307 research outputs found
Quantitative Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Breast Images: Optimization of the Time-to-Peak as a Diagnostic Indicator
Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in the diagnosis of breast cancer and as an aid in the management of this disease. Although DCE-MRI has a high sensitivity for the detection of malignant breast lesions, distinguishing malignant from benign lesions is more challenging for this method and may depend to some extent on how the images are analysed. Although clinical assessment of these images typically involves qualitative assessment by an expert, there is growing interest in the development of quantitative and automated methods to assist the expert assessment. This thesis involves the quantitative analysis of a particular empirical feature of the time evolution of the DCE-MRI signal known as the time-to-peak ( 7 ^ ) . In particular, this thesis investigates die feasibility of applying measures sensitive to 7 ^ heterogeneity as indicators for malignancy in breast DCE-MRI.
Breast lesions in this study were automatically segmented by K-means clustering. Voxel- by-voxel 7\u27peak values were extracted using an empirical model. The / 1th percentile values (p = 10, 20...) of the 7’peak distribution within each lesion, as well as the fractional and absolute hot spot volumes were determined, where hot spot volume refers to the volume of tissue with 7 ^ less than a threshold value. Using the area under the receiver operating characteristic curve (AUC), these measures were tested as indicators for differentiating fibroadenomas from invasive lesions and from ductal carcinoma in situ, as well as for differentiating non-fibroadenoma benign lesions from these malignant lesions. For differentiating fibroadenomas from malignant lesions, low percentile values (p = 10) provided high diagnostic performance. At the optimal threshold (3 min), the hot spot volume provided high diagnostic performance. However, non-fibroadenoma benign lesions were quite difficult to distinguish from malignant lesions. This thesis demonstrates that quantitative analysis of the 7’peak distribution can be optimized for diagnostic performance providing indicators sensitive to intra-lesion r peak heterogeneity
Breast dynamic contrast-enhanced-magnetic resonance imaging and radiomics: State of art
Breast cancer represents the most common malignancy in women, being one of the most frequent cause of cancer-related mortality. Ultrasound, mammography, and magnetic resonance imaging (MRI) play a pivotal role in the diagnosis of breast lesions, with different levels of accuracy. Particularly, dynamic contrast-enhanced MRI has shown high diagnostic value in detecting multifocal, multicentric, or contralateral breast cancers. Radiomics is emerging as a promising tool for quantitative tumor evaluation, allowing the extraction of additional quantitative data from radiological imaging acquired with different modalities. Radiomics analysis may provide novel information through the quantification of lesions heterogeneity, that may be relevant in clinical practice for the characterization of breast lesions, prediction of tumor response to systemic therapies and evaluation of prognosis in patients with breast cancers. Several published studies have explored the value of radiomics with good-to-excellent diagnostic and prognostic performances for the evaluation of breast lesions. Particularly, the integrations of radiomics data with other clinical and histopathological parameters have demonstrated to improve the prediction of tumor aggressiveness with high accuracy and provided precise models that will help to guide clinical decisions and patients management. The purpose of this article in to describe the current application of radiomics in breast dynamic contrast-enhanced MRI
Emerging Techniques in Breast MRI
As indicated throughout this chapter, there is a constant effort to move to more sensitive, specific, and quantitative methods for characterizing breast tissue via magnetic resonance imaging (MRI). In the present chapter, we focus on six emerging techniques that seek to quantitatively interrogate the physiological and biochemical properties of the breast. At the physiological scale, we present an overview of ultrafast dynamic contrast-enhanced MRI and magnetic resonance elastography which provide remarkable insights into the vascular and mechanical properties of tissue, respectively. Moving to the biochemical scale, magnetization transfer, chemical exchange saturation transfer, and spectroscopy (both “conventional” and hyperpolarized) methods all provide unique, noninvasive, insights into tumor metabolism. Given the breadth and depth of information that can be obtained in a single MRI session, methods of data synthesis and interpretation must also be developed. Thus, we conclude the chapter with an introduction to two very different, though complementary, methods of data analysis: (1) radiomics and habitat imaging, and (2) mechanism-based mathematical modeling
Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast-Enhanced Magnetic Resonance Imaging: A Review
Tumor microvasculature possesses a high degree of heterogeneity in its structure and function. These features have been demonstrated to be important for disease diagnosis, response assessment, and treatment planning. The exploratory efforts of quantifying tumor vascular heterogeneity with DCE-MRI have led to promising results in a number of studies. However, the methodological implementation in those studies has been highly variable, leading to multiple challenges in data quality and comparability. This paper reviews several heterogeneity quantification methods, with an emphasis on their applications on DCE-MRI pharmacokinetic parametric maps. Important methodological and technological issues in experimental design, data acquisition, and analysis are also discussed, with the current opportunities and efforts for standardization highlighted
Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis
This Thesis describes the research work performed in the scope of a doctoral research program
and presents its conclusions and contributions. The research activities were carried on in the
industry with Siemens S.A. Healthcare Sector, in integration with a research team.
Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and
complete solutions in the medical sector. The company offers a wide selection of diagnostic
and therapeutic equipment and information systems. Siemens products for medical imaging and
in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis,
magnetic resonance, equipment to angiography and coronary angiography, nuclear
imaging, and many others.
Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically
interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness
in the sector.
The company owns several patents related with self-similarity analysis, which formed the background
of this Thesis. Furthermore, Siemens intended to explore commercially the computer-
aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the
high knowledge acquired by University of Beira Interior in this area together with this Thesis,
will allow Siemens to apply the most recent scienti c progress in the detection of the breast
cancer, and it is foreseeable that together we can develop a new technology with high potential.
The project resulted in the submission of two invention disclosures for evaluation in Siemens
A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index,
two other articles submitted in peer-reviewed journals, and several international conference
papers. This work on computer-aided-diagnosis in breast led to innovative software and novel
processes of research and development, for which the project received the Siemens Innovation
Award in 2012.
It was very rewarding to carry on such technological and innovative project in a socially sensitive
area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na
prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à
doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a
sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até
na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos
para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas.
Um destes métodos foi também adaptado para a classi cação de massas da mama, em
cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas
provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal
usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da
mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças
na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais,
permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram
extraídas por análise multifractal características dos tecidos que permitiram identi car os casos
tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal
3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de
mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método
padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece
informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado
por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a
interpretação dos radiologistas
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PET-MR Imaging of Hypoxia and Vascularity in Breast Cancer
Breast cancer is the most common cancer in the UK and in women globally. Imaging methods like mammography, ultrasound (US) and magnetic resonance imaging (MRI) play an important role in the diagnosis and management of breast cancer; they are generally utilised to provide anatomical or structural description of tumours in the clinical setting. It is widely accepted that the tumour microenvironment influences the phenotype, progression and treatment of breast cancer. This gave the impetus to move beyond tumour visualization in images to radiomics in order to provide additional disease characterisation and early biomarkers of tumour response.
Due to their ability to assess physiological processes in vivo, positron emission tomography (PET) and MRI can provide non-invasive characterisation of the tumour microenvironment, including perfusion, vascular permeability, cellularity and hypoxia, which is associated with poor clinical outcome and metastasis. Clinical imaging studies in breast tumours have hitherto assessed tumour physiological parameters separately, with only few directly comparing data from these modalities. To this end, hybrid PET-MRI represents an attractive option as it can allow examination of functional processes and features of tumours simultaneously, while also conferring methodological advantages to the way imaging information is combined.
The main aim of this thesis is to provide a better understanding of breast cancer pathophysiology using simultaneous PET and multi-parametric MRI. In particular, this work aims to explore relationships between imaging biomarkers of tumour vascularity measured by dynamic contrast-enhanced (DCE) MRI, cellularity using diffusion-weighted imaging (DWI) and hypoxic status using 18F-fluoromisonidazole (18F-FMISO) PET. Correlations between functional PET-MRI parameters and immunohistochemical (IHC) biomarkers of hypoxia and vascularity as well as MRI morphological tumour descriptors are also presented. The thesis concludes with an investigation of the utility of MRI markers of perfusion and surrogate markers of hypoxia to quantitatively monitor and predict pathological response in patients undergoing neoadjuvant chemotherapy (NACT) and provides projections for future work
Advanced perfusion quantification methods for dynamic PET and MRI data modelling
The functionality of tissues is guaranteed by the capillaries, which supply the microvascular
network providing a considerable surface area for exchanges between blood and tissues.
Microcirculation is affected by any pathological condition and any change in the blood supply
can be used as a biomarker for the diagnosis of lesions and the optimization of the treatment.
Nowadays, a number of techniques for the study of perfusion in vivo and in vitro are
available. Among the several imaging modalities developed for the study of microcirculation,
the analysis of the tissue kinetics of intravenously injected contrast agents or tracers is the
most widely used technique. Tissue kinetics can be studied using different modalities: the
positive enhancement of the signal in the computed tomography and in the ultrasound
dynamic contrast enhancement imaging; T1-weighted MRI or the negative enhancement of
T2* weighted MRI signal for the dynamic susceptibility contrast imaging or, finally, the
uptake of radiolabelled tracers in dynamic PET imaging. Here we will focus on the perfusion
quantification of dynamic PET and MRI data. The kinetics of the contrast agent (or the tracer)
can be analysed visually, to define qualitative criteria but, traditionally, quantitative
physiological parameters are extracted with the implementation of mathematical models.
Serial measurements of the concentration of the tracer (or of the contrast agent) in the tissue
of interest, together with the knowledge of an arterial input function, are necessary for the
calculation of blood flow or perfusion rates from the wash-in and/or wash-out kinetic rate
constants. The results depend on the acquisition conditions (type of imaging device, imaging
mode, frequency and total duration of the acquisition), the type of contrast agent or tracer
used, the data pre-processing (motion correction, attenuation correction, correction of the
signal into concentration) and the data analysis method.
As for the MRI, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a
non-invasive imaging technique that can be used to measure properties of tissue
microvasculature. It is sensitive to differences in blood volume and vascular permeability that
can be associated with tumour angiogenesis. DCE-MRI has been investigated for a range of
clinical oncologic applications (breast, prostate, cervix, liver, lung, and rectum) including
cancer detection, diagnosis, staging, and assessment of treatment response. Tumour
microvascular measurements by DCE-MRI have been found to correlate with prognostic
factors (such as tumour grade, microvessel density, and vascular endothelial growth factor
expression) and with recurrence and survival outcomes. Furthermore, DCE-MRI changes
measured during treatment have been shown to correlate with outcome, suggesting a role as
a predictive marker. The accuracy of DCE-MRI relies on the ability to model the
pharmacokinetics of an injected contrast agent using the signal intensity changes on
sequential magnetic resonance images. DCE-MRI data are usually quantified with the
application of the pharmacokinetic two-compartment Tofts model (also known as the
standard model), which represents the system with the plasma and tissue (extravascular
extracellular space) compartments and with the contrast reagent exchange rates between
them. This model assumes a negligible contribution from the vascular space and considers
the system in, what-is-known as, the fast exchange limit, assuming infinitely fast
transcytolemmal water exchange kinetics. In general, the number, as well as any assumption
about the compartments, depends on the properties of the contrast agent used (mainly
gadolinium) together with the tissue physiology or pathology studied. For this reason, the
choice of the model is crucial in the analysis of DCE-MRI data. The value of PET in clinical oncology has been demonstrated with studies in a variety of
cancers including colorectal carcinomas, lung tumours, head and neck tumours, primary and
metastatic brain tumours, breast carcinoma, lymphoma, melanoma, bone cancers, and other
soft-tissue cancers. PET studies of tumours can be performed for several reasons including
the quantification of tumour perfusion, the evaluation of tumour metabolism, the tracing of
radiolabelled cytostatic agents. In particular, the kinetic analysis of PET imaging has showed,
in the past few years, an increasing value in tumour diagnosis, as well as in tumour therapy,
through providing additional indicative parameters. Many authors have showed the benefit
of kinetic analysis of anticancer drugs after labelling with radionuclide in measuring the
specific therapeutic effect bringing to light the feasibility of applying the kinetic analysis to
the dynamic acquisition. Quantification methods can involve visual analysis together with
compartmental modelling and can be applied to a wide range of different tracers. The
increased glycolysis in the most malignancies makes 18F-FDG-PET the most common
diagnostic method used in tumour imaging. But, PET metabolic alteration in the target tissue
can depend by many other factors. For example, most types of cancer are characterized by
increased choline transport and by the overexpression of choline kinase in highly proliferating
cells in response to enhanced demand of phosphatidylcholine (prostate, breast, lung, ovarian
and colon cancers). This effect can be diagnosed with choline-based tracers as the 18Ffluoromethylcholine
(18F-FCH), or the even more stable 18F-D4-Choline. Cellular
proliferation is also imaged with 18F-fluorothymidine (FLT), which is trapped within the
cytosol after being mono phosphorylated by thymidine kinase-1 (TK1), a principal enzyme
in the salvage pathway of DNA synthesis. 18F-FLT has been found to be useful for noninvasive
assessment of the proliferation rate of several types of cancer and showed high
reproducibility and accuracy in breast and lung cancer tumours.
The aim of this thesis is the perfusion quantification of dynamic PET and MRI data of patients
with lung, brain, liver, prostate and breast lesions with the application of advanced models.
This study covers a wide range of imaging methods and applications, presenting a novel
combination of MRI-based perfusion measures with PET kinetic modelling parameters in
oncology. It assesses the applicability and stability of perfusion quantification methods,
which are not currently used in the routine clinical practice.
The main achievements of this work include: 1) the assessment of the stability of perfusion
quantification of D4-Choline and 18F-FLT dynamic PET data in lung and liver lesions,
respectively (first applications in the literature); 2) the development of a model selection in
the analysis of DCE-MRI data of primary brain tumours (first application of the extended
shutter speed model); 3) the multiparametric analysis of PET and MRI derived perfusion
measurements of primary brain tumour and breast cancer together with the integration of
immuohistochemical markers in the prediction of breast cancer subtype (analysis of data
acquired on the hybrid PET/MRI scanner).
The thesis is structured as follows:
- Chapter 1 is an introductive chapter on cancer biology. Basic concepts, including the causes
of cancer, cancer hallmarks, available cancer treatments, are described in this first chapter.
Furthermore, there are basic concepts of brain, breast, prostate and lung cancers (which are
the lesions that have been analysed in this work). - Chapter 2 is about Positron Emission Tomography. After a brief introduction on the basics
of PET imaging, together with data acquisition and reconstruction methods, the chapter
focuses on PET in the clinical settings. In particular, it shows the quantification techniques
of static and dynamic PET data and my results of the application of graphical methods,
spectral analysis and compartmental models on dynamic 18F-FDG, 18F-FLT and 18F-D4-
Choline PET data of patients with breast, lung cancer and hepatocellular carcinoma.
- Chapter 3 is about Magnetic Resonance Imaging. After a brief introduction on the basics of
MRI, the chapter focuses on the quantification of perfusion weighted MRI data. In particular,
it shows the pharmacokinetic models for the quantification of dynamic contrast enhanced
MRI data and my results of the application of the Tofts, the extended Tofts, the shutter speed
and the extended shutter speed models on a dataset of patients with brain glioma.
- Chapter 4 introduces the multiparametric imaging techniques, in particular the combined
PET/CT and the hybrid PET/MRI systems. The last part of the chapter shows the applications
of perfusion quantification techniques on a multiparametric study of breast tumour patients,
who simultaneously underwent DCE-MRI and 18F-FDG PET on a hybrid PET/MRI scanner.
Then the results of a predictive study on the same dataset of breast tumour patients integrated
with immunohistochemical markers. Furthermore, the results of a multiparametric study on
DCE-MRI and 18F-FCM brain data acquired both on a PET/CT scanner and on an MR
scanner, separately. Finally, it will show the application of kinetic analysis in a radiomic
study of patients with prostate cancer
The Impact of Motion Correction on Lesion Characterization in DCE Breast MR Images
ABSTRACT In the context of dynamic contrast enhanced breast MR imaging we analyzed the effect of motion compensating registration on the characterization of lesions. Two registration techniques were applied: 1) rigid registration and 2) elastic registration based on the Navier-Lamé equation. Interpreting voxels that exhibit a decline in image intensity after contrast injection (compared to the non-contrasted native image) as motion outliers, it can be shown that the rate of motion outliers can be largely reduced by both rigid and elastic registration. The performance of lesion features, including maximal signal enhancement ratio and variance of the signal enhancement ratio, was measured by area under the ROC curve as well as Cohen's κ and showed significant improvement for elastic registration, whereas features derived from rigidly registered images did not in general exhibit a significant improvement over the level of unregistered data
Morphological quantitation software in breast MRI: application to neoadjuvant chemotherapy patients
The work in this thesis examines the use of texture analysis techniques and shape descriptors to analyse MR images of the breast and their application as a potential quantitative tool for prognostic indication.Textural information is undoubtedly very heavily used in a radiologist’s decision making process. However, subtle variations in texture are often missed, thus by quantitatively analysing MR images the textural properties that would otherwise be impossible to discern by simply visually inspecting the image can be obtained. Texture analysis is commonly used in image classification of aerial and satellite photography, studies have also focussed on utilising texture in MRI especially in the brain. Recent research has focussed on other organs such as the breast wherein lesion morphology is known to be an important diagnostic and prognostic indicator. Recent work suggests benefits in assessing lesion texture in dynamic contrast-enhanced (DCE) images, especially with regards to changes during the initial enhancement and subsequent washout phases. The commonest form of analysis is the spatial grey-level dependence matrix method, but there is no direct evidence concerning the most appropriate pixel separation and number of grey levels to utilise in the required co-occurrence matrix calculations. The aim of this work is to systematically assess the efficacy of DCE-MRI based textural analysis in predicting response to chemotherapy in a cohort of breast cancer patients. In addition an attempt was made to use shape parameters in order to assess tumour surface irregularity, and as a predictor of response to chemotherapy.In further work this study aimed to texture map DCE MR images of breast patients utilising the co-occurrence method but on a pixel by pixel basis in order to determine threshold values for normal, benign and malignant tissue and ultimately creating functionality within the in house developed software to highlight hotspots outlining areas of interest (possible lesions). Benign and normal data was taken from MRI screening data and malignant data from patients referred with known malignancies.This work has highlighted that textural differences between groups (based on response, nodal status, triple negative and biopsy grade groupings) are apparent and appear to be most evident 1-3 minutes post-contrast administration. Whilst the large number of statistical tests undertaken necessitates a degree of caution in interpreting the results, the fact that significant differences for certain texture parameters and groupings are consistently observed is encouraging.With regards to shape analysis this thesis has highlighted that some differences between groups were seen in shape descriptors but that shape may be limited as a prognostic indicator. Using textural analysis gave a higher proportion of significant differences whilst shape analysis results showed inconsistency across time points.With regards to the mapping this work successfully analysed the texture maps for each case and established lesion detection is possible. The study successfully highlighted hotspots in the breast patients data post texture mapping, and has demonstrated the relationship between sensitivity and false positive rate via hotspot thresholding
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