Optimizing analysis, visualization, and navigation of large image data sets: one 5000-section CT scan can ruin your whole day

Clinical evaluation of semi-automatic landmark-based lesion tracking software for CT-scans

Background

To evaluate a semi-automatic landmark-based lesion tracking software enabling navigation between RECIST lesions in baseline and follow-up CT-scans.

Methods

The software automatically detects 44 stable anatomical landmarks in each thoraco/abdominal/pelvic CT-scan, sets up a patient specific coordinate-system and cross-links the coordinate-systems of consecutive CT-scans. Accuracy of the software was evaluated on 96 RECIST lesions (target- and non-target lesions) in baseline and follow-up CT-scans of 32 oncologic patients (64 CT-scans). Patients had to present at least one thoracic, one abdominal and one pelvic RECIST lesion. Three radiologists determined the deviation between lesions’ centre and the software’s navigation result in consensus.

Results

The initial mean runtime of the system to synchronize baseline and follow-up examinations was 19.4 ± 1.2 seconds, with subsequent navigation to corresponding RECIST lesions facilitating in real-time. Mean vector length of the deviations between lesions’ centre and the semi-automatic navigation result was 10.2 ± 5.1 mm without a substantial systematic error in any direction. Mean deviation in the cranio-caudal dimension was 5.4 ± 4.0 mm, in the lateral dimension 5.2 ± 3.9 mm and in the ventro-dorsal dimension 5.3 ± 4.0 mm.

Conclusion

The investigated software accurately and reliably navigates between lesions in consecutive CT-scans in real-time, potentially accelerating and facilitating cancer staging.

A retrieval-based computer-aided diagnosis system for the characterization of liver lesions in CT scans

RATIONALE AND OBJECTIVES

To evaluate a computer-aided diagnosis (CADx) system for the characterization of liver lesions in computed tomography (CT) scans. The stand-alone predictive performance of the CADx system was assessed and compared to that of three radiologists who were provided with the same amount of image information to which the CADx system had access.

MATERIALS AND METHODS

The CADx system operates as an image search engine exploiting texture analysis of liver lesion image data for the lesion in question and lesions from a database. A region of interest drawn around an indeterminate liver lesion is used as input query. The CADx system retrieves lesions of similar histology (benign/malignant), density (hypodense/hyperdense), or type (cyst/hemangioma/metastasis). The system's performance was evaluated with leave-one-patient-out receiver operating characteristic area under the curve on 685 CT scans from 372 patients that contained 2325 liver lesions (193 <1 cm(3)). Sensitivity, specificity, and positive and negative predictive values were evaluated separately for subcentimeter lesions. Results were compared to those of three radiologists who rated 83 liver lesions (20 hemangiomas, 20 metastases, 20 cysts, 20 hepatocellular carcinomas, and 3 focal nodular hyperplasias) displaying only the liver.

RESULTS

The CADx system's leave-one-patient-out receiver operating characteristic area under the curve was 97.1% for density, 91.4% for histology, and 95.5% for lesion type. For subcentimeter lesions, input of additional semantic information improved the system's performance. The CADx system has been proved to significantly outperform radiologists in discriminating lesion histology and type, provided the radiologists have no access to information other than the image. The radiologists were most reliable in diagnosing hemangioma given the limited image data.

CONCLUSIONS

The CADx system under study discriminated reliably between various liver lesions, even outperforming radiologists when accessing the same image information and demonstrated promising performance in classifying subcentimeter lesions in particular.

Three-dimensional solid texture analysis in biomedical imaging: review and opportunities

Three-dimensional computerized characterization of biomedical solid textures is key to large-scale and high-throughput screening of imaging data. Such data increasingly become available in the clinical and research environments with an ever increasing spatial resolution. In this text we exhaustively analyze the state-of-the-art in 3-D biomedical texture analysis to identify the specific needs of the application domains and extract promising trends in image processing algorithms. The geometrical properties of biomedical textures are studied both in their natural space and on digitized lattices. It is found that most of the tissue types have strong multi-scale directional properties, that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions. The information modeled by the various image processing techniques is analyzed and visualized by displaying their 3-D texture primitives. We demonstrate that non-convolutional approaches are expected to provide best results when the size of structures are inferior to five voxels. For larger structures, it is shown that only multi-scale directional convolutional approaches that are non-separable allow for an unbiased modeling of 3-D biomedical textures. With the increase of high-resolution isotropic imaging protocols in clinical routine and research, these models are expected to best leverage the wealth of 3-D biomedical texture analysis in the future. Future research directions and opportunities are proposed to efficiently model personalized image-based phenotypes of normal biomedical tissue and its alterations. The integration of the clinical and genomic context is expected to better explain the intra class variation of healthy biomedical textures. Using texture synthesis, this provides the exciting opportunity to simulate and visualize texture atlases of normal ageing process and disease progression for enhanced treatment planning and clinical care management.

Scanners and drillers: characterizing expert visual search through volumetric images

Modern imaging methods like computed tomography (CT) generate 3-D volumes of image data. How do radiologists search through such images? Are certain strategies more efficient? Although there is a large literature devoted to understanding search in 2-D, relatively little is known about search in volumetric space. In recent years, with the ever-increasing popularity of volumetric medical imaging, this question has taken on increased importance as we try to understand, and ultimately reduce, errors in diagnostic radiology. In the current study, we asked 24 radiologists to search chest CTs for lung nodules that could indicate lung cancer. To search, radiologists scrolled up and down through a "stack" of 2-D chest CT "slices." At each moment, we tracked eye movements in the 2-D image plane and coregistered eye position with the current slice. We used these data to create a 3-D representation of the eye movements through the image volume. Radiologists tended to follow one of two dominant search strategies: "drilling" and "scanning." Drillers restrict eye movements to a small region of the lung while quickly scrolling through depth. Scanners move more slowly through depth and search an entire level of the lung before moving on to the next level in depth. Driller performance was superior to the scanners on a variety of metrics, including lung nodule detection rate, percentage of the lung covered, and the percentage of search errors where a nodule was never fixated.

Ultrasound imaging on picture archiving and communication systems: are radiologists satisfied?

OBJECTIVES

To evaluate whether picture archiving and communication systems (PACS) adequately satisfy radiologists' needs in ultrasound (US) imaging and which PACS functions may be inadequately implemented for handling US diagnosis.

METHODS

An electronic survey was sent to the membership of the Society of Radiologists in Ultrasound asking them to rate their PACS experience for different modalities, judge the quality of various PACS functions having an impact on US practice and diagnosis, indicate if they felt a need for US-related PACS functions to be implemented or improved, and rate PACS-related improvements for different components of their US practice.

RESULTS

Of the 161 respondents, 112 (70%) used a general radiology PACS. Of these respondents, only 53.2% gave a high rating to the US experience in PACS, significantly lower (P < .0001) than for computed tomography (85.2%), magnetic resonance imaging (84.4%), and radiography (83.2%). The functionality of US-specific display, image-processing, and data management PACS processes were graded significantly lower than basic PACS display functions. Only 0.9% of respondents highly rated PACS handling of 3-dimensional US volume data, whereas 92% highly rated the quality of the black-and-white US image display (P < .0001). Most respondents would like most of these US-specific functions implemented or improved, and most respondents stated that PACS has improved their US practice in different ways, although the contribution in more complex image analysis is lagging.

CONCLUSIONS

Radiologists with a special interest in US believe that the PACS experience for US is lacking. This research helps identify those specific tasks that may further improve work efficiency and diagnostic confidence.

Imaging-based observational databases for clinical problem solving: the role of informatics

Imaging has become a prevalent tool in the diagnosis and treatment of many diseases, providing a unique in vivo, multi-scale view of anatomic and physiologic processes. With the increased use of imaging and its progressive technical advances, the role of imaging informatics is now evolving--from one of managing images, to one of integrating the full scope of clinical information needed to contextualize and link observations across phenotypic and genotypic scales. Several challenges exist for imaging informatics, including the need for methods to transform clinical imaging studies and associated data into structured information that can be organized and analyzed. We examine some of these challenges in establishing imaging-based observational databases that can support the creation of comprehensive disease models. The development of these databases and ensuing models can aid in medical decision making and knowledge discovery and ultimately, transform the use of imaging to support individually-tailored patient care.

Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography

Objective

To evaluate a computer-aided detection (CADe) system for lytic and blastic spinal metastases on computed tomography (CT).

Methods

We retrospectively evaluated the CADe system on 20 consecutive patients with 42 lytic and on 30 consecutive patients with 172 blastic metastases. The CADe system was trained using CT images of 114 subjects with 102 lytic and 308 blastic spinal metastases. Lesions were annotated by experienced radiologists. Detected benign lesions were considered false-positive findings. Detector sensitivity and the number of false-positive findings were calculated as the criteria for detector performance, and free-response receiver operating characteristic (FROC) analysis was conducted. Detailed analysis of false-positive and false-negative findings was performed.

Results

Algorithm runtime is 3 ± 0.5 min per patient. The system achieves a sensitivity of 83 % at 3.5 false positives per patient on average for blastic metastases and a sensitivity of 88 % at 3.7 false positives for lytic metastases. False positives appeared predominantly in the area of degenerative changes in the case of the blastic metastasis detector and in osteoporotic areas in the case of the lytic metastasis detector.

Conclusion

The CADe system reliably detects thoracolumbar spine metastases in real time. An additional study is planned to evaluate how the bone lesion CADe system improves radiologists’ accuracy and efficiency in a clinical setting.

Key Points

• Computer-aided detection (CADe) of bone metastases has been developed for spinal CT.

• The CADe system exhibits high sensitivity with a tolerable false-positive rate.

• Analysis of false-positive detection may further improve the system.

• CADe may reduce the number of missed spinal metastases at CT interpretation.

Multiple diagnostic task performance in CT examination of the chest

Objectives In three experiments, we studied the detection of multiple abnormality types using the satisfaction of search (SOS) paradigm, the provision of a computer-aided detection (CAD) of pulmonary nodules and a focused nodule detection task. Methods 51 chest CT examinations (24 that demonstrated subtle pulmonary nodules and 27 that demonstrated no pulmonary nodules) were read by 15 radiology residents and fellows under two experimental conditions: (1) when there were no other abnormalities present except test abnormalities in the exams (non-SOS condition), and (2) when other abnormalities were present in the exams (SOS condition). Trials from the two conditions were intermixed. Readers were invited to return for two sessions: one in which the SOS condition was repeated with a simulated CAD; another in which only the non-SOS condition was presented. Detection accuracy was measured using receiver operating characteristic (ROC) analysis. Results An SOS effect (reduced detection accuracy for the test nodules in the presence of the diverse added abnormalities) was not found. Average accuracy was much higher when the CAD prompt was provided, without cost in the detection of the added abnormalities. Accuracy for detecting nodules appearing without intermixed SOS trials was also substantially improved. Conclusions CT interpretation was highly task dependent. Nodule detection was poor in the general search task. Therefore, CAD may offer a greater performance improvement than demonstrated in experiments assessing CAD using focused search. The absence of SOS may be due to limited nodule detection even without other abnormalities. Advances in knowledge CAD prompts of nodules increase the detection accuracy of nodules and decrease the time to detection-without impairing the detection accuracy-of non-nodule abnormalities.

Satisfaction of search from detection of pulmonary nodules in computed tomography of the chest

RATIONALE AND OBJECTIVES

We tested whether satisfaction of search (SOS) effects that occur in computed tomography (CT) examination of the chest on detection of native abnormalities are produced by the addition of simulated pulmonary nodules.

MATERIALS AND METHODS

Two experiments were conducted. In the first experiment, 70 CT examinations, half that demonstrated diverse, subtle abnormalities and half that demonstrated no native lesions, were read by 18 radiology residents and fellows under two experimental conditions: presented with and without pulmonary nodules. In a second experiment, many of the examinations were replaced to include more salient native abnormalities. This set was read by 14 additional radiology residents and fellows. In both experiments, detection of the natural abnormalities was studied. Receiver operating characteristic (ROC) curve areas for each reader-treatment combination were estimated using empirical and proper ROC models. Additional analyses focused on decision thresholds and visual search time on abnormality-free CT slice ranges. Institutional review board approval and informed consent from 32 participants were obtained.

RESULTS

Observers more often missed diverse native abnormalities when pulmonary nodules were added, but also made fewer false-positive responses. There was no change in ROC area, but decision criteria grew more conservative. The SOS effect on decision thresholds was accompanied by a reduction in search time on abnormality-free CT slice ranges.

CONCLUSION

The SOS effect in CT examination of the chest is similar to that found in contrast examination of the abdomen, involving induced visual neglect.

Informatics in radiology: what can you see in a single glance and how might this guide visual search in medical images?

Diagnostic accuracy for radiologists is above that expected by chance when they are exposed to a chest radiograph for only one-fifth of a second, a period too brief for more than a single voluntary eye movement. How do radiologists glean information from a first glance at an image? It is thought that this expert impression of the gestalt of an image is related to the everyday, immediate visual understanding of the gist of a scene. Several high-speed mechanisms guide our search of complex images. Guidance by basic features (such as color) requires no learning, whereas guidance by complex scene properties is learned. It is probable that both hardwired guidance by basic features and learned guidance by scene structure become part of radiologists' expertise. Search in scenes may be best explained by a two-pathway model: Object recognition is performed via a selective pathway in which candidate targets must be individually selected for recognition. A second, nonselective pathway extracts information from global or statistical information without selecting specific objects. An appreciation of the role of nonselective processing may be particularly useful for understanding what separates novice from expert radiologists and could help establish new methods of physician training based on medical image perception.

vPSNR: a visualization-aware image fidelity metric tailored for diagnostic imaging

PURPOSE

Often, the large amounts of data generated in diagnostic imaging cause overload problems for IT systems and radiologists. This entails a need of effective use of data reduction beyond lossless levels, which, in turn, underlines the need to measure and control the image fidelity. Existing image fidelity metrics, however, fail to fully support important requirements from a modern clinical context: support for high-dimensional data, visualization awareness, and independence from the original data.

METHODS

We propose an image fidelity metric, called the visual peak signal-to-noise ratio (vPSNR), fulfilling the three main requirements. A series of image fidelity tests on CT data sets is employed. The impact of visualization transform (grayscale window) on diagnostic quality of irreversibly compressed data sets is evaluated through an observer-based study. In addition, several tests were performed demonstrating the benefits, limitations, and characteristics of vPSNR in different data reduction scenarios.

RESULTS

The visualization transform has a significant impact on diagnostic quality, and the vPSNR is capable of representing this effect. Moreover, the tests establish that the vPSNR is broadly applicable.

CONCLUSIONS

vPSNR fills a gap not served by existing image fidelity metrics, relevant for the clinical context. While vPSNR alone cannot fulfill all image fidelity needs, it can be a useful complement in a wide range of scenarios.

Multiple diagnostic task performance in CT examination of the chest

OBJECTIVES

In three experiments, we studied the detection of multiple abnormality types using the satisfaction of search (SOS) paradigm, the provision of a computer-aided detection (CAD) of pulmonary nodules and a focused nodule detection task.

METHODS

51 chest CT examinations (24 that demonstrated subtle pulmonary nodules and 27 that demonstrated no pulmonary nodules) were read by 15 radiology residents and fellows under two experimental conditions: (1) when there were no other abnormalities present except test abnormalities in the exams (non-SOS condition), and (2) when other abnormalities were present in the exams (SOS condition). Trials from the two conditions were intermixed. Readers were invited to return for two sessions: one in which the SOS condition was repeated with a simulated CAD; another in which only the non-SOS condition was presented. Detection accuracy was measured using receiver operating characteristic (ROC) analysis.

RESULTS

An SOS effect (reduced detection accuracy for the test nodules in the presence of the diverse added abnormalities) was not found. Average accuracy was much higher when the CAD prompt was provided, without cost in the detection of the added abnormalities. Accuracy for detecting nodules appearing without intermixed SOS trials was also substantially improved.

CONCLUSIONS

CT interpretation was highly task dependent. Nodule detection was poor in the general search task. Therefore, CAD may offer a greater performance improvement than demonstrated in experiments assessing CAD using focused search. The absence of SOS may be due to limited nodule detection even without other abnormalities. Advances in knowledge CAD prompts of nodules increase the detection accuracy of nodules and decrease the time to detection-without impairing the detection accuracy-of non-nodule abnormalities.

Accuracy of a remote eye tracker for radiologic observer studies: effects of calibration and recording environment

RATIONALE AND OBJECTIVES

To determine the accuracy and reproducibility of a remote eye-tracking system for studies of observer gaze while displaying volumetric chest computed tomography (CT) images.

MATERIALS AND METHODS

Four participants performed calibrations using three different gray-scale backgrounds (black, gray, and white). Each participant then observed a three-dimensional 10-point test pattern embedded in five Digital Imaging and Communications in Medicine (DICOM) datasets (test backgrounds): a full 190-section chest CT scan, 190 copies of a single chest CT section, and three 190-section datasets of homogeneous intensity (black, gray, and white).

RESULTS

Significant variances between participants, calibration backgrounds, and test backgrounds were observed. The least mean systematic error (deviation of recorded gaze position from target) was obtained when the calibration background and test background were black (27 pixels). Systematic error increased when displaying a test background that deviated from the calibration background intensity. Hence, the largest mean systematic error occurred when calibrating to a black background and displaying a white background (67 pixels). For complex chest CT volumes the white calibration background performed best (38 pixels). An angular analysis of the systematic error was performed and demonstrated that the systemic error primarily affects the vertical position of the estimated gaze position.

CONCLUSION

Our findings indicate a potential source of systematic error during gaze recording in a dynamic environment and highlight the importance of configuring the calibration procedure according to the brightness of the display. We recommend that investigators develop routines for postcalibration accuracy measurement and report the effective accuracy for the display environment in which the data are collected.

Multi-touch table system for medical visualization: application to orthopedic surgery planning

Medical imaging plays a central role in a vast range of healthcare practices. The usefulness of 3D visualizations has been demonstrated for many types of treatment planning. Nevertheless, full access to 3D renderings outside of the radiology department is still scarce even for many image-centric specialties. Our work stems from the hypothesis that this under-utilization is partly due to existing visualization systems not taking the prerequisites of this application domain fully into account. We have developed a medical visualization table intended to better fit the clinical reality. The overall design goals were two-fold: similarity to a real physical situation and a very low learning threshold. This paper describes the development of the visualization table with focus on key design decisions. The developed features include two novel interaction components for touch tables. A user study including five orthopedic surgeons demonstrates that the system is appropriate and useful for this application domain.