Oligopeptide-based molecular labelling of (bio)degradable polyester biomaterials.

Nowadays, a very important motivation for the development of new functional materials for medical purposes is not only their performance but also whether they are environmentally friendly. In recent years, there has been a growing interest in the possibility of labelling (bio)degradable polymers, in particular those intended for specific applications, especially in the medical sector, and the potential of information storage in such polymers, making it possible, for example, to track the ultimate environmental fate of plastics. This article presents a straightforward green approach that combines both aspects using an oligopeptide, which is an integral part of polymer material, to store binary information in a physical mixture of polymer and oligopeptide. In the proposed procedure the year of production of polymer films made of poly(l-lactide) (PLLA) and a blend of poly(1,4-butylene adipate-co-1,4-butylene terephthalate) and polylactide (PBAT/PLA) were encoded as the sequence of the appropriate amino acids in the oligopeptide (PEP) added to these polymers. The decoding of the recorded information was carried out using mass spectrometry technique as a new method of decoding, which enabled the successful retrieval and reading of the stored information. Furthermore, the properties of labelled (bio)degradable polymer films and stability during biodegradation of PLLA/PEP film under industrial composting conditions have been investigated. The labelled films exhibited good oligopeptide stability, allowing the recorded information to be retrieved from a green polymer/oligopeptide system before and after biodegradation. The MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide assay) study of the PLLA and PLLA/PBAT using the MRC-5 mammalian fibroblasts was presented for the first time.

High-frequency ultrasound in anti-aging skin therapy monitoring.

Over the last few decades, high-frequency ultrasound has found multiple applications in various diagnostic fields. The fast development of this imaging technique opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. In this paper, being the first in this area, we discuss the usability of HFUS in anti-aging skin therapy assessment. The fully automated algorithm combining high-quality image selection and entry echo layer segmentation steps followed by the dermal parameters estimation enables qualitative and quantitative evaluation of the effectiveness of anti-aging products. Considering the parameters of subcutaneous layers, the proposed framework provides a reliable tool for TCA-peel therapy assessment; however, it can be successfully applied to other skin-condition-related problems. In this randomized controlled clinical trial, forty-six postmenopausal women were randomly assigned to the experimental and control groups. Women were treated four times at one-week intervals and applied skin cream daily between visits. The three month follow-up study enables measurement of the long-term effect of the therapy. According to the results, the TCA-based therapy increased epidermal (entry echo layer) thickness, indicating that the thinning process has slowed down and the skin's condition has improved. An interesting outcome is the obtained growth in the intensity of the upper dermis in the experimental group, which might suggest a reduced photo-aging effect of TCA-peel and increased water content. The same conclusions connected with the anti-aging effect of TCA-peel can be drawn by observing the parameters describing the contribution of low and medium-intensity pixels in the upper dermis. The decreased share of low-intensity pixels and increased share of medium-intensity pixels in the upper dermis suggest a significant increase in local protein synthesis.

High-Frequency Ultrasound Dataset for Deep Learning-Based Image Quality Assessment.

This study aims at high-frequency ultrasound image quality assessment for computer-aided diagnosis of skin. In recent decades, high-frequency ultrasound imaging opened up new opportunities in dermatology, utilizing the most recent deep learning-based algorithms for automated image analysis. An individual dermatological examination contains either a single image, a couple of pictures, or an image series acquired during the probe movement. The estimated skin parameters might depend on the probe position, orientation, or acquisition setup. Consequently, the more images analyzed, the more precise the obtained measurements. Therefore, for the automated measurements, the best choice is to acquire the image series and then analyze its parameters statistically. However, besides the correctly received images, the resulting series contains plenty of non-informative data: Images with different artifacts, noise, or the images acquired for the time stamp when the ultrasound probe has no contact with the patient skin. All of them influence further analysis, leading to misclassification or incorrect image segmentation. Therefore, an automated image selection step is crucial. To meet this need, we collected and shared 17,425 high-frequency images of the facial skin from 516 measurements of 44 patients. Two experts annotated each image as correct or not. The proposed framework utilizes a deep convolutional neural network followed by a fuzzy reasoning system to assess the acquired data's quality automatically. Different approaches to binary and multi-class image analysis, based on the VGG-16 model, were developed and compared. The best classification results reach 91.7% accuracy for the first, and 82.3% for the second analysis, respectively.

Tomographic reconstruction from planar thermal imaging using convolutional neural network.

In this study, we investigate perspectives for thermal tomography based on planar infrared thermal images. Volumetric reconstruction of temperature distribution inside an object is hardly applicable in a way similar to ionizing-radiation-based modalities due to its non-penetrating character. Here, we aim at employing the autoencoder deep neural network to collect knowledge on the single-source heat transfer model. For that purpose, we prepare a series of synthetic 3D models of a cylindrical phantom with assumed thermal properties with various heat source locations, captured at different times. A set of planar thermal images taken around the model is subjected to initial backprojection reconstruction, then passed to the deep model. This paper reports the training and testing results in terms of five metrics assessing spatial similarity between volumetric models, signal-to-noise ratio, or heat source location accuracy. We also evaluate the assumptions of the synthetic model with an experiment involving thermal imaging of a real object (pork) and a single heat source. For validation, we investigate objects with multiple heat sources of a random location and temperature. Our results show the capability of a deep model to reconstruct the temperature distribution inside the object.

Chronic wounds multimodal image database.

A multimodal wound image database was created to allow fast development of computer-aided approaches for wound healing monitoring. The developed system with parallel camera optical axes enables multimodal images: photo, thermal, stereo, and depth map of the wound area to be acquired. As a result of using this system a multimodal database of chronic wound images is introduced. It contains 188 image sets of photographs, thermal images, and 3D meshes of the surfaces of chronic wounds acquired during 79 patient visits. Manual wound outlines delineated by an expert are also included in the dataset. All images of each case are additionally coregistered, and both numerical registration parameters and the transformed images are covered in the database. The presented database is publicly available for the research community at https://chronicwounddatabase.eu. That is the first publicly available database for evaluation and comparison of new image-based algorithms in the wound healing monitoring process with coregistered photographs, thermal maps, and 3D models of the wound area. Easily available database of coregistered multimodal data with the raw data set allows faster development of algorithms devoted to wound healing analysis and monitoring.

Automated size-specific dose estimates using deep learning image processing.

An automated vendor-independent system for dose monitoring in computed tomography (CT) medical examinations involving ionizing radiation is presented in this paper. The system provides precise size-specific dose estimates (SSDE) following the American Association of Physicists in Medicine regulations. Our dose management can operate on incomplete DICOM header metadata by retrieving necessary information from the dose report image by using optical character recognition. For the determination of the patient's effective diameter and water equivalent diameter, a convolutional neural network is employed for the semantic segmentation of the body area in axial CT slices. Validation experiments for the assessment of the SSDE determination and subsequent stages of our methodology involved a total of 335 CT series (60 352 images) from both public databases and our clinical data. We obtained the mean body area segmentation accuracy of 0.9955 and Jaccard index of 0.9752, yielding a slice-wise mean absolute error of effective diameter below 2 mm and water equivalent diameter at 1 mm, both below 1%. Three modes of the SSDE determination approach were investigated and compared to the results provided by the commercial system GE DoseWatch in three different body region categories: head, chest, and abdomen. Statistical analysis was employed to point out some significant remarks, especially in the head category.

Supporting diagnostics and therapy planning for percutaneous ablation of liver and abdominal tumors and pre-clinical evaluation.

Percutaneous ablation methods are used to treat primary and metastatic liver tumors. Image guided navigation support minimally invasive interventions of rigid anatomical structures. When working with the displacement and deformation of soft tissues during surgery, as in the abdomen, imaging navigation systems are in the preliminary implementation stage. In this study a multi-stage approach has been developed to support percutaneous liver tumors ablation. It includes CT image acquisition protocol with the amplitude of respiratory motion that yields images subjected to a semi-automatic method able to deliver personalized abdominal model. Then, US probe and ablation needle calibration, as well as patient position adjustment method during the procedure for the preoperative anatomy model, have been combined. Finally, an advanced module for fusion of the preoperative CT with intraoperative US images was designed. These modules have been tested on a phantom and in the clinical environment. The final average Spatial calibration error was 1,7 mm, the average error of matching the position of the markers was about 2 mm during the entire breathing cycle, and average markers fusion error 495 mm. The obtained results indicate the possibility of using the developed method of navigation in clinical practice.

Biopsy needle tracking technique in US images.

Fast development of imaging techniques in last decades has offered the intra-operative visualization as the integral part of surgical tools. Therefore, on-going research activities still focus on efficient and robust analysis of ultrasound images. The paper meets these requirements targeting in detection of biopsy needle, estimation of the needle trajectory and tracking the needle tip motion inside the examined tissue. The developed novel method uses ultrasound data supported by elastography images. The investigated detection algorithm introduces Histogram of Oriented Gradients and image entropy, whereas the tracking part employs Hough transform, Gabor filter and Kanade-Lucas-Tomasi optical flow estimation technique. The developed methodology is verified by the stereoscopic navigation system. The verification phase proves the accuracy of 3-5mm and encourages the further improvement of the methods.

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration.

PURPOSE: A growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning. However, new modalities, like the time-of-flight (ToF) sensors, widely explored in non-medical fields are powerful and have the potential to become a part of computer aided surgery set-up. Connection of different acquisition systems promises to provide a valuable support for operating room procedures. Therefore, the detailed analysis of the accuracy of such multi-sensors positioning systems is needed. METHODS: We present the system combining pre-operative CT series with intra-operative ToF-sensor and optical tracker point clouds. The methodology contains: optical sensor set-up and the ToF-camera calibration procedures, data pre-processing algorithms, and registration technique. The data pre-processing yields a surface, in case of CT, and point clouds for ToF-sensor and marker-driven optical tracker representation of an object of interest. An applied registration technique is based on Iterative Closest Point algorithm. RESULTS: The experiments validate the registration of each pair of modalities/sensors involving phantoms of four various human organs in terms of Hausdorff distance and mean absolute distance metrics. The best surface alignment was obtained for CT and optical tracker combination, whereas the worst for experiments involving ToF-camera. CONCLUSION: The obtained accuracies encourage to further develop the multi-sensors systems. The presented substantive discussion concerning the system limitations and possible improvements mainly related to the depth information produced by the ToF-sensor is useful for computer aided surgery developers.

Granular computing in model based abdominal organs detection.

Detection of region specific voxel is a true challenge in many segmentation procedures. In this study a concept of implementing granular computing in the detection of anatomical structures in abdominal computed tomography (CT) scans is introduced. After proving the usefulness of the information granules to identify voxels that mark certain organs, an automatic model-based approach has been developed. A three-parameter granule that combines the interval and density distribution of voxels has been introduced and employed to identify organ specific voxels of the liver, spleen and kidneys. The specificity of the information granules varies between 90 and 99% for the liver and spleen and over 85% for the kidneys.

Patient Specific Phantom in bimodal image navigation system.

The paper presents the multistep methodology of bimodal Patient Specific Phantom (PSP) development. First, CT based abdominal digital model is designed. It serves as a source for designing organ moulds manufactured by means of a 3D-printer. The collagen based colloid fills the moulds yielding the organ casts. The PSP permits a bimodal navigation system to be developed that employs a realistic CT-based digital model and US imaging. Highly accurate results were achieved with mean Dice similarity coefficient value of 0.92 and Hausdorff distance 9.67 mm.

Shape and function of the diaphysis of the human tibia.

There is an agreement about the principle that bones are optimized to resist daily loads. This has never been ascertained for the human tibia. One of the main load components in the tibia in vivo is a cantilever load (with a linearly varying bending moment, with its largest component in the sagittal plane). investigated if the cross-section of the diaphysis and its variation along the tibia make it an optimized structure with respect to such loads. Six cadaveric tibias were CT-scanned. The geometry and material properties were extracted from the CT-scans, and analyzed along the tibias. A linear variation along the tibia was found for the second moments of area and inertia, and the section modulus in the sagittal plane (slightly less linear in the frontal plane). Conversely, the other properties (polar moments and cross-section are) were much less linear. This suggests that the structure is optimized to resist a bending moment that varies linearly along the tibia. The tibias were instrumented with 28 triaxial straingauges each. Strain was measured under cantilever loading in the sagittal and frontal planes, under quasi-constant-bending in the sagittal and frontal planes, under torsional loading, and with an axial force. The strain distribution was remarkably uniform when cantilever loading was applied in the sagittal plane and slightly less uniform when cantilever loading was applied in the frontal plane. Strain variations were one order of magnitude larger for all other loading configurations. This shows that the tibia is a uniform-stress structure (i.e. optimized) for cantilever loading.

Suitability of imaging methods (X-ray, CT, MRI) in the diagnostics of Ewing's sarcoma in children - analysis of own material.

BACKGROUND: Ewing sarcoma is a malignant, small round cell bone tumor, presenting predominantly in children and adolescents. Ewing sarcoma may develop in every bone; diaphyses of long bones, ribs and flat bones are the main locations. Local and systemic clinical symptoms are nonspecific - pain, swelling, fever or ill-being. The aim of the study was to assess the role of radiography, computed tomography and magnetic resonance imaging in the analysis of bone lesions in children and young adults with Ewing sarcoma. MATERIAL/METHODS: Twenty-seven patients, aged between 1 year and 10 months, and 17 years and 2 months, with histologically verified Ewing sarcoma of the bone, referred to the Radiological Department of University Hospital No 6., John Paul II Upper Silesian Centre for Child Health Katowice, in the period from 1996 to 2007, were included in the study.Plain radiography was performed in every child, CT in 20 and MRI in 12 individuals. Tumour location, extension of the tumour, soft tissue mass, and periosteal reaction were taken into consideration in the evaluation of the lesion. In some cases, pathological features of the MRI and CT were compared. The prevalence of some radiological features was compared to the literature data. RESULTS: THE MOST COMMON SITE OF TUMOR WAS: ribs (6 children), femoral bone (6 children), pelvis (4 children) and tibia (3 children). In 2 children, a primary tumor was diagnosed in the spine (multifocal in 1 child). X-rays revealed: periosteal reaction in 17 children (63%), soft tissue involvement in 19 children (70%), permeative component in 16 children (59%), and sclerotic component in 5 children (19%). In 10 children (37%), periosteal reaction was not detected. The examination revealed: soft tissue calcifications in 7 cases (26%), a well-delineated focus of destruction within bones in 3 children (11%), cortical thickening in 4 children (15%), cortical destruction in 4 children (15%), saucerisation in 3 children (11%), bone expansion in 3 children (11%), pathological fracture in 2 children (7%), cystic component in 1 child (4%), and vertebra plana in 1 child (4%).Reaction of tumors after i.v. contrast administration, shown on CT, was visible in 16 children - it was useful for a better description of the tumor and extension of the mass within the soft tissue. All MRI examinations (12 children) showed a heterogenous mass with ill-defined borders and a violated cortex. Low signal intensity of the tumor in a T1-weighted image and high signal intensity in a T2-weighted image was shown as well. Heterogenous enhancement of signal intensity on T1-weighted images could be observed after i.v. contrast administration. MRI EXAMINATIONS SHOWED: tumor in an adjacent soft tissue in 11 children, and involvement of the epiphyseal plate or of the joint cavity in 6 children. CONCLUSIONS: X-ray and MRI are essential in diagnostics. CT examination is more useful to estimate periosteal reactions and destruction of bone and marrow cavity, especially in flat bones. However, to recognise a malignancy, it is necessary to perform a histopathological examination. In doubtful cases, the examination has to be verified as well.