Visual MRI: Merging information visualization and non-parametric clustering techniques for MRI dataset analysis

Castellani, Umberto; Cristani, Marco; Combi, Carlo; Murino, Vittorio; Sbarbati, Andrea; Marzola, Pasquina

doi:10.1016/j.artmed.2008.06.006

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…Our method extends our previous work proposed in [3], and brings two advantages to the current state of the DCE-MRI analysis. First, it allows a more stable and robust feature extraction step from DCE-MRI raw data.…”

Section: Introductionsupporting

confidence: 56%

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DCE-MRI Data Analysis for Cancer Area Classification

Castellani¹,

Cristiani²,

Daducci³

et al. 2009

Methods Inf Med

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Section: Introductionsupporting

confidence: 56%

“…In a previous paper [3], we proposed the introduction of the MS clustering on the DCE-MRI data of tumoral regions. In that case, we focused on standard tumor microvessel parameters, such as transendothelial permeability (k PS) and fractional plasma volume ( f PV), obtained voxel-by-voxel from intensity time curves.…”

Section: Introductionmentioning

confidence: 99%

DCE-MRI Data Analysis for Cancer Area Classification

Castellani¹,

Cristiani²,

Daducci³

et al. 2009

Methods Inf Med

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“…MR, for which the datasets can be large and complex, can greatly benefit from effective visualization techniques. Recently, information visualization has been combined with data mining to develop a novel analysis and visualization method for DCE‐MR tumor datasets (59). While this technique has not yet been applied to lung nodules, evidence of correspondence between the boundaries identified by the approach and histologically identified tissues was presented.…”

Section: Pulmonary Nodule Evaluation and Management On Ct And Mrimentioning

confidence: 99%

Recent technological and application developments in computed tomography and magnetic resonance imaging for improved pulmonary nodule detection and lung cancer staging

Sieren

Ohno

Koyama

et al. 2010

Magnetic Resonance Imaging

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This review compares the emerging technologies and approaches in the application of magnetic resonance (MR) and computed tomography (CT) imaging for the assessment of pulmonary nodules and staging of malignant findings. Included in this review is a brief definition of pulmonary nodules and an introduction to the challenges faced. We have highlighted the current status of both MR and CT for the early detection of lung nodules. Developments are detailed in this review for the management of pulmonary nodules using advanced imaging, including: dynamic imaging studies, dual energy CT, computer aided detection and diagnosis, and imaging assisted nodule biopsy approaches which have improved lung nodule detection and diagnosis rates. Recent advancements linking in vivo imaging to corresponding histological pathology are also highlighted. In vivo imaging plays a pivotal role in the clinical staging of pulmonary nodules through TNM assessment. While CT and positron emission tomography (PET)/CT are currently the most commonly clinically employed modalities for pulmonary nodule staging, studies are presented that highlight the augmentative potential of MR. LUNG CANCER remains a challenging disease to effectively identify, diagnose, and treat, as reflected by the 5-year survival rate, which remains similar to that of 15 years ago at 13%. Lung cancer frequently manifests as a peripheral lung nodule or mass. Radiologically identified lesions in the lung that are less than 30 mm in diameter are defined as pulmonary nodules, while those greater than 30 mm are termed masses (1). Both pulmonary nodules and masses present specific challenges in the evaluation of suspected lung cancer; however, the greatest impact of x-ray computed tomography (CT) and magnetic resonance imaging (MRI) developments have been on the detection and characterization of pulmonary nodules. Hence, the scope of this review will focus on the recent advances in CT and MRI of pulmonary nodules and lung cancer staging, highlighting the respective strengths and weaknesses of these modalities. X-ray chest radiography (CXR) is the most widely utilized modality for pulmonary abnormalities and disease screening, although sonography has been utilized in limited situations in the chest. In addition, positron emission tomography (PET) or PET/CT with 2-deoxy-2-[18F]fluoro-D-glucose (FDG) is currently widely utilized for differentiation of malignant lesions from benign lesions, staging of malignant tumors, and assessment of therapeutic effects after conservative therapy. The value of CT and MR for depicting structural anatomy is widely appreciated and applied clinically to noninvasively identify abnormalities and/ or disease. Recent developments of imaging techniques and processing have expanded the potential of these modalities to include combined structure and function. Recent exciting work for the detection, monitoring, characterization, and staging of pulmonary cancer nodules using imaging of both CT and MR modalities will be summarized in this review.While the overall...

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“…Several works are based on the use of machine learning techniques for DCE-MRI tumour analysis [3,4,5,6]. In [3], a visual data-mining approach is proposed to support the medical researchers in tumoral areas characterization by clustering data according to the transendothelial permeability (kPS) and fractional plasma volume (fPV).…”

Section: Introductionmentioning

confidence: 99%

“…In [3], a visual data-mining approach is proposed to support the medical researchers in tumoral areas characterization by clustering data according to the transendothelial permeability (kPS) and fractional plasma volume (fPV). Although kPS and fPV are accepted estimate of tissue vasculature, their instability under small perturbation of the chosen pharmacokinetics model was proved [4,5].…”

Section: Introductionmentioning

confidence: 99%

Learning Approach to Analyze Tumour Heterogeneity in DCE-MRI Data During Anti-cancer Treatment

Daducci

Castellani

Cristani

et al. 2009

Artificial Intelligence in Medicine

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Abstract. The paper proposes a learning approach to support medical researchers in the context of in-vivo cancer imaging, and specifically in the analysis of Dynamic Contrast-Enhanced MRI (DCE-MRI) data. Tumour heterogeneity is characterized by identifying regions with different vascular perfusion. The overall aim is to measure volume differences of such regions for two experimental groups: the treated group, to which an anticancer therapy is administered, and a control group. The proposed approach is based on a three-steps procedure: (i) robust features extraction from raw time-intensity curves, (ii) sample-regions identification manually traced by medical researchers on a small portion of input data, and (iii) overall segmentation by training a Support Vector Machine (SVM) to classify the MRI voxels according to the previously identified cancer areas. In this way a non-invasive method for the analysis of the treatment efficacy is obtained as shown by the promising results reported in our experiments.

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Visual MRI: Merging information visualization and non-parametric clustering techniques for MRI dataset analysis

Cited by 8 publications

References 33 publications

DCE-MRI Data Analysis for Cancer Area Classification

DCE-MRI Data Analysis for Cancer Area Classification

Recent technological and application developments in computed tomography and magnetic resonance imaging for improved pulmonary nodule detection and lung cancer staging

Learning Approach to Analyze Tumour Heterogeneity in DCE-MRI Data During Anti-cancer Treatment

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