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1.
J Magn Reson Imaging ; 50(1): 297-304, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-30447032

RESUMO

BACKGROUND: Non-monoexponential diffusion models are being used increasingly for the characterization and curative effect evaluation of hepatocellular carcinoma (HCC). But the fitting quality of the models and the repeatability of their parameters have not been assessed for HCC. PURPOSE: To evaluate kurtosis, stretched exponential, and statistical models for diffusion-weighted imaging (DWI) of HCC, using b-values up to 2000 s/mm2 , in terms of fitting quality and repeatability. STUDY TYPE: Prospective. POPULATION: Eighteen patients with HCC. FIELD STRENGTH/SEQUENCE: Conventional and DW images (b = 0, 200, 500, 1000, 1500, 2000 s/mm2 ) were acquired at 3.0T. ASSESSMENT: The parameters of the kurtosis, stretched exponential, and statistical models were calculated on regions of interest (ROIs) of each lesion. STATISTICAL TESTS: The fitting quality was evaluated through comparing the fitting residuals produced on the average data of ROI between different models using a paired t-test or Wilcoxon rank-sum test. Repeatability of the fitted parameters at the median values on the voxelwise data of ROI was assessed using the within coefficient of variation (WCV), the intraclass correlation coefficient (ICC), and the 95% Bland-Altman limits of agreements (BA-LA). The repeatability was divided into four levels: excellent, good, acceptable, and poor, referring to the values of ICC and WCV. RESULTS: Among three models, the stretched exponential model provided the best fit to HCC (P < 0.05), whereas the statistical model produced the largest fitting residuals (P < 0.05). The repeatability of K from the kurtosis model was excellent (ICC 0.915; WCV 8.79%), while the distributed diffusion coefficient (DDC) from the stretched model was just acceptable (ICC 0.477; WCV 27.83%). The repeatability was good for other diffusion-related parameters. DATA CONCLUSION: Considering the model fit and repeatability, the kurtosis and stretched exponential models are the preferred models for the description of the DW signals of HCC with respect to the statistical model. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:297-304.


Assuntos
Carcinoma Hepatocelular/diagnóstico por imagem , Imagem de Difusão por Ressonância Magnética , Neoplasias Hepáticas/diagnóstico por imagem , Adulto , Idoso , Algoritmos , Feminino , Humanos , Interpretação de Imagem Assistida por Computador , Processamento de Imagem Assistida por Computador , Masculino , Pessoa de Meia-Idade , Variações Dependentes do Observador , Estudos Prospectivos , Reprodutibilidade dos Testes , Razão Sinal-Ruído
2.
IEEE Trans Biomed Eng ; 66(11): 3220-3230, 2019 11.
Artigo em Inglês | MEDLINE | ID: mdl-30843792

RESUMO

OBJECTIVE: The purpose of this paper is to increase the accuracy of human cardiac diffusion tensor (DT) estimation in diffusion magnetic resonance imaging (dMRI) with a few diffusion gradient directions. METHODS: A structure prior constrained (SPC) method is proposed. The method consists in introducing two regularizers in the conventional nonlinear least squares estimator. The two regularizers penalize the dissimilarity between neighboring DTs and the difference between estimated and prior fiber orientations, respectively. A novel numerical solution is presented to ensure the positive definite estimation. RESULTS: Experiments on ex vivo human cardiac data show that the SPC method is able to well estimate DTs at most voxels, and is superior to state-of-the-art methods in terms of the mean errors of principal eigenvector, second eigenvector, helix angle, transverse angle, fractional anisotropy, and mean diffusivity. CONCLUSION: The SPC method is a practical and reliable alternative to current denoising- or regularization-based methods for the estimation of human cardiac DT. SIGNIFICANCE: The SPC method is able to accurately estimate human cardiac DTs in dMRI with a few diffusion gradient directions.


Assuntos
Técnicas de Imagem Cardíaca/métodos , Imagem de Tensor de Difusão/métodos , Coração/diagnóstico por imagem , Processamento de Imagem Assistida por Computador/métodos , Anisotropia , Simulação por Computador , Humanos
3.
Transl Oncol ; 11(6): 1370-1378, 2018 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-30216762

RESUMO

PURPOSE: To distinguish hepatocellular carcinoma (HCC) from other types of hepatic lesions with the adaptive multi-exponential IVIM model. METHODS: 94 hepatic focal lesions, including 38 HCC, 16 metastasis, 12 focal nodular hyperplasia, 13 cholangiocarcinoma, and 15 hemangioma, were examined in this study. Diffusion-weighted images were acquired with 13 b values (b = 0, 3, …, 500 s/mm2) to measure the adaptive multi-exponential IVIM parameters, namely, pure diffusion coefficient (D), diffusion fraction (fd), pseudo-diffusion coefficient (Di*) and perfusion-related diffusion fraction (fi) of the ith perfusion component. Comparison of the parameters of and their diagnostic performance was determined using Mann-Whitney U test, independent-sample t test, one-way analysis of variance, Z test and receiver-operating characteristic analysis. RESULTS: D, D1* and D2* presented significantly difference between HCCs and other hepatic lesions, whereas fd, f1 and f2 did not show statistical differences. In the differential diagnosis of HCCs from other hepatic lesions, D2* (AUC, 0.927) provided best diagnostic performance among all parameters. Additionally, the number of exponential terms in the model was also an important indicator for distinguishing HCCs from other hepatic lesions. In the benign and malignant analysis, D gave the greatest AUC values, 0.895 or 0.853, for differentiation between malignant and benign lesions with three or two exponential terms. Most parameters were not significantly different between hypovascular and hypervascular lesions. For multiple comparisons, significant differences of D, D1* or D2* were found between certain lesion types. CONCLUSION: The adaptive multi-exponential IVIM model was useful and reliable to distinguish HCC from other hepatic lesions.

4.
PLoS One ; 11(2): e0150161, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-26919477

RESUMO

Orientation distribution functions (ODFs) are widely used to resolve fiber crossing problems in high angular resolution diffusion imaging (HARDI). The characteristics of the ODFs are often assessed using a visual criterion, although the use of objective criteria is also reported, which are directly borrowed from classic signal and image processing theory because they are intuitive and simple to compute. However, they are not always pertinent for the characterization of ODFs. We propose a more general paradigm for assessing the characteristics of ODFs. The idea consists in regarding an ODF as a three-dimensional (3D) point cloud, projecting the 3D point cloud onto an angle-distance map, constructing an angle-distance matrix, and calculating metrics such as length ratio, separability, and uncertainty. The results from both simulated and real data show that the proposed metrics allow for the assessment of the characteristics of ODFs in a quantitative and relatively complete manner.


Assuntos
Imagem de Tensor de Difusão/métodos , Processamento de Imagem Assistida por Computador/métodos , Animais , Encéfalo/anatomia & histologia , Simulação por Computador , Imagem de Tensor de Difusão/estatística & dados numéricos , Macaca/anatomia & histologia , Conceitos Matemáticos , Razão Sinal-Ruído
5.
Phys Med Biol ; 60(21): 8417-36, 2015 Nov 07.
Artigo em Inglês | MEDLINE | ID: mdl-26464329

RESUMO

Diffusion tensor imaging and high angular resolution diffusion imaging are often used to analyze the fiber complexity of tissues. In these imaging techniques, the most commonly calculated metric is anisotropy, such as fractional anisotropy (FA), generalized anisotropy (GA), and generalized fractional anisotropy (GFA). The basic idea underlying these metrics is to compute the deviation from free or spherical diffusion. However, in many cases, the question is not really to know whether it concerns spherical diffusion. Instead, the main concern is to describe and quantify fiber complexity such as fiber crossing in a voxel. In this context, it would be more direct and effective to compute the deviation from a single fiber bundle instead of a sphere. We propose a new metric, called PEAM (PEAnut Metric), which is based on computing the deviation of orientation diffusion functions (ODFs) from a single fiber bundle ODF represented by a peanut. As an example, the proposed PEAM metric is used to classify intravoxel fiber configurations. The results on simulated data, physical phantom data and real brain data consistently showed that the proposed PEAM provides greater accuracy than FA, GA and GFA and enables parallel and complex fibers to be better distinguished.


Assuntos
Algoritmos , Imagem de Difusão por Ressonância Magnética/métodos , Imagem de Tensor de Difusão/métodos , Anisotropia
6.
Comput Med Imaging Graph ; 46 Pt 3: 291-9, 2015 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-26342757

RESUMO

Fiber tractography techniques in diffusion magnetic resonance imaging have become a primary tool for studying the fiber architecture of biological tissues both noninvasively and in vivo. Streamline tracking, as a simple and efficient tractography technique, is widely used to reconstruct fiber pathways. It is however very sensitive to noisy estimation of local fiber orientations. In this paper, we propose a bundle constrained streamline method to accurately reconstruct multifiber pathways. The method introduces neighboring fiber consistency constraint in the tracking process and reconstructs fiber pathways that have optimal tradeoff between consistency with local fiber orientation estimations and similarity with neighboring fiber segment orientations. Results on synthetic, physical phantom and real human brain DW images show that the proposed method allows regular fiber pathways to be reconstructed and outperforms existing techniques.


Assuntos
Algoritmos , Imagem de Tensor de Difusão/métodos , Interpretação de Imagem Assistida por Computador/métodos , Fibras Nervosas Mielinizadas/ultraestrutura , Reconhecimento Automatizado de Padrão/métodos , Substância Branca/anatomia & histologia , Humanos , Aumento da Imagem/métodos , Aprendizado de Máquina , Sensibilidade e Especificidade
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