Katerina Nikiforaki

Diffusion Modelling Tool (DMT) for the analysis of Diffusion Weighted Imaging (DWI) Magnetic Resonance Imaging (MRI) data

This paper presents a graphical user interface (GUI) for empowering users to read, analyze, visualize and quantify the diffusion weighted magnetic resonance imaging (DWI-MRI) data into diffusion related imaging biomarkers. The process of random motion of water molecules in human tissues called diffusivity is a significant physical process that can give an insight in the complexity of tissue microarchitecture. Several mathematical models have been proposed for associating diffusivity with the DWI-MRI data, aiming to provide biomarkers for quantifying this physical process. DMT follows a pipeline workflow comprising: a) reading and preparing the DWI-MRI data for analysis, b) measuring various DWI derived biomarkers from several mathematical models, c) assessing qualitatively the derived biomarkers by visually depicting their generated parametric maps, d) statistically measuring the accuracy of the models, and e) performing histogram analysis to the biomarkers and exporting the results into images and tables.

Whole-liver diffusion-weighted MRI histogram analysis: effect of the presence of colorectal hepatic metastases on the remaining liver parenchyma

Objectives To explore whether whole-liver diffusion-weighted MRI analysis (of the apparently normal liver parenchyma) can help differentiate between patients with colorectal liver metastasis and controls without liver disease. Materials and methods Ten patients with colorectal liver metastasis and 10 controls with no focal/diffuse liver disease underwent liver MRI at 1.5 T including diffusion-weighted imaging (DWI; b-values 0, 50, 100, 500, 750, 1000). Apparent diffusion coefficient (ADC) maps were calculated from the DWI images to carry out quantitative diffusion analyses. An experienced reader performed segmentation of the apparently nondiseased liver (excluding metastases/focal liver lesions) on the ADC maps. Histogram ADC parameters were calculated and compared between the patients and the controls. Results The mean liver ADC was 0.95×10−3 mm2/s for the patients versus 1.03×10−3 mm2/s for the controls (P=0.42). The fifth percentile of the ADC was significantly lower for the patients compared with the controls (0.45 vs. 0.69 10−3 mm2/s, P=0.01). The SD was significantly higher in the patient group (0.30 vs. 0.22, P<0.001). Median, skewness, kurtosis, and 30th–95th percentile were not significantly different between the two groups. Areas under the receiver operator characteristics curves to differentiate patients with metastatic liver involvement from healthy controls without liver disease were 0.79 for the fifth percentile and 0.95 for the SD. Conclusion Whole-liver diffusion-weighted MRI histogram analysis showed a significant shift towards lower fifth percentile ADC values and higher SD in patients with colorectal liver metastasis compared with controls without liver disease.

A model-free approach for imaging tumor hypoxia from DCE-MRI data

Non-invasive imaging biomarkers that assess angiogenic response and tumor microvascular environment at an early stage of therapy could provide useful insights into therapy planning. Tissue hypoxia is related to the insufficient supply of oxygen and is associated with tumor vasculature and perfusion. Thus, knowledge of the hypoxic areas could be of great importance. There is no golden standard for imaging tumor hypoxia yet, however Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is among the most promising non-invasive clinically relevant imaging modalities. In this work, DCE-MRI data from neck sarcoma are analyzed through a pattern recognition technique which results in the separation of the tumor area into well-perfused, hypoxic and necrotic regions.

Apparent Diffusion Coefficient Quantification in Determining the Histological Diagnosis of Malignant Liver Lesions.

Purpose: Diffusion Weighted Imaging is an established diagnostic tool for accurate differential diagnosis between benign and malignant liver lesions. The aim of our study was to evaluate the role of Histogram Analysis of ADC quantification in determining the histological diagnosis as well as the grade of malignant liver tumours. To our knowledge, there is no study evaluating the role of Histogram Analysis of ADC quantification in determining the histological diagnosis as well as the grade of malignant liver tumours. Methods: During five years, 115 patients with known liver lesions underwent Diffusion Weighted Imaging in 3Tesla MR scanner prior to core needle biopsy. Histogram analyses of ADC in regions of interest were drawn and were correlated with biopsy histological diagnosis and grading. Results: Histogram analysis of ADC values shows that 5th and 30th percentile parameters have statistically significant potency of discrimination between primary and secondary lesions groups (p values 0.0036 and 0.0125 respectively). Skewness of the histogram can help discriminate between good and poor differentiated (p value 0.17). Discrimination between primary malignancy site in metastases failed for the present number of patients in each subgroup. Conclusion: Statistical parameters reflecting the shape of the left side of the ADC histogram can be useful for discriminating between primary and secondary lesions and also between well differentiated versus moderate or poor. For the secondary malignancies, they failed to predict the original site of tumour.

Improving hypoxia map estimation by using model-free classification techniques in DCE-MRI images

The vascular microenvironment of tumors is a key determinant of the tumor pathophysiology. Hypoxia, i.e. lack of sufficient oxygen supply, might affect significantly the treatment efficacy of solid tumors making it an important imaging biomarker. The ability to characterize oxygen perfusion of the tumor can provide prognostic information about the tumor progression and risk of metastases. In this work, Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI), a non-invasive method, has been used for the detection of tumor hypoxic areas on neck sarcoma data. Data analysis was performed using a pattern recognition (PR) technique able to automatically identify potential tumor hypoxic regions along with a well-established pharmacokinetic (PK) model for computing perfusion parameters. The paper presents a novel method for the initialization of the PR technique through realistic assumptions in order to overcome instability issues found in random initialization. To this end, the PR technique was initialized using two novel approaches based on the wash-in part of the dynamic acquisition and the ktrans map derived from the PK analysis. The results, from these different implementations show high correlation between them and consistently lead to the separation of the tumor area into well-perfused, hypoxic and necrotic regions.

Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma

The main purpose of this study is to analyze the intrinsic tumor physiologic characteristics in patients with sarcoma through model-free analysis of dynamic contrast enhanced MR imaging data (DCE-MRI). Clinical data were collected from three patients with two different types of histologically proven sarcomas who underwent conventional and advanced MRI examination prior to excision. An advanced matrix factorization algorithm has been applied to the data, resulting in the identification of the principal time-signal uptake curves of DCE-MRI data, which were used to characterize the physiology of the tumor area, described by three different perfusion patterns i.e. hypoxic, well-perfused and necrotic one. The performance of the algorithm was tested by applying different initialization approaches with subsequent comparison of their results. The algorithm was proven to be robust and led to the consistent segmentation of the tumor area in three regions of different perfusion, i.e. well-perfused, hypoxic and necrotic. Results from the model-free approach were compared with a widely used pharmacokinetic (PK) model revealing significant correlations.

Correction to: Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma

The article Pattern recognition and pharmacokinetic methods on DCE-MRI data for tumor hypoxia mapping in sarcoma, written by M. Venianaki, O. Salvetti, E. de Bree, T. Maris, A. Karantanas, E. Kontopodis, K. Nikiforaki, K. Marias, was originally published electronically without open access.

Correction: Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models

Purpose The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. Material and methods Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio. Results All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area. Conclusion No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.

Imaging Biomarker Model-Based Analysis

Magnetic resonance imaging (MRI) is an imaging technique that is based on the interactions of water with external magnetic fields. Magnetic properties of water molecules are analyzed in order to sketch the profile of tissues, and they may be related to a variety of aspects including internal structure, tissue integrity, molecular environment, and others. In order to elucidate tissue properties, it is often necessary to acquire multiple series of images and quantify the progress of a certain parameter in time or the degree of response to an external perturbation. After careful sequence optimization in order to selectively trigger the process, one needs to appreciate all the possible factors affecting the evolution in order to constitute a robust model. Complex phenomena taking place after excitation are decomposed in one or more mathematical terms of an appropriate form and weighting, to comply with the physical rules behind the sequence of events taking place. When model predicted data converge to the experimental measurements, the model can be considered as reliable, in the frame of a carefully designed imaging protocol. In this chapter, we will focus on the most important models used to extract imaging biomarkers related to diffusion and perfusion studies.

Addressing Intravoxel Incoherent Motion challenges through an optimized fitting framework for quantification of perfusion

Diffusion Weighted Imaging (DWI) is a noninvasive imaging technique in Magnetic Resonance Imaging (MRI), providing significant anatomical and functional information in a wide range of clinical and research studies based on the random motion of water molecules. DWI, using appropriate models, can be quantified into clinically relevant biomarkers giving insight in the complexity of the tissue microstructure. Intravoxel Incoherent Motion (IVIM) has been extensively used as a promising model in DWI for assessing cellular density and microcirculation of blood in the tissue, and quantification of these tissue characteristics into biomarkers called true diffusion and micro perfusion. A complex mathematical framework is the basis of IVIM to extract reliable and accurate measures for true diffusion and micro perfusion from the fitting process. However not all biomarkers can be approximated with equal accuracy with micro perfusion being the most sensitive to fitting errors. The most important factors hindering the evaluation are highlighted and alternative methods to mitigate the fitting errors are proposed.