Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging

© 2014 Elsevier Ltd. All rights reserved. Multi-modal image registration is becoming an increasingly powerful tool for medical diagnosis and treatment. The combination of different image modalities facilitates much greater understanding of the underlying condition, resulting in improved patient care. Mutual Information is a popular image similarity measure for performing multi-modal image registration. However, it is recognised that there are limitations with the technique that can compromise the accuracy of the registration, such as the lack of spatial information that is accounted for by the similarity measure. In this paper, we present a two-stage non-rigid registration process using a novel similarity measure, Feature Neighbourhood Mutual Information. The similarity measure efficiently incorporates both spatial and structural image properties that are not traditionally considered by MI. By incorporating such features, we find that this method is capable of achieving much greater registration accuracy when compared to existing methods, whilst also achieving efficient computational runtime. To demonstrate our method, we use a challenging medical image data set consisting of paired retinal fundus photographs and confocal scanning laser ophthalmoscope images. Accurate registration of these image pairs facilitates improved clinical diagnosis, and can be used for the early detection and prevention of glaucoma disease

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T

At ultrahigh field strengths images of the body are hampered by B1-field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a “bias field” to the ideal image. Current bias field correction methods, such as the N4 algorithm, assume a low frequency bias field, which is not sufficiently valid for T2w images at 7 T. In this work we propose a deep learning based bias field correction method to address this issue for T2w prostate images at 7 T. By combining simulated B1-field distributions of a multi-transmit setup at 7 T with T2w prostate images at 1.5 T, we generated artificial 7 T images for which the homogeneous counterpart was available. Using these paired data, we trained a neural network to correct the bias field. We predicted either a homogeneous image (t-Image neural network) or the bias field (t-Biasf neural network). In addition, we experimented with the single-channel images of the receive array and the corresponding sum of magnitudes of this array as the input image. Testing was carried out on four datasets: the test split of the synthetic training dataset, volunteer and patient images at 7 T, and patient images at 3 T. For the test split, the performance was evaluated using the structural similarity index measure, Wasserstein distance, and root mean squared error. For all other test data, the features Homogeneity and Energy derived from the gray level co-occurrence matrix (GLCM) were used to quantify the improvement. For each test dataset, the proposed method was compared with the current gold standard: the N4 algorithm. Additionally, a questionnaire was filled out by two clinical experts to assess the homogeneity and contrast preservation of the 7 T datasets. All four proposed neural networks were able to substantially reduce the B1-field induced inhomogeneities in T2w 7 T prostate images. By visual inspection, the images clearly look more homogeneous, which is confirmed by the increase in Homogeneity and Energy in the GLCM, and the questionnaire scores from two clinical experts. Occasionally, changes in contrast within the prostate were observed, although much less for the t-Biasf network than for the t-Image network. Further, results on the 3 T dataset demonstrate that the proposed learning based approach is on par with the N4 algorithm. The results demonstrate that the trained networks were capable of reducing the B1-field induced inhomogeneities for prostate imaging at 7 T. The quantitative evaluation showed that all proposed learning based correction techniques outperformed the N4 algorithm. Of the investigated methods, the single-channel t-Biasf neural network proves most reliable for bias field correction.</p

Deep learning based correction of RF field induced inhomogeneities for T2w prostate imaging at 7 T

At ultrahigh field strengths images of the body are hampered by B1-field inhomogeneities. These present themselves as inhomogeneous signal intensity and contrast, which is regarded as a “bias field” to the ideal image. Current bias field correction methods, such as the N4 algorithm, assume a low frequency bias field, which is not sufficiently valid for T2w images at 7 T. In this work we propose a deep learning based bias field correction method to address this issue for T2w prostate images at 7 T. By combining simulated B1-field distributions of a multi-transmit setup at 7 T with T2w prostate images at 1.5 T, we generated artificial 7 T images for which the homogeneous counterpart was available. Using these paired data, we trained a neural network to correct the bias field. We predicted either a homogeneous image (t-Image neural network) or the bias field (t-Biasf neural network). In addition, we experimented with the single-channel images of the receive array and the corresponding sum of magnitudes of this array as the input image. Testing was carried out on four datasets: the test split of the synthetic training dataset, volunteer and patient images at 7 T, and patient images at 3 T. For the test split, the performance was evaluated using the structural similarity index measure, Wasserstein distance, and root mean squared error. For all other test data, the features Homogeneity and Energy derived from the gray level co-occurrence matrix (GLCM) were used to quantify the improvement. For each test dataset, the proposed method was compared with the current gold standard: the N4 algorithm. Additionally, a questionnaire was filled out by two clinical experts to assess the homogeneity and contrast preservation of the 7 T datasets. All four proposed neural networks were able to substantially reduce the B1-field induced inhomogeneities in T2w 7 T prostate images. By visual inspection, the images clearly look more homogeneous, which is confirmed by the increase in Homogeneity and Energy in the GLCM, and the questionnaire scores from two clinical experts. Occasionally, changes in contrast within the prostate were observed, although much less for the t-Biasf network than for the t-Image network. Further, results on the 3 T dataset demonstrate that the proposed learning based approach is on par with the N4 algorithm. The results demonstrate that the trained networks were capable of reducing the B1-field induced inhomogeneities for prostate imaging at 7 T. The quantitative evaluation showed that all proposed learning based correction techniques outperformed the N4 algorithm. Of the investigated methods, the single-channel t-Biasf neural network proves most reliable for bias field correction.</p

Aortic dissection type I in a weightlifter with hypertension: A case report

Acute aortic dissection can occur at the time of intense physical exertion in strength-trained athletes like weightlifters, bodybuilders, throwers, and wrestlers

Phase I and pharmacological study of the farnesyltransferase inhibitor tipifarnib (Zarnestra®, R115777) in combination with gemcitabine and cisplatin in patients with advanced solid tumours

This phase I trial was designed to determine the safety and maximum tolerated dose (MTD) of tipifarnib in combination with gemcitabine and cisplatin in patients with advanced solid tumours. Furthermore, the pharmacokinetics of each of these agents was evaluated. Patients were treated with tipifarnib b.i.d. on days 1–7 of each 21-day cycle. In addition, gemcitabine was given as a 30-min i.v. infusion on days 1 and 8 and cisplatin as a 3-h i.v. infusion on day 1. An interpatient dose-escalation scheme was used. Pharmacokinetics was determined in plasma and white blood cells. In total, 31 patients were included at five dose levels. Dose-limiting toxicities (DLTs) consisted of thrombocytopenia grade 4, neutropenia grade 4, febrile neutropenia grade 4, electrolyte imbalance grade 3, fatigue grade 3 and decreased hearing grade 2. The MTD was tipifarnib 200 mg b.i.d., gemcitabine 1000 mg m−2 and cisplatin 75 mg m−2. Eight patients had a confirmed partial response and 12 patients stable disease. No clinically relevant pharmacokinetic interactions were observed. Tipifarnib can be administered safely at 200 mg b.i.d. in combination with gemcitabine 1000 mg m−2 and cisplatin 75 mg m−2. This combination showed evidence of antitumour activity and warrants further evaluation in a phase II setting

Deep Learning-Based Grading of Ductal Carcinoma In Situ in Breast Histopathology Images

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer that can progress into invasive ductal carcinoma (IDC). Studies suggest DCIS is often overtreated since a considerable part of DCIS lesions may never progress into IDC. Lower grade lesions have a lower progression speed and risk, possibly allowing treatment de-escalation. However, studies show significant inter-observer variation in DCIS grading. Automated image analysis may provide an objective solution to address high subjectivity of DCIS grading by pathologists. In this study, we developed a deep learning-based DCIS grading system. It was developed using the consensus DCIS grade of three expert observers on a dataset of 1186 DCIS lesions from 59 patients. The inter-observer agreement, measured by quadratic weighted Cohen's kappa, was used to evaluate the system and compare its performance to that of expert observers. We present an analysis of the lesion-level and patient-level inter-observer agreement on an independent test set of 1001 lesions from 50 patients. The deep learning system (dl) achieved on average slightly higher inter-observer agreement to the observers (o1, o2 and o3) ($\kappa_{o1,dl}=0.81, \kappa_{o2,dl}=0.53, \kappa_{o3,dl}=0.40$) than the observers amongst each other ($\kappa_{o1,o2}=0.58, \kappa_{o1,o3}=0.50, \kappa_{o2,o3}=0.42$) at the lesion-level. At the patient-level, the deep learning system achieved similar agreement to the observers ($\kappa_{o1,dl}=0.77, \kappa_{o2,dl}=0.75, \kappa_{o3,dl}=0.70$) as the observers amongst each other ($\kappa_{o1,o2}=0.77, \kappa_{o1,o3}=0.75, \kappa_{o2,o3}=0.72$). In conclusion, we developed a deep learning-based DCIS grading system that achieved a performance similar to expert observers. We believe this is the first automated system that could assist pathologists by providing robust and reproducible second opinions on DCIS grade

Effects of pre- and postnatal exposure to chlorinated dioxins and furans on human neonatal thyroid hormone concentrations.

Animal studies have shown that dioxins influence plasma thyroid hormone concentrations. To investigate the effect of chlorinated dioxins and furans on thyroid hormone concentrations in humans, we studied 38 healthy breast-fed infants. The study population was divided into two groups according to the dioxin concentrations in milk fat of their mothers. Blood samples were taken at birth and at the ages of 1 and 11 weeks. At birth a tendency to higher total thyroxine (tT4) concentrations was found in the high exposure group. At the ages of 1 and 11 weeks the increase of mean tT4 concentrations and tT4/thyroxine-binding globulin ratios in the high exposure group reached significance as compared to the low exposure group. At birth and 1 week after birth, mean thyrotropin (TSH) concentrations were similar in both groups, but at the age of 11 weeks the mean TSH concentrations were significantly higher in the high exposure group. We postulate that the observed plasma tT4 elevation in infants exposed to dioxins before and after birth is the result of an effect on the thyroid hormone regulatory system

"Is social inclusion through PE, sport and PA still a rhetoric?" evaluating the relationship between physical education, sport and social inclusion

This Special Issue is part of Educational Review’s Hall of Fame, comprising the journal’s most read and highly cited papers. As part of this I will be critiquing a milestone paper within the field(s) of Sport, PE and (I will extend to) PA by Professor Richard Bailey. The paper has been amongst the most-cited in the journal and I have personally cited the paper numerous times in my own work thus far. Upon its original publication (nearly 13 years ago), the article (managed to provide a very useful distinction between PE and sport (and PA), which is important given the constant slippage between the terms in many articles since. In this response article, I will try to provide a brief summary of the paper from Bailey, but at the same time examine closely the notion of social inclusion through sport and PE by summarising work that has subsequently been conducted. I will conclude by summarising that some 13 years later spurious claims about effective inclusive practices through sport abound, and we still lack clear evidence to support the rhetoric about the ways in which sport and PE can contribute to social inclusion

Consensus recommendations on training and competing in the heat

Exercising in the heat induces thermoregulatory and other physiological strain that can lead to impairments in endurance exercise capacity. The purpose of this consensus statement is to provide up-to-date recommendations to optimize performance during sporting activities undertaken in hot ambient conditions. The most important intervention one can adopt to reduce physiological strain and optimize performance is to heat acclimatize. Heat acclimatization should comprise repeated exercise–heat exposures over 1–2 weeks. In addition, athletes should initiate competition and training in an euhydrated state and minimize dehydration during exercise. Following the development of commercial cooling systems (e.g., cooling vests), athletes can implement cooling strategies to facilitate heat loss or increase heat storage capacity before training or competing in the heat. Moreover, event organizers should plan for large shaded areas, along with cooling and rehydration facilities, and schedule events in accordance with minimizing the health risks of athletes, especially in mass participation events and during the first hot days of the year. Following the recent examples of the 2008 Olympics and the 2014 FIFA World Cup, sport governing bodies should consider allowing additional (or longer) recovery periods between and during events for hydration and body cooling opportunities when competitions are held in the heat