1,721,001 research outputs found
Applications of imaging processing to MRgFUS treatment for fibroids: A review
No full textMagnetic resonance guided focused ultrasound (MRgFUS) is an innovative technology that can treat many oncological diseases. Among these, uterine fibroids are well suited to be treated by focused ultrasound, because the treatment, unlike traditional surgical resection, is non-invasive and thus preserves the desired reproductive capacity of patients. There are some methodological issues in MRgFUS treatment that should be addressed. First, there is operator dependence; this is related to the use of manual approaches for the segmentation of regions of interest (ROI) for treatment, both in the initial stages of treatment planning and in the post-treatment evaluation of the ablated area. From this scenario, we understand the need to integrate MRgFUS technology with methods for the automatic detection of the regions affected by the treatment. Temperature monitoring techniques, based on proton resonance shift (PRF), although not always able to provide correct measurements, are the most used in the treatments guided by magnetic resonance. The dependence on a reference image makes the thermal maps obtained through PRF subject to artefacts and, consequently, to temperature measurement errors. It is therefore crucial to develop new techniques, perhaps based on referenceless thermometry approaches, in order to avoid overheating in unwanted areas that could lead to patients suffering burns. Closely linked to the two above mentioned problems, there is the motion compensation that would improve the current method (this requires re-planning MRgFUS treatment in cases of patient movement) as well as temperature monitoring since it would overcome limitations related to a fixed baseline image, uncorrelated to the real patient position. This paper addresses these important issues, providing for each of them a high-level discussion able to give a brief overview. Specific technical details are provided about one of the approaches in the literature, in order to make the discussed problem more complete and easier to understand
Can magnetic resonance image-guided focused ultrasound surgery replace local oncology treatments? A review
No full textMagnetic resonance image-guided focused ultrasound surgery (MRgFUS) is an innovative technology in the new panorama of treatment using ultrasound. It combines two well-known and distinct methodologies: high-intensity focused ultrasound (HIFU) and a magnetic resonance imaging system (MRI). This review on MRgFUS is focused on the technical aspects and the current clinical applications in oncology.More precisely, the advantages/disadvantages of MRgFUS compared to other local approaches such as surgery and radiotherapy are discussed in detail
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A fully automatic 2D segmentation method for uterine fibroid in MRgFUS treatment evaluation
No full textPurpose: Magnetic Resonance guided Focused UltraSound (MRgFUS) represents a non-invasive surgical approach that uses thermal ablation to treat uterine fibroids. After the MRgFUS treatment, an operator must manually segment the treated fibroid areas to evaluate the NonPerfused Volume (NPV). This manual approach is operator-dependent, introducing issues of result reproducibility, which could lead to errors in the subsequent follow-up phase. Moreover, manual segmentation is time-consuming, and can have a negative impact on the optimization of both machine-time and operator-time. Method: To address these issues, in this paper a novel fully automatic method based on the unsupervised Fuzzy C-Means clustering and iterative optimal threshold selection algorithms for uterus and fibroid segmentation is proposed. The developed method could be used to enhance the current manual methodology performed by healthcare operators for post-operative NPV evaluation in uterine fibroid MRgFUS treatments. Results: The proposed method was tested on 15 MR datasets of 15 different patients with uterine fibroids and evaluated using area-based and distance-based metrics. A comparison of extracted volume was also performed. Average values for fibroid (ROT) segmentation are SDI=88.67%, JI=80.70%, SE=89.79%, SP=88.73%, MAD=2.200 [pixels], MAXD=6.233 [pixels] and HD=2.988 [pixels]. Moreover, to make a quantitative evaluation of this method, our experimental results were compared with similar literature approaches. Conclusions: The proposed method provides a practical approach for the automatic evaluation of the boundary and volume of ablated fibroid regions, without any external user input. The achieved segmentation results show the validity and the effectiveness of the proposed solution
Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments
No full textUterine fibroids are benign tumors that can affect female patients during reproductive years. Magnetic resonance-guided focused ultrasound (MRgFUS) represents a noninvasive approach that uses thermal ablation principles to treat symptomatic fibroids. During traditional treatment planning, uterus, fibroids, and surrounding organs at risk must be manually marked on MR images by an operator. After treatment, an operator must segment, again manually, treated areas to evaluate the non-perfused volume (NPV) inside the fibroids. Both pre- and post-treatment procedures are time-consuming and operator-dependent. This paper presents a novel method, based on an advanced direct region detection model, for fibroid segmentation in MR images to address MRgFUS post-treatment segmentation issues. An incremental procedure is proposed: split-and-merge algorithm results are employed as multiple seed-region selections by an adaptive region growing procedure. The proposed approach segments multiple fibroids with different pixel intensity, even in the same MR image. The method was evaluated using area-based and distance-based metrics and was compared with other similar works in the literature. Segmentation results, performed on 14 patients, demonstrated the effectiveness of the proposed approach showing a sensitivity of 84.05 %, a specificity of 92.84 %, and a speedup factor of 1.56× with respect to classic region growing implementations (average values)
NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique
No full textStereotactic neuro-radiosurgery is a well-established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft-tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and growth. Therefore, particular attention must be deserved to these hypoxic areas that could lead to recurrent cancers and resistance to therapeutic damage. This article proposes a novel fully automatic method for necrosis extraction (NeXt), using the Fuzzy C-Means algorithm, after the GTV segmentation. This unsupervised Machine Learning technique detects and delineates the necrotic regions also in heterogeneous cancers. The overall processing pipeline is an integrated two-stage segmentation approach useful to support neuro-radiosurgery. NeXt can be exploited for dose escalation, allowing for a more selective strategy to increase radiation dose in hypoxic radioresistant areas. Moreover, NeXt analyzes contrast-enhanced T1-weighted MR images alone and does not require multispectral MRI data, representing a clinically feasible solution. This study considers an MRI dataset composed of 32 brain metastatic cancers, wherein 20 tumors present necroses. The segmentation accuracy of NeXt was evaluated using both spatial overlap-based and distance-based metrics, achieving these average values: Dice similarity coefficient 95.93% ± 4.23% and mean absolute distance 0.225 ± 0.229 (pixels)
Multimodal medical image registration using Particle Swarm Optimization: A review
No full textIntensity-based registration techniques have been increasingly used in multimodal image co-registration, which is a fundamental task in medical imaging, because it enables to integrate different images into a single representation such that complementary information can be easily accessed and fused. These schemes usually require the optimization of some similarity metric (e.g., Mutual Information) calculated on the input images. Local optimization methods often do not obtain good results, possibly leading to premature convergence to local optima, especially with non-smooth fitness functions. In these cases, we can adopt global optimization methods, and Swarm Intelligence techniques represent a very effective and efficient solution. This paper focuses on biomedical image registration using Particle Swarm Optimization (PSO). Several literature approaches are critically reviewed, by investigating modifications and hybridizations with Evolutionary Strategies. Since biomedical image registration represents a challenging clinical task, the experimental findings encourage further research studies in the near future
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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