Digital Imaging And Communications In Medicine Images Research Articles

Watermarking algorithm based on phase-only CGH in fractional Hartley domain for DICOM images

In this manuscript, we proposed a watermarking algorithm based on phase-only computer-generated holography (CGH) in the fractional Hartley domain for digital imaging and communications in medicine (DICOM) images. The proposed algorithm improves the security of the CGH-based algorithm. The cascaded use of fractional Hartley transform and attenuation factor β increases the keyspace of the proposed watermarking algorithm. The robustness and effectiveness of the proposed watermarking algorithm is validated using simulations on DICOM images. The effectiveness of the proposed watermarking algorithm is assessed using statistical tools in terms of mean-squared error, information entropy, correlation coefficient, histogram, and mesh plots. The robustness is evaluated by testing the proposed algorithm’s performance under real-time threats, including contamination and data loss attacks. Furthermore, the security of the proposed algorithm is also tested for existing cryptographic attacks, such as chosen-plaintext attacks, and known-plaintext attacks. The simulation results indicate that the proposed watermarking algorithm is robust and effective.

Artificial intelligence-assisted automated heart failure detection and classification from electronic health records.

Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients. We used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies. Our study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes.

Software development to anonymize patient information from clinical computed tomography images

A free software for digital imaging and communications in medicine (DICOM) image anonymization is needed to protect patient information from third parties. This study aimed to develop a software for the anonymization of patient information in computed tomography (CT) image. There was a total of 17 informations to be anonymized, such as Patient’s Name, Other Patient Names, Patient’s ID, Patient’s Birth Date, Patient’s Sex, Patient’s Age, Study ID, Study Description, Series Description, Institution Name, Institution Adress, Referring Phisician’s Name, Consulting Physician’s Name, Performing Physician’s Name, Name of Physician(s) Reading Study, Operator’s Name, and Protocol Name. In every information, its initial value was replaced with a dummy value with the string value of "N/A". For testing, patient CT images from four different hospitals with different scanners were collected. It is found that each scanner had different information stored in DICOM information. However, the anonymization process in the four hospitals works well with accuracy of 100%. The developed software can anonymize DICOM images flexibly and successfully. This software can be used for anonymization of patient information in order to protect patient information.

A Novel Framework for Securing ECDH Encrypted DICOM Pixel Data Stored Over Cloud Using IPFS

The future holds the possibility of hospitals sharing medical images obtained through non-invasive systems to patients remotely. The advent of cloud and the storage and deployment of medical healthcare images in the cloud has resulted in the increased need for application of Cryptographic techniques to protect them from unauthorized access and malicious attacks. The Digital Imaging and Communication in Medicine (DICOM) standard is more compatible across medical imaging instruments globally. The pixel data of DICOM images requires more privacy and security. A novel ECDS based cryptographic approach is suggested to encrypt the original DICOM image as well as the ROI pixel data extracted from DICOM images. Results computed experimentally have proved that medical image encryption via ECDH is more robust, efficient and faster than existing medical image encryption schemes.

Application of Medical Image 3D Visualization Web Platform in Auxiliary Diagnosis and Preoperative Planning

Three-dimensional visualization of medical image data can enable doctors to observe images from more angles and higher dimensions. It is of great significance for doctors to assist in diagnosis and preoperative planning. Most 3D visualization systems are based on desktop applications, which are too dependent on hardware and operating system. This makes it difficult to use across platforms and maintain. Web-based systems tend to have limited capabilities. To this end, we developed a web application, which not only provides DICOM (Digital Imaging and Communications in Medicine) image browsing and annotation functions, but also provides three-dimensional post-processing functions of multiplanar reconstruction, volume rendering, lung parenchyma segmentation and brain MRI (Magnetic Resonance Imaging) analysis. In order to improve the rendering speed, we use the Marching Cube algorithm for 3D reconstruction in the background in an asynchronous way, and save the reconstructed model as glTF (GL Transmission Format). At the same time, Draco compression algorithm is used to optimize the glTF model to achieve more efficient rendering. After performance evaluation, the system reconstructed a CT (Computed Tomography) series of 242 slices and the optimized model was only 6.37mb with a rendering time of less than 2.5s. Three-dimensional visualization of the lung parenchyma clearly shows the volume, location, and shape of pulmonary nodules. The segmentation and reconstruction of different brain tissues can reveal the spatial three-dimensional structure and adjacent relationship of glioma in the brain, which has great application value in auxiliary diagnosis and preoperative planning.

Is Intraoral Scanning Accurate to Evaluate Dental Implant Position? An In-Vitro Study

The position of dental implants is generally verified through imaging exams, even though its use exposes patients to radiation. Intraoral scanning (IOS) may be a suitable alternative to using radiographic imaging to verify implant position. Using polyurethane jaw models, the purpose of this in-vitro study was to measure and compare implant positions determined by IOS and cone-bean computed tomography (CBCT). One hundred twenty implants were installed in 30 edentulous polyurethane jaws, 4 dental implants in each prototype. Four scanbodies were attached to the implants, and a scanning of each mandible was acquired using an intraoral scanner (CS 3600). All prototypes were also submitted to CBCT. Then, the 3D scan files in STL (Standard Tessellation Language) format were superimposed on the DICOM (Digital Imaging and Communications in Medicine) images of the tomographic mandibles. The accuracy of IOS was evaluated by the metric analyses of deviations between the position of the implants projected by the IOS versus the detected tomographically, in which CBCT served as the gold standard, using a free software for digital planning (Bluesky 4 - Grayslake, IL, USA). The following measures were analyzed: radial deviations at the shoulder (Xc) and at the apex of the implants (Xa), height deviation (Xh) and axial deviation. Bland-Altman and a paired t-test were applied to verify the reproducibility between measurements and a t-test for a mean was applied to compare the measurements with zero value. The results showed Xc and Xa deviation means of 0.14±0.09mm and 0.12±0.12mm, respectively. The Xh mean was 0.2±0.12mm and the axial deviation mean was 0.71°±0.66°. T-test showed a statistically significant difference when the 4 means were compared to zero value, represented by the CBCT (P<.0001). There was a statistically significant difference IN the scanned measures compared to CBCT as the standard, but the differences may not be clinically significant. The IOS utilization to evaluate the position of dental implants is a radiation-free and reproducible method, with the advantage of not generating metal artifacts. Further clinical studies are needed to validate this new method of postoperative evaluation.

Bringing Radiology Education to a New Reality: A Pilot Study of Using Virtual Reality as a Remote Educational Tool.

Purpose: We investigated virtual reality (VR) during a 2-week, undergraduate, radiology elective to determine if it improved learning outcomes and user satisfaction. Methods: Eighteen students enrolled between August 2021 and February 2022. Each student had a collaborative Zoom teaching session with a preceptor using a Picture Archive and Communications System (PACS)-like viewing system Online DICOM Image Navigator (ODIN), followed by a teaching session using a VR, Digital Imaging and Communications in Medicine (DICOM) viewer (SieVRt). After each teaching session, the students independently reviewed 8 imaging cases and completed case related questions. The students completed a survey, rating their subjective experiences using ODIN and SieVRt. Results: There was no difference in total test scores between the two learning strategies. However, students did perform statistically better on two of five questions designed to test the detection/measurement capabilities of SieVRt vs ODIN. Students stated that they preferred using SieVRt over ODIN and agreed that they were able to view subtle imaging findings and abnormalities better using SieVRt. However, students found that some of the functions of SieVRt (measuring angles/lengths, and multitasking) were difficult. There were technical challenges with VR and minor undesirable physical effects (dizziness, nausea, etc.). Conclusions: Virtual reality has the potential to enhance radiology education by providing an immersive and engaging experience. Objectively, students were able to perform two tasks better with SieVRt. Subjectively, the VR platform received favourable reviews from students for a variety of features. There were reported technical and physical challenges related to using VR. Future developments in VR systems should focus on improving the user experience.

Study of secondary particles resulting along 50–150 MeV accelerated protons in liver tissues using MCNPX and abdomen DICOM images

PurposeThis paper aims to study the resulting Brag peak and secondary particles (neutrons, photons, deuterons, alpha, helium_3, and tritons) along protons’ path in tissue.Design/methodology/approachMATLAB program and MCNP code were used to read abdomen Digital Imaging and Communications in Medicine (DICOM) images and build a 3D phantom to liver in purpose to study resulting Bragg peak and secondary particles (neutrons, photons, deuterons, alpha, helium_3 and tritons) along protons’ path.FindingsFrom the study, it was found that Bragg peak varies from a 2 cm depth within the tissue for 50 MeV protons to a 14.2 cm depth for 150 MeV protons; in the other hand, the total deposited energy decreases from 0.656 [MeV/g]/proton, at the depth 2 cm and 50 MeV protons, to the value 0.220 [MeV/g]/proton, at the depth 14 cm and 150 MeV protons.Originality/valueAs for the flow rate of secondary neutrons and photons, the flow rate of secondary neutrons takes a maximum value (peak) in the middle of the proton path, i.e. when the energy of the protons drops to the value of 30 MeV, and this maximum value of the neutrons flow rate is accompanied by a maximum value of the photon flow rate, as for the rest of the secondary particles produced (alpha particles, deuterons, electrons, tritons and triple helium), they deposit most of their energy locally.

Clinical Applications of Mixed Reality and 3D Printing in Congenital Heart Disease.

Understanding the anatomical features and generation of realistic three-dimensional (3D) visualization of congenital heart disease (CHD) is always challenging due to the complexity and wide spectrum of CHD. Emerging technologies, including 3D printing and mixed reality (MR), have the potential to overcome these limitations based on 2D and 3D reconstructions of the standard DICOM (Digital Imaging and Communications in Medicine) images. However, very little research has been conducted with regard to the clinical value of these two novel technologies in CHD. This study aims to investigate the usefulness and clinical value of MR and 3D printing in assisting diagnosis, medical education, pre-operative planning, and intraoperative guidance of CHD surgeries through evaluations from a group of cardiac specialists and physicians. Two cardiac computed tomography angiography scans that demonstrate CHD of different complexities (atrial septal defect and double outlet right ventricle) were selected and converted into 3D-printed heart models (3DPHM) and MR models. Thirty-four cardiac specialists and physicians were recruited. The results showed that the MR models were ranked as the best modality amongst the three, and were significantly better than DICOM images in demonstrating complex CHD lesions (mean difference (MD) = 0.76, p = 0.01), in enhancing depth perception (MD = 1.09, p = 0.00), in portraying spatial relationship between cardiac structures (MD = 1.15, p = 0.00), as a learning tool of the pathology (MD = 0.91, p = 0.00), and in facilitating pre-operative planning (MD = 0.87, p = 0.02). The 3DPHM were ranked as the best modality and significantly better than DICOM images in facilitating communication with patients (MD = 0.99, p = 0.00). In conclusion, both MR models and 3DPHM have their own strengths in different aspects, and they are superior to standard DICOM images in the visualization and management of CHD.

Can the method of CBCT interpretation influence endodontic diagnosis?

Aim: This study evaluated the influence of the method used for visualization and analysis of cone-beam computed tomography (CBCT) on the diagnosis of endodontic conditions. Materials and methods: Twenty clinical cases containing twelve different endodontic conditions were analyzed by two specialists in dental radiology and one specialist in endodontics. Initially, the evaluators viewed the cases in Portable Document Format (PDF) containing a selection of digital images and, by consensus, described their diagnostic hypotheses for each case. One week later, the evaluators reassessed the cases, this time using multiplanar reconstructions with a Digital Imaging and Communications in Medicine (DICOM) format image viewer. Once more, by consensus, they recorded their diagnostic hypotheses. Results: In 10% of the cases there was a discrepancy between the diagnoses made using preselected digital images in PDFs and by viewing multiplanar reconstructions. The visualization of the PDF images obtained a sensitivity of 0.714, specificity of 0.966, and 90% accuracy. Discussion: In the majority of these cases, endodontic conditions identified using the DICOM image viewer (multiplanar reconstruction) were not detected using the PDFs of preselected images. Conclusion: Although more studies are needed, the authors reiterate that using multiplanar reconstructions should always be preferred to other forms of analysis for CBCT, so that the maximum diagnostic potential of the imaging exam can be achieved.

A blind steganography approach for hiding privacy details in images of digital imaging and communications in medicine using QR code

&lt;span&gt;This study aims to hide patient’s privacy details of digital imaging and communications in medicine (DICOM) files using the quick response (QR) code images with the same size using steganographic technique. The proposed method is based on the properties of the discrete cosine transform (DCT) of the DICOM images to embed a QR code image. The proposed method includes two parts: data embedding and extraction process. Moreover, the stego DICOM image could be blindly used to produce the embedded QR code image without the existence of the original DICOM image. The performances of proposed method were evaluated using the metrics of the peak signal to noise ratio (PSNR), the structural similarity index (SSIM), the universal quality index (UQI), the correlation coefficient (R) and the bit error rate (BER) values. The experimental results scored a high PSNR after the embedding process by embedding a QR code image into the DICOM image with the same size.&lt;/span&gt;

A simple embed over encryption scheme for DICOM images using Bülban Map.

With the onset of any pandemic, the medical image database is bound to increase. These medical images are prone to attack by hackers for their medical data and patient health information. To safeguard these medical images, a new algorithm is proposed. The algorithm involves secretly embedding the patient identification number into the medical image and encrypting the medical image, protecting the patient’s identity and the patient’s medical condition from hackers. The encryption algorithm involved a single stage of confusion and two stages of diffusion. The confusion operation was performed using the key generated by the Bülban map. The first stage of diffusion was done in the transform domain, using 5/3 transformation. The second diffusion stage was performed in the spatial domain by altering the pixel values using the key. The algorithm was tested on over 30 DICOM (Digital Imaging and Communications in Medicine) images taken from Open Science Framework (OSF), a public database for COVID-19 patients. The algorithm could resist the statistical attacks upon analysis, providing a PSNR of 7.084 dB and entropy of 15.9815 bits for the cipher image. The correlation coefficients for the cipher image were 0.0275, -0.0027, 0.018 in horizontal, vertical and diagonal directions. The keyspace was 2((M−1) ×N)×16, with M the number of rows and N the number of columns in the image. The key sensitivity was high. The test results and metric analysis prove that the algorithm is an effective one for embedding and encryption.Graphical abstract

Secure Watermarking Scheme for Color DICOM Images in Telemedicine Applications

Teleradiology plays a vital role in the medical field, which permits transmitting medical and imaging data over a communication network. It ensures data reliability and provides convenient communication for clinical interpretation and diagnostic purposes. The transmission of this medical data over a network raises the problems of legal, ethical issues, privacy, and copyright authenticity. The copyright protection of medical images is a significant issue in the medical field. Watermarking schemes are used to address these issues. A gray-level or binary image is used as a watermark frequently in color image watermarking schemes. In this paper, the authors propose a novel non-blind medical image watermarking scheme based on 2-D Lifting Wavelet Transform (LWT), Multiresolution Singular Value Decomposition (MSVD), and LU factorization to improve the robustness and authenticity of medical images. In this scheme, multiple color watermarks are embedded into the colored DICOM (Digital Imaging and Communications in Medicine) images obtained from Color Doppler images (DICOM format), and the average results achieved by our proposed scheme is 46.84 db for Peak Signal-to-Noise Ratio (PSNR), 37.46 db for Signal-to-Noise Ratio (SNR), 0.99 for Quality of Image and 0.998 for Normalized Correlation for various image processing attacks. These results make our watermarking technique an ideal candidate for medical image watermarking.

Validation and comparison of volume measurements using 1 multidetector computed tomography and 5 cone-beam computed tomography protocols: An in vitro study.

The purpose of this study was to compare volume measurements obtained using 2 image software packages on Digital Imaging and Communications in Medicine (DICOM) images acquired from 1 multidetector computed tomography and 5 cone-beam computed tomography devices, using different protocols for physical volume measurements. Four pieces of bovine leg were prepared. Marrow was removed from 3 pieces, leaving cortical bone exposed. The resulting space of 1 piece was filled with water, another was filled with propylene glycol, and the third was left unfilled. The marrow in the fourth sample was left fully intact. Volume measurements were obtained after importing DICOM images into the Dolphin Imaging 11.95 and ITK-SNAP software programs. Data were analyzed using 3-way analysis of variance with a generalized linear model to determine the effects of voxel size, software, and content on percentage mean volume differences between tomographic protocols. A significance level of 0.05 was used. The intraclass correlation coefficients for intraobserver and interobserver reliability were, respectively, 0.915 and 0.764 for the Dolphin software and 0.894 and 0.766 for the ITK-SNAP software. Three sources of statistically significant variation were identified: the interaction between software and content (P=0.001), the main effect of content (P=0.014), and the main effect of software (P=0.001). Voxel size was not associated with statistically significant differences in volume measurements. Both content and software influenced the accuracy of volume measurements, especially when the content had gray values similar to those of the adjacent tissues.

Developed DICOM standard schema with DSpace

PurposeThis paper aims to present a novel DSpace-based medical image repository system planned explicitly for storing and retrieving clinical images using digital imaging and communication in medicine (DICOM) metadata standards. DSpace institutional repository software is widely used in an academic environment for accessing and mainly storing text-related files. DICOM images are particular types of images embedded with much system-generated metadata and organised using DICOM metadata standards.Design/methodology/approachThe present paper talks about institutional repository software (DSpace) in archiving DICOM images. In the current study, the authors have tried to integrate the DICOM metadata standard with DSpace, which was compatible with Dublin Core (DC) and open archives initiative – protocol for metadata harvesting (OAI-PMH). After combining the DICOM standard with DSpace and the repository tested with a sample of 5,000 images, the retrieval results using various DICOM tags was very satisfactory. This study paves for the use of open source software (OSS) in storing and retrieving medical images.FindingsThe author has provided the DSpace software to recognised DICOM (.dcm) files in the first stage. In the second stage, a patch was developed to identify the DICOM metadata standard in Dspace, which has inbuilt DC metadata standards. Finally, in the third stage, retrieval efficiency was tested with a 5,000 .dcm image using the DICOM tag and the results were very fruitful.Research limitations/implicationsA major limitation of this study was the size of the data (5,000 DICOM images) with which the authors have tested the system. The system scalability has to be tested on various fronts like on cloud and local servers with different configurations, for which a separate study has to be done.Practical implicationsOnce this system is in place, DICOM users can stock, retrieve and access the image from the Web platform. Furthermore, this proposed repository will be the warehouse of various DICOM images with reasonable storage costs.Originality/valueIn addition to exploring the opportunities of free open source software (FOSS) implementation in medical science, this study includes issues related to the performance of an open-source repository for retrieving and preserving medical images. It created and developed Open Source DICOM Medical Image Library with DICOM metadata standard with the help of DSpace. Thus, the study will generate value for library professionals and medical professionals and FOSS vendors to understand the medical market in the context of FOSS.

Quality control of radiomic features using 3D-printed CT phantoms.

.Purpose: The lack of standardization in quantitative radiomic measures of tumors seen on computed tomography (CT) scans is generally recognized as an unresolved issue. To develop reliable clinical applications, radiomics must be robust across different CT scan modes, protocols, software, and systems. We demonstrate how custom-designed phantoms, imprinted with human-derived patterns, can provide a straightforward approach to validating longitudinally stable radiomic signature values in a clinical setting.Approach: Described herein is a prototype process to design an anatomically informed 3D-printed radiomic phantom. We used a multimaterial, ultra-high-resolution 3D printer with voxel printing capabilities. Multiple tissue regions of interest (ROIs), from four pancreas tumors, one lung tumor, and a liver background, were extracted from digital imaging and communication in medicine (DICOM) CT exam files and were merged together to develop a multipurpose, circular radiomic phantom (18 cm diameter and 4 cm width). The phantom was scanned 30 times using standard clinical CT protocols to test repeatability. Features that have been found to be prognostic for various diseases were then investigated for their repeatability and reproducibility across different CT scan modes.Results: The structural similarity index between the segment used from the patients’ DICOM image and the phantom CT scan was 0.71. The coefficient variation for all assessed radiomic features was across 30 repeat scans of the phantom. The percent deviation (pDV) from the baseline value, which was the mean feature value determined from repeat scans, increased with the application of the lung convolution kernel, changes to the voxel size, and increases in the image noise. Gray level co-occurrence features, contrast, dissimilarity, and entropy were particularly affected by different scan modes, presenting with .Conclusions: Previously discovered prognostic and popular radiomic features are variable in practice and need to be interpreted with caution or excluded from clinical implementation. Voxel-based 3D printing can reproduce tissue morphology seen on CT exams. We believe that this is a flexible, yet practical, way to design custom phantoms to validate and compare radiomic metrics longitudinally, over time, and across systems.

Novel Geometries for Stereotactic Localizers

Introduction: The N-localizer is generally utilized in a 3-panel or, more rarely, a 4-panel system for computing stereotactic positions. However, a stereotactic frame that incorporates a 2-panel (bipanel) N-localizer system with panels affixed to only the left and right sides of the frame offers several advantages: improved ergonomics to attach the panels, reduced claustrophobia for the patient, mitigation of posterior panel contact with imaging systems, and reduced complexity. A bipanel system that comprises two standard N-localizer panels yields only two three-dimensional (3D) coordinates, which are insufficient to solve for the stereotactic matrix without further information. While additional information to determine the stereotactic positions could include scalar distances from Digital Imaging and Communications in Medicine (DICOM) metadata or 3D regression across the imaging volume, both have risks related to noise and error propagation. Therefore, we sought to develop new stereotactic localizers that comprise only lateral fiducials (bipanel) that leave the front and back regions of the patient accessible but that contain enough information to solve for the stereotactic matrix using each image independently. Methods: To solve the stereotactic matrix, we assumed the need to compute three or more 3D points from a single image. Several localizer options were studied using Monte Carlo simulations to understand the effect of errors on the computed target location. The simulations included millions of possible combinations for computing the stereotactic matrix in the presence of random errors of 1mm magnitude. The matrix then transformed coordinates for a target that was placed 50mm anterior, 50mm posterior, 50mm lateral, or 50mm anterior and 50mm lateral to the centre of the image. Simulated cross-sectional axial images of the novel localizer systems were created and converted into DICOM images representing computed tomography (CT) images. Results: Three novel models include the M-localizer, F-localizer, and Z-localizer. For each of these localizer systems, optimized results were obtained using an overdetermined system of equations made possible by more than three diagonal bars. In each case, the diagonal bar position was computed using standard N-localizer mathematics. Additionally, the M-localizer allowed adding a computation using the Sturm-Pastyr method. Monte Carlo simulation demonstrated that the Z-localizer provided optimal results.Conclusion: The three proposed novel models meet our design objectives. Of the three, the Z-localizer produced the least propagation of error. The M-localizer was simpler and had slightly more error than the Z-localizer. The F-localizer produced more error than either the Z-localizer or M-localizer. Further study is needed to determine optimizations using these novel models.

Estimation of patient skin dose in fluoroscopy: summary of a joint report by AAPM TG357 and EFOMP.

Physicians use fixed C-arm fluoroscopy equipment with many interventional radiological and cardiological procedures. The associated effective dose to a patient is generally considered low risk, as the benefit-risk ratio is almost certainly highly favorable. However, X-ray-induced skin injuries may occur due to high absorbed patient skin doses from complex fluoroscopically guided interventions (FGI). Suitable action levels for patient-specific follow-up could improve the clinical practice. There is a need for a refined metric regarding follow-up of X-ray-induced patient injuries and the knowledge gap regarding skin dose-related patient information from fluoroscopy devices must be filled. The most useful metric to indicate a risk of erythema, epilation or greater skin injury that also includes actionable information is the peak skin dose, that is, the largest dose to a region of skin. The report is based on a comprehensive review of best practices and methods to estimate peak skin dose found in the scientific literature and situates the importance of the Digital Imaging and Communication in Medicine (DICOM) standard detailing pertinent information contained in the Radiation Dose Structured Report (RDSR) and DICOM image headers for FGI devices. Furthermore, the expertise of the task group members and consultants have been used to bridge and discuss different methods and associated available DICOM information for peak skin dose estimation. The report contributes an extensive summary and discussion of the current state of the art in estimating peak skin dose with FGI procedures with regard to methodology and DICOM information. Improvements in skin dose estimation efforts with more refined DICOM information are suggested and discussed. The endeavor of skin dose estimation is greatly aided by the continuing efforts of the scientific medical physics community, the numerous technology enhancements, the dose-controlling features provided by the FGI device manufacturers, and the emergence and greater availability of the DICOM RDSR. Refined and new dosimetry systems continue to evolve and form the infrastructure for further improvements in accuracy. Dose-related content and information systems capable of handling big data are emerging for patient dose monitoring and quality assurance tools for large-scale multihospital enterprises.

3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation.

The 3D convolutional neural network is able to make use of the full nonlinear 3D context information of lung nodule detection from the DICOM (Digital Imaging and Communications in Medicine) images, and the Gradient Class Activation has shown to be useful for tailoring classification tasks and localization interpretation for fine-grained features and visual explanation for the internal working. Gradient-weighted class activation plays a crucial role for clinicians and radiologists in terms of trusting and adopting the model. Practitioners not only rely on a model that can provide high precision but also really want to gain the respect of radiologists. So, in this paper, we explored the lung nodule classification using the improvised 3D AlexNet with lightweight architecture. Our network employed the full nature of the multiview network strategy. We have conducted the binary classification (benign and malignant) on computed tomography (CT) images from the LUNA 16 database conglomerate and database image resource initiative. The results obtained are through the 10-fold cross-validation. Experimental results have shown that the proposed lightweight architecture achieved a superior classification accuracy of 97.17% on LUNA 16 dataset when compared with existing classification algorithms and low-dose CT scan images as well.

Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction Arrays

Deep learning is becoming increasingly popular and available to new users, particularly in the medical field. Deep learning image segmentation, outcome analysis, and generators rely on presentation of Digital Imaging and Communications in Medicine (DICOM) images and often radiation therapy (RT) structures as masks. Although the technology to convert DICOM images and RT structures into other data types exists, no purpose-built Python module for converting NumPy arrays into RT structures exists. The 2 most popular deep learning libraries, Tensorflow and PyTorch, are both implemented within Python, and we believe a set of tools built in Python for manipulating DICOM images and RT structures would be useful and could save medical researchers large amounts of time and effort during the preprocessing and prediction steps. Our module provides intuitive methods for rapid data curation of RT-structure files by identifying unique region of interest (ROI) names and ROI structure locations and allowing multiple ROI names to represent the same structure. It is also capable of converting DICOM images and RT structures into NumPy arrays and SimpleITK Images, the most commonly used formats for image analysis and inputs into deep learning architectures and radiomic feature calculations. Furthermore, the tool provides a simple method for creating a DICOM RT-structure from predicted NumPy arrays, which are commonly the output of semantic segmentation deep learning models. Accessing DicomRTTool via the public Github project invites open collaboration, and the deployment of our module in PyPi ensures painless distribution and installation. We believe our tool will be increasingly useful as deep learning in medicine progresses.