Showing papers in "Biomedical Physics & Engineering Express in 2021"

Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning.

Abstract: Purpose Synthetic CT generation is the focus of many studies, however, only models on data with the same dataset were tested. Therefore, how well the trained model will work for data from different hospitals and MR protocols is still unknown. In this study, we addressed the model generalization problem for the MR-to-CT conversion task. Methods Brain T2 MR and corresponding CT images were collected from one hospital and brain T1-FLAIR, T1-POST MR, and corresponding CT images were collected from another hospital. To investigate the model's generalizability ability, four potential solutions were proposed: source model, target model, combined model, and adapted model. All models were trained using the CycleGAN network. The source model was trained with a source domain dataset from scratch and tested with a target domain dataset. The target model was trained with a target domain dataset and tested with a target domain dataset. The combined model was trained with both source domain and target domain datasets, and tested with the target domain dataset. The adapted model used a transfer learning strategy to train a CycleGAN model with a source domain dataset and retrain the pre-trained model with a target domain dataset. MAE, RMSE, PSNR, and SSIM were used to quantitatively evaluate model performance on a target domain dataset. Results The adapted model achieved best quantitative results of 74.56±8.61, 193.18±17.98, 28.30±0.83, and 0.84±0.01 for MAE, RMSE, PSNR, and SSIM using the T1-FLAIR dataset and 74.89±15.64, 195.73±31.29, 27.72±1.43, and 0.83±0.04 for MAE, RMSE, PSNR, and SSIM using the T1-POST dataset. The source model had the poorest performance. Conclusions This work indicates high generalization ability to generate synthetic CT images from small training datasets of MR images using pre-trained CycleGAN. The quantitative results of the test data, including different scanning protocols and different acquisition centers, indicated the proof of this concept.

Prediction of diabetic foot ulcer progression: a computational study.

Abstract: The development of foot ulcers is a common consequence of severe diabetes. Due to vascular disorders and impeded healing caused by the disease, most foot ulcers have been reported to be affected by body weight and progress with time. Also, abnormal distribution of plantar pressures has been observed to cause the formation of additional ulcers, which may collectively lead to traumatic amputations. While a study of such pathophysiology is not possible through experiments, a few computational modelling works have investigated diabetic foot ulcers. To date, ulcers with a few sizes and locations have been studied, and their effect on the plantar stresses has been quantified. In this work, we have attempted to study the effect of all possible ulcer locations on the generated plantar peak stresses and peak stress locations where additional ulcers may form. Also, the effect of ulcer location on the possible ulcer growth was investigated. A full-scale foot model was developed and a total of 52 ulcer locations were simulated separately, with standing and walking loads. The generated stresses were normalised with the foot size and statistically analysed to develop novel formulations for predicting peak plantar stresses and their locations for any known ulcer location. The results from this study are anticipated to provide important guidelines to doctors and medical practitioners for predicting foot ulcer progression in diabetic patients with existing ulcers and allow the administration of timely preventive interventions.

VHEE beam dosimetry at CERN Linear Electron Accelerator for Research under ultra-high dose rate conditions.

Abstract: The aim of this work is the dosimetric characterization of a plane parallel ionization chamber under defined beam setups at the CERN Linear Electron Accelerator for Research (CLEAR). A laser driven electron beam with energy of 200 MeV at two different field sizes of approximately 3.5 mm FWHM and approximately 7 mm FWHM were used at different pulse structures. Thereby the dose-per-pulse range varied between approximately 0.2 and 12 Gy per pulse. This range represents approximately conventional dose rate range beam conditions up to ultra-high dose rate (UHDR) beam conditions. The experiment was based on a water phantom which was integrated into the horizontal beamline and radiochromic films and an Advanced Markus ionization chamber was positioned in the water phantom. In addition, the experimental setup were modelled in the Monte Carlo simulation environment FLUKA. In a first step the radiochromic film measurements were used to verify the beamline setup. Depth dose distributions and dose profiles measured by radiochromic film were compared with Monte Carlo simulations to verify the experimental conditions. Second, the radiochromic films were used for reference dosimetry to characterize the ionization chamber. In particular, polarity effects and the ion collection efficiency of the ionization chamber were investigated for both field sizes and the complete dose rate range. As a result of the study, significant polarity effects and recombination loss of the ionization chamber were shown and characterized. However, the work shows that the behavior of the ionization chamber at the laser driven beam line at the CLEAR facility is comparable to classical high dose-per-pulse electron beams. This allows the use of ionization chambers on the CLEAR system and thus enables active dose measurement during the experiment. Compared to passive dose measurement with film, this is an important step forward in the experimental equipment of the facility.

Faster super-resolution ultrasound imaging with a deep learning model for tissue decluttering and contrast agent localization.

Abstract: Super-resolution ultrasound (SR-US) imaging allows visualization of microvascular structures as small as tens of micrometers in diameter. However, use in the clinical setting has been impeded in part by ultrasound (US) acquisition times exceeding a breath-hold and by the need for extensive offline computation. Deep learning techniques have been shown to be effective in modeling the two more computationally intensive steps of microbubble (MB) contrast agent detection and localization. Performance gains by deep networks over conventional methods are more than two orders of magnitude and in addition the networks can localize overlapping MBs. The ability to separate overlapping MBs allows use of higher contrast agent concentrations and reduces US image acquisition time. Herein we propose a fully convolutional neural network (CNN) architecture to perform the operations of MB detection as well as localization in a single model. Termed SRUSnet, the network is based on the MobileNetV3 architecture modified for 3-D input data, minimal convergence time, and high-resolution data output using a flexible regression head. Also, we propose to combine linear B-mode US imaging and nonlinear contrast pulse sequencing (CPS) which has been shown to increase MB detection and further reduce the US image acquisition time. The network was trained within silicodata and tested onin vitrodata from a tissue-mimicking flow phantom, and onin vivodata from the rat hind limb (N = 3). Images were collected with a programmable US system (Vantage 256, Verasonics Inc., Kirkland, WA) using an L11-4v linear array transducer. The network exceeded 99.9% detection accuracy onin silicodata. The average localization accuracy was smaller than the resolution of a pixel (i.e.λ/8). The average processing time on a Nvidia GeForce 2080Ti GPU was 64.5 ms for a 128 × 128-pixel image.

A staggered 3-layer DOI PET detector using BaSO 4 reflector for enhanced crystal identification and inter-crystal scattering event discrimination capability.

Abstract: The spatial resolution of small animal positron emission tomography (PET) scanners can be improved by the use of crystals with fine pitch and rejection of inter-crystal scattering (ICS) events, which leads to a better quantification of radiopharmaceuticals. On the other hand, depth-of-interaction (DOI) information is essential to preserve the spatial resolution at the PET field-of-view (FOV) periphery while keeping the sensitivity. In this study we proposed a novel staggered 3-layer DOI detector using BaSO4reflector material for an enhanced crystal identification performance as well as ICS event rejection capability over those of ESR reflector based DOI detectors. The proposed staggered 3-layer DOI detector had 3-layer staggered LYSO crystal arrays (crystal pitch = 1 mm), an acrylic light guide, and a 4 × 4 SiPM array. The 16 SiPM anode signals were read out by using a resistive network to encode the crystal position and energy information while the timing signal was extracted from the common cathode. The crystal map quality was substantially enhanced by using the BaSO4reflector material as compared to that of the ESR reflector due to the low optical crosstalk between the LYSO crystals. The ICS events can be rejected with BaSO4by using simple pulse height discrimination thanks to the light collection efficiency difference that depends on the crystal layers. As a result, the total number of events was decreased around 26% with BaSO4as compared to that of ESR. The overall energy resolution and coincidence timing resolution with BaSO4were 19.7 ± 5.6% and 591 ± 160 ps, respectively which were significantly worse than 10.9 ± 2.2% and 308 ± 23 ps values of ESR because of the relatively low light collection efficiency with BaSO4(1057 ± 308 ADC) compared to that of ESR (1808 ± 118 ADC). In conclusion, we found the proposed staggered 3-layer DOI detector using the BaSO4reflector material with ICS event rejection capability can be a cost-effective solution for realizing high resolution and highly sensitive small animal PET scanners while minimizing the complexity of the SiPM readout circuit.

Study of optical reflectors for a 100ps coincidence time resolution TOF-PET detector design.

Abstract: Positron Emission Tomography (PET) reconstructed image signal-to-noise ratio (SNR) can be improved by including the 511 keV photon pair coincidence time-of-flight (TOF) information. The degree of SNR improvement from this TOF capability depends on the coincidence time resolution (CTR) of the PET system, which is essentially the variation in photon arrival time differences over all coincident photon pairs detected for a point positron source placed at the system center. The CTR is determined by several factors including the intrinsic properties of the scintillation crystals and photodetectors, crystal-to-photodetector coupling configurations, reflective materials, and the electronic readout configuration scheme. The goal of the present work is to build a novel TOF-PET system with 100 picoseconds (ps) CTR, which provides an additional factor of 1.5-2.0 improvement in reconstructed image SNR compared to state-of-the-art TOF-PET systems which achieve 225-400 ps CTR. A critical parameter to understand is the optical reflector's influence on scintillation light collection and transit time variations to the photodetector. To study the effects of the reflector covering the scintillation crystal element on CTR, we have tested the performance of four different reflector materials: Enhanced Specular Reflector (ESR) -coupled with air or optical grease to the scintillator; Teflon tape; BaSO4paint alone or mixed with epoxy; and TiO2paint. For the experimental set-up, we made use of 3 × 3 × 10 mm3fast-LGSO:Ce scintillation crystal elements coupled to an array of silicon photomultipliers (SiPMs) using a novel 'side-readout' configuration that has proven to have lower variations in scintillation light collection efficiency and transit time to the photodetector.Results: show CTR values of 102.0 ± 0.8, 100.2 ± 1.2, 97.3 ± 1.8 and 95.0 ± 1.0 ps full-width-half-maximum (FWHM) with non-calibrated energy resolutions of 10.2 ± 1.8, 9.9 ± 1.2, 7.9 ± 1.2, and 8.6 ± 1.7% FWHM for the Teflon, ESR (without grease), BaSO4(without epoxy) and TiO2paint treatments, respectively.

Pattern recognition of EMG signals for low level grip force classification.

Abstract: Grasping of the objects is the most frequent activity performed by the human upper limb. The amputations of the upper limb results in the need for prosthetic devices. The myoelectric prosthetic devices use muscle signals and apply control techniques for identification of different levels of hand gesture and force levels. In this study; a different level force contraction experiment was performed in which Electromyography (EMGs) signals and fingertip force signals were acquired. Using this experimental data; a two-step feature selection process is applied for the designing of a pattern recognition algorithm for the classification of different force levels. The two step feature selection process consist of generalized feature ranking using ReliefF, followed by personalized feature selection using Neighborhood Component Analysis (NCA) from the shortlisted features by earlier technique. The classification algorithms applied in this study were Support Vector Machines (SVM) and Random Forest (RF). Besides feature selection; optimization of the number of muscles during classification of force levels was also performed using designed algorithm. Based on this algorithm; the maximum classification accuracy using SVM classifier and two muscle set was achieved as high as 99%. The optimal feature set consisted features such as Auto Regressive coefficients, Willison Amplitude and Slope Sign Change. The mean classification accuracy for different subjects, achieved using SVM and RF was 94.5% and 91.7% respectively.

On the use of trajectory log files for machine & patient specific QA.

Abstract: Purpose:Trajectory log files are increasingly being utilized clinically for machine and patient specific QA. The process of converting the DICOM-RT plan to a deliverable trajectory by the linac control software introduces some uncertainty that is inherently incorporated into measurement-based patient specific QA but is not necessarily included for trajectory log file-based methods. Roughly half of prior studies have included this uncertainty in the analysis while the remaining studies have ignored it, and it has yet to be quantified in the literature.Methods:We collected DICOM-RT files from the treatment planning system and the trajectory log files from four TrueBeam linear accelerators for 25 IMRT and 10 VMAT plans. We quantified the DICOM-RT Conversion to Trajectory Residual (DCTR, difference between 'planned' MLC position from TPS DICOM-RT file and 'expected' MLC position (the deliverable MLC positions calculated by the linac control software) from trajectory log file) and compared it to the discrepancy between actual and expected machine parameters recorded in trajectory log files.Results:RMS of the DCTR was 0.0845 mm (range of RMS per field/arc: 0.0173-0.1825 mm) for 35 plans (114 fields/arcs) and was independent of treatment technique, with a maximum observed discrepancy at any control point of 0.7255 mm. DCTR was correlated with MLC velocity and was consistent over the course of treatment and over time, with a slight change in magnitude observed after a linac software upgrade. For comparison, the RMS of trajectory log file reported delivery error for moving MLCs was 0.0205 mm, thus DCTR is about four times the recorded delivery error in the trajectory log file.Conclusion:The uncertainty introduced from the conversion process by the linac control software from DICOM-RT plan to a deliverable trajectory is 3-4 times larger than the discrepancy between actual and expected machine parameters recorded in trajectory log files. This uncertainty should be incorporated into the analysis when using trajectory log file-based methods for analyzing MLC performance or patient-specific QA.

Feasibility of imaging amyloid in the brain using small-angle x-ray scattering.

Abstract: Small-angle x-ray scattering (SAXS) imaging may have the potential to image β amyloid plaques in vivo in the brain without tracers for assessment of Alzheimer's disease (AD). We use a laboratory SAXS system for planar imaging of AD model and control mouse brains slices to detect regions with high density of amyloid plaques. These regions were validated with histology methods. Using Monte Carlo techniques, we simulate SAXS computed tomography (SAXS-CT) system to study the potential of selectively differentiating amyloid targets in mouse and human head phantoms with detailed anatomy. We found contrast between amyloid and brain tissue at small q (below 0.8 nm-1) in the neocortex region of the transgenic brain slices as supported by histology. We observed similar behavior through planar SAXS imaging of an amyloid-like fibril deposit with a 0.8 mm diameter at a known location on a wild type mouse brain. In our SAXS-CT simulations, we found that 33-keV x rays provide increase plaque visibility in the mouse head for targets of at least 0.1 mm in diameter, while in the human head, 70-keV x rays were capable of detecting plaques as small as 2 mm. To increase radiation efficiency, we used a weighted-sum image visualization approach allowing the dose deposited by 70-keV x rays per SAXS-CT slice of the human head to be reduced by a factor of 10 to 71 mGy for gray matter and 63 mGy for white matter. The findings suggest that a dedicated SAXS-CT system for in vivo amyloid imaging in small animals and humans can be successfully developed with further system optimization to detect regions with amyloid plaques in the brain with a safe level of radiation dose.

Efficient brain tumor detection and classification using magnetic resonance imaging.

Abstract: Magnetic Resonance Imaging (MRI) inputs are most noticeable in diagnosing brain tumors via computer and manual clinical understanding. Multi-level detection and classification of the images utilizing computer-aided processing depend on labels and annotations. Though the two processes are dynamic and time-consuming, without which the precise accuracy is less assured. For augmenting the accuracy in processing un-labeled or annotation-less images, this article introduces Absolute Classification-Detection Model (AC-DM). This model uses a conventional neural network for training the morphological variations proficient of achieving label-less classification and tumor detection. The traditional neural network trains the images based on differential lattice morphology for classification and detection. In this process, training for the lattices and their corresponding gradients is validated to improve the precision of the regional analysis. This helps to retain the precision of identifying tumors. The variations are recognized for their lattice mapping in the detected boundaries of the input image. The detected boundaries help to map accurate lattices for adapting morphological transforms. Thus, the partial and complex processing in detecting tumors is restrained in the suggested model, adapting to the classification. The efficiency of the suggested model is verified utilizing accuracy, precision, sensitivity, and classification time.

The future of bone regeneration: integrating AI into tissue engineering.

Abstract: Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body's natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome.

Radiation-induced airway changes and downstream ventilation decline in a swine model.

Abstract: Purpose.To investigate indirect radiation-induced changes in airways as precursors to atelectasis post radiation therapy (RT).Methods.Three Wisconsin Miniature Swine (WMSTM) underwent a research course of 60 Gy in 5 fractions delivered to a targeted airway/vessel in the inferior left lung. The right lung received a max point dose <5 Gy. Airway segmentation was performed on the pre- and three months post-RT maximum inhale phase of the four-dimensional (4D) computed tomography (CT) scans. Changes in luminal area (Ai) and square root of wall area (WA) for each airway were investigated. Changes in ventilation were assessed using the Jacobian ratio and were measured in three different regions: the inferior left lung <5 Gy (ILL), the superior left lung <5 Gy (SLL), and the contralateral right lung <5 Gy (RL).Results.Airways (n = 25) in the right lung for all swine showed no significant changes (p = 0.48) in Ai post-RT compared to pre-RT. Airways (n = 28) in the left lung of all swine were found to have a significant decrease (p < 0.001) in Ai post-RT compared to pre-RT, correlated (Pearson R = -0.97) with airway dose. Additionally,WAdecreased significantly (p < 0.001) with airway dose. Lastly, the Jacobian ratio of the ILL (0.883) was lower than that of the SLL (0.932) and the RL (0.955).Conclusions.This work shows that for the swine analyzed, there were significant correlations between Ai andWAchange with radiation dose. Additionally, there was a decrease in lung function in the regions of the lung supplied by the irradiated airways compared to the regions supplied by unirradiated airways. These results support the hypothesis that airway dose should be considered during treatment planning in order to potentially preserve functional lung and reduce lung toxicities.

Advanced fuzzy cognitive maps: state-space and rule-based methodology for coronary artery disease detection.

Abstract: According to the World Health Organization, 50% of deaths in European Union are caused by Cardiovascular Diseases (CVD), while 80% of premature heart diseases and strokes can be prevented. In this study, a Computer-Aided Diagnostic model for a precise diagnosis of Coronary Artery Disease (CAD) is proposed. The methodology is based on State Space Advanced Fuzzy Cognitive Maps (AFCMs), an evolution of the traditional Fuzzy Cognitive Maps. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the proposed system and the interpretability of the decision mechanism. The proposed method is evaluated utilizing a CAD dataset from the Department of Nuclear Medicine of the University Hospital of Patras, in Greece. Several experiments are conducted to define the optimal parameters of the proposed AFCM. Furthermore, the proposed AFCM is compared with the traditional FCM approach and the literature. The experiments highlight the effectiveness of the AFCM approach, obtaining 85.47% accuracy in CAD diagnosis, showing an improvement of +7% over the traditional approach. It is demonstrated that the AFCM approach in developing Fuzzy Cognitive Maps outperforms the conventional approach, while it constitutes a reliable method for the diagnosis of Coronary Artery Disease.

ZnO nanoparticles stimulate oxidative stress to induce apoptosis of B16F10 melanoma cells: In vitro and in vivo studies

Abstract: Melanoma is one of the most aggressive skin cancers. However, there remain many limitations in the current clinical treatments of it. Zinc oxide nanoparticles (ZnO NPs) have been considered to be a promising antitumor drug due to their excellent biocompatibility, biodegradability and biofunctionality. In this study, we prepared spherical ZnO NPs with an average diameter of less than 10 nm by a simple chemical method. According to the in vitro cytotoxicity assay, ZnO NPs in a certain concentration range (20-35 μg/mL) showed significant cytotoxicity to B16F10 melanoma cells, while having little effect on the viability of 3T3L1 fibroblasts. When cultured with B16F10 melanoma cells, ZnO NPs induced the generation of reactive oxygen and mitochondrial superoxide through the release of Zn2+, leading to oxidative stress in the cells, further reducing the mitochondrial membrane potential and decreasing the number of mitochondrial cristae. Furthermore, damaged mitochondria induced the release of apoptosis factors to promote cell apoptosis. FITC-Annexin V/propidium iodide double staining assay was used to analyze different apoptosis stages of B16F10 cells induced by ZnO NPs. A polymer hydrogel (Gel-F127-ZnO NPs) with Pluronic F127 as the carrier of ZnO NPs was fabricated for evaluating the antitumor effect of ZnO NPs in vivo. The in vivo experiment indicated that the tumor recurrence was significantly inhibited in tumor-bearing mice after treated with Gel-F127-ZnO NPs. Conclusively, ZnO NPs showed a strong antitumor effect both in vitro and in vivo.

Denoising PET images for proton therapy using a residual U-net.

Abstract: The use of proton therapy has the advantage of high dose concentration as it is possible to concentrate the dose on the tumor while suppressing damage to the surrounding normal organs However, the range uncertainty significantly affects the actual dose distribution in the vicinity of the proton range, limiting the benefit of proton therapy for reducing the dose to normal organs By measuring the annihilation gamma rays from the produced positron emitters, it is possible to obtain a proton induced positron emission tomography (pPET) image according to the irradiation region of the proton beam Smoothing with a Gaussian filter is generally used to denoise PET images; however, this approach lowers the spatial resolution Furthermore, other conventional smoothing processing methods may deteriorate the steep region of the pPET images In this study, we proposed a denoising method based on a Residual U-Net for pPET images We conducted the Monte Carlo simulation and irradiation experiment on a human phantom to obtain pPET data The accuracy of the range estimation and the image similarity were evaluated for pPET images using the Residual U-Net, a Gaussian filter, a median filter, the block-matching and 3D-filtering (BM3D), and a total variation (TV) filter Usage of the Residual U-Net yielded effective results corresponding to the range estimation; however, the results of peak-signal-to-noise ratio were identical to those for the Gaussian filter, median filter, BM3D, and TV filter The proposed method can contribute to improving the accuracy of treatment verification and shortening the PET measurement time

Evaluation of acrylamide-based molecularly imprinted polymer thin-sheets for specific protein capture-a myoglobin model.

Abstract: We evaluate a series of thin-sheet hydrogel molecularly imprinted polymers (MIPs), using a family of acrylamide-based monomers, selective for the target protein myoglobin (Mb). The simple production of the thin-sheet MIP offers an alternative biorecognition surface that is robust, stable and uniform, and has the potential to be adapted for biosensor applications. The MIP containing the functional monomer N-hydroxymethylacrylamide (NHMAm), produced optimal specific rebinding of the target protein (Mb) with 84.9 % (± 0.7) rebinding and imprinting and selectivity factors of 1.41 and 1.55, respectively. The least optimal performing MIP contained the functional monomer N,Ndimethylacrylamide (DMAm) with 67.5 % (± 0.7) rebinding and imprinting and selectivity factors of 1.11 and 1.32, respectively. Hydrogen bonding effects, within a protein-MIP complex, were investigated using computational methods and Fourier transform infrared (FTIR) spectroscopy. The quantum mechanical calculations predictions of a red shift of the monomer carbonyl peak is borneout within FTIR spectra, with three of the MIPs, acrylamide, N-(hydroxymethyl) acrylamide, and N-(hydroxyethyl) acrylamide, showing peak downshifts of 4, 11, and 8 cm-1, respectively.

Influence of anthopometric variables on the mechanical properties of human rib cortical bone

Abstract: Objective. The mechanical properties of ribs from a large number ofpost-mortemhuman subjects (PMHS) were analyzed to search for variation according to age, sex or BMI in the sample. A large sample of specimens from different donors (N= 64) with a very wide range of ages and anthropometric characteristics was tested.Methods. Uniaxial tensile tests were used for a sample of coupons machined from cortical bone tissue in order to isolate the purely mechanical properties from the geometrically influenced properties of the rib. Each coupon is about 25 mm long and has a thickness of about 0.5 mm. The mechanical properties measured for each specimen/coupon include YM, yield stress, ultimate stress (maximum failure stress), ultimate strain, and resilience (energy to fracture of SED). The study provides new methodological improvements in DIC techniques.Results. This study is notable for using an atypically large sample of number of PMHS. The size of the sample allowed the authors to determine that age has a significant effect on failure stress (p< 0.0001), yield stress (p= 0.0047), ultimate strain (p< 0.0001) and resilience (p< 0.0001) [numbers in parentheses represent the correspondingp- values]. Finally, there is a combined effect, so that for a given age, an increase of BMI leads to a decrease of the maximum strain (i.e. cortical bone is less stiff when both age and BMI are higher).

Biomaterials in bone and mineralized tissue engineering using 3D printing and bioprinting technologies.

Abstract: This review focuses on recently developed printable biomaterials for bone and mineralized tissue engineering. 3D printing or bioprinting is an advanced technology to design and fabricate complex functional 3D scaffolds, mimicking native tissue for in vivo applications. We categorized the biomaterials into two main classes: 3D printing and bioprinting. Various biomaterials, including natural, synthetic biopolymers and their composites, have been studied. Biomaterial inks or bioinks used for bone and mineralized tissue regeneration include hydrogels loaded with minerals or bioceramics, cells, and growth factors. In 3D printing, the scaffold is created by acellular biomaterials (biomaterial inks), while in 3D bioprinting, cell-laden hydrogels (bioinks) are used. Two main classes of bioceramics, including bioactive and bioinert ceramics, are reviewed. Bioceramics incorporation provides osteoconductive properties and induces bone formation. Each biopolymer and mineral have its advantages and limitations. Each component of these composite biomaterials provides specific properties, and their combination can ameliorate the mechanical properties, bioactivity, or biological integration of the 3D printed scaffold. Present challenges and future approaches to address them are also discussed.

Does participant's age impact on tDCS induced fields? Insights from computational simulations.

Abstract: Objective: Understanding the induced current flow from transcranial direct current stimulation (tDCS) is essential for determining the optimal dose and treatment. Head tissue conductivities play a key role in the resulting electromagnetic fields. However, there exists a complicated relationship between skull conductivity and participant age, that remains unclear. We explored how variations in skull electrical conductivities, particularly as a suggested function of age, affected tDCS induced electric fields.Approach: Simulations were employed to compare tDCS outcomes for different intensities across head atlases of varying age. Three databases were chosen to demonstrate differing variability in skull conductivity with age and how this may affect induced fields. Differences in tDCS electric fields due to proposed age-dependent skull conductivity variation, as well as deviations in grey matter, white matter and scalp, were compared and the most influential tissues determined.Main results: tDCS induced peak electric fields significantly negatively correlated with age, exacerbated by employing proposed age-appropriate skull conductivity (according to all three datasets). Uncertainty in skull conductivity was the most sensitive to changes in peak fields with increasing age. These results were revealed to be directly due to changing skull conductivity, rather than head geometry alone. There was no correlation between tDCS focality and age.Significance: Accurate and individualised head anatomy andin vivoskull conductivity measurements are essential for modelling tDCS induced fields. In particular, age should be taken into account when considering stimulation dose to precisely predict outcomes.

Automated measurements of morphological parameters of muscles and tendons

Abstract: Capturing accurate representations of musculoskeletal system morphology is a core aspect of musculoskeletal modelling of the upper limb. Measurements of important geometric parameters such as the thickness of muscles and tendons are key descriptors of the underlying morphology. Though the measurement of those parameters can be estimated manually using cadaveric measurements, this is not an appropriate technique for constructing a personalised musculoskeletal model for an individual. Therefore, this work proposes and applies a novel method for evaluating the geometric parameters of the upper extremity based on automated ultrasound image analysis. The proposed algorithm involves advanced techniques from artificial intelligence and image processing to outline the necessary details of the musculoskeletal morphology from appropriately enhanced ultrasound images. The ultrasound images were collected from 25 healthy volunteers from different parts of upper limb. The results were compared with measurements of a manual evaluation. Our results showed that the average discrepancy between the manual and automatic measures of triceps thickness is 0.115 mm. This represents improved accuracy compared to several current approaches.

Comparison of modeling accuracy between Radixact®and CyberKnife®Synchrony®respiratory tracking system.

Abstract: Synchrony Respiratory Tracking system adapted from CyberKnife has been introduced in Radixact to compensate the tumor motion caused by respiration. This study aims to compare the modeling accuracy of the Synchrony system between Radixact and CyberKnife. Two Synchrony plans based on fiducial phantoms were created for CyberKnife and Radixact, respectively. Different respiratory motion traces were used to drive a motion platform to move along the superoinferior and left-right direction. The cycle time and the amplitude of target/surrogate motion of one selected motion trace were scaled to investigate the dependence of modeling accuracy on the motion characteristic. The predicted target position, the correlation error, potential difference (Radixact only) and standard error (CyberKnife only) were extracted from raw data or log files of the two systems. The modeling accuracy was evaluated by calculating the root-mean-square (RMS) error between the predicted target positions and the input motion trace. A threshold T95 within which 95% of the potential difference or the standard error lay was defined and evaluated. Except for the motion trace with a small amplitude and a good (linear) correlation between target and surrogate motion, Radixact showed smaller RMS errors than CyberKnife. The RMS error of both systems increased with the motion amplitude and showed a decreasing trend with the increasing cycle time. No correlation was found between the RMS error and the amplitude of surrogate motion. T95 could be a good estimator of modeling accuracy for CyberKnife rather than Radixact. The correlation error defined in Radixact were largely affected by the number of fiducial markers and the setup error. In general, the modeling accuracy of the Radixact Synchrony system is better than that of the CyberKnife Synchrony system under unfavorable conditions.

A deep convolutional neural network model for hand gesture recognition in 2D near-infrared images.

Abstract: The hand gesture recognition (HGR) process is one of the most vital components in human-computer interaction systems. Especially, these systems facilitate hearing-impaired people to communicate with society. This study aims to design a deep learning CNN model that can classify hand gestures effectively from the analysis of near-infrared and colored natural images. This paper proposes a new deep learning model based on CNN to recognize hand gestures improving recognition rate, training, and test time. The proposed approach includes data augmentation to boost training. Furthermore, five popular deep learning models are used for transfer learning, namely VGG16, VGG19, ResNet50, DenseNet121, and InceptionV3 and compared their results. These models are applied to recognize 10 different hand gestures for near-infrared images and 24 ASL hand gestures for colored natural images. The proposed CNN model, VGG16, VGG19, Resnet50, DenseNet121, and InceptionV3 models achieve recognition rates of 99.98%, 100%, 99.99%, 91.63%, 82.42% and 81.84%, respectively on near-infrared images. For colored natural ASL images, the models achieve recognition rates of 99.91%, 99.31%, 98.67%, 91.97%, 93.37%, and 93.21%, respectively. The proposed model achieves promising results spending the least amount of time.

In vivo biocompatibility and biodegradability of poly(lactic acid)/poly(ε-caprolactone) blend compatibilized with poly(ε-caprolactone-b-tetrahydrofuran) in Wistar rats.

Abstract: Poly(lactic acid) (PLA) and poly(ɛ-caprolactone) (PCL) are two important aliphatic esters known for their biodegradability and bioresorbability properties; the former is stiffer and brittle while the smaller modulus of the latter allows a suitable elongation. The new biomaterials being developed from the blend of these two polymers (PLA and PCL) is opportune due to the reducing interfacial tension between their immiscible phases. In a previous study, PLA/PCL immiscible blend when compatibilized with poly(e-caprolactone-b-tetrahydrofuran) resulted in enhanced ductility and toughness no cytotoxic effect in vitro tests. There is little published data on the effect of poly(e-caprolactone-b-tetrahydrofuran) on PLA and PCL biocompatibility and biodegradability in vivo tests. This study focuses on evaluating the behavioral response and polymer-tissue interaction of compatibilized PLA/PCL blend compared to neat PLA implanted via intraperitoneal (IP) and subcutaneous (SC) in male Wistar rats, distributed in four experimental groups: neat PLA, PLA/PCL blend, sham, and control at 2-, 8- and 24-weeks post-implantation (WPI). Open-field test was performed to appraise emotionality and spontaneous locomotor activity. Histopathological investigation using hematoxylin-eosin (H&E) and picrosirius-hematoxylin (PSH) was used to assess polymer-tissue interaction. Modifications in PLA and the PLA / PCL blend's surface morphology were determined by scanning electron microscopy (SEM). PLA group defecated more often than PLA/PCL rats 2 and 8 WPI. Conjunctive capsule development around implants, cell adhesion, angiogenesis, and giant cells of a foreign body to the biomaterial was observed in light microscopy. Both groups displayed a fibrous reaction along with collagen deposition around the biomaterials. In the SEM, the images showed a higher degradation rate for the PLA/PCL blend in both implantation routes. The polymers implanted via IP exhibited a higher degradation rate compared to SC. These findings emphasize the biocompatibility of the PLA/PCL blend compatibilized with poly(e-caprolactone-b-tetrahydrofuran), making this biopolymer an acceptable alternative in a variety of biomedical applicatio.

Initial clinical experience of patient-specific QA of treatment delivery in online adaptive radiotherapy using a 1.5 T MR-Linac.

Abstract: Purpose This study aims to evaluate the performance of a commercial 1.5T MR-Linac by analyzing its patient-specific quality assurance (QA) data collected during one full year of clinical operation. Methods and materials The patient-specific QA system consisted of offline delivery QA (DQA) and online calculation-based QA. Offline DQA was based on ArcCHECK-MR combined with an ionization chamber. Online QA was performed using RadCalc that calculated and compared the point dose calculation with the treatment planning system (TPS). A total of 24 patients with 189 treatment fractions were enrolled in this study. Gamma analysis was performed and the threshold that encompassed 95% of QA results (T95) was reported. The plan complexity metric was calculated for each plan and compared with the dose measurements to determine whether any correlation existed. Results All point dose measurements were within 5% deviation. The mean gamma passing rates of the group data were found to be 96.8 ± 4.0% and 99.6 ± 0.7% with criteria of 2%/2mm and 3%/3mm, respectively. T95 of 87.4% and 98.2% was reported for the overall group with the two passing criteria, respectively. No statistically significant difference was found between adaptive treatments with adapt-to-position (ATP) and adapt-to-shape (ATS), whilst the category of pelvis data showed a better passing rate than other sites. Online QA gave a mean deviation of 0.2 ± 2.2%. The plan complexity metric was positively correlated with the mean dose difference whilst the complexity of the ATS cohort had larger variations than the ATP cohort. Conclusions A patient-specific QA system based on ArcCHECK-MR, solid phantom and ionization chamber has been well established and implemented for validation of treatment delivery of a 1.5T MR-Linac. Our QA data obtained over one year confirms that good agreement between TPS calculation and treatment delivery was achieved.

In-vivo viscoelastic properties estimation in subcutaneous adipose tissue by integration of poroviscoelastic-mass transport model (pve-MTM) into wearable electrical impedance tomography (w-EIT)

Abstract: In-vivo viscoelastic properties have been estimated in human subcutaneous adipose tissue (SAT) by integration of poroviscoelastic-mass transport model (pve-MTM) into wearable electrical impedance tomography (w-EIT) under the influence of external compressive pressure -P. The pve-MTM predicts the ion concentration distribution cmod (t) by coupling the poroviscoelastic and mass transport model to describe the hydrodynamics, rheology, and transport phenomena inside SAT. The w-EIT measures the time-difference conductivity distribution ∆γ(t) in SAT resulted from the ion transport. Based on the integration, the two viscoelastic properties which are viscoelastic shear modulus of SAT Gv and relaxation time of SAT τv are estimated by applying an iterative curve-fitting between the normalized average ion concentration distribution 〈cmod〉(t) predicted from pve-MTM and the experimental normalized average ion concentration distribution 〈cexp〉(t) derived from w-EIT. The in-vivo experiments were conducted by applying external compressive pressure -P on human calf boundary to induce interstitial fluid flow and ion movement in SAT. As a result, the value of Gv was range from 4.9-6.3 kPa and the value of τv was range from 27.50-38.5 s with the value of average goodness-of-fit curve fitting R2> 0.76. These value of Gv and τv were compared to the human and animal tissue from the literature in order to verify this method. The results from pve-MTM provide evidence that Gv and τv plays a role in the predicted value of cmod.

Deep learning-based thoracic CBCT correction with histogram matching.

Abstract: Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup accuracy. The clinical applications of CBCTs are constrained, however, by poor soft tissue contrast, image artifacts, and instability of Hounsfield unit (HU) values. Here, we propose a new deep learning-based method to generate synthetic CTs (sCT) from thoracic CBCTs. A deep-learning model which integrates histogram matching (HM) into a cycle-consistent adversarial network (Cycle-GAN) framework, called HM-Cycle-GAN, was trained to learn mapping between thoracic CBCTs and paired planning CTs. Perceptual supervision was adopted to minimize blurring of tissue interfaces. An informative maximizing loss was calculated by feeding CBCT into the HM-Cycle-GAN to evaluate the image histogram matching between the planning CTs and the sCTs. The proposed algorithm was evaluated using data from 20 SBRT patients who each received 5 fractions and therefore 5 thoracic CBCTs. To reduce the effect of anatomy mismatch, original CBCT images were pre-processed via deformable image registrations with the planning CT before being used in model training and result assessment. We used planning CTs as ground truth for the derived sCTs from the correspondent co-registered CBCTs. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC) indices were adapted as evaluation metrics of the proposed algorithm. Assessments were done using Cycle-GAN as the benchmark. The average MAE, PSNR, and NCC of the sCTs generated by our method were 66.2 HU, 30.3 dB, and 0.95, respectively, over all CBCT fractions. Superior image quality and reduced noise and artifact severity were seen using the proposed method compared to the results from the standard Cycle-GAN method. Our method could therefore improve the accuracy of IGRT and corrected CBCTs could help improve online adaptive RT by offering better contouring accuracy and dose calculation.

Breast tumor parameter estimation and interactive 3D thermal tomography using discrete thermal sensor data.

Abstract: This work uses a simple low-cost wearable device embedded with discrete thermal sensors to map the breast skin surface temperature. A methodology has been developed to estimate diameter, blood perfusion, metabolic heat generation and location in X, Y, Z coordinate of tumor from this discrete set of data. An interactive 3D thermal tomography was developed which provides a detailed 3D thermal view of the breast anatomy. Using this system, the user can interactively rotate and slice the 3D thermal image of the breast for a detailed study of the tumor. Finite element method (FEM) and an evolution-based inverse method were used for the parameter estimation. The method was first validated using phantom experiments and the results obtained were within an error of 10% (0.005 W cm-3) for heat generation and 15% (0.3 cm) for heater location. Further validation was carried out through clinical trials on 60 human subjects. Estimated blood perfusion rate and metabolic heat generation rate exhibit distinguishable difference between cancerous and non-cancerous breast. Estimated diameter and location of tumor in cancerous breast shows good agreement with the actual clinical reports. We have obtained a sensitivity of 82.78% and specificity of 87.09%. Proposed breast tumor parameter estimation methodology with interactive 3D thermal tomography is a good screening tool for breast cancer detection and also useful for clinicians to find out location including depth.

Comparison between patient-specific deep brain stimulation simulations and commercial system SureTune3.

Abstract: Objective. Software to visualize estimated volume of tissue activated (VTA) in deep brain stimulation assuming a homogeneous tissue surrounding such as SureTune3 has recently become available for clinical use. The objective of this study is to compare SureTune3 with homogeneous and heterogeneous patient-specific finite element method (FEM) simulations of the VTA to elucidate how well they coincide in their estimates.Approach. FEM simulations of the VTA were performed in COMSOL Multiphysics and compared with VTA from SureTune3 with variation of voltage and current amplitude, pulse width, axon diameter, number of active contacts, and surrounding homogeneous grey or white matter. Patient-specific simulations with heterogeneous tissue were also performed.Main results. The VTAs corresponded well for voltage control in homogeneous tissue, though with the smallest VTAs being slightly larger in SureTune3 and the largest VTAs being slightly larger in the FEM simulations. In current control, FEM estimated larger VTAs in white matter and smaller VTAs in grey matter compared to SureTune3 as grey matter has higher electric conductivity than white matter and requires less voltage to reach the same current. The VTAs also corresponded well in the patient-specific cases except for one case with a cyst of highly conductive cerebrospinal fluid (CSF) near the active contacts.Significance. The VTA estimates without taking the surrounding tissue into account in SureTune3 are in good agreement with patient-specific FEM simulations when using voltage control in the absence of CSF-filled cyst. In current control or when CSF is present near the active contacts, the tissue characteristics are important for the VTA and needs consideration.Clinical. trial ethical approval: Local ethics committee at Linkoping University (2012/434-31).

Effect of the addition of Chitosan and TiO2 nanoparticles on antibacterial properties of an orthodontic composite in fixed orthodontic treatment: a randomized clinical trial study.

Abstract: Due to the existing demands for methods independent of patient co-operation in preventing and overcoming the incidence of white spot lesions (WSLs) and caries in fixed orthodontic treatments, several studies have considered the modification of orthodontic composites using antimicrobial nanomaterials. In this regard, the aim of this study is to investigate the effect of the addition of chitosan nanoparticles (NPs) and TiO2NPs onStreptococcus mutans(S. mutans) counts and the enamel mineral content in fixed orthodontic patients. A double-blind randomized clinical trial study was carried out in 24 patients (i.e., 48 upper second premolars and 48 maxillary lateral incisors) who were candidates for fixed orthodontic treatment. In the case of the control group, the bracket was bonded to the tooth with an orthodontic adhesive (Transbond XT, 3M Unitek, USA) while, in the experimental group, the bracket was bonded to the tooth with Transbond XT containing 1% chitosan NPs and 1% TiO2NPs. For the maxillary lateral incisor and upper second premolar teeth, theS. mutanscounts around the brackets were measured, through the usage of real-time PCR, at the time points of 1 day, 2 months, and 6 months after bonding the brackets to the tooth. Furthermore, the enamel mineral content measurement was also performed around the brackets at 1 day, 2 months, and 6 months after bonding the brackets to the tooth. TheS. mutanscounts were analyzed using Friedman and Mann-Whitney U tests. The Repeated measures ANOVA test and Independent samples T-test were also applied, in order to evaluate the mineral content. According to the results, there was a significant reduction in theS. mutanscounts of experimental group at the time points of 1 day, 2 months, and 6 months in both maxillary lateral incisor and upper second premolar teeth. However, we did not observe any significant differences in the control group between the reports at 1 day, 2 months, and 6 months in both maxillary lateral incisor and upper second premolar teeth. The outcomes of this study indicate that, with regard to maxillary lateral incisor teeth, there were no significant differences between the results of the experimental group and control group at the time points of 1 day, 2 months, and 6 months. Furthermore, with respect to the upper second premolar teeth, no significant differences were observed between the two groups at 1 day and 2 months; however,S. mutanscounts were significantly lower in the experimental group than in the control group at the time point of 6 months. Moreover, our gathered data confirmed the absence of any significant differences between the experimental group and control group, in terms of enamel mineral content, at the time intervals of 1 day, 2 months, and 6 months. In conclusion, the incorporation of chitosan NPs and TiO2NPs in orthodontic composites induces an antibacterial property in the resultant adhesive to be used for fixed orthodontic treatment.

Segmenting lung lesions of COVID-19 from CT images via pyramid pooling improved Unet.

Abstract: Segmenting lesion regions of Coronavirus Disease 2019 (COVID-19) from computed tomography (CT) images is a challenge owing to COVID-19 lesions characterized by high variation, low contrast between infection lesions and around normal tissues, and blurred boundaries of infections. Moreover, a shortage of available CT dataset hinders deep learning techniques applying to tackling COVID-19. To address these issues, we propose a deep learning-based approach known as PPM-Unet to segmenting COVID-19 lesions from CT images. Our method improves an Unet by adopting pyramid pooling modules instead of the conventional skip connection and then enhances the representation of the neural network by aiding the global attention mechanism. We first pre-train PPM-Unet on COVID-19 dataset of pseudo labels containing1600 samples producing a coarse model. Then we fine-tune the coarse PPM-Unet on the standard COVID-19 dataset consisting of 100 pairs of samples to achieve a fine PPM-Unet. Qualitative and quantitative results demonstrate that our method can accurately segment COVID-19 infection regions from CT images, and achieve higher performance than other state-of-the-art segmentation models in this study. It offers a promising tool to lay a foundation for quantitatively detecting COVID-19 lesions.