BOSS: Bones, organs and skin shape model.

A virtual anatomical model of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions. For these tasks, it is highly desirable that the patient's surface and internal organs are of high quality for any pose and shape estimate. At present, the majority of statistical shape models (SSMs) are restricted to a small number of organs or bones or do not adequately represent the general population. To address this, we propose a deformable human shape and pose model that combines skin, internal organs, and bones, learned from CT images. By modeling the statistical variations in a pose-normalized space using probabilistic PCA while also preserving joint kinematics, our approach offers a holistic representation of the body that can be beneficial for automation in various medical applications. In an interventional setup, our model could, for example, facilitate automatic system/patient positioning, organ-specific iso-centering, automated collimation or collision prediction. We assessed our model's performance on a registered dataset, utilizing the unified shape space, and noted an average error of 3.6 mm for bones and 8.8 mm for organs. By utilizing solely skin surface data or patient metadata like height and weight, we find that the overall combined error for bone-organ measurement is 8.68 mm and 8.11 mm, respectively. To further verify our findings, we conducted additional tests on publicly available datasets with multi-part segmentations, which confirmed the effectiveness of our model. In the diverse TotalSegmentator dataset, the errors for bones and organs are observed to be 5.10mm and 8.72mm, respectively. Our work shows that anatomically parameterized statistical shape models can be created accurately and in a computationally efficient manner. The proposed approach enables the construction of shape models that can be directly integrated into to various medical applications.

Vitamin D composition of Australian game products.

The vitamin D content of many Australian game products is unknown. These foods are potential sources of vitamin D for remote-dwelling Aboriginal and Torres Strait Islander people, of whom 39% are vitamin D deficient (serum 25-hydroxyvitamin D3 (25(OH)D3) concentrations < 50 nmol/L). Vitamin D3, 25(OH)D3, vitamin D2 and 25(OH)D2 were measured by liquid chromatography-triple quadrupole mass spectrometry (LC-QQQ) in raw meat (camel, crocodile, emu, kangaroo), emu eggs and emu oil. Vitamin D3 (range, 0.5-14.5 µg/100 g) was found in all products except camel and kangaroo. All samples except kangaroo contained 25(OH)D3; some camel samples contained relatively high concentrations (range, 0.4-5.2 µg/100 g). Vitamin D2 was found in emu products and some kangaroo samples. We detected trace amounts of 25(OH)D2 in some camel and kangaroo samples. This study provides valuable insight into foods with a paucity of data on vitamin D content, showing that some are potentially useful sources of vitamin D.

Learning-based occupational x-ray scatter estimation.

Objective.During x-ray-guided interventional procedures, the medical staff is exposed to scattered ionizing radiation caused by the patient. To increase the staff's awareness of the invisible radiation and monitor dose online, computational scatter estimation methods are convenient. However, such methods are usually based on Monte Carlo (MC) simulations, which are inherently computationally expensive. Yet, in the interventional environment, immediate feedback to the personnel is desirable.Approach. In this work, we propose deep neural networks to mitigate the computational effort of MC simulations. Our learning-based models consider detailed models of the (outer) patient shape and (inner) anatomy, additional objects in the room, and the x-ray tube spectrum to cover imaging settings encountered in real interventional settings. We investigate two cases of scatter prediction. First, we employ network architectures to estimate the full three-dimensional (3D) scatter distribution. Second, we investigate the prediction of two-dimensional (2D) intensity projections that facilitate the intra-procedural visualization.Main results.Depending on the dimensionality of the estimated scatter distribution and the network architecture, the mean relative error of each network is in the range of 12% and 14% compared to MC simulations. However, 3D scatter distributions can be estimated within 60 ms and 2D distributions within 15 ms.Significance.Overall, our method is suitable to support the online assessment of scattered ionizing radiation in the interventional environment and can help to lower the occupational radiation risk.

Robust learning-based x-ray image denoising-potential pitfalls, their analysis and solutions.

Purpose:Since guidance based on x-ray imaging is an integral part of interventional procedures, continuous efforts are taken towards reducing the exposure of patients and clinical staff to ionizing radiation. Even though a reduction in the x-ray dose may lower associated radiation risks, it is likely to impair the quality of the acquired images, potentially making it more difficult for physicians to carry out their procedures.Method:We present a robust learning-based denoising strategy involving model-based simulations of low-dose x-ray images during the training phase. The method also utilizes a data-driven normalization step-based on an x-ray imaging model-to stabilize the mixed signal-dependent noise associated with x-ray images. We thoroughly analyze the method's sensitivity to a mismatch in dose levels used for training and application. We also study the impact of differing noise models used when training for low and very low-dose x-ray images on the denoising results.Results:A quantitative and qualitative analysis based on acquired phantom and clinical data has shown that the proposed learning-based strategy is stable across different dose levels and yields excellent denoising results, if an accurate noise model is applied. We also found that there can be severe artifacts when the noise characteristics of the training images are significantly different from those in the actual images to be processed. This problem can be especially acute at very low dose levels. During a thorough analysis of our experimental results, we further discovered that viewing the results from the perspective of denoising via thresholding of sub-band coefficients can be very beneficial to get a better understanding of the proposed learning-based denoising strategy.Conclusion:The proposed learning-based denoising strategy provides scope for significant x-ray dose reduction without the loss of important image information if the characteristics of noise is accurately accounted for during the training phase.

Vitamin D composition of Australian foods.

Australia needs accurate vitamin D food composition data to support public health initiatives. Previously, limitations in analytical methodology have precluded development of a comprehensive database. We used liquid chromatography with triple quadrupole mass spectrometry (LC-QQQ) to analyse 149 composite samples representing 98 foods (primary samples n = 896) in duplicate for vitamin D3, 25-hydroxyvitamin D3 (25(OH)D3), vitamin D2, 25(OH)D2. The greatest concentrations of vitamin D3 were found in canned salmon and a malted chocolate drink powder (fortified); chicken eggs and chicken leg meat contained the most 25(OH)D3. Margarine (fortified) and chocolate contained the greatest concentrations of vitamin D2, with smaller amounts found in various meat products. 25(OH)D2 was detected in various foods, including meats, and was quantitated in lamb liver. These data advance knowledge of dietary vitamin D in Australia and highlight the importance of analysis of these four forms of vitamin D to accurately represent the vitamin D content of food.

X-Ray Scatter Estimation Using Deep Splines.

X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics' constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results. Unfortunately, in the context of medical imaging, those may be misleading and could lead to wrong conclusions. To overcome this problem, we propose to embed B-splines as a known operator into neural networks. This inherently constrains their predictions to well-behaved and smooth functions. In a study using synthetic head and thorax data as well as real thorax phantom data, we found that our approach performed on par with U-net when comparing both algorithms based on quantitative performance metrics. However, our approach not only reduces runtime and parameter complexity, but we also found it much more robust to unseen noise levels. While the U-net responded with visible artifacts, the proposed approach preserved the X-ray signal's frequency characteristics.

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images.

In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation. Point-wise features that comprise spatial positions (and surface normal vectors in the case of structured meshes), and their corresponding image features, are used to encode the observation and represent the underlying 3D volume. The actor and value networks are applied iteratively to estimate a sequence of transformations that enable accurate delineation of object boundaries. The proposed approach was extensively evaluated in both segmentation and registration tasks using a variety of challenging clinical datasets. Our method has fewer trainable parameters and lower computational complexity compared to the 3D U-Net, and it is independent of the volume resolution. We show that the proposed method is applicable to mono- and multi-modal segmentation tasks, achieving significant improvements over the state-of-the-art for the latter. The flexibility of the proposed framework is further demonstrated for a multi-modal registration application. As we learn to predict actions rather than a target, the proposed method is more robust compared to the 3D U-Net when dealing with previously unseen datasets, acquired using different protocols or modalities. As a result, the proposed method provides a promising multi-purpose segmentation and registration framework, particular in the context of image-guided interventions.

XDose: toward online cross-validation of experimental and computational X-ray dose estimation.

PURPOSE: As the spectrum of X-ray procedures has increased both for diagnostic and for interventional cases, more attention is paid to X-ray dose management. While the medical benefit to the patient outweighs the risk of radiation injuries in almost all cases, reproducible studies on organ dose values help to plan preventive measures helping both patient as well as staff. Dose studies are either carried out retrospectively, experimentally using anthropomorphic phantoms, or computationally. When performed experimentally, it is helpful to combine them with simulations validating the measurements. In this paper, we show how such a dose simulation method, carried out together with actual X-ray experiments, can be realized to obtain reliable organ dose values efficiently. METHODS: A Monte Carlo simulation technique was developed combining down-sampling and super-resolution techniques for accelerated processing accompanying X-ray dose measurements. The target volume is down-sampled using the statistical mode first. The estimated dose distribution is then up-sampled using guided filtering and the high-resolution target volume as guidance image. Second, we present a comparison of dose estimates calculated with our Monte Carlo code experimentally obtained values for an anthropomorphic phantom using metal oxide semiconductor field effect transistor dosimeters. RESULTS: We reconstructed high-resolution dose distributions from coarse ones (down-sampling factor 2 to 16) with error rates ranging from 1.62 % to 4.91 %. Using down-sampled target volumes further reduced the computation time by 30 % to 60 %. Comparison of measured results to simulated dose values demonstrated high agreement with an average percentage error of under [Formula: see text] for all measurement points. CONCLUSIONS: Our results indicate that Monte Carlo methods can be accelerated hardware-independently and still yield reliable results. This facilitates empirical dose studies that make use of online Monte Carlo simulations to easily cross-validate dose estimates on-site.

Data-driven estimation of noise variance stabilization parameters for low-dose x-ray images.

PURPOSE: Denoising x-ray images corrupted by signal-dependent mixed noise is usually approached either by considering noise statistics directly or by using noise variance stabilization (NVS) techniques. An advantage of the latter is that the noise variance can be stabilized to a known constant throughout the image, facilitating the application of denoising algorithms designed for the removal of additive Gaussian noise. A well-performing NVS is the generalized Anscombe transform (GAT). To calculate the GAT, the system gain as well as the variance of electronic noise are required. Unfortunately, these parameters are difficult to predict from the x-ray tube settings in clinical practice, because the system gain observed at the detector depends on the beam hardening caused by the patient. MATERIALS AND METHODS: We propose a data-driven method for estimating the parameters required to carry out an NVS using the GAT. It utilizes the energy compaction property of the discrete cosine transform to obtain the NVS parameters using a robust regression approach relying on a linear Poisson-Gaussian model. The method has been experimentally validated with respect to beam hardening as well as denoising performance for different dose and scatter levels. RESULTS: Across a range of low-dose x-ray settings, the proposed robust regression approach has estimated both system gain and electronic noise level with an average error of only 4.2%. When used to perform a GAT followed by the denoising of low-dose x-ray images, performance gains of 5% for peak-signal-to-noise ratio and 4% for structural similarity index can be obtained. CONCLUSION: The parameters needed to calculate the GAT can be estimated efficiently and robustly using a data-driven approach. The improved parameter estimation method facilitates a more accurate GAT-based NVS and, hence, better denoising of low-dose x-ray images when algorithms designed for additive Gaussian noise are applied.

Physics-driven learning of x-ray skin dose distribution in interventional procedures.

PURPOSE: Radiation doses accumulated during very complicated image-guided x-ray procedures have the potential to cause stochastic, but also deterministic effects, such as skin rashes or even hair loss. To monitor and reduce radiation-related risks to patients' skin, x-ray imaging devices are equipped with online air kerma monitoring components. Traditionally, such measurements have been used to estimate skin entrance dose by (a) estimating air kerma at the interventional reference point (IRP), (b) forward projecting the dose distribution, and (c) considering a backscatter factor among other correction factors. Unfortunately, the complicated interaction between incident x-ray photons, secondary electrons, and skin tissue cannot be properly accounted for by assuming a linear relationship between forward projected air kerma and a backscatter factor. Gold standard skin dose models are therefore determined using Monte Carlo (MC) techniques. However, MC simulations are computationally complex in general and possible acceleration mainly depends on the employed hardware and variance reduction techniques. To obtain reliable and fast dose estimates, we propose to combine MC-based simulations with learning-based methods. METHODS: The basic idea of our method is to approximate the radiation physics to calculate a first-order exposure estimate quickly. This initial estimate is then refined using prior knowledge derived from MC simulations. To this end, the primary photon propagation inside a voxelized patient model is estimated using a less accurate but fast photon ray casting (RC) simulation based on the Beer-Lambert law. The results of the RC simulation are then fed into a convolutional neural network (CNN), which maps the propagation of primary photons to the dose deposition inside the patient model. Additionally, the patient model itself including anatomy and material properties, such as mass density and mass energy-absorption coefficients, are fed into the CNN as well. The CNN is trained using smoothed results of MC simulations as output and RC simulations of identical imaging settings and patient models as input. RESULTS: In total, 163 MC and associated RC simulations are carried out for the head, thorax, abdomen, and pelvis in three different voxel phantoms. We used 10 8 or 10 9 primarily emitted photons sampled from a 125 kV peak voltage spectrum, respectively. Edge-preserving smoothing (EPS) is applied to reduce (a) general stochastic uncertainties and (b) stochastic uncertainty concerning MC simulations of less primary photons. The CNN is trained using seven imaging settings of the abdomen in a single phantom. Testing its performance on the remaining datasets, the CNN is capable of estimating skin dose with an error of below 10% for the majority of test cases. CONCLUSION: The combination of deep neural networks and MC simulation of particle physics has the potential to decrease the computational complexity of accurate skin dose estimation. The proposed approach can provide dose distributions in under one second when running on high-end hardware. On lower cost hardware, it took up to 2 min to arrive at the same result. This makes our approach applicable in high-end environments as well as in budget solutions. Furthermore, the number of primary photons only affects the training time, while the execution time is independent of the number of primary photons.

Preliminary results of DSA denoising based on a weighted low-rank approach using an advanced neurovascular replication system.

PURPOSE: 2D digital subtraction angiography (DSA) has become an important technique for interventional neuroradiology tasks, such as detection and subsequent treatment of aneurysms. In order to provide high-quality DSA images, usually undiluted contrast agent and a high X-ray dose are used. The iodinated contrast agent puts a burden on the patients' kidneys while the use of high-dose X-rays expose both patients and medical staff to a considerable amount of radiation. Unfortunately, reducing either the X-ray dose or the contrast agent concentration usually results in a sacrifice of image quality. MATERIALS AND METHODS: To denoise a frame, the proposed spatiotemporal denoising method utilizes the low-rank nature of a spatially aligned temporal sequence where variation is introduced by the flow of contrast agent through a vessel tree of interest. That is, a constrained weighted rank-1 approximation of the stack comprising the frame to be denoised and its temporal neighbors is computed where the weights are used to prevent the contribution of non-similar pixels toward the low-rank approximation. The method has been evaluated using a vascular flow phantom emulating cranial arteries into which contrast agent can be manually injected (Vascular Simulations Replicator, Vascular Simulations, Stony Brook NY, USA). For the evaluation, image sequences acquired at different dose levels as well as different contrast agent concentrations have been used. RESULTS: Qualitative and quantitative analyses have shown that with the proposed approach, the dose and the concentration of the contrast agent could both be reduced by about 75%, while maintaining the required image quality. Most importantly, it has been observed that the DSA images obtained using the proposed method have the closest resemblance to typical DSA images, i.e., they preserve the typical image characteristics best. CONCLUSION: Using the proposed denoising approach, it is possible to improve the image quality of low-dose DSA images. This improvement could enable both a reduction in contrast agent and radiation dose when acquiring DSA images, thereby benefiting patients as well as clinicians. Since the resulting images are free from artifacts and as the inherent characteristics of the images are also preserved, the proposed method seems to be well suited for clinical images as well.

An analytical approach for the simulation of realistic low-dose fluoroscopic images.

PURPOSE: The quality of X-ray images plays an important role in computer-assisted interventions. Although learning-based denoising techniques have been shown to be successful in improving the image quality, they often rely on pairs of associated low- and high-dose X-ray images that are usually not possible to acquire at different dose levels in a clinical scenario. Moreover, since data variation is an important requirement for learning-based methods, the use of phantom data alone may not be sufficient. A possibility to address this issue is a realistic simulation of low-dose images from their related high-dose counterparts. METHOD: We introduce a novel noise simulation method based on an X-ray image formation model. The method makes use of the system parameters associated with low- and high-dose X-ray image acquisitions, such as system gain and electronic noise, to preserve the image noise characteristics of low-dose images. RESULTS: We have compared several corresponding regions of the associated real and simulated low-dose images-obtained from two different imaging systems-visually as well as statistically, using a two-sample Kolmogorov-Smirnov test at 5% significance. In addition to being visually similar, the hypothesis that the corresponding regions-from 80 pairs of real and simulated low-dose regions-belonging to the same distribution has been accepted in 81.43% of the cases. CONCLUSION: The results suggest that the simulated low-dose images obtained using the proposed method are almost indistinguishable from real low-dose images. Since extensive calibration procedures required in previous methods can be avoided using the proposed approach, it allows an easy adaptation to different X-ray imaging systems. This in turn leads to an increased diversity of the training data for potential learning-based methods.

A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model.

PURPOSE: With the recent introduction of fully assisting scanner technologies by Siemens Healthineers (Erlangen, Germany), a patient surface model was introduced to the diagnostic imaging device market. Such a patient representation can be used to automate and accelerate the clinical imaging workflow, manage patient dose, and provide navigation assistance for computed tomography diagnostic imaging. In addition to diagnostic imaging, a patient surface model has also tremendous potential to simplify interventional imaging. For example, if the anatomy of a patient was known, a robotic angiography system could be automatically positioned such that the organ of interest is positioned in the system's iso-center offering a good and flexible view on the underlying patient anatomy quickly and without any additional X-ray dose. METHOD: To enable such functionality in a clinical context with sufficiently high accuracy, we present an extension of our previous patient surface model by adding internal anatomical landmarks associated with certain (main) bones of the human skeleton, in particular the spine. We also investigate different approaches to positioning of these landmarks employing CT datasets with annotated internal landmarks as training data. The general pipeline of our proposed method comprises the following steps: First, we train an active shape model using an existing avatar database and segmented CT surfaces. This stage also includes a gravity correction procedure, which accounts for shape changes due to the fact that the avatar models were obtained in standing position, while the CT data were acquired with patients in supine position. Second, we match the gravity-corrected avatar patient surface models to surfaces segmented from the CT datasets. In the last step, we derive the spatial relationships between the patient surface model and internal anatomical landmarks. RESULT: We trained and evaluated our method using cross-validation using 20 datasets, each containing 50 internal landmarks. We further compared the performance of four different generalized linear models' setups to describe the positioning of the internal landmarks relative to the patient surface. The best mean estimation error over all the landmarks was achieved using lasso regression with a mean error of [Formula: see text]. CONCLUSION: Considering that interventional X-ray imaging systems can have detectors covering an area of about [Formula: see text] ([Formula: see text]) at iso-center, this accuracy is sufficient to facilitate automatic positioning of the X-ray system.

Influence of acid hydrolysis, saponification and sample clean-up on the measurement of phytosterols in dairy cattle feed using GC-MS and GC with flame ionization detection.

The fortification of processed foods including dairy products is increasingly commonplace with phytosterols among many compounds used to improve the nutritional value of food products. It is also increasingly common practice for some dairy cattle feeds to be fortified for their potential to increase phytosterol levels in milk. In this study, a combined, streamlined protocol using acid hydrolysis, saponification and sample clean-up was developed to enable the rapid and reliable measurement of phytosterols. The method was developed with focus on streamlining the overall technique to make it suitable for commercial laboratories, to reduce labor and consumable costs, while maintaining accuracy. A total of 12 different feed types commonly used in the dairy industry were analyzed with the highest and lowest sterol contents found in cotton seed oil and tannin with average phytosterol contents of 256 and <30 mg per 100 g, respectively. With a limit of reporting of 30 mg/kg for individual sterols and a correlation coefficient > 0.99, the method was validated for milk to enable feed comparison studies with respect to the total phytosterol content in raw milk.

Vitamin D Content of Australian Native Food Plants and Australian-Grown Edible Seaweed.

Vitamin D has previously been quantified in some plants and algae, particularly in leaves of the Solanaceae family. We measured the vitamin D content of Australian native food plants and Australian-grown edible seaweed. Using liquid chromatography with triple quadrupole mass spectrometry, 13 samples (including leaf, fruit, and seed) were analyzed in duplicate for vitamin D2, vitamin D3, 25-hydroxyvitamin D2, and 25-hydroxyvitamin D3. Five samples contained vitamin D2: raw wattleseed (Acacia victoriae) (0.03 µg/100 g dry weight (DW)); fresh and dried lemon myrtle (Backhousia citriodora) leaves (0.03 and 0.24 µg/100 g DW, respectively); and dried leaves and berries of Tasmanian mountain pepper (Tasmannia lanceolata) (0.67 and 0.05 µg/100 g DW, respectively). Fresh kombu (Lessonia corrugata) contained vitamin D3 (0.01 µg/100 g DW). Detected amounts were low; however, it is possible that exposure to ultraviolet radiation may increase the vitamin D content of plants and algae if vitamin D precursors are present.

A photon recycling approach to the denoising of ultra-low dose X-ray sequences.

PURPOSE: Clinical procedures that make use of fluoroscopy may expose patients as well as the clinical staff (throughout their career) to non-negligible doses of radiation. The potential consequences of such exposures fall under two categories, namely stochastic (mostly cancer) and deterministic risks (skin injury). According to the "as low as reasonably achievable" principle, the radiation dose can be lowered only if the necessary image quality can be maintained. METHODS: Our work improves upon the existing patch-based denoising algorithms by utilizing a more sophisticated noise model to exploit non-local self-similarity better and this in turn improves the performance of low-rank approximation. The novelty of the proposed approach lies in its properly designed and parameterized noise model and the elimination of initial estimates. This reduces the computational cost significantly. RESULTS: The algorithm has been evaluated on 500 clinical images (7 patients, 20 sequences, 3 clinical sites), taken at ultra-low dose levels, i.e. 50% of the standard low dose level, during electrophysiology procedures. An average improvement in the contrast-to-noise ratio (CNR) by a factor of around 3.5 has been found. This is associated with an image quality achieved at around 12 (square of 3.5) times the ultra-low dose level. Qualitative evaluation by X-ray image quality experts suggests that the method produces denoised images that comply with the required image quality criteria. CONCLUSION: The results are consistent with the number of patches used, and they demonstrate that it is possible to use motion estimation techniques and "recycle" photons from previous frames to improve the image quality of the current frame. Our results are comparable in terms of CNR to Video Block Matching 3D-a state-of-the-art denoising method. But qualitative analysis by experts confirms that the denoised ultra-low dose X-ray images obtained using our method are more realistic with respect to appearance.

Vitamin D3 and 25-Hydroxyvitamin D3 Content of Retail White Fish and Eggs in Australia.

Dietary vitamin D may compensate for inadequate sun exposure; however, there have been few investigations into the vitamin D content of Australian foods. We measured vitamin D3 and 25-hydroxyvitamin D3 (25(OH)D3) in four species of white fish (barramundi, basa, hoki and king dory), and chicken eggs (cage and free-range), purchased from five Australian cities. Samples included local, imported and wild-caught fish, and eggs of varying size from producers with a range of hen stocking densities. Raw and cooked samples were analysed using high performance liquid chromatography with photodiode array. Limits of reporting were 0.2 and 0.1 µg/100 g for vitamin D3 and 25(OH)D3, respectively. The vitamin D3 content of cooked white fish ranged from <0.1 to 2.3 µg/100 g, and the 25(OH)D3 content ranged from 0.3 to 0.7 µg/100 g. The vitamin D3 content of cooked cage eggs ranged from 0.4 to 0.8 µg/100 g, and the 25(OH)D3 content ranged from 0.4 to 1.2 µg/100 g. The vitamin D3 content of cooked free-range eggs ranged from 0.3 to 2.2 µg/100 g, and the 25(OH)D3 content ranged from 0.5 to 0.8 µg/100 g. If, as has been suggested, 25(OH)D3 has five times greater bioactivity than vitamin D3, one cooked serve (100 g) of white fish, and one cooked serve of cage or free-range eggs (120 g) may provide 50% or 100%, respectively, of the current guidelines for the adequate intake of vitamin D (5 µg) for Australians aged 1-50 years.

Rapid measurement of phytosterols in fortified food using gas chromatography with flame ionization detection.

A novel method for the measurement of total phytosterols in fortified food was developed and tested using gas chromatography with flame ionization detection. Unlike existing methods, this technique is capable of simultaneously extracting sterols during saponification thus significantly reducing extraction time and cost. The rapid method is suitable for sterol determination in a range of complex fortified foods including milk, cheese, fat spreads, oils and meat. The main enhancements of this new method include accuracy and precision, robustness, cost effectiveness and labour/time efficiencies. To achieve these advantages, quantification and the critical aspects of saponification were investigated and optimised. The final method demonstrated spiked recoveries in multiple matrices at 85-110% with a relative standard deviation of 1.9% and measurement uncertainty value of 10%.

Contrast-Based 3D/2D Registration of the Left Atrium: Fast versus Consistent.

For augmented fluoroscopy during cardiac ablation, a preoperatively acquired 3D model of a patient's left atrium (LA) can be registered to X-ray images recorded during a contrast agent (CA) injection. An automatic registration method that works also for small amounts of CA is desired. We propose two similarity measures: The first focuses on edges of the patient anatomy. The second computes a contrast agent distribution estimate (CADE) inside the 3D model and rates its consistency with the CA as seen in biplane fluoroscopic images. Moreover, temporal filtering on the obtained registration results of a sequence is applied using a Markov chain framework. Evaluation was performed on 11 well-contrasted clinical angiographic sequences and 10 additional sequences with less CA. For well-contrasted sequences, the error for all 73 frames was 7.9 ± 6.3 mm and it dropped to 4.6 ± 4.0 mm when registering to an automatically selected, well enhanced frame in each sequence. Temporal filtering reduced the error for all frames from 7.9 ± 6.3 mm to 5.7 ± 4.6 mm. The error was typically higher if less CA was used. A combination of both similarity measures outperforms a previously proposed similarity measure. The mean accuracy for well contrasted sequences is in the range of other proposed manual registration methods.

Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach.

Cryo-balloon catheters have attracted an increasing amount of interest in the medical community as they can reduce patient risk during left atrial pulmonary vein ablation procedures. As cryo-balloon catheters are not equipped with electrodes, they cannot be localized automatically by electro-anatomical mapping systems. As a consequence, X-ray fluoroscopy has remained an important means for guidance during the procedure. Most recently, image guidance methods for fluoroscopy-based procedures have been proposed, but they provide only limited support for cryo-balloon catheters and require significant user interaction. To improve this situation, we propose a novel method for automatic cryo-balloon catheter detection in fluoroscopic images by detecting the cryo-balloon catheter's built-in X-ray marker. Our approach is based on a blob detection algorithm to find possible X-ray marker candidates. Several of these candidates are then excluded using prior knowledge. For the remaining candidates, several catheter specific features are introduced. They are processed using a machine learning approach to arrive at the final X-ray marker position. Our method was evaluated on 75 biplane fluoroscopy images from 40 patients, from two sites, acquired with a biplane angiography system. The method yielded a success rate of 99.0% in plane A and 90.6% in plane B, respectively. The detection achieved an accuracy of 1.00 mm±0.82 mm in plane A and 1.13 mm±0.24 mm in plane B. The localization in 3-D was associated with an average error of 0.36 mm±0.86 mm.