William W. Boonn

Presentation, complications, and natural history of penetrating atherosclerotic ulcer disease

OBJECTIVES Increased utilization of computed tomography angiography (CTA) has increased the radiologic diagnosis of penetrating atherosclerotic ulcers (PAUs), which are defined as the ulceration of atherosclerotic plaque through the internal elastic lamina into the aortic media. However, the presentation, treatment indications, and natural history of this disease process remain unclear. METHODS The radiology database at a single university hospital was searched retrospectively for the CTA diagnosis of PAU from January 2003 to June 2009. All scans were interpreted by a cardiovascular radiologist. Information on PAU characteristics and need for surgical repair due to PAU disease was collected. PAU stability or progression was assessed by follow-up CTA, if available. Only PAUs in the aortic arch, descending thoracic aorta, and abdominal aorta were included. RESULTS Three hundred eighty-eight PAUs were diagnosed by CTA interpretation. PAU location was in the aortic arch in 27 (6.8%) cases, the descending thoracic aorta in 243 (61.2%) cases, and the abdominal aorta in 118 (29.7%) cases. Two hundred twenty-four (57.7%) PAUs were isolated (without saccular aneurysm or intramural hematoma); 108 (27.8%) PAUs had associated saccular aneurysms; and 56 (14.4%) PAUs had associated intramural hematoma. Rupture was present in 16 (4.1%) cases. Fifty (12.9%) PAUs underwent repair with thoracic endovascular aortic repair (TEVAR) (n = 30), endovascular aneurysm repair (EVAR) (n = 10), or open surgery (n = 10); primary indications for repair were saccular aneurysm (n = 26), rupture (n = 16), and persistent or recurrent symptoms (n = 8). Even if initially treated conservatively with resolution of pain, symptomatic PAU disease was more likely to require repair than asymptomatic PAU disease (36.2% vs 7.8%, P < .001). Follow-up CTA was available for 87 PAUs, 20 (23.0%) of which demonstrated radiographic disease progression at a mean follow-up of 8.4 ± 10.3 months. Symptomatic PAU disease was more likely to progress than asymptomatic disease (42.9% vs 16.7%, P = .029). CONCLUSIONS For PAUs diagnosed on CTA at a single institution, 4.1% were ruptured and 12.9% underwent repair. Close follow-up imaging appears to be indicated for PAUs, particularly in the case of symptomatic disease, which is more likely to require repair and to undergo radiographic progression.

RADIANCE: An automated, enterprise-wide solution for archiving and reporting CT radiation dose estimates.

There is growing interest in the ability to monitor, track, and report exposure to radiation from medical imaging. Historically, however, dose information has been stored on an image-based dose sheet, an arrangement that precludes widespread indexing. Although scanner manufacturers are beginning to include dose-related parameters in the Digital Imaging and Communications in Medicine (DICOM) headers of imaging studies, there remains a vast repository of retrospective computed tomographic (CT) data with image-based dose sheets. Consequently, it is difficult for imaging centers to monitor their dose estimates or participate in the American College of Radiology (ACR) Dose Index Registry. An automated extraction software pipeline known as Radiation Dose Intelligent Analytics for CT Examinations (RADIANCE) has been designed that quickly and accurately parses CT dose sheets to extract and archive dose-related parameters. Optical character recognition of information in the dose sheet leads to creation of a text file, which along with the DICOM study header is parsed to extract dose-related data. The data are then stored in a relational database that can be queried for dose monitoring and report creation. RADIANCE allows efficient dose analysis of CT examinations and more effective education of technologists, radiologists, and referring physicians regarding patient exposure to radiation at CT. RADIANCE also allows compliance with the ACRs dose reporting guidelines and greater awareness of patient radiation dose, ultimately resulting in improved patient care and treatment.

Informatics in Radiology: Automated Structured Reporting of Imaging Findings Using the AIM Standard and XML

Quantitative and descriptive imaging data are a vital component of the radiology report and are frequently of paramount importance to the ordering physician. Unfortunately, current methods of recording these data in the report are both inefficient and error prone. In addition, the free-text, unstructured format of a radiology report makes aggregate analysis of data from multiple reports difficult or even impossible without manual intervention. A structured reporting work flow has been developed that allows quantitative data created at an advanced imaging workstation to be seamlessly integrated into the radiology report with minimal radiologist intervention. As an intermediary step between the workstation and the reporting software, quantitative and descriptive data are converted into an extensible markup language (XML) file in a standardized format specified by the Annotation and Image Markup (AIM) project of the National Institutes of Health Cancer Biomedical Informatics Grid. The AIM standard was created to allow image annotation data to be stored in a uniform machine-readable format. These XML files containing imaging data can also be stored on a local database for data mining and analysis. This structured work flow solution has the potential to improve radiologist efficiency, reduce errors, and facilitate storage of quantitative and descriptive imaging data for research.

Automated Extraction of Radiation Dose Information for CT Examinations

Exposure to radiation as a result of medical imaging is currently in the spotlight, receiving attention from Congress as well as the lay press. Although scanner manufacturers are moving toward including effective dose information in the Digital Imaging and Communications in Medicine headers of imaging studies, there is a vast repository of retrospective CT data at every imaging center that stores dose information in an image-based dose sheet. As such, it is difficult for imaging centers to participate in the ACRs Dose Index Registry. The authors have designed an automated extraction system to query their PACS archive and parse CT examinations to extract the dose information stored in each dose sheet. First, an open-source optical character recognition program processes each dose sheet and converts the information to American Standard Code for Information Interchange (ASCII) text. Each text file is parsed, and radiation dose information is extracted and stored in a database which can be queried using an existing pathology and radiology enterprise search tool. Using this automated extraction pipeline, it is possible to perform dose analysis on the >800,000 CT examinations in the PACS archive and generate dose reports for all of these patients. It is also possible to more effectively educate technologists, radiologists, and referring physicians about exposure to radiation from CT by generating report cards for interpreted and performed studies. The automated extraction pipeline enables compliance with the ACRs reporting guidelines and greater awareness of radiation dose to patients, thus resulting in improved patient care and management.

Radiologist Use of and Perceived Need for Patient Data Access

Given the increasing volume of radiological exams, the decreasing frequency of direct communication with the referring provider, and the distribution of patient data over many clinical systems, radiologists often do not have adequate clinical information at the time of interpretation. We have performed a survey of radiologists to determine the need and actual utilization of patient data at the time of image interpretation. Our findings demonstrate that most radiologists want more clinical information when interpreting images and that this information would impact their report, but they are discouraged by the time it takes to access this information. In addition, current mechanisms for monitoring necessary patient follow-up are inadequate.

A modern experience with saccular aortic aneurysms

OBJECTIVE Repair of saccular aortic aneurysms (SAAs) is frequently recommended based on a perceived predisposition to rupture, despite little evidence that these aneurysms have a more malignant natural history than fusiform aortic aneurysms. METHODS The radiology database at a single university hospital was searched for the computed tomographic (CT) diagnosis of SAA between 2003 and 2011. Patient characteristics and clinical course, including the need for surgical intervention, were recorded. SAA evolution was assessed by follow-up CT, where available. Multivariate analysis was used to examine potential predictors of aneurysm growth rate. RESULTS Three hundred twenty-two saccular aortic aneurysms were identified in 284 patients. There were 153 (53.7%) men and 131 women with a mean age of 73.5±10.0 years. SAAs were located in the ascending aorta in two (0.6%) cases, the aortic arch in 23 (7.1%), the descending thoracic aorta in 219 (68.1%), and the abdominal aorta in 78 (24.2%). One hundred thirteen (39.8%) patients underwent surgical repair of SAA. Sixty-two patients (54.9%) underwent thoracic endovascular aortic repair, 22 underwent endovascular aneurysm repair (19.5%), and 29 (25.6%) required open surgery. The average maximum diameter of SAA was 5.0±1.6 cm. In repaired aneurysms, the mean diameter was 5.4±1.4 cm; in unrepaired aneurysms, it was 4.4±1.1 cm (P<.001). Eleven patients (3.9%) had ruptured SAAs on initial scan. Of the initial 284 patients, 50 patients (with 54 SAA) had CT follow-up after at least 3 months (23.2±19.0 months). Fifteen patients (30.0%) ultimately underwent surgical intervention. Aneurysm growth rate was 2.8±2.9 mm/yr, and was only weakly related to initial aortic diameter (R2=.19 by linear regression, P=.09 by multivariate regression). Decreased calcium burden (P=.03) and increased patient age (P=.05) predicted increased aneurysm growth by multivariate analysis. CONCLUSIONS While SAA were not found to have a higher growth rate than their fusiform counterparts, both clinical and radiologic follow-up is necessary, as a significant number ultimately require surgical intervention. Further clinical research is necessary to determine the optimal management of SAA.

The role of GPU computing in medical image analysis and visualization

The role of computers in medical image display and analysis continues to be one of the most computationally demanding tasks facing modern computers. Recent advances in GPU architecture have allowed for a new programming paradigm which utilized the massively parallel computational capacity of GPUs for general purpose computing. These parallel processors provide substantial performance benefits in image analysis and manipulation. Automated segmentation algorithms gain the most benefit from incorporation of GPU computing into the image processing workflow. There are also new visualization paradigms, such as stereoscopic 3D, which have been made possible by the continued increase in computational capacity of GPUs. These two key functions of modern GPUs will enable medical imagers to keep pace with the increasing size of scan data sets while allowing for new and innovative analysis and interaction paradigms.

MR microscopy of normal human brain

MR microscopy at 9.4T depicts the architecture of the brain in exquisite detail, including the individual laminae of the cortex, the individual nuclei of the basal ganglia, the thalami, subthalami and metathalami, and the orientations and relationship among the dominant nuclei and white matter tracts of the brain. The authors believe that these anatomic relations will ultimately be displayed in vivo as clinical MR scanners begin to operate at field strengths of 4.7T, 7T, and 8T. Then, those familiar with this anatomy will be able to interpret patient images with far greater sophistication.

Patient-Centered Imaging: Opportunities and Challenges

Patient-centered medicine is the latest mantra for the emerging relationship between patients and health care providers. Fueled principally by advances in genomics and facilitated by the gargantuan leap of medical informatics, the attempt to individualize care on the basis of the unique patient resonates well with the Hippocratic oath and is a fresh bulwark against the parallel move toward greater standardization. It will come as no surprise to radiologists that they will be at the forefront of this venture, given the ubiquity of imaging and, equally, the potential latitude of individualization within imaging. In this commentary, we identify some of the opportunities and challenges of patient-centered imaging, a natural offshoot of patient-centered medicine.

An Algorithm for Intelligent Sorting of CT-Related Dose Parameters

Imaging centers nationwide are seeking innovative means to record and monitor computed tomography (CT)-related radiation dose in light of multiple instances of patient overexposure to medical radiation. As a solution, we have developed RADIANCE, an automated pipeline for extraction, archival, and reporting of CT-related dose parameters. Estimation of whole-body effective dose from CT dose length product (DLP)—an indirect estimate of radiation dose—requires anatomy-specific conversion factors that cannot be applied to total DLP, but instead necessitate individual anatomy-based DLPs. A challenge exists because the total DLP reported on a dose sheet often includes multiple separate examinations (e.g., chest CT followed by abdominopelvic CT). Furthermore, the individual reported series DLPs may not be clearly or consistently labeled. For example, “arterial” could refer to the arterial phase of the triple liver CT or the arterial phase of a CT angiogram. To address this problem, we have designed an intelligent algorithm to parse dose sheets for multi-series CT examinations and correctly separate the total DLP into its anatomic components. The algorithm uses information from the departmental PACS to determine how many distinct CT examinations were concurrently performed. Then, it matches the number of distinct accession numbers to the series that were acquired and anatomically matches individual series DLPs to their appropriate CT examinations. This algorithm allows for more accurate dose analytics, but there remain instances where automatic sorting is not feasible. To ultimately improve radiology patient care, we must standardize series names and exam names to unequivocally sort exams by anatomy and correctly estimate whole-body effective dose.