Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical simulation of microwave ablation incorporating tissue contraction based on thermal dose

Tissue contraction plays an important role during high temperature tumor ablation, particularly during device characterization, treatment planning and imaging follow up. We measured such contraction in 18 ex vivo bovine liver samples during microwave ablation by tracking fiducial motion on CT imaging. Contraction was then described using a thermal dose dependent model and a negative thermal expansion coefficient based on the empirical data. FEM simulations with integrated electromagnetic wave propagation, heat transfer, and structural mechanics were evaluated using temperature-dependent dielectric properties and the negative thermal expansion models. Simulated temperature and displacement curves were then compared with the ex vivo experimental results on different continuous output powers. The optimized thermal dose model indicated over 50% volumetric contraction occurred at the temperature over 102.1 °C. The numerical simulation results on temperature and contraction-induced displacement showed a good agreement with experimental results. At microwave powers of 55 W, the mean errors on temperature between simulation and experimental results were 8.25%, 2.19% and 5.67% at 5 mm, 10 mm and 20 mm radially from the antenna, respectively. The simulated displacements had mean errors of 16.60%, 14.08% and 23.45% at the same radial locations. Compared to the experimental results, the simulations at the other microwave powers had larger errors with 10–40% mean errors at 40 W, and 10–30% mean errors at 25 W. The proposed model is able to predict temperature elevation and simulate tissue deformation during microwave ablation, and therefore may be incorporated into treatment planning and clinical translation from numerical simulations.
The correction of time and temperature effects in MR-based 3D Fricke xylenol orange dosimetry

Previously developed MR-based three-dimensional (3D) Fricke-xylenol orange (FXG) dosimeters can provide end-to-end quality assurance and validation protocols for pre-clinical radiation platforms. FXG dosimeters quantify ionizing irradiation induced oxidation of Fe2+ ions using pre- and post-irradiation MR imaging methods that detect changes in spin-lattice relaxation rates (R1 = T1−1) caused by irradiation induced oxidation of Fe2+. Chemical changes in MR-based FXG dosimeters that occur over time and with changes in temperature can decrease dosimetric accuracy if they are not properly characterized and corrected. This paper describes the characterization, development and utilization of an empirical model-based correction algorithm for time and temperature effects in the context of a pre-clinical irradiator and a 7 T pre-clinical MR imaging system. Time and temperature dependent changes of R1 values were characterized using variable TR spin-echo imaging. R1-time and R1-temperature dependencies were fit using non-linear least squares fitting methods. Models were validated using leave-one-out cross-validation and resampling. Subsequently, a correction algorithm was developed that employed the previously fit empirical models to predict and reduce baseline R1 shifts that occurred in the presence of time and temperature changes. The correction algorithm was tested on R1-dose response curves and 3D dose distributions delivered using a small animal irradiator at 225 kVp. The correction algorithm reduced baseline R1 shifts from −2.8 × 10−2 s−1 to 1.5 × 10−3 s−1. In terms of absolute dosimetric performance as assessed with traceable standards, the correction algorithm reduced dose discrepancies from approximately 3% to approximately 0.5% (2.90 ± 2.08% to 0.20 ± 0.07%, and 2.68 ± 1.84% to 0.46 ± 0.37% for the 10 × 10 and 8 × 12 mm2 fields, respectively). Chemical changes in MR-based FXG dosimeters produce time and temperature dependent R1 values for the time intervals and temperature changes found in a typical small animal imaging and irradiation laboratory setting. These changes cause baseline R1 shifts that negatively affect dosimeter accuracy. Characterization, modeling and correction of these effects improved in-field reported dose accuracy to less than 1% when compared to standardized ion chamber measurements.
Granulocytes as models for human protein marker identification following nicotine exposure

Nicotinic acetylcholine receptors (nAChRs) are pentameric cation channels expressed in the mammalian CNS, in the peripheral nervous system, and in skeletal muscle. Neuronal‐type nAChRs are also found in several non‐neuronal cell types, including leukocytes. Granulocytes are a subtype of leukocytes that include basophils, eosinophils, and neutrophils. Granulocytes, also known as polymorphonuclear leukocytes, are characterized by their ability to produce, store, and release compounds from intracellular granules. Granulocytes are the most abundant type of leukocyte circulating in the peripheral blood. Granulocyte abundance, nAChR expression, and nAChR upregulation following chronic nicotine administration makes granulocytes interesting models for identifying protein markers of nicotine exposure. Nicotinic receptor subunits and several non‐nAChR proteins have been identified as protein markers of granulocyte nicotine exposure. We review methods to isolate granulocytes from human tissue, summarize present data about the expression of nAChRs in the three granulocyte cell types (basophils, eosinophils, and neutrophils), describe current knowledge of the effects of nicotine exposure on human granulocyte protein expression, and highlight areas of interest for future investigation.
Generalized PSF modeling for optimized quantitation in PET imaging

Point-spread function (PSF) modeling offers the ability to account for resolution degrading phenomena within the PET image generation framework. PSF modeling improves resolution and enhances contrast, but at the same time significantly alters image noise properties and induces edge overshoot effect. Thus, studying the effect of PSF modeling on quantitation task performance can be very important. Frameworks explored in the past involved a dichotomy of PSF versus no-PSF modeling. By contrast, the present work focuses on quantitative performance evaluation of standard uptake value (SUV) PET images, while incorporating a wide spectrum of PSF models, including those that under- and over-estimate the true PSF, for the potential of enhanced quantitation of SUVs. The developed framework first analytically models the true PSF, considering a range of resolution degradation phenomena (including photon non-collinearity, inter-crystal penetration and scattering) as present in data acquisitions with modern commercial PET systems. In the context of oncologic liver FDG PET imaging, we generated 200 noisy datasets per image-set (with clinically realistic noise levels) using an XCAT anthropomorphic phantom with liver tumours of varying sizes. These were subsequently reconstructed using the OS-EM algorithm with varying PSF modelled kernels. We focused on quantitation of both SUVmean and SUVmax, including assessment of contrast recovery coefficients, as well as noise-bias characteristics (including both image roughness and coefficient of-variability), for different tumours/iterations/PSF kernels. It was observed that overestimated PSF yielded more accurate contrast recovery for a range of tumours, and typically improved quantitative performance. For a clinically reasonable number of iterations, edge enhancement due to PSF modeling (especially due to over-estimated PSF) was in fact seen to lower SUVmean bias in small tumours. Overall, the results indicate that exactly matched PSF modeling does not offer optimized PET quantitation, and that PSF overestimation may provide enhanced SUV quantitation. Furthermore, generalized PSF modeling may provide a valuable approach for quantitative tasks such as treatment-response assessment and prognostication.
A study to determine if dried moringa leaf powder is an acceptable supplement to combine with maize meal for Malawian children

The population of Malawi is just over 14 million. The staple crop in Malawi is maize, but in the past 10 years Malawi has experienced at least five severe droughts and a number of floods that have led to periods of famine. As a consequence of famine, 5% of the children are wasted or severely malnourished and 22% are underweight or malnourished. A sustainable approach to preventing malnutrition has become increasingly important in recent years with climate change, population pressures and global economic decline. It has been suggested that dried moringa leaves (Moringa oleifera), a locally grown product has the potential to go someway towards meeting the needs of Malawian children, as it is relatively rich in protein and micronutrients; however, it apparently has a bitter taste and an unfamiliar green colour. One-hundred-and-twenty orphaned children less than 6 years of age were randomly split into 3 groups. Over a course of 6 weeks they were each provided a porridge meal everyday. One group was given a standard maize meal porridge (Likuni Phala), a second group was given a maize/moringa/soya mix and the third group was given a maize and moringa mix. The porridges had a similar protein and energy content. Using plate wastage, questionnaires and observations, a level of acceptability for each porridge was determined. Cooks were also asked to evaluate the practicalities of using Moringa in the porridge. Analysis showed that in the first 3 weeks of the trial, the moringa and maize porridge (group 3) was less acceptable than the other two porridges. However, by week 6 there was no significant difference between the acceptability of the porridge mixes. This was reflected in both questionnaires responses and plate wastage. The cooks observed that although the three porridges required different cooking methods, all were acceptable. Due to the high level of acceptance in the final weeks of the trial, it is concluded that moringa leaf powder has the potential to be a very important source of local sustainable nutrition in the Malawian diet.
Determining the mechanical properties of a radiochromic silicone-based 3D dosimeter

New treatment modalities in radiotherapy (RT) enable delivery of highly conformal dose distributions in patients. This creates a need for precise dose verification in three dimensions (3D). A radiochromic silicone-based 3D dosimetry system has recently been developed. Such a dosimeter can be used for dose verification in deformed geometries, which requires knowledge of the dosimeter’s mechanical properties. In this study we have characterized the dosimeter’s elastic behaviour under tensile and compressive stress. In addition, the dose response under strain was determined. It was found that the dosimeter behaved as an incompressible hyperelastic material with a non-linear stress/strain curve and with no observable hysteresis or plastic deformation even at high strains. The volume was found to be constant within a 2% margin at deformations up to 60%. Furthermore, it was observed that the dosimeter returned to its original geometry within a 2% margin when irradiated under stress, and that the change in optical density per centimeter was constant regardless of the strain during irradiation. In conclusion, we have shown that this radiochromic silicone-based dosimeter’s mechanical properties make it a viable candidate for dose verification in deformable 3D geometries.
Groupwise registration of MR brain images with tumors

A novel groupwise image registration framework is developed for registering MR brain images with tumors. Our method iteratively estimates a normal-appearance counterpart for each tumor image to be registered and constructs a directed graph (digraph) of normal-appearance images to guide the groupwise image registration. Particularly, our method maps each tumor image to its normal appearance counterpart by identifying and inpainting brain tumor regions with intensity information estimated using a low-rank plus sparse matrix decomposition based image representation technique. The estimated normal-appearance images are groupwisely registered to a group center image guided by a digraph of images so that the total length of ‘image registration paths’ to be the minimum, and then the original tumor images are warped to the group center image using the resulting deformation fields. We have evaluated our method based on both simulated and real MR brain tumor images. The registration results were evaluated with overlap measures of corresponding brain regions and average entropy of image intensity information, and Wilcoxon signed rank tests were adopted to compare different methods with respect to their regional overlap measures. Compared with a groupwise image registration method that is applied to normal-appearance images estimated using the traditional low-rank plus sparse matrix decomposition based image inpainting, our method achieved higher image registration accuracy with statistical significance (p = 7.02 × 10−9).
Use of CPR in hemorrhagic shock, a dog model

INTRODUCTION Cardiopulmonary resuscitation was designed for sudden cardiac events usually triggered by thrombotic phenomena. Despite this, it is routinely used in trauma resuscitations as per the American Heart guidelines. There is no data supporting the use of chest compressions in hemorrhagic shock. An evidence-based cardiopulmonary resuscitation (CPR) protocol has been developed for dogs. We sought to determine the effects and outcomes of chest compressions in hemorrhagic shock in a canine model. METHODS Eighteen dogs were randomized to three treatment groups—chest compressions only after hemorrhagic shock (CPR), CPR with fluid resuscitation after hemorrhagic shock (CPR + FLU), and fluid resuscitation alone after hemorrhagic shock (FLU). Under anesthesia, dogs were hemorrhaged until pulse was lost; they were maintained pulseless for 30 minutes and then resuscitated over 20 minutes. Vital signs and laboratory values were recorded at determined intervals. Echocardiography was performed throughout the study. Upon termination of the study, kidney, liver, heart, and brain tissue histology was evaluated for end organ damage. Statistical significance was p < 0.05 with a Bonferroni correction for multiple comparisons. RESULTS Blood loss and mean time to loss of pulse were similar between the groups. Dogs in the CPR group had significantly lower mean arterial pressure and higher pulse at all points compared to CPR + FLU and FLU (p < 0.05). Ejection fraction was lower in the CPR group at 5 and 10 minutes compared to the other groups (p < 0.05). Vital signs and laboratory results between CPR + FLU and FLU were equivalent. Two of six dogs in the CPR group died, while no dogs died in the CPR + FLU or FLU groups. Dogs in the CPR group were found to have more episodes of end organ damage. CONCLUSION There was no benefit to chest compressions in the hypovolemic animals. Chest compressions in addition to fluid did not reverse signs of shock better than fluid alone. Further research is needed to define if there is a role of CPR in the trauma patient with hemorrhagic shock.
A deep learning framework for supporting the classification of breast lesions in ultrasound images

In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.
Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study

Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT + BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D0.1/1/2cc, and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.
Early initiation of extracorporeal membrane oxygenation improves survival in adult trauma patients with severe adult respiratory distress syndrome

Background The use of extracorporeal membrane oxygenation (ECMO) in the trauma population has been reported to have a mortality benefit in patients with severe refractory hypoxic respiratory failure. This study compares the early initiation of ECMO for the management of severe adult respiratory distress syndrome (ARDS) versus a historical control immediately preceding the use of ECMO for trauma patients. Methods A retrospective study was conducted at a single verified Level I trauma center. The study population was limited to trauma patients diagnosed with severe ARDS using the Berlin definition (PaO2/FIO2 ratio < 100). Patients managed with ECMO versus conventional ventilation (CONV) were compared. The primary outcome of interest was mortality; secondary outcomes included hospital length of stay, intensive care unit–free days, and ventilator-free days. Results Fifteen ECMO patients managed from March 2013 to November 2014 were identified, as were 14 CONV patients managed from March 2012 to February 2013 who met the Berlin definition of severe ARDS. Data related to age, Injury Severity Scores (ISSs), admission lactic acid levels, base deficit, the number of transfused red blood cell units within the first 24 hours, and presence of severe traumatic brain injury were collected and were not statistically different between the groups. Likewise, Murray Lung Injury (MLI), Sequential Organ Failure Assessment (SOFA), and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores determined at the onset of severe ARDS were not statistically different between the groups. Median hospital stay (CONV, 28.0 days [14.0–47.0]; ECMO, 43.5 days [30.0–93.0]; p = 0.15), intensive care unit–free days (CONV, 0.0 days [0.0–5.0]; ECMO, 5.0 days [0.0–7.0]; p = 0.26), and ventilator-free days (CONV, 0.0 days [0.0–10.0]; ECMO, 8.0 days [0.0–19.0]; p = 0.13) were not statistically different between the groups. Mortality in the ECMO group was significantly reduced compared with the CONV group (ECMO, 13.3%; CONV, 64%; p = 0.01). Timing from the onset of severe ARDS to ECMO intervention occurred at a mean 1.9 ± 1.4 days. Conclusion Patients who were treated with ECMO for severe ARDS had an improved mortality compared with historical controls. ECMO should be considered at the early onset of severe ARDS to improve survival. LEVEL OF EVIDENCE Therapeutic study, level IV.
Towards an in-plane methodology to track breast lesions by using mammograms and patient-specific finite element simulations.

In breast cancer screening or diagnosis, it is usual to combine different images in order to locate a lesion as accurately as possible. These images are generated by using a single or several imaging techniques. As X-ray based mammography is used widespread, a breast lesion is located in the same plane of the image (mammogram), but tracking of a breast lesion across mammograms corresponding to different views is a daring task for medical physicians. According to this, simulation tools and methodologies that use patient-specific numerical models can facilitate the task of fusing information from different images. Additionally, these tools need to be as straightforward as possible to facilitate its translation to the clinical area. This paper presents a patient-specific, finite element (FE) based and semi-automated simulation methodology to track breast lesions across mammograms. A realistic, three-dimensional, computer model of a patient's breast was generated from magnetic resonance imaging (MRI) to simulate mammographic compressions in cranio-caudal (CC, head-to-toe) and medio-lateral oblique (MLO, shoulder-to-opposite hip) directions. For each compression being simulated, a virtual mammogram was obtained and posteriorly superimposed to the corresponding real mammogram, by sharing the nipple as a common feature. Two-dimensional rigid-body transformations were applied, and the error distance measured between the centroids of the tumor previously located on each image was 3.84 mm and 2.41 mm for CC and MLO compression, respectively. Considering that the scope of this work is to conceive a methodology translatable to the clinical practice, the results indicate that it could be helpful to support tracking of breast lesions.
A new free graft applied to the reconstruction of the nostril

IN making a new nose after its destruction by accident or disease three methods have been in constant use for the formation of the nostril margin : ( I ) A flap is turned in from the neighbourhood so that it forms the lining of the nostril, but requires a considerable waiting period between the graft and the making of the nose. The new nostril is liable to be lacking in support and apt to contract and form an atresia of the naris. (3) An extra piece of the covering flap is