Digital Imaging And Communications In Medicine Format Research Articles

DICOM® integrated EEG data: A first clinical implementation of the new DICOM standard for neurophysiology data

ObjectiveDemonstrating a pilot implementation of the Digital Imaging and Communication in Medicine (DICOM) neurophysiology standard published in 2020. MethodsAn automated workflow for converting EEG data from a proprietary vendor EEG format to standardized and interoperable DICOM format was developed and tested. ResultsRetrieval of proprietary EEG data, associated videos, annotations and metadata from the vendor EEG archive and their subsequent conversion to DICOM EEG was possible without changes to the departmental workflow. To transfer DICOM EEG data to the central radiology DICOM archive, only minor extensions in the parameterization of the archive’s DICOM interfaces were necessary. Linkage with the electronic health record (EHR) and display in a DICOM EEG viewer could be demonstrated. A random sample of 88 DICOM EEG studies was compared to the original vendor files and EEG and video file sizes were comparable. ConclusionsStoring and reviewing EEG data in standardized DICOM format is feasible, facilitated by existing DICOM infrastructure, and therefore allows for vendor independent access to EEG data. SignificanceWe report the first implementation of the DICOM neurophysiology standard, thus promoting standardization in the field of neurophysiology as well as data exchange and access to legacy recordings in an interoperable vendor independent format.

Volume of Aneurysmal Subarachnoid Hematoma Measured Quantitatively by 3D Slicer to Predict Symptomatic Cerebral Vasospasm.

Aneurysmal subarachnoid hemorrhage is strongly associated with symptomatic cerebral vasospasm. This study aims to investigate whether a quantitative measure of aneurysmal subarachnoid hematoma by 3D Slicer is more valuable for vasospasm risk prediction compared with modified Fisher scale and the new scale by Eagles. We performed a retrospective research of Digital Imaging and Communications in Medicine format (DICOM) in aneurysmal patients treated at our institution from 2019 to 2020. Association between vasospasm and hematoma volume by 3D Slicer was explored by univariate and multivariate analyses. Value of risk prediction was compared through area under the receiver operator characteristic curve (AUC) among the modified Fisher scale, the new scale by Eagles, and hematoma volume by 3D Slicer. Hematoma volume by 3D Slicer had a significant relationship with vasospasm both by one-way analysis of variance (ANOVA; F = 19.37, P < 0.001) and a binary logistic regression analysis (odds ratio [OR] = 1.05, P = 0.016). Hematoma volume by 3D Slicer had a significantly higher AUC (0.708; 95% confidence interval [CI]: 0.618-0.798, P < 0.001) than the modified Fisher scale and the new scale by Eagles. The optimal diagnostic threshold for hematoma volume by 3D Slicer was 15.98 ml, and the sensitivity and specificity were 73.5% and 58.6%, respectively. Volume of aneurysmal subarachnoid hematoma measured quantitatively by 3D Slicer can improve the predictive value for symptomatic cerebral vasospasm.

Diffusion Weighted Imaging (DWI) Classification and Apparent Diffusion Coefficient (ADC) Value Tendency Based on Cerebral Glioma Grading in Patients at Dr. Soetomo General Academic Hospital in 2016-2020

Highlight: Many studies conclude that the prognosis of gliomas can be determined using the DWI sequence and ADC value on MRI The correlation between DWI and ADC values with glioma grading is related to tumor cellularity, nuclear-cytoplasmic ratio, and extracellular space, which affect the movement of water molecules. ABSTRACT Introduction: Glioma grading is crucial to know its prognosis. Magnetic resonance imaging (MRI) is used as a preoperative examination that contains diffusion-weighted imaging (DWI) sequences confirmed by an apparent diffusion coefficient (ADC) value that helps assess tissue based on water diffusion. Objective: To prove the relationship between DWI and ADC values with cerebral glioma grading in patients at Dr. Soetomo General Academic Hospital in 2016—2020. Methods: This retrospective study collected medical records and MRI files in DICOM (Digital Imaging and Communications in Medicine) format. Gender, age, tumor histopathology, and glioma grading were collected. DWI and ADC values were obtained using the RadiAnt DICOM Viewer application. The data were analyzed using descriptive and analytical statistics. The chi-square test was used to analyze the relationship of DWI with glioma grading, and the spearman rank test was used to analyze the relationship of ADC value with glioma grading. Results: The majority of 35 patients were male (54.3%), aged 31–40 years old (22.9%), and the most common histopathology was glioblastoma (37.1%), WHO grade IV. On DWI, most low-grade glioma (LGG) patients showed unrestricted diffusion, and most high-grade glioma (HGG) patients showed restricted diffusion. The ADC value of HGG was lower than the ADC value of LGG. Statistical tests showed a relationship between DWI and glioma grading (p&lt;0.05) and a relationship between the ADC value and glioma grading (p&lt;0.05). Conclusion: There was a relationship between DWI and ADC with glioma grading in Dr. Soetomo General Academic Hospital patients for the period 2016—2020

The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.4 Million Screening and Diagnostic Mammographic Images.

Supplemental material is available for this article. Keywords: Mammography, Breast, Machine Learning © RSNA, 2023.

A software program for automated compressive vertebral fracture detection on elderly women's lateral chest radiograph: Ofeye 1.0.

BackgroundBecause osteoporotic vertebral fracture (OVF) on chest radiographs is commonly missed in radiological reports, we aimed to develop a software program which offers automated detection of compressive vertebral fracture (CVF) on lateral chest radiographs, and which emphasizes CVF detection specificity with a low false positivity rate.MethodsFor model training, we retrieved 3,991 spine radiograph cases and 1,979 chest radiograph cases from 16 sources, with among them in total 1,404 cases had OVF. For model testing, we retrieved 542 chest radiograph cases and 162 spine radiograph cases from four independent clinics, with among them 215 cases had OVF. All cases were female subjects, and except for 31 training data cases which were spine trauma cases, all the remaining cases were post-menopausal women. Image data included DICOM (Digital Imaging and Communications in Medicine) format, hard film scanned PNG (Portable Network Graphics) format, DICOM exported PNG format, and PACS (Picture Archiving and Communication System) downloaded resolution reduced DICOM format. OVF classification included: minimal and mild grades with <20% or ≥20–25% vertebral height loss respectively, moderate grade with ≥25–40% vertebral height loss, severe grade with ≥40%–2/3 vertebral height loss, and collapsed grade with ≥2/3 vertebral height loss. The CVF detection base model was mainly composed of convolution layers that include convolution kernels of different sizes, pooling layers, up-sampling layers, feature merging layers, and residual modules. When the model loss function could not be further decreased with additional training, the model was considered to be optimal and termed ‘base-model 1.0’. A user-friendly interface was also developed, with the synthesized software termed ‘Ofeye 1.0’.ResultsCounting cases and with minimal and mild OVFs included, base-model 1.0 demonstrated a specificity of 97.1%, a sensitivity of 86%, and an accuracy of 93.9% for the 704 testing cases. In total, 33 OVFs in 30 cases had a false negative reading, which constituted a false negative rate of 14.0% (30/215) by counting all OVF cases. Eighteen OVFs in 15 cases had OVFs of ≥ moderate grades missed, which constituted a false negative rate of 7.0% (15/215, i.e., sensitivity 93%) if only counting cases with ≥ moderate grade OVFs missed. False positive reading was recorded in 13 vertebrae in 13 cases (one vertebra in each case), which constituted a false positivity rate of 2.7% (13/489). These vertebrae with false positivity labeling could be readily differentiated from a true OVF by a human reader. The software Ofeye 1.0 allows ‘batch processing’, for example, 100 radiographs can be processed in a single operation. This software can be integrated into hospital PACS, or installed in a standalone personal computer.ConclusionsA user-friendly software program was developed for CVF detection on elderly women’s lateral chest radiographs. It has an overall low false positivity rate, and for moderate and severe CVFs an acceptably low false negativity rate. The integration of this software into radiological practice is expected to improve osteoporosis management for elderly women.

Effect of 3-Dimensional, Virtual Reality Models for Surgical Planning of Robotic Prostatectomy on Trifecta Outcomes: A Randomized Clinical Trial.

Planning complex operations such as robotic-assisted radical prostatectomy requires surgeons to review 2-dimensional magnetic resonance imaging (MRI) cross-sectional images to understand 3-dimensional (3D), patient-specific anatomy. We sought to determine surgical outcomes for robotic-assisted radical prostatectomy when surgeons reviewed 3D, virtual reality (VR) models for operative planning. A multicenter, randomized, single-blind clinical trial was conducted from January 2019 to December 2020. Patients undergoing robotic-assisted laparoscopic radical prostatectomy were prospectively enrolled and randomized to either a control group undergoing usual preoperative planning with prostate biopsy results and MRI only or to an intervention group where MRI and biopsy results were supplemented with a 3D VR model. The primary outcome measure was margin status, and secondary outcomes were oncologic control, sexual function and urinary function. Ninety-two patients were analyzed, with trends toward lower positive margin rates (33% vs 25%) in the intervention group, no significant difference in functional outcomes and no difference in traditional operative metrics (p >0.05). Detectable postoperative prostate specific antigen was significantly lower in the intervention group (31% vs 9%, p=0.036). In 32% of intervention cases, the surgeons modified their operative plan based on the model. When this subset was compared to the control group, there was a strong trend toward increased bilateral nerve sparing (78% vs 92%), and a significantly lower rate of postoperative detectable prostate specific antigen in the intervention subset (31% vs 0%, p=0.038). This randomized clinical trial demonstrated patients whose surgical planning involved 3D VR models have better oncologic outcomes while maintaining functional outcomes.

Pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert organ contours.

Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging. The dataset collection consists of axial CT images in Digital Imaging and Communications in Medicine (DICOM) format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children's Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of 7 years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise. The data are available on The Cancer Imaging Archive (TCIA_ (https://www.cancerimagingarchive.net/) under the collection Pediatric-CT-SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are listed in Table 2. This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient-specific organ dose estimation.

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.

A pilot study on deep learning-based grading of corners of vertebral bodies for assessment of radiographic progression in patients with ankylosing spondylitis.

Background:Radiographs are widely used to evaluate radiographic progression with modified stoke ankylosing spondylitis spinal score (mSASSS).Objective:This pilot study aimed to develop a deep learning model for grading the corners of the cervical and lumbar vertebral bodies for computer-aided detection of mSASSS in patients with ankylosing spondylitis (AS).Methods:Digital radiographic examination of the spine was performed using Discovery XR656 (GE Healthcare) and Digital Diagnost (Philips). The disk points were detected between the bodies using a key-point detection deep learning model from the image obtained in DICOM (digital imaging and communications in medicine) format from the cervical and lumbar spinal radiographs. After cropping the vertebral regions around the disk point, the lower and upper corners of the vertebral bodies were classified as grade 3 (total bony bridges) or grades 0, 1, or 2 (non-bridges). We trained a convolutional neural network model to predict the grades in the lower and upper corners of the vertebral bodies. The performance of the model was evaluated in a validation set, which was separate from the training set.Results:Among 1280 patients with AS for whom mSASSS data were available, 5,083 cervical and 5245 lumbar lateral radiographs were reviewed. The total number of corners where mSASSS was measured in the cervical and lumbar vertebrae, including the upper and lower corners, was 119,414. Among them, the number of corners in the training and validation sets was 110,088 and 9326, respectively. The mean accuracy, sensitivity, and specificity for mSASSS scoring in one corner of the vertebral body were 0.91604, 0.80288, and 0.94244, respectively.Conclusion:A high-performance deep learning model for grading the corners of the vertebral bodies was developed for the first time. This model must be improved and further validated to develop a computer-aided tool for assessing mSASSS in the future.

Automatic classification of medical image modality and anatomical location using convolutional neural network.

Modern radiologic images comply with DICOM (digital imaging and communications in medicine) standard, which, upon conversion to other image format, would lose its image detail and information such as patient demographics or type of image modality that DICOM format carries. As there is a growing interest in using large amount of image data for research purpose and acquisition of large amount of medical image is now a standard practice in the clinical setting, efficient handling and storage of large amount of image data is important in both the clinical and research setting. In this study, four classes of images were created, namely, CT (computed tomography) of abdomen, CT of brain, MRI (magnetic resonance imaging) of brain and MRI of spine. After converting these images into JPEG (Joint Photographic Experts Group) format, our proposed CNN architecture could automatically classify these 4 groups of medical images by both their image modality and anatomic location. We achieved excellent overall classification accuracy in both validation and test sets (> 99.5%), specificity and F1 score (> 99%) in each category of this dataset which contained both diseased and normal images. Our study has shown that using CNN for medical image classification is a promising methodology and could work on non-DICOM images, which could potentially save image processing time and storage space.

Computer Tools to Analyze Lung CT Changes after Radiotherapy

The paper describes a computer tool dedicated to the comprehensive analysis of lung changes in computed tomography (CT) images. The correlation between the dose delivered during radiotherapy and pulmonary fibrosis is offered as an example analysis. The input data, in DICOM (Digital Imaging and Communications in Medicine) format, is provided from CT images and dose distribution models of patients. The CT images are processed using convolution neural networks, and next, the selected slices go through the segmentation and registration algorithms. The results of the analysis are visualized in graphical format and also in numerical parameters calculated based on the images analysis.

Design and Implementation of the Pre-Clinical DICOM Standard in Multi-Cohort Murine Studies.

The small animal imaging Digital Imaging and Communications in Medicine (DICOM) acquisition context structured report (SR) was developed to incorporate pre-clinical data in an established DICOM format for rapid queries and comparison of clinical and non-clinical datasets. Established terminologies (i.e., anesthesia, mouse model nomenclature, veterinary definitions, NCI Metathesaurus) were utilized to assist in defining terms implemented in pre-clinical imaging and new codes were added to integrate the specific small animal procedures and handling processes, such as housing, biosafety level, and pre-imaging rodent preparation. In addition to the standard DICOM fields, the small animal SR includes fields specific to small animal imaging such as tumor graft (i.e., melanoma), tissue of origin, mouse strain, and exogenous material, including the date and site of injection. Additionally, the mapping and harmonization developed by the Mouse-Human Anatomy Project were implemented to assist co-clinical research by providing cross-reference human-to-mouse anatomies. Furthermore, since small animal imaging performs multi-mouse imaging for high throughput, and queries for co-clinical research requires a one-to-one relation, an imaging splitting routine was developed, new Unique Identifiers (UID’s) were created, and the original patient name and ID were saved for reference to the original dataset. We report the implementation of the small animal SR using MRI datasets (as an example) of patient-derived xenograft mouse models and uploaded to The Cancer Imaging Archive (TCIA) for public dissemination, and also implemented this on PET/CT datasets. The small animal SR enhancement provides researchers the ability to query any DICOM modality pre-clinical and clinical datasets using standard vocabularies and enhances co-clinical studies.

Server-Aided 3D DICOM Viewer for Mobile Platforms

Digital Imaging and Communications in Medicine (DICOM) is an international standard to transfer, store, retrieve, print, process and display medical imaging information. It provides a standardized method to store medical images from many types of imaging devices. Typically, CT and MRI scans, which are composed of 2D slice images in DICOM format, can be inspected and analyzed with DICOM-compatible imaging software. Additionally, the DICOM format provides important information to assemble cross-sections into 3D volumetric datasets. Not many DICOM viewers are available for mobile platforms (smartphones and tablets), and most of them are 2D-based with limited functionality and user interaction. This paper reports on our efforts to design and implement a volumetric 3D DICOM viewer for mobile devices with real-time rendering, interaction, a full transfer function editor and server access capabilities. 3D DICOM image sets, either loaded from the device or downloaded from a remote server, can be rendered at up to 60 fps on Android devices. By connecting to our server, users can a) get pre-computed image quality metrics and organ segmentation results, and b) share their experience and synchronize views with other users on different platforms.

Reconstructing and resizing 3D images from DICOM files

Current reconstruction of 3D images from DICOM (Digital Imaging and Communications in Medicine) files requires strict supervision for the reconstructed images to have the same metadata including slice thickness, spacing between slices, and image resolution. We propose an algorithm for reconstructing 3D images based on medical images in the DICOM format with varied metadata and resizing the 3D images while preserving the annotations. The 3D image resizing may facilitate processing because most current systems cannot handle the huge 3D image data sizes. After resizing the 3D images, the original annotations that can be used as ground truths to train and evaluate machine learning method are preserved by projection. Experimental results show that the proposed method can handle various DICOM files and correctly project annotations onto the resized image.

DICOM Format and Protocol Standardization-A Core Requirement for Digital Pathology Success.

As the use of digital techniques in toxicologic pathology expands, challenges of scalability and interoperability come to the fore. Proprietary formats and closed single-vendor platforms prevail but depend on the availability and maintenance of multiformat conversion libraries. Expedient for small deployments, this is not sustainable at an industrial scale. Primarily known as a standard for radiology, the Digital Imaging and Communications in Medicine (DICOM) standard has been evolving to support other specialties since its inception, to become the single ubiquitous standard throughout medical imaging. The adoption of DICOM for whole slide imaging (WSI) has been sluggish. Prospects for widespread commercially viable clinical use of digital pathology change the incentives. Connectathons using DICOM have demonstrated its feasibility for WSI and virtual microscopy. Adoption of DICOM for digital and computational pathology will allow the reuse of enterprise-wide infrastructure for storage, security, and business continuity. The DICOM embedded metadata allows detached files to remain useful. Bright-field and multichannel fluorescence, Z-stacks, cytology, and sparse and fully tiled encoding are supported. External terminologies and standard compression schemes are supported. Color consistency is defined using International Color Consortium profiles. The DICOM files can be dual personality Tagged Image File Format (TIFF) for legacy support. Annotations for computational pathology results can be encoded.

Optimizing humeral stem fixation in revision reverse shoulder arthroplasty with the cement-within-cement technique

The purpose of this study was to report on the clinical outcomes of patients undergoing revision reverse shoulder arthroplasty (RSA) by the cement-within-cement technique, as well as to identify whether surgical technique can affect subsequent humeral loosening. In 98 patients, cemented humeral components that were revised to RSA using the cement-within-cement technique were identified and included in this study. We compared 8 patients in whom humeral stem loosening developed with 90 patients whose stem remained fixed. Preoperative and postoperative radiographs of each patient were downloaded in DICOM (Digital Imaging and Communications in Medicine) format and analyzed in Mimics. The total area of the cement mantle (in square millimeters) and of the stem (in square millimeters), as visualized on 2-dimensional plain films, was measured in each subject on both preoperative and postoperative radiographs. Outcomes at a minimum of 2 years of follow-up were analyzed. Clinical outcomes were available in 57 patients, with a mean follow-up period of 54 months (range, 21-156 months). Patients demonstrated significantly improved functional outcomescores and shoulder range of motion. In the group without loosening, the mean increase in the cement mantle area was 4380 ± 12701 mm2 (P < .0001). In the group with loosening, the mean increase in the cement mantle area was only 811 ± 4014 mm2 (P = .484). Use of the cement-within-cement technique for fixation of the humeral component in revision RSA is effective in improving functional outcome scores and shoulder range of motion. Furthermore, these findings suggest that efforts to maximize the cement volume during reimplantation may lessen the chance of humeral stem loosening requiring additional revision.

Xeno-Hybrid Composite Scaffold Manufactured with CAD/CAM Technology for Horizontal Bone-Augmentation in Edentulous Atrophic Maxilla: A Short Communication

The present short communication described a new procedure for the reconstruction of the horizontal severely resorbed edentulous maxilla with custom-made deproteinized bovine bone block, fabricated using three-dimensional imaging of the patient and computer-aided design/computer-aided manufacturing (CAD/CAM) technology. The protocol consisted of three phases. In the diagnosis and treatment planning, cone-beam computed tomographic scans of the patient were saved in DICOM (digital imaging and communication in medicine) format, anatomic and prosthetic data were imported into a dedicated diagnostic and medical imaging software, the prosthetic-driven position of the implants, and the graft blocks perfectly adapted to the residual bone structure were virtually planned. In the manufacturing of customized graft blocks, the CAD-CAM technology and the bovine-derived xenohybrid composite bone (SmartBone® on Demand - IBI SA - Industrie Biomediche Insubri SA Switzerland) were used to fabricate the grafts in the exact shape of the 3D planning virtual model. In the surgical and prosthetic procedure, the maxillary ridge augmentation with custom-made blocks and implant-supported full-arch screw-retained rehabilitation were performed. The described protocol offered some advantages when compared to conventional augmentation techniques. The use of deproteinized bovine bone did not require additional surgery for bone harvesting, avoided the risk of donor site morbidity, and provided unlimited biomaterial availability. The customization of the graft blocks reduced the surgical invasiveness, shorting operating times because the manual shaping of the blocks and its adaptation at recipient sites are not necessary and less dependent on the clinician’s skill and experience.

G-Code Generator from Bone DICOM CT with Cloud

A three dimensional (3D) medical image can provide a sense of reality for a doctor. A 3D printer can transform a 3D image into a 3D object, which provides communication between the doctor and his/her patients. One step to make the 3D object is to generate the g-code using a software. However, the existing software for creating the g-code still uses the user interface (a manual process). This study aims to generate g-code from Digital Imaging and Communications in Medicine (DICOM) format in Computerized Tomography (CT) through the cloud. The g-code generator is implemented using Phyton version 3 and runs on a computer using Linux (Ubuntu 18.4) as the operating system. The computer specification is intel core i9-9900K with motherboard Gigabyte Z390 Aorus Elite and graphic card Gigabyte GV-N208TGAMING OC11GC (memory 11GB). The medical image object is a bone that comprises craniofacial bone and thoracic vertebra, taken from multi-slice CT in DICOM format. The g-code file is suitable for a specific 3D printer. The performance criteria are the transferred data and visual shape comparison between the output from InVesalius 3.1 software and 3D printer (manually). The justification of the visual shape match from three radiology doctors for craniofacial bone and a thoracic vertebra is approximately 100% matching.

Application of computer-assisted design for anterolateral thigh flap in oral and maxillofacial reconstruction

Objective To investigate the feasibility and accuracy of using digital technology to design anterolateral thigh flap (ALTF) in oral and maxillofacial defect reconstruction. Methods Ten cases underwent oral and maxillofacial defects reconstruction with ALTFs in Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology from June 2019 to Oct. 2019 were enrolled. There were 7 males and 3 females with the mean age of 47.1 years. Preoperative high frequency color Doppler ultrasound examination was performed to detect the perforators of ALTF. CT data of the thigh was imported in DICOM (digital imaging and communications in medicine) format to the Proplan CMF 3.0 software (Materalise, Belgium), then virtual harvest of ALTF was performed according to the points of perforators detected by high frequency color Doppler ultrasound and the virtual flap volume was calculated by Proplan CMF 3.0 software. ALTF was harvested followed by preoperative virtual design, and the actual flap volume of ALTF was measured by the draining method during the surgery. Finally, the accuracy rate of using high frequency color Doppler ultrasound to detect perforators of ALTFs was calculated, and the differences between the virtual flap volume measured by Propaln CMF 3.0 software and the actual volume of ALTF by the draining method were compared using paired samples T test. Results Fifteen perforators in the flaps area of 10 patients who underwent oral and maxillofacial defects reconstruction with ALTFs were detected by high frequency color Doppler ultrasound, and 16 perforators were identified during the surgery, with the accuracy rate of 87.5%. The flaps size ranged from 5 cm×7 cm to 8 cm×15 cm, all the 10 flaps survived. The donor sites were primarily closed without skin graft, and no surgery complication was found on the donor site. The mean flap volume measured by Propaln CMF 3.0 software was 71.4 cm³ (range: 36.1-188.4 cm³), and the mean volume measured by the draining method was 70.7 cm³ (range: 38.3-172.5 cm³). There was no significant difference between the virtual flap volume measured by Propaln CMF 3.0 software and the actual volume measured by draining method (t=0.318; P=0.758). Conclusion Preoperative virtual design of ALTF has good feasibility and accuracy and can be used to guide the harvest of ALTF during operation.

ODIN: Adaptation of an HTML 5-Compatible DICOM Viewer for MIRC-TFS, Enhancing the Incorporation of Clinical Images into the Health Science Curriculum.

The use of diagnostic radiology images while teaching has been proven to enhance student learning. The incorporation of images in the Digital Imaging and Communications in Medicine (DICOM) format has been hindered by the lack of integrated, DICOM image-compatible, viewing software. A project was developed to facilitate the migration of clinical diagnostic radiology images, in their native DICOM format into the curriculum at the undergraduate level, including the Medical School, while anonymizing the images and presenting them in a way that simulates a clinical Picture Archive and Communication System (PACS). These images can be directly incorporated into Blackboard Learn or into other applications using embedded URLs. A teaching file server was deployed using the Medical Imaging Resource Community-Teaching File System (MIRC-TFS) platform. An HTML5, DICOM image viewing server was developed and deployed. Plugins were created to integrate the anonymized images in the MIRC-TFS server with the HTML5 DICOM viewer and Blackboard Learn. The solution was deployed at an undergraduate level. This project has resulted in the successful development and deployment of a variety of solutions, including an HTML 5 DICOM viewer, that has allowed for the incorporation of anonymized DICOM images from a clinical imaging repository into the undergraduate curriculum.