Showing papers in "Journal of Digital Imaging in 2008"
TL;DR: Experimental results show that the amount of TI is closely related to both image noise and image blurring, which demonstrates the usefulness of the proposed method for evaluation of physical image quality in medical imaging.
Abstract: This paper presents a simple and straightforward method for synthetically evaluating digital radiographic images by a single parameter in terms of transmitted information (TI). The features of our proposed method are (1) simplicity of computation, (2) simplicity of experimentation, and (3) combined assessment of image noise and resolution (blur). Two acrylic step wedges with 0–1–2–3–4–5 and 0–2–4–6–8–10 mm in thickness were used as phantoms for experiments. In the present study, three experiments were conducted. First, to investigate the relation between the value of TI and image noise, various radiation doses by changing exposure time were employed. Second, we examined the relation between the value of TI and image blurring by shifting the phantoms away from the center of the X-ray beam area toward the cathode end when imaging was performed. Third, we analyzed the combined effect of deteriorated blur and noise on the images by employing three smoothing filters. Experimental results show that the amount of TI is closely related to both image noise and image blurring. The results demonstrate the usefulness of our method for evaluation of physical image quality in medical imaging.
TL;DR: It is demonstrated that the RadLex terminology can be translated into an ontology, a representation of terminologies that is both human-browsable and machine-processable, and that creating this ontology permits computational analysis of RadLex and enables its use in a variety of computer applications.
Abstract: The radiology community has recognized the need to create a standard terminology to improve the clarity of reports, to reduce radiologist variation, to enable access to imaging information, and to improve the quality of practice. This need has recently led to the development of RadLex, a controlled terminology for radiology. The creation of RadLex has proved challenging in several respects: It has been difficult for users to peruse the large RadLex taxonomies and for curators to navigate the complex terminology structure to check it for errors and omissions. In this work, we demonstrate that the RadLex terminology can be translated into an ontology, a representation of terminologies that is both human-browsable and machine-processable. We also show that creating this ontology permits computational analysis of RadLex and enables its use in a variety of computer applications. We believe that adopting an ontology representation of RadLex will permit more widespread use of the terminology and make it easier to collect feedback from the community that will ultimately lead to improving RadLex.
TL;DR: The proposed near-lossless method is proven to effectively detect a tampered medical image and recover the original ROI image.
Abstract: Digital medical images are very easy to be modified for illegal purposes. For example, microcalcification in mammography is an important diagnostic clue, and it can be wiped off intentionally for insurance purposes or added intentionally into a normal mammography. In this paper, we proposed two methods to tamper detection and recovery for a medical image. A 1024 × 1024 x-ray mammogram was chosen to test the ability of tamper detection and recovery. At first, a medical image is divided into several blocks. For each block, an adaptive robust digital watermarking method combined with the modulo operation is used to hide both the authentication message and the recovery information. In the first method, each block is embedded with the authentication message and the recovery information of other blocks. Because the recovered block is too small and excessively compressed, the concept of region of interest (ROI) is introduced into the second method. If there are no tampered blocks, the original image can be obtained with only the stego image. When the ROI, such as microcalcification in mammography, is tampered with, an approximate image will be obtained from other blocks. From the experimental results, the proposed near-lossless method is proven to effectively detect a tampered medical image and recover the original ROI image. In this study, an adaptive robust digital watermarking method combined with the operation of modulo 256 was chosen to achieve information hiding and image authentication. With the proposal method, any random changes on the stego image will be detected in high probability.
TL;DR: In non-academic settings, utilizing radiologists as transcriptionists results in more error ridden radiology reports and increased costs compared with conventional transcription services.
Abstract: Continuous voice recognition dictation systems for radiology reporting provide a viable alternative to conventional transcription services with the promise of shorter report turnaround times and increased cost savings. While these benefits may be realized in academic institutions, it is unclear how voice recognition dictation impacts the private practice radiologist who is now faced with the additional task of transcription. In this article, we compare conventional transcription services with a commercially available voice recognition system with the following results: 1) Reports dictated with voice recognition took 50% longer to dictate despite being 24% shorter than those conventionally transcribed, 2) There were 5.1 errors per case, and 90% of all voice recognition dictations contained errors prior to report signoff while 10% of transcribed reports contained errors. 3). After signoff, 35% of VR reports still had errors. Additionally, cost savings using voice recognition systems in non-academic settings may not be realized. Based on average radiologist and transcription salaries, the additional time spent dictating with voice recognition costs an additional $6.10 per case or $76,250.00 yearly. The opportunity costs may be higher. Informally surveyed, all radiologists expressed dissatisfaction with voice recognition with feelings of frustration, and increased fatigue. In summary, in non-academic settings, utilizing radiologists as transcriptionists results in more error ridden radiology reports and increased costs compared with conventional transcription services.
TL;DR: Improvement in classification accuracy may be gained by using selected combinations of shape, edge-sharpness, and texture features in breast masses computed from 111 regions in mammograms.
Abstract: Breast masses due to benign disease and malignant tumors related to breast cancer differ in terms of shape, edge-sharpness, and texture characteristics. In this study, we evaluate a set of 22 features including 5 shape factors, 3 edge-sharpness measures, and 14 texture features computed from 111 regions in mammograms, with 46 regions related to malignant tumors and 65 to benign masses. Feature selection is performed by a genetic algorithm based on several criteria, such as alignment of the kernel with the target function, class separability, and normalized distance. Fisher’s linear discriminant analysis, the support vector machine (SVM), and our strict two-surface proximal (S2SP) classifier, as well as their corresponding kernel-based nonlinear versions, are used in the classification task with the selected features. The nonlinear classification performance of kernel Fisher’s discriminant analysis, SVM, and S2SP, with the Gaussian kernel, reached 0.95 in terms of the area under the receiver operating characteristics curve. The results indicate that improvement in classification accuracy may be gained by using selected combinations of shape, edge-sharpness, and texture features.
TL;DR: Two detection and restoration systems are proposed to cope with forgery of medical images using the lossless data-embedding techniques, which have the ability to recover the whole blocks of the image and only a particular region where a physician will be interested in, with a better visual quality.
Abstract: Over the past few years, the billows of the digital trends and the exploding growth of electronic networks, such as worldwide web, global mobility networks, etc., have drastically changed our daily lifestyle. In view of the widespread applications of digital images, medical images, which are produced by a wide variety of medical appliances, are stored in digital form gradually. These digital images are very easy to be modified imperceptively by malicious intruders for illegal purposes. The well-known adage that “seeing is believing” seems not always a changeless truth. Therefore, protecting images from being altered becomes an important issue. Based on the lossless data-embedding techniques, two detection and restoration systems are proposed to cope with forgery of medical images in this paper. One of them has the ability to recover the whole blocks of the image and the other enables to recover only a particular region where a physician will be interested in, with a better visual quality. Without the need of comparing with the original image, these systems have a great advantage of detecting and locating forged parts of the image with high possibility. And then it can also restore the counterfeited parts. Furthermore, once an image is announced authentic, the original image can be derived from the stego-image losslessly. The experimental results show that the restored version of a tampered image in the first method is extremely close to the original one. As to the second method, the region of interest selected by a physician can be recovered without any loss, when it is tampered.
TL;DR: In this paper, methods are presented for automatic detection of the nipple and the pectoral muscle edge in mammograms via image processing in the Radon domain using Radon-domain information for the detection of straight-line candidates with high gradient.
Abstract: In this paper, methods are presented for automatic detection of the nipple and the pectoral muscle edge in mammograms via image processing in the Radon domain. Radon-domain information was used for the detection of straight-line candidates with high gradient. The longest straight-line candidate was used to identify the pectoral muscle edge. The nipple was detected as the convergence point of breast tissue components, indicated by the largest response in the Radon domain. Percentages of false-positive (FP) and false-negative (FN) areas were determined by comparing the areas of the pectoral muscle regions delimited manually by a radiologist and by the proposed method applied to 540 mediolateral-oblique (MLO) mammographic images. The average FP and FN were 8.99% and 9.13%, respectively. In the detection of the nipple, an average error of 7.4 mm was obtained with reference to the nipple as identified by a radiologist on 1,080 mammographic images (540 MLO and 540 craniocaudal views).
TL;DR: The main purpose of this work is to review the theoretical and practical aspects of calibration of LCDs to the GSDF, and the influence of ambient light on calibration and perception is discussed.
Abstract: Consistent presentation of digital radiographic images at all locations within a medical center can help ensure a high level of patient care. Currently, liquid crystal displays (LCDs) are the electronic display technology of choice for viewing medical images. As the inherent luminance (and thereby perceived contrast) properties of different LCDs can vary substantially, calibration of the luminance response of these displays is required to ensure that observer perception of an image is consistent on all displays. The digital imaging and communication in medicine (DICOM) grayscale standard display function (GSDF) defines the luminance response of a display such that an observer’s perception of image contrast is consistent throughout the pixel value range of a displayed image. The main purpose of this work is to review the theoretical and practical aspects of calibration of LCDs to the GSDF. Included herein is a review of LCD technology, principles of calibration, and other practical aspects related to calibration and observer perception of images presented on LCDs. Both grayscale and color displays are considered, and the influence of ambient light on calibration and perception is discussed.
TL;DR: The present study was performed to evaluate the potential for clinical application of digital linear tomosynthesis in imaging hip prostheses and the effectiveness of this method in enhancing visibility of a prosthesis case was quantified in terms of the signal-to-noise ratio (SNR).
Abstract: The present study was performed to evaluate the potential for clinical application of digital linear tomosynthesis in imaging hip prostheses. Volumetric x-ray digital linear tomosysnthesis was used to image hip prostheses. The tomosynthesis was compared to metal artifact reduction (MAR) computed tomography (CT), and non-MAR CT scans of a prosthesis case. The effectiveness of this method in enhancing visibility of a prosthesis case was quantified in terms of the signal-to-noise ratio (SNR), and removal of ghosting artifacts in a prosthesis case was quantified in terms of the artifact spread function (ASF). In the near in-focus plane, the contrast is greater in the MAR CT or tomosynthesis relative to the non-MAR CT. The order of ASF performance of the algorithm was as follows: (1) tomosynthesis; (2) MAR-CT; (3) non-MAR CT. The potential usefulness of digital linear tomosynthesis for evaluation of hip prostheses was demonstrated. Further studies are required to determine the ability of digital linear tomosynthesis to quantify the spatial relationships between the metallic components of these devices as well as to identify bony changes with diagnostic consequences.
TL;DR: A PACS-integrated digital dashboard function designed to alert radiologists to their unsigned report queue status, coupled with an actionable link to the report signing application, resulted in a 24% reduction in the time between transcription and report finalization.
Abstract: As radiology departments transition to near-complete digital information management, work flows and their supporting informatics infrastructure are becoming increasingly complex. Digital dashboards can integrate separate computerized information systems and summarize key work flow metrics in real time to facilitate informed decision making. A PACS-integrated digital dashboard function designed to alert radiologists to their unsigned report queue status, coupled with an actionable link to the report signing application, resulted in a 24% reduction in the time between transcription and report finalization. The dashboard was well received by radiologists who reported high usage for signing reports. Further research is needed to identify and evaluate other potentially useful work flow metrics for inclusion in a radiology clinical dashboard.
TL;DR: A new prostate detection method using multiresolution autocorrelation texture features and clinical features such as location and shape of tumor, which can detect cancerous tissues efficiently and high sensitivity by the measurement of the number of correctly classified pixels.
Abstract: In this paper, we propose a new prostate detection method using multiresolution autocorrelation texture features and clinical features such as location and shape of tumor. With the proposed method, we can detect cancerous tissues efficiently with high specificity (about 90–95%)and high sensitivity (about 92–96%) by the measurement of the number of correctly classified pixels. Multiresolution autocorrelation can detect cancerous tissues efficiently, and clinical knowledge helps to discriminate the cancer region by location and shape of the region and increases specificity. The support vector machine is used to classify tissues based on those features. The proposed method will be helpful in formulating a more reliable diagnosis, increasing diagnosis efficiency.
TL;DR: The BRCA1/BRCA2 gene mutation carriers and low-risk women have different mammographic parenchymal patterns, and it is expected that women identified as high risk by computerized feature analyses might potentially be more aggressively screened for breast cancer.
Abstract: Purpose: The purpose of the study was to evaluate the usefulness of power law spectral analysis on mammographic parenchymal patterns in breast cancer risk assessment. Materials and Methods: Mammograms from 172 subjects (30 women with the BRCA1/BRCA2 gene mutation and 142 low-risk women) were retrospectively collected and digitized. Because age is a very important risk factor, 60 low-risk women were randomly selected from the 142 low-risk subjects and were age matched to the 30 gene mutation carriers. Regions of interest were manually selected from the central breast region behind the nipple of these digitized mammograms and subsequently used in power spectral analysis. The power law spectrum of the form was evaluated for the mammographic patterns. The performance of exponent β as a decision variable for differentiating between gene mutation carriers and low-risk women was assessed using receiver operating characteristic analysis for both the entire database and the age-matched subset. Results: Power spectral analysis of mammograms demonstrated a statistically significant difference between the 30 BRCA1/BRCA2 gene mutation carriers and the 142 low risk women with an average β values of 2.92 (±0.28) and 2.47(±0.20), respectively. An Az value of 0.90 was achieved in distinguishing between gene mutation carriers and low-risk women in the entire database, with an Az value of 0.89 being achieved on the age-matched subset. Conclusions: The BRCA1/BRCA2 gene mutation carriers and low-risk women have different mammographic parenchymal patterns. It is expected that women identified as high risk by computerized feature analyses might potentially be more aggressively screened for breast cancer.
TL;DR: Custom software to increase automation of the Cobb angle measurement from posteroanterior radiographs was developed using active shape models and was reliable for moderate-sized curves, and did detect vertebrae in larger curves with a modified training set of larger curves.
Abstract: Choosing the most suitable treatment for scoliosis relies heavily on accurate and reproducible Cobb angle measurement from successive radiographs. The objective is to reduce variability of Cobb angle measurement by reducing user intervention and bias. Custom software to increase automation of the Cobb angle measurement from posteroanterior radiographs was developed using active shape models. Validity and reliability of the automated system against a manual and semiautomated measurement method was conducted by two examiners each performing measurements on three occasions from a test set (N = 22). A training set (N = 47) of radiographs representative of curves seen in a scoliosis clinic was used to train the software to recognize vertebrae from T4 to L4. Images with a maximum Cobb angle between 20° and 50°, excluding surgical cases, were selected for training and test sets. Automated Cobb angles were calculated using best-fit slopes of the detected vertebrae endplates. Intraclass correlation coefficient (ICC) and standard error of measurement (SEM) showed high intraexaminer (ICC > 0.90, SEM 2°–3°) and interexaminer (ICC > 0.82, SEM 2°–4°), but poor intermethod reliability (ICC = 0.30, SEM 8°–9°). The automated method underestimated large curves. The reliability improved (ICC = 0.70, SEM 4°–5°) with exclusion of the four largest curves (>40°) in the test set. The automated method was reliable for moderate-sized curves, and did detect vertebrae in larger curves with a modified training set of larger curves.
TL;DR: A novel spline-based dynamic range technique is presented in detail, which has the advantage of obtaining a high level of contrast in the intensity range of interest without discarding the intensity information outside of this range while maintaining a user interface similar to the standard window/level windowing procedure.
Abstract: A new application, Fusion Viewer, available for free, has been designed and implemented with a modular object-oriented design. The viewer provides both traditional and novel tools to fuse 3D data sets such as CT (computed tomography), MRI (magnetic resonance imaging), PET (positron emission tomography), and SPECT (single photon emission tomography) of the same subject, to create maximum intensity projections (MIP) and to adjust dynamic range. In many situations, it is desirable and advantageous to acquire biomedical images in more than one modality. For example, PET can be used to acquire functional data, whereas MRI can be used to acquire morphological data. In some situations, a side-by-side comparison of the images provides enough information, but in most of the cases it may be necessary to have the exact spatial relationship between the modalities presented to the observer. To accomplish this task, the images need to first be registered and then combined (fused) to create a single image. In this paper, we discuss the options for performing such fusion in the context of multimodal breast imaging. Additionally, a novel spline-based dynamic range technique is presented in detail. It has the advantage of obtaining a high level of contrast in the intensity range of interest without discarding the intensity information outside of this range while maintaining a user interface similar to the standard window/level windowing procedure.
TL;DR: Speech recognition decreases turnaround times and may thus speed up the whole patient care process by facilitating online reporting and encourage the utilization of SR, which improves the productivity and accelerates the workflow with excellent end-user satisfaction.
Abstract: Speech recognition (SR), available since the 1980s, has only recently become sufficiently reliable to allow utilization in medical environment. This study measured the effect of SR for the radiological dictation process and estimated differences in report turnaround times (RTTs). During the transition from cassette-based reporting to SR, the workflow of 14 radiologists was periodically followed up for 2 years in a university hospital. The sample size was more than 20,000 examinations, and the radiologists were the same throughout the study. A RTT was defined as the time from imaging at the modality to the time when the report was available for the clinician. SR cut down RTTs by 81% and the standard deviation by 83%. The proportion of reports available within 1 h escalated from 26% to 58%. The proportion of reports created by SR increased during a follow-up time of this study from 0% up to 88%. SR decreases turnaround times and may thus speed up the whole patient care process by facilitating online reporting. SR was easily adopted and well accepted by radiologists. Our findings encourage the utilization of SR, which improves the productivity and accelerates the workflow with excellent end-user satisfaction.
TL;DR: This paper presents a powerful user interface for CBIR that provides all three mechanisms for extended query refinement and has a significant impact for medical CBIR applications.
Abstract: The impact of image pattern recognition on accessing large databases of medical images has recently been explored, and content-based image retrieval (CBIR) in medical applications (IRMA) is researched. At the present, however, the impact of image retrieval on diagnosis is limited, and practical applications are scarce. One reason is the lack of suitable mechanisms for query refinement, in particular, the ability to (1) restore previous session states, (2) combine individual queries by Boolean operators, and (3) provide continuous-valued query refinement. This paper presents a powerful user interface for CBIR that provides all three mechanisms for extended query refinement. The various mechanisms of man–machine interaction during a retrieval session are grouped into four classes: (1) output modules, (2) parameter modules, (3) transaction modules, and (4) process modules, all of which are controlled by a detailed query logging. The query logging is linked to a relational database. Nested loops for interaction provide a maximum of flexibility within a minimum of complexity, as the entire data flow is still controlled within a single Web page. Our approach is implemented to support various modalities, orientations, and body regions using global features that model gray scale, texture, structure, and global shape characteristics. The resulting extended query refinement has a significant impact for medical CBIR applications.
TL;DR: This work makes use of the existing image annotation to address second and third issues of image annotation, and proposes an automatic multilevel code generation for image classification and multileVEL image annotation.
Abstract: Image retrieval at the semantic level mostly depends on image annotation or image classification. Image annotation performance largely depends on three issues: (1) automatic image feature extraction; (2) a semantic image concept modeling; (3) algorithm for semantic image annotation. To address first issue, multilevel features are extracted to construct the feature vector, which represents the contents of the image. To address second issue, domain-dependent concept hierarchy is constructed for interpretation of image semantic concepts. To address third issue, automatic multilevel code generation is proposed for image classification and multilevel image annotation. We make use of the existing image annotation to address second and third issues. Our experiments on a specific domain of X-ray images have given encouraging results.
TL;DR: An intuitive and flexible program structure, as well as the program GUI for CBCT acquisition, is presented in this note, designed to control a custom-built CBCT system but has been also used in a standard angiographic suite.
Abstract: Construction of a cone-beam computed tomography (CBCT) system for laboratory research usually requires integration of different software and hardware components. As a result, building and operating such a complex system require the expertise of researchers with significantly different backgrounds. Additionally, writing flexible code to control the hardware components of a CBCT system combined with designing a friendly graphical user interface (GUI) can be cumbersome and time consuming. An intuitive and flexible program structure, as well as the program GUI for CBCT acquisition, is presented in this note. The program was developed in National Instrument’s Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) graphical language and is designed to control a custom-built CBCT system but has been also used in a standard angiographic suite. The hardware components are commercially available to researchers and are in general provided with software drivers which are LabVIEW compatible. The program structure was designed as a sequential chain. Each step in the chain takes care of one or two hardware commands at a time; the execution of the sequence can be modified according to the CBCT system design. We have scanned and reconstructed over 200 specimens using this interface and present three examples which cover different areas of interest encountered in laboratory research. The resulting 3D data are rendered using a commercial workstation. The program described in this paper is available for use or improvement by other researchers.
TL;DR: A statistical method for error detection that can be applied after transcription is presented, showing an error detection rate as high as 96% in some cases.
Abstract: Despite the potential to dominate radiology reporting, current speech recognition technology is thus far a weak and inconsistent alternative to traditional human transcription. This is attributable to poor accuracy rates, in spite of vendor claims, and the wasted resources that go into correcting erroneous reports. A solution to this problem is post-speech-recognition error detection that will assist the radiologist in proofreading more efficiently. In this paper, we present a statistical method for error detection that can be applied after transcription. The results are encouraging, showing an error detection rate as high as 96% in some cases.
TL;DR: It is proposed to use a signature based on the turning angle function of contours of breast masses to derive features that capture the characteristics of shape roughness as described above and methods to derive an index of the presence of convex regions (XRTA, VRTA, CXTA, FDTA, and FDdTA from theturn angle function.
Abstract: Malignant breast tumors and benign masses appear in mammograms with different shape characteristics: the former usually have rough, spiculated, or microlobulated contours, whereas the latter commonly have smooth, round, oval, or macrolobulated contours. Features that characterize shape roughness and complexity can assist in distinguishing between malignant tumors and benign masses. Signatures of contours may be used to analyze their shapes. We propose to use a signature based on the turning angle function of contours of breast masses to derive features that capture the characteristics of shape roughness as described above. We propose methods to derive an index of the presence of convex regions (XRTA), an index of the presence of concave regions (VRTA), an index of convexity (CXTA), and two measures of fractal dimension (FDTA and FDdTA) from the turning angle function. The methods were tested with a set of 111 contours of 65 benign masses and 46 malignant tumors with different parameters. The best classification accuracies in discriminating between benign masses and malignant tumors, obtained for XRTA, VRTA, CXTA, FDTA, and FDdTA in terms of the area under the receiver operating characteristics curve, were 0.92, 0.92, 0.93, 0.93, and, 0.92, respectively.
TL;DR: 3D imaging by computed tomography has long been desirable for research and treatment of cochlear-implant patients, but technical challenges have limited its wide application.
Abstract: While 3-dimensional (3D) imaging by computed tomography has long been desirable for research and treatment of cochlear-implant patients, technical challenges have limited its wide application. Recent developments in scanner hardware and image processing techniques now allow image quality improvements that make clinical applications feasible. Validation experiments were performed to characterize a new methodology and its imaging performance.
TL;DR: The data present gender-specific and age-related normative reference data for computer-aided joint space analysis, which provide a valid and reliable differentiation between disease-related joint space narrowing andAge-related Joint space narrowing, particularly in patients with osteoarthritis of the fingers.
Abstract: Purpose The study introduces reference data for a computer-aided analysis. The semiautomated computer-aided diagnostic system provides the estimation of joint space width at the distal interphalangeal joints, considering gender-specific and age-related changes.
TL;DR: Experimental results demonstrated that the AIANN model attained higher average results than those obtained using published methods for real MRI data and simulated MRI data, especially at low levels of noise.
Abstract: In this paper, a new neural network model inspired by the biological immune system functions is presented. The model, termed Artificial Immune-Activated Neural Network (AIANN), extracts classification knowledge from a training data set, which is then used to classify input patterns or vectors. The AIANN is based on a neuron activation function whose behavior is conceptually modeled after the chemical bonds between the receptors and epitopes in the biological immune system. The bonding is controlled through an energy measure to ensure accurate recognition. The AIANN model was applied to the segmentation of 3-dimensional magnetic resonance imaging (MRI) data of the brain and a contextual basis was developed for the segmentation problem. Evaluation of the segmentation results was performed using both real MRI data obtained from the Center for Morphometric Analysis at Massachusetts General Hospital and simulated MRI data generated using the McGill University BrainWeb MRI simulator. Experimental results demonstrated that the AIANN model attained higher average results than those obtained using published methods for real MRI data and simulated MRI data, especially at low levels of noise.
TL;DR: Applications for report encoding need to be developed to validate the lexicon against a larger sample of reports and address the issue of automatic relationship encoding.
Abstract: Introduction: To validate a preliminary version of a radiological lexicon (RadLex) against terms found in thoracic CT reports and to index report content in RadLex term categories. Material and Methods: Terms from a random sample of 200 thoracic CT reports were extracted using a text processor and matched against RadLex. Report content was manually indexed by two radiologists in consensus in term categories of Anatomic Location, Finding, Modifier, Relationship, Image Quality, and Uncertainty. Descriptive statistics were used and differences between age groups and report types were tested for significance using Kruskal–Wallis and Mann–Whitney Test (significance level <0.05). Results: From 363 terms extracted, 304 (84%) were found and 59 (16%) were not found in RadLex. Report indexing showed a mean of 16.2 encoded items per report and 3.2 Finding per report. Term categories most frequently encoded were Modifier (1,030 of 3,244, 31.8%), Anatomic Location (813, 25.1%), Relationship (702, 21.6%) and Finding (638, 19.7%). Frequency of indexed items per report was higher in older age groups, but no significant difference was found between first study and follow up study reports. Frequency of distinct findings per report increased with patient age (p < 0.05). Conclusion: RadLex already covers most terms present in thoracic CT reports based on a small sample analysis from one institution. Applications for report encoding need to be developed to validate the lexicon against a larger sample of reports and address the issue of automatic relationship encoding.
TL;DR: Mammographic calcifications are correlated with HER-2/neu overexpression in primary breast carcinomas and its clinical perspective is assessed.
Abstract: HER-2/neu is a valuable therapeutic and prognostic marker in primary breast carcinomas. The aim of this study was to evaluate the association between mammographic calcifications and HER-2/neu overexpression in primary breast carcinomas and assess its clinical perspective. A retrospective study of 152 preoperative mammograms in patients with breast carcinoma was performed. Expression of HER-2/neu was determined by immunohistochemical staining on 152 tissues that comprised specimens of 11 ductal carcinoma in situ (DCIS) and 141 primary invasive carcinomas. Mammographic calcifications were evaluated according to the Breast Imaging Reporting and Data System (BI-RADS), fourth edition. Calcifications were found in 73 (48.0%) out of 152 patients by mammography finding. Calcifications were more common in carcinomas with HER-2/neu overexpression (45:73, 61.6%) than in those without HER-2/neu overexpression (28:79, 35.4%; P = 0.001). Of the 73 carcinomas with calcifications on mammography, mass with spiculated margin as an associated finding of calcifications was more significantly frequent in carcinomas with HER-2/neu overexpression (15 of 45, 33.3%) than in those without HER-2/neu overexpression (2 of 28, 7.1%; P = 0.036). Fine linear morphology was more common in carcinomas with HER-2/neu overexpression (15:45, 33.3%) when compared with those without HER-2/neu overexpression (2:28, 7.1%; P = 0.036). Clustered distribution of calcifications was more common in carcinomas with HER-2/neu overexpression (26:45, 57.8%) compared with carcinomas without HER-2/neu overexpression (6:28, 21.4%; P = 0.048). Mammographic calcifications are correlated with HER-2/neu overexpression in primary breast carcinomas. Calcifications detected during screening mammography are not only of diagnostic value but of use in determining therapeutic options and prognosis.
TL;DR: The proposed procedure was applied to nine X-ray computed tomographic exams of four pediatric patients with neuroblastoma and good agreement was observed between the results of segmentation and the reference contours drawn by the radiologist, with an average mean distance to the closest point of 5.85 mm.
Abstract: Segmentation of the internal organs in medical images is a difficult task. By incorporating a priori information regarding specific organs of interest, results of segmentation may be improved. Landmarking (i.e., identifying stable structures to aid in gaining more knowledge concerning contiguous structures) is a promising segmentation method. Specifically, segmentation of the diaphragm may help in limiting the scope of segmentation methods to the abdominal cavity; the diaphragm may also serve as a stable landmark for identifying internal organs, such as the liver, the spleen, and the heart. A method to delineate the diaphragm is proposed in the present work. The method is based upon segmentation of the lungs, identification of the lower surface of the lungs as an initial representation of the diaphragm, and the application of least-squares modeling and deformable contour models to obtain the final segmentation of the diaphragm. The proposed procedure was applied to nine X-ray computed tomographic (CT) exams of four pediatric patients with neuroblastoma. The results were evaluated against the boundaries of the diaphragm as identified independently by a radiologist. Good agreement was observed between the results of segmentation and the reference contours drawn by the radiologist, with an average mean distance to the closest point of 5.85 mm over a total of 73 CT slices including the diaphragm.
TL;DR: Breathing chest radiography using FPD was shown to be capable of quantifying relative ventilation in local lung area and detecting regional differences in ventilation and timing of airway closure.
Abstract: This study was performed to investigate the ability of breathing chest radiography using flat-panel detector (FPD) to quantify relative local ventilation. Dynamic chest radiographs during respiration were obtained using a modified FPD system. Imaging was performed in three different positions, ie, standing and right and left decubitus positions, to change the distribution of local ventilation. We measured the average pixel value in the local lung area. Subsequently, the interframe differences, as well as difference values between maximum inspiratory and expiratory phases, were calculated. The results were visualized as images in the form of a color display to show more or less x-ray translucency. Temporal changes and spatial distribution of the results were then compared to lung physiology. In the results, the average pixel value in each lung was associated with respiratory phase. In all positions, respiratory changes of pixel value in the lower area were greater than those in the upper area (P < 0.01), which was the same tendency as the regional differences in ventilation determined by respiratory physiology. In addition, in the decubitus position, it was observed that areas with large respiratory changes in pixel value moved up in the vertical direction during expiration, which was considered to be airway closure. In conclusion, breathing chest radiography using FPD was shown to be capable of quantifying relative ventilation in local lung area and detecting regional differences in ventilation and timing of airway closure. This method is expected to be useful as a new diagnostic imaging modality for evaluating relative local ventilation.
TL;DR: Although summation and AIP techniques produce images that differ from PR images, these differences are not easily perceived by radiologists, and the summation or AIP Techniques can substitute for PR for the primary interpretation of abdominal CT.
Abstract: We hypothesized that that the summation or axial slab average intensity projection (AIP) techniques can substitute for the primary reconstruction (PR) from a raw projection data for abdominal applications. To compare with PR datasets (5-mm thick, 20% overlap) in 150 abdominal studies, corresponding summation and AIP datasets were calculated from 2-mm thick images (50% overlap). The root-mean-square error between PR and summation images was significantly greater than that between PR and AIP images (9.55 [median] vs. 7.12, p < 0.0001, Wilcoxon signed-ranks test). Four radiologists independently compared 2,000 test images (PR [as control], summation, or AIP) and their corresponding PR images to prove that the identicalness of summation or AIP images to PR images was not 1% less than the assessed identicalness of PR images to themselves (Wald-type test for clustered matched-pair data in a non-inferiority design). For each reader, both summation and AIP images were not inferior to PR images in terms of being rated identical to PR (p < 0.05). Although summation and AIP techniques produce images that differ from PR images, these differences are not easily perceived by radiologists. Thus, the summation or AIP techniques can substitute for PR for the primary interpretation of abdominal CT.
TL;DR: A novel 3D navigation tool has been designed and developed that is based on an alternative input device that may help to fully exploit the diagnostic potential of volumetric imaging by allowing for a more efficient reading process compared to currently deployed solutions based on conventional mouse and keyboard.
Abstract: Volumetric imaging (computed tomography and magnetic resonance imaging) provides increased diagnostic detail but is associated with the problem of navigation through large amounts of data. In an attempt to overcome this problem, a novel 3D navigation tool has been designed and developed that is based on an alternative input device. A 3D mouse allows for simultaneous definition of position and orientation of orthogonal or oblique multiplanar reformatted images or slabs, which are presented within a virtual 3D scene together with the volume-rendered data set and additionally as 2D images. Slabs are visualized with maximum intensity projection, average intensity projection, or standard volume rendering technique. A prototype has been implemented based on PC technology that has been tested by several radiologists. It has shown to be easily understandable and usable after a very short learning phase. Our solution may help to fully exploit the diagnostic potential of volumetric imaging by allowing for a more efficient reading process compared to currently deployed solutions based on conventional mouse and keyboard.
TL;DR: A method to correct all pixels in the mammography image according to the excess or lack on radiation to which these have been submitted as a result of the Heel effect is presented.
Abstract: The most significant radiation field nonuniformity is the well-known Heel effect. This nonuniform beam effect has a negative influence on the results of computer-aided diagnosis of mammograms, which is frequently used for early cancer detection. This paper presents a method to correct all pixels in the mammography image according to the excess or lack on radiation to which these have been submitted as a result of the this effect. The current simulation method calculates the intensities at all points of the image plane. In the simulated image, the percentage of radiation received by all the points takes the center of the field as reference. In the digitized mammography, the percentages of the optical density of all the pixels of the analyzed image are also calculated. The Heel effect causes a Gaussian distribution around the anode-cathode axis and a logarithmic distribution parallel to this axis. Those characteristic distributions are used to determine the center of the radiation field as well as the cathode-anode axis, allowing for the automatic determination of the correlation between these two sets of data. The measurements obtained with our proposed method differs on average by 2.49 mm in the direction perpendicular to the anode-cathode axis and 2.02 mm parallel to the anode-cathode axis of commercial equipment. The method eliminates around 94% of the Heel effect in the radiological image and the objects will reflect their x-ray absorption. To evaluate this method, experimental data was taken from known objects, but could also be done with clinical and digital images.