IN-VIVO CT DOSIMETRY DURING VIRTUAL COLONOSCOPY

Virtual colonoscopy (VC) is a minimally invasive means for identifying colorectal polyps and colorectal lesions by insufflating a patient’s bowel, applying contrast agent via rectal catheter, and performing multi-detector computed tomography (MDCT) scans. The technique is recommended for colonic health screening by the American Cancer Society but not funded by the Centers for Medicare and Medicaid Services (CMS) partially because of potential risks from radiation exposure. To date, no in‐vivo organ dose measurements have been performed for MDCT scans; thus, the accuracy of any current dose estimates is currently unknown. In this study, two TLDs were affixed to the inner lumen of standard rectal catheters used in VC, and in-vivo rectal dose measurements were obtained within 6 VC patients. In order to calculate rectal dose, TLD-100 powder response was characterized at diagnostic doses such that appropriate correction factors could be determined for VC. A third-order polynomial regression with a goodness of fit factor of R2=0.992 was constructed from this data. Rectal dose measurements were acquired with TLDs during simulated VC within a modified anthropomorphic phantom configured to represent three sizes of patients undergoing VC. The measured rectal doses decreased in an exponential manner with increasing phantom effective diameter, with R2=0.993 for the exponential regression model and a maximum percent coefficient of variation (%CoV) of 4.33%. In-vivo measurements yielded rectal doses ranged from that decreased exponentially with increasing patient effective diameter, in a manner that was also favorably predicted by the size specific dose estimate (SSDE) model for all VC patients that were of similar age, body composition, and TLD placement. The measured rectal dose within a younger patient was favorably predicted by the anthropomorphic phantom dose regression model due to similarities in the percentages of highly attenuating material at the respective measurement locations and in the placement of the TLDs. The in-vivo TLD response did not increase in %CoV with decreasing dose, and the largest %CoV was 10.0%.

Colonoscopia virtual e DNA fecal : estudo de viabilidade como testes de rastreio do cancro colorretal

Tese de doutoramento, Cirurgia Geral (Medicina), Universidade de Lisboa, Faculdade de Medicina, 2014

Colonoscopia virtual em cães

Pós-graduação em Medicina Veterinária - FMVZ

Colonografia por tomografia computorizada : colonoscopia virtual a evidência actual

Trabalho Final do Curso de Mestrado Integrado em Medicina, Faculdade de Medicina, Universidade de Lisboa, 2013

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Prospective study on bright lumen magnetic resonance colonography in comparison with conventional colonoscopy

The aim of this prospective trial was to evaluate sensitivity and specificity of bright lumen magnetic resonance colonography (MRC) in comparison with conventional colonoscopy (CC). A total of 120 consecutive patients with clinical indications for CC were prospectively examined using MRC (1.5 Tesla) which was then followed by CC. Prior to MRC, the cleansed colon was filled with a gadolinium-water solution. A 3D GRE sequence was performed with the patient in the prone and supine position, each acquired during one breathhold period. After division of the colon into five segments, interactive data analysis was carried out using three-dimensional post-processing, including a virtual intraluminal view. The results of CC served as a reference standard. In all patients MRC was performed successfully and no complications occurred. Image quality was diagnostic in 92% (574/620 colonic segments). On a per-patient basis, the results of MRC were as follows: sensitivity 84% (95% CI 71.7-92.3%), specificity 97% (95% CI 89.0-99.6%). Five flat adenomas and 6/16 small polyps (< or =5 mm) were not identified by MRC. MRC offers high sensitivity and excellent specificity rates in patients with clinical indications for CC. Improved MRC techniques are needed to detect small polyps and flat adenomas.

Exploring the effectiveness of virtual product placement on recall and recognition of new brands

This paper investigates the effectiveness of virtual product placement as a marketing tool by examining the relationship between brand recall and recognition and virtual product placement. It also aims to address a gap in the existing academic literature by focusing on the impact of product placement on recall and recognition of new brands. The growing importance of product placement is discussed and a review of previous research on product placement and virtual product placement is provided. The research methodology used to study the recall and recognition effects of virtual product placement are described and key findings presented. Finally, implications are discussed and recommendations for future research provided.