Biomedical Physics & Engineering Express最新文献

Background.The MTF has difficulties being determined (according to the provisions of the IEC standards) in the hospital setting due to the lack of resources.Purpose.The objective of this work is to propose a quantitative method for obtaining the point spread function (PSF) and the modulation transfer function (MTF) of a digital mammography system from an image of a bar pattern.Methods.The method is based on the measurement of the contrast transfer function (CTF) of the system over the image of the bar pattern. In addition, a theoretical model for thePSFis proposed, from which the theoreticalCTFof the system is obtained by means of convolution with a square wave (mathematical simulation of the bar pattern). Through an iterative process, the free parameters of thePSFmodel are varied until the experimentalCTFcoincides with the one calculated by convolution. Once thePSFof the system is obtained, we calculate theMTFby means of its Fourier transform. TheMTFcalculated from the modelPSFhave been compared with those calculated from an image of a 65μm diameter gold wire using an oversampling process.Results.TheCTFhas been calculated for three digital mammographic systems (DMS 1, DMS 2 and DMS 3), no differences of more than 5 % were found with the CTF obtained with the PSF model. The comparison of theMTFshows us the goodness of thePSFmodel.Conclusions.The proposed method for obtainingPSFandMTFis a simple and accessible method, which does not require a complex configuration or the use of phantoms that are difficult to access in the hospital world. In addition, it can be used to calculate other magnitudes of interest such as the normalized noise power spectrum (NNPS) and the detection quantum efficiency (DQE).

A hip joint fracture includes a break in the thigh (femur) or coxa bone near the pelvis. During fracture healing, stability and weight bearing by the affected limb are key indicators to measure patients' improvement. Conventionally, the rehabilitation effectiveness is monitored through clinical examinations, patients' feedback, and few studies also reported instrumented gait evaluations. A gap remains there to numerically quantify the recovery in patients' stability and weight bearing in response to rehabilitation therapies. This study introduces Nyquist and Bode (N&B) methods to analyse the instrumented gait signals further and evaluate gait stability in hip fracture patients during weight loading and unloading transitions. The centre of pressure (CoP) data was recorded using force plates for conditions: coxa hip fracture (HC), femur hip fracture (HF), and normal hip joint (NH). The time rate of CoP signals illustrated two major impulses during the loading and unloading phases which were modelled in time and frequency domains. The frequency models were further analysed by applying N&B methods and stability margins were computed for both impaired and healthy conditions. Results illustrated a significant decrease (Kruskal-Wallis's test, p < 0.001) in the intralimb walking stability of both fracture conditions. Further, Spearman's correlation between CoP velocities of fractured and intact limbs illustrated significant interlimb dependencies to maintain walking stability (p < 0.001) during weight loading and unloading transitions. Overall, the HF impairment illustrated the least intralimb walking stability and relatively greater interlimb dependencies. Clinically, these methods and findings are important to measure the recovery in patients undergoing rehabilitation after a hip joint or other lower limb impairments.

Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the content of an image, has garnered much attention. The main objective of this task is to separate objects from the background, thereby simplifying and enhancing the significance of the image. However, existing methods for image segmentation have their limitations when applied to certain types of images. This survey paper aims to highlight the importance of image segmentation techniques by providing a thorough examination of their advantages and disadvantages. The accurate detection of cancer regions in medical images is crucial for ensuring effective treatment. In this study, we have also extensive analysis of Computer-Aided Diagnosis (CAD) systems for cancer identification, with a focus on recent research advancements. The paper critically assesses various techniques for cancer detection and compares their effectiveness. Convolutional neural networks (CNNs) have attracted particular interest due to their ability to segment and classify medical images in large datasets, thanks to their capacity for self- learning and decision-making.

This study evaluated the feasibility of the femoral bone after fixation using biphasic calcium phosphate cement-augmentation of the proximal femoral nail antirotation (PFNA) compared with PFNA without cement. This study presented to compare the stiffness, fatigue testing, and compressive strength between stable (AO31-A2.1) and unstable (AO31-A3.3) intertrochanteric fractures treated by cement augmented PFNA of the cadaveric femoral. Biphasic calcium phosphate cement was injected to align and compatible with PFNA and the reconstructive procedure was monitored the cement placement using x-ray imaging during operation. The testing demonstrated that the cement could be injected through a small needle (13 G, 16 cm length, 1.8 mm inner diameter) within a suitable operating time. The feasibility study of the biomechanical testing was divided into three tests: stiffness test, fatigue cyclic load, and compression test. The results showed that the cement-augmented specimens exhibited higher stiffness than the control specimens without cement. The cement-augmented specimens also showed lower strain energy during the fatigue test, resulting in higher compressive strength (4730.7 N) compared to the control specimens (3857.4 N). There is a correlation between BMD and fracture load and the increase in compression load of the cement-augmented femoral compared to the controls as well as an increase in strain energy of fatigue cyclic testing was found. Biphasic calcium phosphate cement-augmented of the PFNA biomechanically enhanced the cut-out resistance in intertrochanteric fracture. This procedure is especially efficient for unstable intertrochanteric fracture suggesting the potential benefits of using biphasic calcium phosphate cement in medical applications.

X-ray phase-contrast imaging has become a valuable tool for biomedical research due to its improved contrast abilities over regular attenuation-based imaging. The recently emerged Talbot-Lau interferometer can provide quantitative attenuation, phase-contrast and dark-field image data, even with low-brilliance x-ray tube sources. Thus, it has become a valid option for clinical environments. In this study, we analyze the effects of x-ray tube voltage and total number of images on the contrast-to-noise ratio (CNR) and dose-weighted CNR (CNRD) calculated from tomographic transmission and phase-contrast data of a phantom sample. Constant counting statistics regardless of the voltage was ensured by adjusting the image exposure time for each voltage setting. The results indicate that the x-ray tube voltage has a clear effect on both image contrast and noise. This effect is amplified in the case of phase-contrast images, which is explained by the polychromatic x-ray spectrum and the dependence of interferometer visibility on the spectrum. CNRD is additionally affected by the total imaging time. While submerging the sample into a water container effectively reduces image artefacts and improves the CNR, the additional attenuation of the water must be compensated with a longer exposure time. This reduces dose efficiency. Both the CNR and CNRD are higher in the phase-contrast images compared to transmission images. For transmission images, and phase-contrast images without the water container, CNRD can be increased by using higher tube voltages (in combination with a lower exposure time). For phase-contrast images with the water container, CNRD is increased with lower tube voltages. In general, the CNRD does not strongly depend on the number of tomographic angles or phase steps used.

Understanding the brain response to thermal stimuli is crucial in the sensory experience. This study focuses on non-painful thermal stimuli, which are sensations induced by temperature changes without causing discomfort. These stimuli are transmitted to the central nervous system through specific nerve fibers and are processed in various regions of the brain, including the insular cortex, the prefrontal cortex, and anterior cingulate cortex. Despite the prevalence of studies on painful stimuli, non-painful thermal stimuli have been less explored. This research aims to bridge this gap by investigating brain functional connectivity during the perception of non-painful warm and cold stimuli using electroencephalography (EEG) and the partial directed coherence technique (PDC). Our results demonstrate a clear contrast in the direction of information flow between warm and cold stimuli, particularly in the theta and alpha frequency bands, mainly in frontal and temporal regions. The use of PDC highlights the complexity of brain connectivity during these stimuli and reinforces the existence of different pathways in the brain to process different types of non-painful warm and cold stimuli.

Being able to image the microstructure of growth cartilage is important for understanding the onset and progression of diseases such as osteochondrosis and osteoarthritis, as well as for developing new treatments and implants. Studies of cartilage using conventional optical brightfield microscopy rely heavily on histological staining, where the added chemicals provide tissue-specific colours. Other microscopy contrast mechanisms include polarization, phase- and scattering contrast, enabling non-stained or 'label-free' imaging that significantly simplifies the sample preparation, thereby also reducing the risk of artefacts. Traditional high-performance microscopes tend to be both bulky and expensive.Computational imagingdenotes a range of techniques where computers with dedicated algorithms are used as an integral part of the image formation process. Computational imaging offers many advantages like 3D measurements, aberration correction and quantitative phase contrast, often combined with comparably cheap and compact hardware. X-ray microscopy is also progressing rapidly, in certain ways trailing the development of optical microscopy. In this study, we first briefly review the structures of growth cartilage and relevant microscopy characterization techniques, with an emphasis on Fourier ptychographic microscopy (FPM) and advanced x-ray microscopies. We next demonstrate with our own results computational imaging through FPM and compare the images with hematoxylin eosin and saffron (HES)-stained histology. Zernike phase contrast, and the nonlinear optical microscopy techniques of second harmonic generation (SHG) and two-photon excitation fluorescence (TPEF) are explored. Furthermore, X-ray attenuation-, phase- and diffraction-contrast computed tomography (CT) images of the very same sample are presented for comparisons. Future perspectives on the links to artificial intelligence, dynamic studies andin vivopossibilities conclude the article.

Purpose. This study aims to introduce an innovative noninvasive method that leverages a single image for both grading and staging prediction. The grade and the stage of cervix cancer (CC) are determined from diffusion-weighted imaging (DWI) in particular apparent diffusion coefficient (ADC) maps using deep convolutional neural networks (DCNN).Methods. datasets composed of 85 patients having annotated tumor stage (I, II, III, and IV), out of this, 66 were with grade (II and III) and the remaining patients with no reported grade were retrospectively collected. The study was IRB approved. For each patient, sagittal and axial slices containing the gross tumor volume (GTV) were extracted from ADC maps. These were computed using the mono exponential model from diffusion weighted images (b-values = 0, 100, 1000) that were acquired prior to radiotherapy treatment. Balanced training sets were created using the Synthetic Minority Oversampling Technique (SMOTE) and fed to the DCNN. EfficientNetB0 and EfficientNetB3 were transferred from the ImageNet application to binary and four-class classification tasks. Five-fold stratified cross validation was performed for the assessment of the networks. Multiple evaluation metrics were computed including the area under the receiver operating characteristic curve (AUC). Comparisons with Resnet50, Xception, and radiomic analysis were performed.Results. for grade prediction, EfficientNetB3 gave the best performance with AUC = 0.924. For stage prediction, EfficientNetB0 was the best with AUC = 0.931. The difference between both models was, however, small and not statistically significant EfficientNetB0-B3 outperformed ResNet50 (AUC = 0.71) and Xception (AUC = 0.89) in stage prediction, and demonstrated comparable results in grade classification, where AUCs of 0.89 and 0.90 were achieved by ResNet50 and Xception, respectively. DCNN outperformed radiomic analysis that gave AUC = 0.67 (grade) and AUC = 0.66 (stage).Conclusion.the prediction of CC grade and stage from ADC maps is feasible by adapting EfficientNet approaches to the medical context.

Introduction. The positioning ofγray interactions in positron emission tomography (PET) detectors is commonly made through the evaluation of the Anger logic flood histograms. machine learning techniques, leveraging features extracted from signal waveform, have demonstrated successful applications in addressing various challenges in PET instrumentation.Aim. This paper evaluates the use of artificial neural networks (NN) forγray interaction positioning in pixelated scintillators coupled to a multiplexed array of silicon photomultipliers (SiPM).Methods. An array of 16 Cerium doped Lutetium-based (LYSO) crystal pixels (cross-section 2 × 2 mm²) coupled to 16 SiPM (S13360-1350) were used for the experimental setup. Data from each of the 16 LYSO pixels was recorded, a total of 160000 events. The detectors were irradiated by 511 keV annihilationγrays from a Sodium-22 (²²Na) source. Another LYSO crystal was used for electronic collimation. Features extracted from the signal waveform were used to train the model. Two models were tested: i) single multiple-class neural network (mcNN), with 16 possible outputs followed by a softmax and ii) 16 binary classification neural networks (bNN), each one specialized in identifying events occurred in each position.Results. Both NN models showed a mean positioning accuracy above 85% on the evaluation dataset, although the mcNN is faster to train.DiscussionThe method's accuracy is affected by the introduction of misclassified events that interacted in the neighbour's crystals and were misclassified during the dataset acquisition. Electronic collimation reduces this effect, however results could be improved using a more complex acquisition setup, such as a light-sharing configuration.ConclusionsThe methods comparison showed that mcNN and bNN can surpass the Anger logic, showing the feasibility of using these models in positioning procedures of future multiplexed detector systems in a linear configuration.

Lentiviral transduction is widely used in research, has shown promise in clinical trials involving gene therapy and has been approved for CAR-T cell immunotherapy. However, most modifications are doneex vivoand rely on systemic administration of large numbers of transduced cells for clinical applications. A novel approach utilizingin situbiomaterial-based gene delivery can reduce off-target side effects while enhancing effectiveness of the manipulation process. In this study, poly(ethylene glycol) diacrylate (PEGDA)-based scaffolds were developed to enablein situlentivirus-mediated transduction. Compared to other widely popular biomaterials, PEGDA stands out due to its robustness and cost-effectiveness. These scaffolds, prepared via cryogelation, are capable of flowing through surgical needles in bothin vitroandin vivoconditions, and promptly regain their original shape. Modification with poly(L-lysine) (PLL) enables lentivirus immobilization while interconnected macroporous structure allows cell infiltration into these matrices, thereby facilitating cell-virus interaction over a large surface area for efficient transduction. Notably, these preformed injectable scaffolds demonstrate hemocompatibility, cell viability and minimally inflammatory response as shown by ourin vitroandin vivostudies involving histology and immunophenotyping of infiltrating cells. This study marks the first instance of using preformed injectable scaffolds for delivery of lentivectors, which offers a non-invasive and localized approach for delivery of factors enablingin situlentiviral transduction suitable for both tissue engineering and immunotherapeutic applications.