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We developed positron emission tomography (PET)/computed tomography (CT) viewing software (PETviewer) that can display co-registered PET and CT images obtained by PET/CT and stored on picture archiving and communication systems (PACS). PETviewer has tools for presetting windows for CT display; control bars for PET window level; zoom, pan, and pseud...
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... K (MBq/cc) is a pixel value calibrated to megabecquerels per cubic centimeter and decay corrected to the scan start time. Dose is the injected dose in megabecquerel at injection time, decay corrected to the scan start time. The injection time must be part of the dataset. Wt is the patient weight in kilograms. The factor 1,000 corresponds to the number of cubic centimeters per kilogram for water, with an approximate conver- sion of patient weight to distribution volume. SUV max is the maximum SUV value in a ROI and SUV mean is the mean SUV value in a ROI. SUV max_mean is the mean SUV of nine neighbor SUVs included maximum SUV Image fusion was achieved in a PACS workstation (Petavision®, Asan Medical Center) 13 . Whole body PET and CT images were acquired with a PET/CT scanner. Reconstructed datasets of PET/CT were transferred and archived in PACS servers through a DICOM gateway. The clinical application of fusion images was evaluated. Phantom experiments were performed to evaluate the validity of image fusion and registration. The SUV was calculated using activity, weight, and injected dose information in DICOM header files. After performing the MIP of the PET image with scanner software, MIP, axial PET, and CT images were transmitted to the PACS developed by our hospital (Petavision®). Images were acquired using a Biograph Sensation 16 PET/CT scanner (Siemens, Knoxville, TN, USA), a combined system that integrates a PET scanner based on the ECAT ACCEL (CPS Innovations) with a spiral CT scanner (Sensation 16; Siemens Medical Solutions using the syngo multimodality computer platform; Siemens, Knoxville, TN, USA). The Sensation 16 is a 16-slice CT scanner that can acquire images with slice thick- nesses of 0.75 to 10 mm. The PET component of the Biograph is an ECAT ACCEL scanner (CPS Innovations), which acquires 47 transaxial images simultaneously 14 . All patients fasted for at least 6 h before undergoing PET/CT. Scans were performed 60 min after intravenous injection of 555 MBq (15 mCi) of 18 F-FDG. After the imaging field had been determined with an initial topogram scan, a 20- to 40-s whole body CT acquisition was performed using the following parameters: 120 kV(p), 110 mAs, 5-mm slice collimation, and a bed speed of 15 mm/s. On completion of the CT portion, the PET emission data were acquired for 2 min/bed position for all patients. Imaging included six to seven bed positions per patient. Patients were instructed to breathe shallowly during the PET and CT portions of the study to minimize misregistration and attenuation artifacts between PET and CT images. The PET images were reconstructed using an attenuation-weighted ordered-subsets expectation maximization algorithm (two iterations, 16 sub- sets) followed by a postreconstruction smoothing Gaussian filter (full width at half-maximum, 6 mm). The images were reconstructed in a 128× 128 matrix without zooming, resulting in a pixel size of 5.3 mm. These processing parameters are representative of those typically used in routine clinical studies. The PACS (Petavision®) used in this work was organized into three subsystems: the acquisition subsystem, database and storage subsystem, and display subsystem (Fig. 1). The display subsystem was a personal computer (PC)-based clinical workstation developed by ASAN Medical Center. The hardware components used in the ...
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The usability of information systems, known as Hospital Information System (HIS), Radiology Information System (RIS), and Picture Archiving and Communication System, for electronic medical records has shown a good impact for actors in the hospital. The objective is to help and make their work easier; such as for a physician to access the patient's...
The DICOM standard has now become the uncontested standard for the exchange and management of biomedical images. Everyone acknowledges its prominent role in the emergence of multi-vendor Picture Archiving and Communication Systems (PACS), and their successful integration with Hospital Information Systems and Radiology Information Systems, thanks to...
The usability and simulation of information systems, known as Hospital Information System (HIS), Radiology Information System (RIS), and Picture Archiving, Communication System, for electronic medical records has shown a good impact for actors in the hospital. The objective is to help and make their work easier; such as for a nurse or administratio...
To present an adapted Clinical Trial Processor (CTP) test set-up for receiving, anonymising and saving Digital Imaging and Communications in Medicine (DICOM) data using external input from the original database of an existing clinical study information system to guide the anonymisation process.
Two methods are presented for an adapted CTP test set-...
Citations
... Therefore, SNQ of the humeral site (SNQh), mid-substance site (SNQm), and glenoid site (SNQg) were evaluated. The measurements were completed on a picture archiving and communication system with a precision of 0.001 ( Fig. 2; Petavision ® , South Korea) [9]. The MRI images were analysed by two independent raters (a radiologist and an orthopaedic surgeon) in a double-blind manner. ...
Purpose:
Superior capsular reconstruction (SCR) using fascia lata autograft has been performed for irreparable rotator cuff tear recently. The signal-to-noise quotient (SNQ) of the graft on magnetic resonance imaging (MRI) may reflect the degree of graft maturity and healing. However, how SNQ changes with graft remodelling and time and whether this change correlates with postoperative clinical outcomes after SCR remain unknown. This study aimed to explore the correlation between SNQ of the fascia lata autograft and clinical functional outcomes after SCR.
Methods:
Patients with irreparable posterosuperior rotator cuff tear undergoing SCR using fascia lata autograft between 2013 and 2017 were retrospectively analysed. For clinical outcomes, the American Shoulder and Elbow Surgeons (ASES) score, Constant-Murley score, Single Assessment Numeric Evaluation (SANE), and Visual Analogue Scale (VAS) for pain and range of motion (ROM; forward flexion and external rotation) were evaluated at postoperative 6 and 12 months. Signal intensity of the humeral, mid-substance, and glenoid sites and background were measured to calculate the SNQ values on follow-up MRI at 3 and 12 months. The correlations between clinical outcomes and SNQ at different time points were then analysed.
Results:
A total of 15 patients were enrolled in the study. The mean postoperative VAS score significantly increased at postoperative 6 months and significantly decreased at postoperative 12 months. Except for forward flexion, all other functional outcomes were improved at postoperative 6 months. Analysis of MRI showed SNQ at the humeral (SNQh), mid-substance, and glenoid sites decreased from postoperative 3 to 12 months with a statistical significance detected in SNQh (P < 0.01). Correlation analyses showed that the SNQh values negatively correlated with VAS, ASES, Constant-Murley score, SANE, ROM (forward flexion), and ROM (external rotation) (all P < 0.05).
Conclusion:
SNQ of the fascia lata autograft decreased with time in patients receiving SCR. SNQ at the humeral site was negatively correlated with clinical outcomes.
Level of evidence:
III.
... Two-dimensional superimposition of antemortem and postmortem CT images, Postmortem CT images (red) were superimposed over the antemortem CT images (gray scale), The shapes of the whole skull overlap one another (Fig. 6a-d), The shapes of the fractures on the both images overlap perfectly (Fig. 6a, e), The shapes of other anatomical structures i.e. frontal sinuses, sphenoidal sinuses, orbits, and skull base structures also overlap ( Fig. 6b-d, f). software for the purpose of "fusing" two different types of images of the same patient scanned by different modalities or scanned at different times [20][21][22]. In the present study, we used it to superimpose the AM and PM data sets for forensic analysis to determine whether the images belonged to the same person. ...
... The axial view of the transcerebellar diameter (TCD) and the anteroposterior length of the corpus callosum on sagittal view were measured (Fig 1) with the use of the entirely digital measuring system of the PACS of Asan Medical Center (PetaVision, version 2.1). 21 The TCD was measured as the longest transverse diameter perpendicular to the cerebellar vermis on the axial view that showed both nasal septum and orbital area, and the corpus callosal length was measured as the anteroposterior length on the midline sagittal image in which the pituitary gland was seen (Fig 1). ...
Background and purpose:
An increased prevalence of disabilities is being observed as more preterm infants survive. This study was conducted to evaluate correlations between brain MR imaging measurements taken at a term-equivalent age and neurodevelopmental outcome at 2 years' corrected age among very low-birth-weight infants.
Materials and methods:
Of the various brain MR imaging measurements obtained at term-equivalent ages, reproducible measurements of the transcerebellar diameter and anteroposterior length of the corpus callosum on sagittal images were compared with neurodevelopmental outcomes evaluated by the Bayley Scales of Infant Development (II) at 2 years' corrected age (mean ± standard deviation, 16.1 ± 6.4 months of age).
Results:
Ninety infants were enrolled. The mean gestational age at birth was 27 weeks and the mean birth weight was 805.5 g. A short corpus callosal length was associated with a Mental Developmental Index <70 (P = .047) and high-risk or diagnosed cerebral palsy (P = .049). A small transcerebellar diameter was associated with a Psychomotor Developmental Index <70 (P = .003), Mental Developmental Index <70 (P = .004), and major neurologic disability (P = .006).
Conclusions:
A small transcerebellar diameter and short corpus callosal length on brain MR imaging at a term-equivalent age are related to adverse neurodevelopmental outcomes at a corrected age of 2 years and could be a useful adjunctive tool for counseling parents about future developmental outcomes.
... The images in nuclear medicine should be acquired and stored in DICOM format without loss of functional data as done in NMIS. There are some efforts to develop interfaces that allow navigating between multimodality image sets (9). In our institution same storage system is used both by nuclear medicine and radiology departments. ...
Objective: Radiology information system (RIS) is basically evolved for the need of radiologists and ignores the vital steps needed for a proper work flow of Nuclear Medicine Department. Moreover, CT/MRI oriented classical PACS systems are far from satisfying Nuclear Physicians like storing dynamic data for reprocessing and quantitative analysis of colored images. Our purpose was to develop a workflow based Nuclear Medicine Information System (NMIS) that fulfills the needs of Nuclear Medicine Department and its integration to hospital PACS system.
Material and Methods: Workflow in NMIS uses HL7 (health level seven) and steps include, patient scheduling and retrieving information from HIS (hospital information system), radiopharmacy, acquisition, digital reporting and approval of the reports using Nuclear Medicine specific diagnostic codes. Images and dynamic data from cameras of are sent to and retrieved from PACS system (Corttex©) for reprocessing and quantitative analysis.
Results: NMIS has additional functions to the RIS such as radiopharmaceutical management program which includes stock recording of both radioactive and non-radioactive substances, calculation of the radiopharmaceutical dose for individual patient according to body weight and maximum permissible activity, and calculation of radioactivity left per unit volume for each radionuclide according their half lives. Patient scheduling and gamma camera patient work list settings were arranged according to specific Nuclear Medicine procedures. Nuclear Medicine images and reports can be retrieved and viewed from HIS.
Conclusion: NMIS provides functionality to standard RIS and PACS system according to the needs of Nuclear Medicine.
Conflict of interest:None declared.
This report addresses the technical aspects of de-identification of medical images of human subjects and biospecimens, such that re-identification risk of ethical, moral, and legal concern is sufficiently reduced to allow unrestricted public sharing for any purpose, regardless of the jurisdiction of the source and distribution sites. All medical images, regardless of the mode of acquisition, are considered, though the primary emphasis is on those with accompanying data elements, especially those encoded in formats in which the data elements are embedded, particularly Digital Imaging and Communications in Medicine (DICOM). These images include image-like objects such as Segmentations, Parametric Maps, and Radiotherapy (RT) Dose objects. The scope also includes related non-image objects, such as RT Structure Sets, Plans and Dose Volume Histograms, Structured Reports, and Presentation States. Only de-identification of publicly released data is considered, and alternative approaches to privacy preservation, such as federated learning for artificial intelligence (AI) model development, are out of scope, as are issues of privacy leakage from AI model sharing. Only technical issues of public sharing are addressed.
Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT significantly increases the risk of ionizing radiation exposure for patients. We propose a deep learning framework to learn the relationship mapping between attenuation corrected (AC) PET and non-attenuation corrected (NAC) PET images to estimate PET attenuation maps and generate pseudo-CT images for medical observation. In this study, 5760, 1608 and 1351 pairs of transverse PET-CT slices were used as the training, validation, and testing sets, respectively, to implement the proposed framework. A pix2pix model was adopted to predict AC PET images from NAC PET images, which allowed the calculation of PET attenuation maps (µ-maps). The same model was then applied to generate realistic CT images from the calculated µ-maps. The quality of predicted AC PET and CT was assessed using normalized root mean square error (NRMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and Pearson correlation coefficient (PCC). Relative to true AC PET, the synthetic AC PET achieved superior quantitative performances with 2.20 ± 1.17% NRMSE, 34.03 ± 4.73 dB PSNR, 97.90 ± 1.22% SSIM and 98.45 ± 1.31% PCC. The synthetic CT and synthetic AC PET images were deemed acceptable by radiologists who rated the images, as they provided sufficient anatomical and functional information, respectively. This work demonstrates that the proposed deep learning framework is a promising method in clinical applications, such as radiotherapy and low-dose imaging.
In the digital age, the healthcare industry is generating a huge amount of data and information. Although there are structured data such as EHRs, the major data type is unstructured data such as clinical text. The sources of health data are also diversified, including medical data, clinical data, patient-generated data, and social media data. Different methods are applied to analyze the variety of data and obtain health information. When the various types of information are generated, information retrieval and extraction techniques can be used for further decision-making. Data and information-enabled decision-making is a complex process. Many tools and methods are developed to support decision-making in healthcare. Along with the benefits of integrating business intelligence in healthcare, issues and challenges exist. This chapter discusses the health data and information and how they support decision-making in healthcare.
Lung cancer (LC) will decrease the yield, which will have a negative impact on the economy. Therefore, primary and accurate the attack finding is a priority for the agro-dependent state. In several modern technologies for early detection of LC, image processing has become a one of the essential tool so that it cannot only early to find the disease accurately, but also successfully measure it. Various approaches have been developed to detect LC based on background modelling. Most of them focus on temporal information but partially or completely ignore spatial information, making it sensitive to noise. In order to overcome these issues an improved hybrid semantic feature descriptor technique is introduced based on Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP) and histogram of oriented gradients (HOG) feature extraction algorithms. And also to improve the LC segmentation problems a fuzzy c-means clustering algorithm (FCM) is used. Experiments and comparisons on publically available LIDC-IBRI dataset. To evaluate the proposed feature extraction performance three different classifiers are analysed such as artificial neural networks (ANN), recursive neural network and recurrent neural networks (RNNs).
Purpose:
The purpose of this study was to propose a method for segmentation and volume measurement of graft liver and spleen of pediatric transplant recipients on digital imaging and communications in medicine (DICOM) -format images using U-Net and three-dimensional (3-D) workstations (3DWS) .
Method:
For segmentation accuracy assessments, Dice coefficients were calculated for the graft liver and spleen. After verifying that the created DICOM-format images could be imported using the existing 3DWS, accuracy rates between the ground truth and segmentation images were calculated via mask processing.
Result:
As per the verification results, Dice coefficients for the test data were as follows: graft liver, 0.758 and spleen, 0.577. All created DICOM-format images were importable using the 3DWS, with accuracy rates of 87.10±4.70% and 80.27±11.29% for the graft liver and spleen, respectively.
Conclusion:
The U-Net could be used for graft liver and spleen segmentations, and volume measurement using 3DWS was simplified by this method.
In the digital age, the healthcare industry is generating a huge amount of data and information. Although there are structured data such as EHRs, the major data type is unstructured data such as clinical text. The sources of health data are also diversified, including medical data, clinical data, patient-generated data, and social media data. Different methods are applied to analyze the variety of data and obtain health information. When the various types of information are generated, information retrieval and extraction techniques can be used for further decision-making. Data and information-enabled decision-making is a complex process. Many tools and methods are developed to support decision-making in healthcare. Along with the benefits of integrating business intelligence in healthcare, issues and challenges exist. This chapter discusses the health data and information and how they support decision-making in healthcare.
