TIFF Images Research Articles

Potential of PSMA for breast cancer in nuclear medicine: digital quantitative immunohistochemical analysis and implications for a theranostic approach

BackgroundFurther research is still needed to fully understand the potential of prostate-specific membrane antigen (PSMA) in breast cancer (BC) and to develop and optimize targeted therapies and imaging modalities. The objective of this study was to present a comprehensive analysis of immunohistochemistry data on PSMA staining in BC and to discuss its potential value in a theranostic approach.MethodsFifty-eight male and female patients were randomly selected from a retrospective database of patients who underwent surgery for breast cancer between January 2012 and December 2017 and for whom a specimen is available in our tumour library. Immunodetection of PSMA and CD31 was performed on serial slides. The digitized slides were reviewed and analysed by an experienced pathologist. Additionally, the corresponding TIFF images were processed to calculate the percentage of positive neovessels.ResultsEighteen patients (31.6%) had no expression, 29 (50.9%) had PSMA neovascular expression scored as “1”, and 10 (17.5%) had neovascular expression scored as “2”. Digital immunohistochemistry analysis for this last specific group of patients showed a median proportion of positive neovessels equal to 5% (range: 3–19). A multivariable logistic regression demonstrated that the odds of PSMA positivity were 4.55 times higher in non-luminal tumours and decreased by a factor of 0.12 in lobular subtypes. There was no association between sex or the presence of a germline BRCA1/2 mutation and PSMA expression in tumours.ConclusionsOur study highlights generally low neovascular expression of PSMA in specific histopathological subtypes of breast cancer, which will likely hamper the development of an adequate theranostic strategy.Trial registrationThe procedure has been retrospectively registered to the French National Institute for Health Data (N° F20220615153900).

Optimising ovarian tumor classification using a novel CT sequence selection algorithm

Gynaecological cancers, especially ovarian cancer, remain a critical public health issue, particularly in regions like India, where there are challenges related to cancer awareness, variable pathology, and limited access to screening facilities. These challenges often lead to the diagnosis of cancer at advanced stages, resulting in poorer outcomes for patients. The goal of this study is to enhance the accuracy of classifying ovarian tumours, with a focus on distinguishing between malignant and early-stage cases, by applying advanced deep learning methods. In our approach, we utilized three pre-trained deep learning models—Xception, ResNet50V2, and ResNet50V2FPN—to classify ovarian tumors using publicly available Computed Tomography (CT) scan data. To further improve the model’s performance, we developed a novel CT Sequence Selection Algorithm, which optimises the use of CT images for a more precise classification of ovarian tumours. The models were trained and evaluated on selected TIFF images, comparing the performance of the ResNet50V2FPN model with and without the CT Sequence Selection Algorithm. Our experimental results show the Comparative evaluation against the ResNet50V2 FPN model, both with and without the CT Sequence Selection Algorithm, demonstrates the superiority of the proposed algorithm over existing state-of-the-art methods. This research presents a promising approach for improving the early detection and management of gynecological cancers, with potential benefits for patient outcomes, especially in areas with limited healthcare resources.

Reprocessing and interpretation of legacy seismic data using machine learning from the Granada Basin, Spain

The Granada Basin (Spain) is a Neogene sedimentary depression with irregular geomorphology and deep depocenters. It is located in the most seismically hazardous part of the Iberian Peninsula with an historically experienced extremely destructive earthquakes, followed by periods of low to moderate seismicity. In 1980s the Chevron Oil Company collected a set of 30 deep seismic reflection sections in this Basin of which only the results on paper are kept. Due to the fact that many of these seismic profiles are currently located in urban areas and the economic cost of carrying out a similar exploration, it was decided to recover these old data and apply a post-stack treatment to improve their quality. The purpose of this study is to show the applied reprocessing flow and, with the new sections, to present a spatial model of the basin. The first stage of recovery and enhacement of seismic sections has consisted in three phases: first, high-resolution scanning of paper copies to TIFF images followed by the transformation of TIFF images to SEG-Y format; second, poststack processing workflow to increasing resolution and lateral coherence of these seismic lines; and third, it has been used a machine learning algorithm, among others, increasing the spatial resolution, signal-to-noise ratio, and coherence of the seismic signals. In addition, basement horizons, as well as three sedimentary sequences, were identified in all seismic sections and interpolated to create a three-dimensional basement model composed by normal faults, horst and grabens related to the seismotectonic behavior of the basin. As an overall assessment, this work is an example of the usefulness of ‘recycling’ legacy seismic data, which nowadays are usually in archived boxes, but at the time required a great economic and acquisition effort.

Development of an Artificial-Intelligence-Based Tool for Automated Assessment of Cellularity in Bone Marrow Biopsies in Ph-Negative Myeloproliferative Neoplasms.

The cellularity assessment in bone marrow biopsies (BMBs) for the diagnosis of Philadelphia chromosome (Ph)-negative myeloproliferative neoplasms (MPNs) is a key diagnostic feature and is usually performed by the human eyes through an optical microscope with consequent inter-observer and intra-observer variability. Thus, the use of an automated tool may reduce variability, improving the uniformity of the evaluation. The aim of this work is to develop an accurate AI-based tool for the automated quantification of cellularity in BMB histology. A total of 55 BMB histological slides, diagnosed as Ph- MPN between January 2018 and June 2023 from the archives of the Pathology Unit of University "Luigi Vanvitelli" in Naples (Italy), were scanned on Ventana DP200 or Epredia P1000 and exported as whole-slide images (WSIs). Fifteen BMBs were randomly selected to obtain a training set of AI-based tools. An expert pathologist and a trained resident performed annotations of hematopoietic tissue and adipose tissue, and annotations were exported as .tiff images and .png labels with two colors (black for hematopoietic tissue and yellow for adipose tissue). Subsequently, we developed a semantic segmentation model for hematopoietic tissue and adipose tissue. The remaining 40 BMBs were used for model verification. The performance of our model was compared with an evaluation of the cellularity of five expert hematopathologists and three trainees; we obtained an optimal concordance between our model and the expert pathologists' evaluation, with poorer concordance for trainees. There were no significant differences in cellularity assessments between two different scanners.

Surface Heat Monitoring with High-Resolution UAV Thermal Imaging: Assessing Accuracy and Applications in Urban Environments

The urban heat island (UHI) effect, where urban areas experience higher temperatures than surrounding rural regions, necessitates effective monitoring to estimate and address its diverse impacts. Many existing studies on urban heat dynamics rely on satellite data with coarse resolutions, posing challenges in analyzing heterogeneous urban surfaces. Unmanned aerial vehicles (UAVs) offer a solution by providing thermal imagery at a resolution finer than 1 m. Despite UAV thermal imaging being extensively explored in agriculture, its application in urban environments, specifically for surface temperatures, remains underexplored. A pilot project conducted in Athens, Georgia, utilized a UAV with a FLIR Vue Pro R 640 thermal camera to collect thermal data from two neighborhoods. Ground data, obtained using a handheld FLIR E6-XT infrared imaging camera, were compared with UAV thermal imagery. The study aimed to assess the accuracy of the UAV camera and the handheld camera for urban monitoring. Initial testing revealed the handheld’s accuracy but tendency to underpredict, while UAV camera testing highlighted considerations for altitude in both the rjpg and tiff image pixel conversion models. Despite challenges, the study demonstrates the potential of UAV-derived thermal data for monitoring urban surface temperatures, emphasizing the need for careful model considerations in data interpretation.

Whole slide images as non-fungible tokens: A decentralized approach to secure, scalable data storage and access

BackgroundDistributed ledger technology (DLT) enables the creation of tamper-resistant, decentralized, and secure digital ledgers. A non-fungible token (NFT) represents a record on-chain associated with a digital or physical asset, such as a whole-slide image (WSI). The InterPlanetary File System (IPFS) represents an off-chain network, hypermedia, and file sharing peer-to-peer protocol for storing and sharing data in a distributed file system. Today, we need cheaper, more efficient, highly scalable, and transparent solutions for WSI data storage and access of medical records and medical imaging data. MethodsWSIs were created from non-human tissues and H&E-stained sections were scanned on a Philips Ultrafast WSI scanner at 40× magnification objective lens (1 μm/pixel). TIFF images were stored on IPFS, while NFTs were minted on the Ethereum blockchain network in ERC-1155 standard. WSI-NFTs were stored on MetaMask and OpenSea was used to display the WSI-NFT collection. Filebase storage application programing interface (API) were used to create dedicated gateways and content delivery networks (CDN). ResultsA total of 10 WSI-NFTs were minted on the Ethereum blockchain network, found on our collection “Whole Slide Images as Non-fungible Tokens Project” on Open Sea: https://opensea.io/collection/untitled-collection-126765644. WSI TIFF files ranged in size from 1.6 to 2.2 GB and were stored on IPFS and pinned on 3 separate nodes. Under optimal conditions, and using a dedicated CDN, WSI reached retrieved at speeds of over 10 mb/s, however, download speeds and WSI retrieval times varied significantly depending on the file and gateway used. Overall, the public IPFS gateway resulted in variably poorer WSI download retrieval performance compared to gateways provided by Filebase storage API. ConclusionWhole-slide images, as the most complex and substantial data files in healthcare, demand innovative solutions. In this technical report, we identify pitfalls in IPFS, and demonstrate proof-of-concept using a 3-layer architecture for scalable, decentralized storage, and access. Optimized through dedicated gateways and CDNs, which can be effectively applied to all medical data and imaging modalities across the healthcare sector. DLT and off-chain network solutions present numerous opportunities for advancements in clinical care, education, and research. Such approaches uphold the principles of equitable healthcare data ownership, security, and democratization, and are poised to drive significant innovation.

Analysis of the Neutron Radiographic Image Quality and Beam Intensity Produced by a Compact Multichannel Collimator

We investigate the image quality and beam intensity of thermal neutron radiography after replacing a standard single-channel neutron collimator with a compact array of microcollimators. In this study, the MCNP6 Monte Carlo computer code was used to simulate a 2 × 2-cm-area isotropic thermal neutron source, which then was collimated by an array of micron-sized neutron collimators that measured 29.8 μm in diameter and with lengths that varied from 0.6 to 3 mm. These microcollimators were spaced 30 μm apart and assembled into a 2 × 2-cm array. The image quality of the neutron beams produced by the resulting collimator arrays was assessed by imaging the edge of a very thin (~0.01-mm) gadolinium foil to obtain the image Modulation Transfer Function (MTF). The MCNP6 resulting flux map from each simulation then was converted into a grayscale .tiff image and the image’s resulting MTF obtained using the ImageJ computer program with the imaging beam geometric unsharpness, which is a limiting factor in the image resolution determined at the 10% value of the MTF curve. In this study, we found that a 2 × 2× 0.298-cm microcollimator, corresponding to a length-to–hole diameter ratio of 100:1 and a collimator length of 2.98 mm produced a beam with a geometric unsharpness of 32 μm. Compared to a standard single-channel collimator with a 2 × 2-cm aperture, the single-channel collimator would need to be 660 cm long to produce an equivalent geometric sharpness. Yet because of its shorter length, the imaging beam intensity from our 2.98-mm-thick collimator array was approximately 50 times greater than that of an equivalent single-channel collimator.

MEYEstro software: an automatic tool for standardized refractive surgery outcomes reporting

BackgroundStandardization for reporting medical outcomes enables clinical study comparisons and has a fundamental role in research reproducibility. In this context, we present mEYEstro, a free novel standalone application for automated standardized refractive surgery graphs. mEYEstro can be used for single and multiple group comparisons in corneal and intraocular refractive surgery patients. In less than 30 s and with minimal user manipulation, mEYEstro automatically creates the required journal standard graphs while simultaneously performing valid statistical analyses.ResultsThe software produces the following 11 standard graphs; Efficacy: 1. Cumulative uncorrected (UDVA) and corrected visual acuity (CDVA), 2. Difference between UDVA and CDVA, Safety: 3. Change in line of CDVA, Accuracy: 4. Spherical equivalent (SEQ) to intended target, 5. Attempted vs. achieved SEQ, 6. Defocus equivalent (DEQ) accuracy, 7. Refractive astigmatism accuracy, 8. Target-induced astigmatism vs. Surgically-induced astigmatism, 9. Correction index histogram, 10. Angle of error histogram, Stability: 11. SEQ stability over time. Percent proportions, means, standard deviations, Cohen's d effect sizes, and p-values are calculated and displayed on each graph. All graphs can be easily exported as high-resolution TIFF images for figures to use in scientific manuscripts and presentations.ConclusionsmEYEstro software enables clinicians, surgeons, and researchers, to easily and efficiently analyze refractive surgery outcomes using the standardized methodology required by several peer-reviewed ophthalmology journals.

Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium

Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research.

Data Type and Data Sources for Agricultural Big Data and Machine Learning

Sustainable agriculture is currently being challenged under climate change scenarios since extreme environmental processes disrupt and diminish global food production. For example, drought-induced increases in plant diseases and rainfall caused a decrease in food production. Machine Learning and Agricultural Big Data are high-performance computing technologies that allow analyzing a large amount of data to understand agricultural production. Machine Learning and Agricultural Big Data are high-performance computing technologies that allow the processing and analysis of large amounts of heterogeneous data for which intelligent IT and high-resolution remote sensing techniques are required. However, the selection of ML algorithms depends on the types of data to be used. Therefore, agricultural scientists need to understand the data and the sources from which they are derived. These data can be structured, such as temperature and humidity data, which are usually numerical (e.g., float); semi-structured, such as those from spreadsheets and information repositories, since these data types are not previously defined and are stored in No-SQL databases; and unstructured, such as those from files such as PDF, TIFF, and satellite images, since they have not been processed and therefore are not stored in any database but in repositories (e.g., Hadoop). This study provides insight into the data types used in Agricultural Big Data along with their main challenges and trends. It analyzes 43 papers selected through the protocol proposed by Kitchenham and Charters and validated with the PRISMA criteria. It was found that the primary data sources are Databases, Sensors, Cameras, GPS, and Remote Sensing, which capture data stored in Platforms such as Hadoop, Cloud Computing, and Google Earth Engine. In the future, Data Lakes will allow for data integration across different platforms, as they provide representation models of other data types and the relationships between them, improving the quality of the data to be integrated.

Which Histometric Analysis Approach Is More Reliable for Assessing Histological Bone Tissue Samples?

This study aims to evaluate the grid of Merz and ImageJ methods for histometric quantification, verifying which is more reliable and defining which is most suitable based on the time required to perform. Thirty histological samples of maxillary sinuses grafted with xenografts were evaluated using an optical light microscope attached to an image capture camera and connected to a microcomputer. The images were digitalized and recorded as a TIFF image, and the new bone formation was evaluated using the grid of Merz and ImageJ. The Bland–Altman analysis was used to identify the agreement between the methods and determine suitable future research options. The timing of the quantification was also performed to identify a possible advantage. The mean value for the quantification analysis timing for the grid of Merz was 194.9 ± 72.0 s and for ImageJ was 871.7 ± 264.4, with statistical significance between the groups (p = 0.0001). The Bland–Altman analysis demonstrated a concordance between the methods, due to the bias being next to the maximum concordance (−1.25) in addition to the graphic showing the scattering points next to the mean of differences and inside of limits of agreement. Thus, it was demonstrated that the grid of Merz presents reliable outcomes and advantages over the ImageJ methodology regarding the time spent to contour the areas of interest.

Compass Fundus-Guided Perimetry in Geographic Atrophy.

Purpose To evaluate compass (CMP), a recently introduced device that combines scanning ophthalmoscopy, automated perimetry, and eye tracking, for fundus-guided perimetry (microperimetry) with the purpose of correlating perimetric retinal sensitivity (PRS) and retinal geographic atrophy (GA) features. Materials and Methods A retrospective, cross-sectional study was performed in 56 eyes of 43 patients affected by GA. All patients underwent compass 10-2 perimetry, consisting of a full-threshold visual field on fundus photography and an infrared (IR) image of the central 30° of the retina. Data were exported to an Excel sheet. Binarization with black/white (B/W) variables was applied on the compass photo fundus and matched with visual field scores. Patients underwent autofluorescence (AF) and IR images (Heidelberg, Germany): CMP and Heidelberg IR images were homologated by using GIMP software (https://www.gimp.org), and then atrophic areas were manually measured with the ImageJ program. CMP perimetric grid was overlapped with AF and IR pictures by using GIMP, obtaining composite TIFF images, which were then analyzed with the ImageJ greyscale score (GSS) tool. A hyperautofluorescent halo was identified on the GA edges of some patients. Pearson's correlation between GA size on IR compass and IR Heidelberg and between GSS and PRS values has been calculated; the independent t-test was realized to calculate the correlation between GSS and B/W variables identified on the CMP photo fundus. The Spearman correlation between total deviation and pattern deviation was calculated. Results The AUC-ROC score between CMP scores and B/W variables was 93,4%. The Spearman correlation between total deviation and pattern deviation was highly significant (p = 0,00). The correlation between AF GSS values and PRS was significant (p value = 0,00), the correlation between GSS of hyperautofluorescent points and PRS was significant (p value = 0,00), and the correlation between IR GSS and PRS was significant (p value = 0,00). The correlation between AF GSS and B/W variables was significant (p value = 0,002), the correlation between hyperautofluorescent points and B/W was not significant (p value = 0,40), and the correlation between IR GSS and B/W was significant (p = 0,00). Conclusions Based on our preliminary results, compass seems to be a reliable, quick, and safe device for the anatomical and functional study of GA. The direct visualization of the visual field on the fundus photography as a background allows a precise assessment and clinical monitoring of this disease.

Effect of Process Parameters on the Performance of Drop-On-Demand 3D Inkjet Printing: Geometrical-Based Modeling and Experimental Validation.

As additive manufacturing has evolved, 3D inkjet printing (IJP) has become a promising alternative manufacturing method able to manufacture functional multi-material parts in a single process. However, issues with part quality in terms of dimensional errors and lack of precision still restrict its industrial and commercial applications. This study aims at improving the dimensional accuracy of 3D IJP parts by developing an optimization-oriented simulation tool of droplet behavior during the drop-on-demand 3D IJP process. The simulation approach takes into consideration the effect of droplet volume, droplet center-to-center distance, coverage percentage of jetted droplets, the contact angle of the ink on the solid substrate and coalescence performance of overlapping droplets, in addition to the number of printed layers. Following the development of the simulation tool using MATLAB, its feasibility was experimentally validated and the results showed a good agreement with a maximum deviation of 2.25% for horizontal features. In addition, the simulated horizontal features are compared with the results of “Inkraster” software, which also illustrates droplet behavior, however, only in 2D. For vertical features, a dial gauge indicator is used to measure the sample height, and the validation results show that the simulation tool can predicate the height of the sample with an average error of 10.89% for a large droplet diameter and 8.09% for a small diameter. The simulation results were found to be in a good agreement with the dimensions of the printed parts. The developed tool was then used to elucidate the effect of resolution of processed TIFF image and droplet diameter on the dimensional accuracy of 3D IJP parts.

Morphological Examination of Vertebral Bodies (Centrum) From Grass Carp (Ctenopharyngodon idella) Using High‐Resolution Micro‐CT Scans

The vertebral column, a defining trait of all vertebrates, is organized as a concatenated chain of vertebrae, and therefore its support to the body depends on individual vertebral morphology. Consequently, studying the morphology of the vertebral body (centrum) is of anatomical and clinical importance. In this study, we have investigated the morphology of Grass carp (Ctenopharyngodon idella) vertebral centrum using high‐resolution micro‐CT scans. Grass carp is a member of the interclass Teleostei (teleost fish), part of the class Actinopterygii, which accounts for the majority of all vertebrate species; thus, Grass carp vertebral morphology can help us understand normal vertebrate vertebral morphology and development. Based on previous studies done on teleost fish, we hypothesized that vertebral centrum will demonstrate bilateral symmetry along the sagittal (left/right) and transverse (cranial/caudal) planes. To this end, three vertebrae (numbers V9‐11) from eight Grass carps that were housed in a Living Stream Systems for 8 weeks at 15⁰C were harvested and micro‐CT scanned at a resolution of 19.29 µm (SkyScan1173, Bruker Inc). The scans were reconstructed into 16‐bit TIFF image stacks and then imported into Dragonfly (ORS Inc.) for segmentation using an Otsu algorithm. After centering each vertebrae along its three principal axes various morphological measurements were performed. Grass carp vertebral centrum demonstrated an hourglass shape where both endplates are concave. While the vertebral centrum demonstrated bilateral symmetry along the sagittal plane (left/right, in line with our hypothesis), vertebral centrum demonstrated deviation from bilateral symmetry along the transverse plane (cranial/caudal, refuting our hypothesis). The vertebral centrum cranial endplate size (perimeter and area) was found to be smaller and less circular compared to the caudal endplate. This implies that the vertebral body tapers off from caudal to cranial. In addition, the cranial endplate was found to be shallower (i.e. more obtuse angles, α > β, see figure 1) compared to the caudal endplate. As a result of the shallower cranial endplate, the center of the vertebrae (narrowest part of the hourglass, between the two endplates) was shifted cranially (A < B, see figure 1). The sum of these findings implies that the caudal aspect of Grass carp vertebral centrum is bigger and more robust. Currently we have no information whether this is due to nature, e.g. differences in gene expression, or nurture, e.g. environmental effect. As the vertebral transverse and spinous processes are slanted caudally, the direction of muscle action during swimming may create a gradient of stresses from cranial to caudal, resulting in a more robust caudal aspect of the vertebral centrum. Expanding our study to include additional quadrupedal and bipedal (i.e. humans) vertebrae, as well as testing if these morphological aspects of the vertebrae are indeed plastic and can be affected by environmental factors (i.e. temperature or other stressors) may help answering this interesting question.

Fast and flexible processing of large FRET image stacks using the FRET-IBRA toolkit.

Ratiometric time-lapse FRET analysis requires a robust and accurate processing pipeline to eliminate bias in intensity measurements on fluorescent images before further quantitative analysis can be conducted. This level of robustness can only be achieved by supplementing automated tools with built-in flexibility for manual ad-hoc adjustments. FRET-IBRA is a modular and fully parallelized configuration file-based tool written in Python. It simplifies the FRET processing pipeline to achieve accurate, registered, and unified ratio image stacks. The flexibility of this tool to handle discontinuous image frame sequences with tailored configuration parameters further streamlines the processing of outliers and time-varying effects in the original microscopy images. FRET-IBRA offers cluster-based channel background subtraction, photobleaching correction, and ratio image construction in an all-in-one solution without the need for multiple applications, image format conversions, and/or plug-ins. The package accepts a variety of input formats and outputs TIFF image stacks along with performance measures to detect both the quality and failure of the background subtraction algorithm on a per frame basis. Furthermore, FRET-IBRA outputs images with superior signal-to-noise ratio and accuracy in comparison to existing background subtraction solutions, whilst maintaining a fast runtime. We have used the FRET-IBRA package extensively to quantify the spatial distribution of calcium ions during pollen tube growth under mechanical constraints. Benchmarks against existing tools clearly demonstrate the need for FRET-IBRA in extracting reliable insights from FRET microscopy images of dynamic physiological processes at high spatial and temporal resolution. The source code for Linux and Mac operating systems is released under the BSD license and, along with installation instructions, test images, example configuration files, and a step-by-step tutorial, is freely available at github.com/gmunglani/fret-ibra.

A method for time-independent film dosimetry: Can we obtain accurate patient-specific QA results at any time postirradiation?

AimThe aim of this work was twofold. (1) To investigate and present a comparison between EBT3 and EBT‐XD in terms of postirradiation color changes. (2) Create an automated workflow to allow radiochromic film (EBT3/XD) to be scanned and converted to dose accurately at any postirradiation time.Materials and MethodsTen GafChromic EBT‐XD calibration films were exposed in 2 Gy increments up to 18 Gy. Calibrates were then scanned at 5‐min intervals postirradiation over 24 h using an AutoHotKey script, resulting in 288 TIFF images. Following the 24‐h scanning period, a MATLAB script was used to automatically read the tiff images and create a series of 288 calibration curves distinct in time which is termed as the “Temporal Calibration Model” (TCM). The model is saved as a series of polynomial fit coefficients to net optical density as a function of dose, timestamped in 5‐min increments. Ten patient‐specific film measurements (5 × EBT‐XD and 5 × EBT3) were then carried out and scanned using the same 5‐min scan intervals from 5 min postirradiation to 24 h postirradiation. The TCM was then automatically applied using eFilmQA software to convert the patient‐specific QA films to dose by applying the relevant calibration curve from the TCM, corresponding to the arbitrary postirradiation time that the film was scanned. Each dose plane at postirradiation scan intervals of 5 min up to 20 h was then compared to the ground‐truth dose plane using gamma analysis.ResultsGamma pass rates using the TCM at time t, normalized to the pass rate after 20 h postirradiation, were found to have a maximum coefficient of variation of 3% over any postirradiation time. Conversely, not using the TCM resulted in coefficients of variation of up to 39%. Clinical implementation of this method showed an average accuracy of 2.8% when comparing the clinical result to the TCM result.ConclusionsWe have developed a methodology that allows radiochromic film to be accurately used as a dosimeter at any arbitrary scan postirradiation time, whereas previously, waiting periods of 16–24 h before readout were needed to ensure the postirradiation changes had stabilized. The creation of a TCM can enable results from radiochromic film measurements to be obtained quickly postirradiation. Using a conventional single calibration curve generated at 20 h postirradiation can result in gamma pass‐rate difference of up to 75% for measurement films scanned at a much shorter postirradiation time.

Ontology of Heterogeneous Image File Formats and their Disparate Applications

Different image formats are available in the world today which are used for various purposes, this paper elaborates the Ontology of different Image File Formats and their various applications. Digital images are saved in various Image File Formats which have different properties and features which are ideal for a particular use. A digital image is primarily classified into two types, raster or vector type. Image format elucidate how the information in the image will be stored. Image file format is a systematic way of storing and arranging digital images. Image file format can store data in compressed format (which may be lossy or lossless), uncompressed format or a vector format. Some Image format are suitable for a particular purpose while some are not. TIFF Image type is good for printing whereas PNG or JPG, are best for web. Analysis of the basic Image File Format have been carried out practically and the result is displayed in the coming section

309 Visualizing the immunotherapy-induced spatial reorganization of the tumor-immune microenvironment by CODEX multiplex imaging

BackgroundTumors contain spatially organized microenvironments in which the location and composition of the cellular components ultimately determine tumor fate. We previously characterized CD45+ cells infiltrating T3 sarcomas using complementary forms of high-dimensional profiling (scRNAseq and CyTOF) and identified key immune cell populations that became associated either with growing tumors in mice treated with control antibody (cmAb) or rejecting tumors in mice treated with immune-checkpoint therapy (ICT) i.e., anti-PD-1 and/or anti-CTLA-4. To better understand the effects of the intratumoral immune cell populations on one another and on the tumor itself, we used CODEX multiplex imaging to simultaneously characterize expression of 35 distinct immune cell markers on tumor tissue sections thereby maintaining the spatial relationships between effector cells and their cognate tumor cell targetsMethodsT3 tumor bearing syngeneic mice were treated with cmAb or a-PD-1/a-CTLA-4 on days 6,9 and 12, harvested on different days, fresh frozen, cut in 8µM sections, transferred to coverslips, and stained with a panel of commercially available and custom-made CODEX antibodies following optimized staining conditions using the manufacturer’s protocol. Whole tumor tissue raw TIFF images were subjected to an image processing pipeline developed by Akoya Biosciences, Inc, where dapi+ cells used for cell segmentation by the Startdist method. Unsupervised clustering using Scanpy toolkit based on normalized marker expression profiles identified and validated 12 unique cell clustersResultsWhen compared to the cmAb group, a-PD-1/a-CTLA-4 tumors harvested on day 10 displayed a marked increase in percentage as well as density of CD4+ and CD8+ T cells, Ly6G neutrophils, MHC-II+cd11c+cd24+DCs and a decrease of CD140a+ tumor cells. Spatial analysis indicated statistically significant differences in the organization of these cell types upon treatment with a-PD-1/a-CTLA-4 compared to cmAb treatment. Specifically, immune cells were found at the border of the tumor in cmAb treated mice and heavily infiltrated tumors following ICT. Neighborhood identification analysis revealed changes in the composition of the main neighborhood clusters, of which the top three clusters present in cmAb treated tumors (macrophage, tumor, and other immune cells) were substituted by clusters of CD4+ and CD8+ T cells, neutrophils, and macrophages.ConclusionsThese results not only confirm the remodeling of both the lymphoid and myeloid compartments previously observed with scRNAseq and CyTOF but also suggest that changes in the spatial organization of immune cells are drivers of the tumor rejection process upon treatment with ICT.

Measurement Extraction using Fuzzy Set Rule for Segmented Features of Brain Tumor in T-1 & T-2 Weighted Images

Aims: For neuro radiologist it becomes hard to accumulate features with minute dissimilarity in plenty of cases, so it is hard to make a correct decision. Therefore, the need is to generate some rules for prediction of degree of malignancy in tumors.
 Design: The pre-operative analysis of brain lesion is based on magnetic resonance imaging and clinical data set. Analysis of MRI finding and medical data set gives the relationship between regular pattern & interpretable pattern to acquire desired degree of malignancy. Until now the edge detection, segmentation and morphological operators are used to detect exact location of brain tumor. As uncertainty exits; here fuzzy set rules are evaluated to predict the degree by which a benign tumor is converted into malignant tumor.
 Methods: Fuzzy extraction theory has been applied along with image progressing algorithms like edge detection; segmentation and morphological operation based on spectral transformation are used to detect exact location of brain tumor to predict the degree malignancy. Step of Image analysis: a) Preprocessing: input 2D gif or tiff image b) Filtering of image using Anisodiff filter c) Thresholding, applying morphological operators and tumor line detection.
 Statistical Analysis used: A diagnostic feature includes blood flow, mass effect, temperature, calcification, edema, signal intensity & so on. Numerous features can be taken into consideration for better outcome.
 Results: Fuzzy set rule is one of the promising methods along with MR finding to achieve accuracy higher than 85% by considering few of the medical symptoms on different features.
 Conclusions: This research is limited to specific region and type of glioma and thus cannot deal heterogeneous cases in which situation is much complicated. The result evaluated here are usually retroactive. As studied, by analyzing signal intensity of T-1 & T-2 weighted image alone, accuracy of 60-70% has been achieved. So in order to get higher accuracy feature like cyst generation, oedema, blood supply are included to achieve 85% accuracy.

Characterization of Gafchromic™ film response against radionuclide activity

PurposeIn this study, we used Gafchromic™ film XR-QA2 and RT-QA2 to characterize the film energy response against various radionuclides. We introduce a neutron depletion theoretical model that can describe film response as a function of cumulated activity. The film response was investigated with respect to different backscatter media such as polystyrene, perspex, lead and corrugated fibreboard carton (CFC). The sensitivity of the two types of film to different energies was also studied. Lastly, a film stack method was tested to allow the user to obtain sequential, cumulative doses at different time points. MethodsPieces of Gafchromic™ film XR-QA2 and RT-QA2 were exposed to Am-241, Cs-137, Tc-99m, and I-131 to obtain various cumulative activities. After 24 h, each film piece was digitized by scanning it with an Epson Perfection V330 flatbed scanner to obtain 48-bit RGB TIFF images. Afterwards, each image was processed with the Image J software package. The film response was fitted to a theoretically derived function based on the neutron depletion model and the Beer-Lambert Law and compared with an existing fitting function. Layers of the film were also placed together and irradiated with the above-mentioned radionuclides to investigate the possibility of increasing the sensitivity of the film as a dosimeter. The energy response of the two types of film was investigated by irradiating pieces of film with different photon energies. ResultsThe theoretical response model fits OD vs cumulative activity accurately. XR-QA2 film shows good energy film response by using CFC as a backscatter material when using radionuclides. From the results, it is also evident that XR-QA2 is more sensitive to low energy gamma rays than RT-QA2. Its OD sensitivity can be increased by 2 ± 0.2 when using a double layer film and by 2.8 ± 0.3 when using a triple-layer film. By using a film stack, the experimental time can be decreased by using the second-order polynomial relationship obtained to relate the stacked film data to the single film data. ConclusionsThe neutron depletion theoretical model is accurate and contains less free parameters than higher-order polynomial fits. The Gafchromic™ XR-QA2 film is also better to use in nuclear medicine because of its higher sensitivity. The sensitivity of the film as a dosimeter can also be increased by using multiple layers of film. Experiment times can also be decreased by using the film stack method.