Publications by authors named "Jinhua Sheng"

19 Publications

  • Page 1 of 1

Strategies for multivariate analyses of imaging genetics study in Alzheimer's disease.

Neurosci Lett 2021 09 29;762:136147. Epub 2021 Jul 29.

School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China.

Alzheimer's disease (AD) is an incurable neurodegenerative disease primarily affecting the elderly population. Early diagnosis of AD is critical for the management of this disease. Imaging genetics examines the influence of genetic variants (i.e., single nucleotide polymorphisms (SNPs)) on brain structure and function and many novel approaches of imaging genetics are proposed for studying AD. We review and synthesize the Alzheimer's Disease Neuroimaging Initiative (ADNI) genetic associations with quantitative disease endophenotypes including structural and functional neuroimaging, diffusion tensor imaging (DTI), positron emission tomography (PET), and fluid biomarker assays. In this review, we survey recent publications using neuroimaging and genetic data of AD, with a focus on methods capturing multivariate effects accommodating the large number variables from both imaging data and genetic data. We review methods focused on bridging the imaging and genetic data by establishing genotype-phenotype association, including sparse canonical correlation analysis, parallel independent component analysis, sparse reduced rank regression, sparse partial least squares, genome-wide association study, and so on. The broad availability and wide scope of ADNI genetic and phenotypic data has advanced our understanding of the genetic basis of AD and has nominated novel targets for future pharmaceutical therapy and biomarker development.
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http://dx.doi.org/10.1016/j.neulet.2021.136147DOI Listing
September 2021

Identifying and characterizing different stages toward Alzheimer's disease using ordered core features and machine learning.

Heliyon 2021 Jun 11;7(6):e07287. Epub 2021 Jun 11.

College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China.

Based on the joint HCPMMP parcellation method we developed before, which divides the cortical brain into 360 regions, the concept of ordered core features (OCF) is first proposed to reveal the functional brain connectivity relationship among different cohorts of Alzheimer's disease (AD), late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI) and healthy controls (HC). A set of core network features that change significantly under the specifically progressive relationship were extracted and used as supervised machine learning classifiers. The network nodes in this set mainly locate in the frontal lobe and insular, forming a narrow band, which are responsible for cognitive impairment as suggested by previous finding. By using these features, the accuracy ranged from 86.0% to 95.5% in binary classification between any pair of cohorts, higher than 70.1%-91.0% when using all network features. In multi-group classification, the average accuracy was 75% or 78% for HC, EMCI, LMCI or EMCI, LMCI, AD against baseline of 33%, and 53.3% for HC, EMCI, LMCI and AD against baseline of 25%. In addition, the recognition rate was lower when combining EMCI and LMCI patients into one group of mild cognitive impairment (MCI) for classification, suggesting that there exists a big difference between early and late MCI patients. This finding supports the EMCI/LMCI inclusion criteria introduced by ADNI based on neuropsychological assessments.
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http://dx.doi.org/10.1016/j.heliyon.2021.e07287DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8220177PMC
June 2021

Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling.

Sci Rep 2021 04 26;11(1):9005. Epub 2021 Apr 26.

Beijing Hospital, Beijing, 100730, China.

Generalized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calculation, a small number of auto-calibration signal (ACS) lines need to be sampled at the center of the k-space. Therefore, the sampling pattern used in this type of method is to full sample data in the middle area and undersample in the outer k-space with nominal reduction factors. In this paper, we propose a novel reconstruction method with a multiple variable density sampling (MVDS) that is different from traditional sampling patterns. Our method can significantly improve the image quality using multiple reduction factors with fewer ACS lines. Specifically, the traditional sampling pattern only uses a single reduction factor to uniformly undersample data in the region outside the ACS, but we use multiple reduction factors. When sampling the k-space data, we keep the ACS lines unchanged, use a smaller reduction factor for undersampling data near the ACS lines and a larger reduction factor for the outermost part of k-space. The error is lower after reconstruction of this region by undersampled data with a smaller reduction factor. The experimental results show that with the same amount of data sampled, using NL-GRAPPA to reconstruct the k-space data sampled by our method can result in lower noise and fewer artifacts than traditional methods. In particular, our method is extremely effective when the number of ACS lines is small.
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http://dx.doi.org/10.1038/s41598-021-88567-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8076203PMC
April 2021

Characteristics and variability of functional brain networks.

Neurosci Lett 2020 06 30;729:134954. Epub 2020 Apr 30.

College of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China.

Functional brain networks were constructed from functional magnetic resonance imaging (fMRI) data originating from 96 healthy adults. These networks possessed a total of 360 nodes, derived from the latest multi-modal brain parcellation method. A novel group network (overlay network) analysis model is proposed to study common attributes as well as differences found in the human brain by analysis of the functional brain network. Currently, the mean network is generally used to represent the group network. But mean networks have a modularity problem making them distinct from real networks. The overlay network is constructed by calculating the connections between the whole brain network regions, and then filtering the connections by limiting the threshold value. We find that the overlay network is closer to the real network condition of the group in terms of network characteristics related to modularity. Multiple network features are applied to investigate the discrepancies between the new group network and the mean network. Individual divergences between brain regions of everyone are also explored. Results show that the brain network of different people has a high consistency in the global measures, while there exist great differences for local measures in brain regions. Some brain regions show variability over other brain regions on most measures. In addition, we explored the impact of different thresholds on the overlay network and find that different thresholds have a greater impact on the clustering coefficient, maximized modularity, strength, and global efficiency.
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http://dx.doi.org/10.1016/j.neulet.2020.134954DOI Listing
June 2020

Alzheimer's disease, mild cognitive impairment, and normal aging distinguished by multi-modal parcellation and machine learning.

Sci Rep 2020 03 25;10(1):5475. Epub 2020 Mar 25.

College of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China.

A 360-area surface-based cortical parcellation is extended to study mild cognitive impairment (MCI) and Alzheimer's disease (AD) from healthy control (HC) using the joint human connectome project multi-modal parcellation (JHCPMMP) proposed by us. We propose a novel classification method named as JMMP-LRR to accurately identify different stages toward AD by integrating the JHCPMMP with the logistic regression-recursive feature elimination (LR-RFE). In three-group classification, the average accuracy is 89.0% for HC, MCI, and AD compared to previous studies using other cortical separation with the best classification accuracy of 81.5%. By counting the number of brain regions whose feature is in the feature subset selected with JMMP-LRR, we find that five brain areas often appear in the selected features. The five core brain areas are Fusiform Face Complex (L-FFC), Area 10d (L-10d), Orbital Frontal Complex (R-OFC), Perirhinal Ectorhinal (L-PeEc) and Area TG dorsal (L-TGd, R-TGd). The features corresponding to the five core brain areas are used to form a new feature subset for three classifications with the average accuracy of 80.0%. Results demonstrate the importance of the five core brain regions in identifying different stages toward AD. Experiment results show that the proposed method has better accuracy for the classification of HC, MCI, AD, and it also proves that the division of brain regions using JHCPMMP is more scientific and effective than other methods.
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http://dx.doi.org/10.1038/s41598-020-62378-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7096533PMC
March 2020

A novel joint HCPMMP method for automatically classifying Alzheimer's and different stage MCI patients.

Behav Brain Res 2019 06 2;365:210-221. Epub 2019 Mar 2.

College of Computer Science, Hangzhou Dianzi University, Hangzhou, Zhejiang, China.

A 360-area surface-based cortical parcellation was recently generated using multimodal data in a group average of 210 healthy young adults from the Human Connectome Project (HCP). In order to automatically and accurately identify mild cognitive impairment (MCI) at its two levels (early MCI and late MCI), Alzheimer's disease (AD) and healthy control (HC), a novel joint HCP MMP method was first proposed to delineate the cortical architecture and function connectivity in a group of non healthy adults. The proposed method was applied to register a dataset of 96 resting-state functional connectomes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to Connectivity Informatics Technology Initiative (CIFTI) space and parcellated brain into human connectome project multi-modal parcellation (HCPMMP) with 360 areas. Various network features in each node of the connectivity network were considered as the candidate features for classification.The fine-grained multi-modal based on HCP-MMP combined with machine learning in identification for EMCI, LMCI, AD and HC. Applying various network features, including strength, betweenness centrality, clustering coefficient, local efficiency, eigenvector centrality, etc, we trained and tested several machine learning models. Thousands of features were processed by filter and wrapper feature selection procedures, and finally there were thirty features to be selected to achieve classification accuracies of 93.8% for EMCI vs. HC, 95.8% for LMCI vs. HC, 95.8% for AD vs. HC, and 91.7% for LMCI vs. AD, respectively by using support vector machine (SVM) algorithm. Most of the selected features locate in the region of temporal or cingulate cortex. Compared with previous studies, our results demonstrate the superiority of the proposed method over existing techniques.
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http://dx.doi.org/10.1016/j.bbr.2019.03.004DOI Listing
June 2019

KerNL: Kernel-Based Nonlinear Approach to Parallel MRI Reconstruction.

IEEE Trans Med Imaging 2019 01 7;38(1):312-321. Epub 2018 Aug 7.

The conventional calibration-based parallel imaging method assumes a linear relationship between the acquired multi-channel k-space data and the unacquired missing data, where the linear coefficients are estimated using some auto-calibration data. In this paper, we first analyze the model errors in the conventional calibration-based methods and demonstrate the nonlinear relationship. Then, a much more general nonlinear framework is proposed for auto-calibrated parallel imaging. In this framework, kernel tricks are employed to represent the general nonlinear relationship between acquired and unacquired k-space data without increasing the computational complexity. Identification of the nonlinear relationship is still performed by solving linear equations. Experimental results demonstrate that the proposed method can achieve reconstruction quality superior to GRAPPA and NL-GRAPPA at high net reduction factors.
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http://dx.doi.org/10.1109/TMI.2018.2864197DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6422679PMC
January 2019

Pseudo-Bootstrap Network Analysis-an Application in Functional Connectivity Fingerprinting.

Front Hum Neurosci 2017 13;11:351. Epub 2017 Jul 13.

Department of Psychological and Brain Sciences, Indiana University, BloomingtonIN, United States.

Brain parcellation divides the brain's spatial domain into small regions, which are represented by nodes within the network analysis framework. While template-based parcellations are widely used, the parcels on the template do not necessarily match individual's functional nodes. A new method is developed to overcome the inconsistent network analysis results by by-passing the difficulties of parcellating the brain into functionally meaningful areas. First, roughly equal-sized parcellations are obtained. Second, these random parcellations are applied to individual subjects multiple times and a pseudo-bootstrap (PBS) of the network is obtained for statistical inferences. It was found that the variation of mean global network metrics from PBS sampling is smaller compared with inter-subject variation or within-subject variation between two diffusion MRI scans. Using the mean global network metrics from PBS sampling, the intra-class correlation is always higher than the average obtained from using a single random parcellation. As one application, the PBS method was tested on the Human Connectome Project resting state dataset to identify individuals across scan sessions based on the mean functional connectivity (FC)-a trivial network property that has little information about the connectivity between nodes. An accuracy rate of ∼90% was achieved by simply finding the maximum correlation of mean FC of PBS samples between two scan sessions.
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http://dx.doi.org/10.3389/fnhum.2017.00351DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5507998PMC
July 2017

DATA SYNTHESIS AND METHOD EVALUATION FOR BRAIN IMAGING GENETICS.

Proc IEEE Int Symp Biomed Imaging 2014 May;2014:1202-1205

Radiology and Imaging Sciences, BioHealth Informatics, Indiana University, IN, USA.

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. We present initial efforts on evaluating a few SCCA methods for brain imaging genetics. This includes a data synthesis method to create realistic imaging genetics data with known SNP-QT associations, application of three SCCA algorithms to the synthetic data, and comparative study of their performances. Our empirical results suggest, approximating covariance structure using an identity or diagonal matrix, an approach used in these SCCA algorithms, could limit the SCCA capability in identifying the underlying imaging genetics associations. An interesting future direction is to develop enhanced SCCA methods that effectively take into account the covariance structures in the imaging genetics data.
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http://dx.doi.org/10.1109/ISBI.2014.6868091DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4232947PMC
May 2014

Integrated variable projection approach (IVAPA) for parallel magnetic resonance imaging.

Comput Med Imaging Graph 2012 Oct 22;36(7):552-9. Epub 2012 Jun 22.

Beijing Hospital, Ministry of Health, Beijing, China.

Parallel magnetic resonance imaging (pMRI) is a fast method which requires algorithms for the reconstructing image from a small number of measured k-space lines. The accurate estimation of the coil sensitivity functions is still a challenging problem in parallel imaging. The joint estimation of the coil sensitivity functions and the desired image has recently been proposed to improve the situation by iteratively optimizing both the coil sensitivity functions and the image reconstruction. It regards both the coil sensitivities and the desired images as unknowns to be solved for jointly. In this paper, we propose an integrated variable projection approach (IVAPA) for pMRI, which integrates two individual processing steps (coil sensitivity estimation and image reconstruction) into a single processing step to improve the accuracy of the coil sensitivity estimation using the variable projection approach. The method is demonstrated to be able to give an optimal solution with considerably reduced artifacts for high reduction factors and a low number of auto-calibration signal (ACS) lines, and our implementation has a fast convergence rate. The performance of the proposed method is evaluated using a set of in vivo experiment data.
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http://dx.doi.org/10.1016/j.compmedimag.2012.05.005DOI Listing
October 2012

Characteristics and variability of structural networks derived from diffusion tensor imaging.

Neuroimage 2012 Jul 17;61(4):1153-64. Epub 2012 Mar 17.

Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.

Structural brain networks were constructed based on diffusion tensor imaging (DTI) data of 59 young healthy male adults. The networks had 68 nodes, derived from FreeSurfer parcellation of the cortical surface. By means of streamline tractography, the edge weight was defined as the number of streamlines between two nodes normalized by their mean volume. Specifically, two weighting schemes were adopted by considering various biases from fiber tracking. The weighting schemes were tested for possible bias toward the physical size of the nodes. A novel thresholding method was proposed using the variance of number of streamlines in fiber tracking. The backbone networks were extracted and various network analyses were applied to investigate the features of the binary and weighted backbone networks. For weighted networks, a high correlation was observed between nodal strength and betweenness centrality. Despite similar small-worldness features, binary networks and weighted networks are distinctive in many aspects, such as modularity and nodal betweenness centrality. Inter-subject variability was examined for the weighted networks, along with the test-retest reliability from two repeated scans on 44 of the 59 subjects. The inter-/intra-subject variability of weighted networks was discussed in three levels - edge weights, local metrics, and global metrics. The variance of edge weights can be very large. Although local metrics show less variability than the edge weights, they still have considerable amounts of variability. Weighting scheme one, which scales the number of streamlines by their lengths, demonstrates stable intra-class correlation coefficients against thresholding for global efficiency, clustering coefficient and diversity. The intra-class correlation analysis suggests the current approach of constructing weighted network has a reasonably high reproducibility for most global metrics.
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http://dx.doi.org/10.1016/j.neuroimage.2012.03.036DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3500617PMC
July 2012

Optimization of seed density in DTI tractography for structural networks.

J Neurosci Methods 2012 Jan 29;203(1):264-72. Epub 2011 Sep 29.

Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.

Diffusion tensor imaging (DTI) has been used for mapping the structural network of the human brain. The network can be constructed by choosing various brain regions as nodes and fiber tracts connecting those regions as links. The structural network generated from DTI data can be affected by noise in the scans and the choice of tractography algorithm. This study aimed to examine the effect of the number of seeds in tractography on the variance of structural networks. The variance of the network was characterized using an approach similar to the National Electrical Manufacturers Association (NEMA) standards for measurement of image noise. It was shown that the variance of the network is inversely related to the square root of seed density. Consequently, the number of seeds has a large impact on local characteristics and metrics of the network architecture. As the number of seeds increased, increased stability of structural network metrics was observed. However, more seeds can also lead to more spurious fibers and thus affect nodal degrees and edge weights, and proper thresholding may be necessary to create an appropriate weighted network. Because the variance of the network is also influenced by other imaging factors, further increase in the number of seeds has little effect in reducing the network variance. The selection of the seed number should be a balance between the network variance and computational effort.
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http://dx.doi.org/10.1016/j.jneumeth.2011.09.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3500612PMC
January 2012

Integrated analysis of gene expression and copy number data on gene shaving using independent component analysis.

IEEE/ACM Trans Comput Biol Bioinform 2011 Nov-Dec;8(6):1568-79

Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 950 West Walnut Street, Indianapolis, IN 46202, USA.

DNA microarray gene expression and microarray-based comparative genomic hybridization (aCGH) have been widely used for biomedical discovery. Because of the large number of genes and the complex nature of biological networks, various analysis methods have been proposed. One such method is "gene shaving," a procedure which identifies subsets of the genes with coherent expression patterns and large variation across samples. Since combining genomic information from multiple sources can improve classification and prediction of diseases, in this paper we proposed a new method, "ICA gene shaving" (ICA, independent component analysis), for jointly analyzing gene expression and copy number data. First we used ICA to analyze joint measurements, gene expression and copy number, of a biological system and project the data onto statistically independent biological processes. Next, we used these results to identify patterns of variation in the data and then applied an iterative shaving method. We investigated the properties of our proposed method by analyzing both simulated and real data. We demonstrated that the robustness of our method to noise using simulated data. Using breast cancer data, we showed that our method is superior to the Generalized Singular Value Decomposition (GSVD) gene shaving method for identifying genes associated with breast cancer.
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http://dx.doi.org/10.1109/TCBB.2011.71DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146966PMC
April 2012

An elliptical SPECT system with slit-slat collimation for cardiac imaging.

Authors:
Jinhua Sheng

Comput Med Imaging Graph 2011 Jan;35(1):9-15

Department of Electrical Engineering and Computer Science, University of Missouri, Kansas City, MO 64112, USA.

Cardiac studies are a good candidate for SPECT (single photon emission computed tomography) because of the large clinical demand and the need for improved image quality. But SPECT imaging suffers from poor spatial resolution and high statistical noise. A new SPECT system with slit-slat collimation arranged on an elliptical arc for cardiac imaging is proposed in this paper. Simulated emission computed tomography data are generated along an elliptical moving orbit with system configuration parameters. The iterative reconstruction techniques are used to implement the cardiac imaging of the proposed SPECT system. Image reconstruction can be done using the OS-EM algorithm from the data collected. This system is developed to improve the reconstructed image if attenuation correction and depth-dependent correction are included in the reconstruction, and the body contour is used in the reconstruction for severely truncated data. The simulation results show that the present methods can provide significant improvement in the spatial resolution and the image quality of SPECT sets.
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http://dx.doi.org/10.1016/j.compmedimag.2010.08.006DOI Listing
January 2011

Metabolomic imaging for human prostate cancer detection.

Sci Transl Med 2010 Jan;2(16):16ra8

Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.

As current radiological approaches cannot accurately localize prostate cancer in vivo, biopsies are conducted at random within prostates for patients at risk for prostate cancer, leading to high false-negative rates. Metabolomic imaging can map cancer-specific biomolecular profile values onto anatomical structures to direct biopsy. In this preliminary study, we evaluated five whole prostates removed during prostatectomy from biopsy-proven cancer patients on a 7-tesla human whole-body magnetic resonance scanner. Localized, multi-cross-sectional, multivoxel magnetic resonance spectra were used to construct a malignancy index based on prostate cancer metabolomic profiles obtained from previous intact tissue analyses with a 14-tesla spectrometer. This calculated malignancy index is linearly correlated with lesion size and demonstrates a 93 to 97% overall accuracy for detecting the presence of prostate cancer lesions, suggesting the potential clinical utility of this approach.
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http://dx.doi.org/10.1126/scitranslmed.3000513DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2857699PMC
January 2010

Regularized sensitivity encoding (SENSE) reconstruction using Bregman iterations.

Magn Reson Med 2009 Jan;61(1):145-52

Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53211, USA.

In parallel imaging, the signal-to-noise ratio (SNR) of sensitivity encoding (SENSE) reconstruction is usually degraded by the ill-conditioning problem, which becomes especially serious at large acceleration factors. Existing regularization methods have been shown to alleviate the problem. However, they usually suffer from image artifacts at high acceleration factors due to the large data inconsistency resulting from heavy regularization. In this paper, we propose Bregman iteration for SENSE regularization. Unlike the existing regularization methods where the regularization function is fixed, the method adaptively updates the regularization function using the Bregman distance at different iterations, such that the iteration gradually removes the aliasing artifacts and recovers fine structures before the noise finally comes back. With a discrepancy principle as the stopping criterion, our results demonstrate that the reconstructed image using Bregman iteration preserves both sharp edges lost in Tikhonov regularization and fines structures missed in total variation (TV) regularization, while reducing more noise and aliasing artifacts.
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http://dx.doi.org/10.1002/mrm.21799DOI Listing
January 2009

Improved self-calibrated spiral parallel imaging using JSENSE.

Med Eng Phys 2009 Jun 21;31(5):510-4. Epub 2008 Nov 21.

Department of Electrical Engineering and Computer Science, University of Wisconsin, Milwaukee, 3200N. Cramer Street, Milwaukee, WI 53211, United States.

Spiral MRI has several advantages over Cartesian MRI such as faster acquisitions and reduced demand in gradient. In parallel imaging, spiral trajectories are especially of great interest due to their inherent self-calibration capabilities, which is especially useful for dynamic imaging applications such as fMRI and cardiac imaging. The existing self-calibration techniques use the central spiral data that are sampled densely in the accelerated acquisition for coil sensitivity estimation. However, the resulting sensitivities are not sufficiently accurate for SENSE reconstruction due to the data truncation. In this paper, JSENSE which has been successfully used in Cartesian trajectories is extended to spiral trajectory such that the coil sensitivities and the desired image are reconstructed jointly to improve accuracy through alternating optimization. The improved sensitivities lead to a more accurate SENSE reconstruction. The results from both phantom and in vivo data are shown to demonstrate the effectiveness of JSENSE for spiral trajectory.
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http://dx.doi.org/10.1016/j.medengphy.2008.09.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2726790PMC
June 2009

Joint image reconstruction and sensitivity estimation in SENSE (JSENSE).

Magn Reson Med 2007 Jun;57(6):1196-202

Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA.

Parallel magnetic resonance imaging (pMRI) using multichannel receiver coils has emerged as an effective tool to reduce imaging time in various applications. However, the issue of accurate estimation of coil sensitivities has not been fully addressed, which limits the level of speed enhancement achievable with the technology. The self-calibrating (SC) technique for sensitivity extraction has been well accepted, especially for dynamic imaging, and complements the common calibration technique that uses a separate scan. However, the existing method to extract the sensitivity information from the SC data is not accurate enough when the number of data is small, and thus erroneous sensitivities affect the reconstruction quality when they are directly applied to the reconstruction equation. This paper considers this problem of error propagation in the sequential procedure of sensitivity estimation followed by image reconstruction in existing methods, such as sensitivity encoding (SENSE) and simultaneous acquisition of spatial harmonics (SMASH), and reformulates the image reconstruction problem as a joint estimation of the coil sensitivities and the desired image, which is solved by an iterative optimization algorithm. The proposed method was tested on various data sets. The results from a set of in vivo data are shown to demonstrate the effectiveness of the proposed method, especially when a rather large net acceleration factor is used.
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http://dx.doi.org/10.1002/mrm.21245DOI Listing
June 2007

A fast image reconstruction algorithm based on penalized-likelihood estimate.

Med Eng Phys 2005 Oct;27(8):679-86

Department of Medical Physics, Rush University, Chicago, IL 60607, USA.

Statistical iterative methods for image reconstruction like maximum likelihood expectation maximization (ML-EM) are more robust and flexible than analytical inversion methods and allow for accurately modeling the counting statistics and the photon transport during acquisition. They are rapidly becoming the standard for image reconstruction in emission computed tomography. The maximum likelihood approach provides images with superior noise characteristics compared to the conventional filtered back projection algorithm. But a major drawback of the statistical iterative image reconstruction is its high computational cost. In this paper, a fast algorithm is proposed as a modified OS-EM (MOS-EM) using a penalized function, which is applied to the least squares merit function to accelerate image reconstruction and to achieve better convergence. The experimental results show that the algorithm can provide high quality reconstructed images with a small number of iterations.
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http://dx.doi.org/10.1016/j.medengphy.2005.02.004DOI Listing
October 2005
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