synthesis dictionaries have shown promise in CS based MRI, the idea of learning sparsifying transforms has not received much attention. In this paper, we propose a novel framework for MR image reconstruction that simultaneously adapts the transform and reconstructs the image from highly undersampled k-space measurements. The proposed approach is significantly faster (>10x) than previous approaches involving synthesis dictionaries, while also providing comparable or better reconstruction quality. This makes it more amenable for adoption for clinical use.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129264967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A FEM deformable mesh for active region segmentation","authors":"K. Popuri, Dana Cobzas, Martin Jägersand","doi":"10.1109/ISBI.2013.6556673","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556673","url":null,"abstract":"We propose a novel template-based multi-region segmentation method using a finite element method (FEM) deformation model with diffusion-based regularization. Our proposed method is computationally more efficient than the traditional template-based segmentation methods that use non-parametric or B-spline based deformation models, as it significantly reduces the number of degrees of freedom (DOF) associated with the energy minimization that arises in template-based segmentation. Further, like all template-based segmentation approaches our method is able to preserve topology of the initial regions of interest (ROIs) defined in the template, which is very useful for segmentation of anatomical structures. Segmentation results on medical images with various anatomical structures show that the proposed method improves computational efficiency without compromising segmentation accuracy.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128763155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Variational approach for small-size lung nodule segmentation","authors":"A. Farag","doi":"10.1109/ISBI.2013.6556417","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556417","url":null,"abstract":"This paper describes a novel variational approach for segmentation of small-size lung nodules which may be detected in low dose CT (LDCT) scans. These nodules do not possess distinct shape or appearance characteristics; hence, their segmentation is enormously difficult, especially at small size (≤ 1 cm). Variational methods hold promise in these scenarios despite the difficulties in estimation of the energy function parameters and the convergence. The proposed method is analytic and has a clear implementation strategy for LDCT scans. We show the effectiveness of the algorithm for segmenting various types of nodules regardless of their location in the lung tissue.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125505583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Negahdar, N. Dunlap, A. Zacarias, A. Civelek, S. Woo, A. Amini
{"title":"Comparison of indices of regional lung function from 4-D X-ray CT: Jacobian vs. strain of deformation","authors":"M. Negahdar, N. Dunlap, A. Zacarias, A. Civelek, S. Woo, A. Amini","doi":"10.1109/ISBI.2013.6556558","DOIUrl":"https://doi.org/10.1109/ISBI.2013.6556558","url":null,"abstract":"In the literature, a widely adopted approach for assessing the regional lung function in patients undergoing radiation therapy for lung cancer from 4-D X-ray CT data is the Jacobian of deformation. The Jacobian which describes regional lung volume changes however lacks consideration of directional length changes during respiration. Previously, we proposed the use of strain for measurement of regional deformation in the Lung [1]. In this paper, we perform physiologic validation of lung strain and compare the results with those from the Jacobian measure. Lung deformation fields were computed through application of a recently proposed 3-D optical flow technique (MOFID) [2] to 4-D X-ray CT data sets collected in seven subjects diagnosed with non-small cell primary lung cancer. In addition to 4-D CT data, both SPECT ventilation (VSPECT), and SPECT perfusion (QSPECT) data were acquired in all subjects. For each case, voxel-wise statistical correlation of the Jacobian as well as principal strains of deformation (CT-derived pulmonary function images) with both ventilation and perfusion SPECT was performed. The results indicate that the maximum principal strain has a higher correlation with both SPECT ventilation and SPECT perfusion than other indices including the Jacobian.","PeriodicalId":178011,"journal":{"name":"2013 IEEE 10th International Symposium on Biomedical Imaging","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122584115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}