Emergence of teleradiology, PACS, and other radiology IT solutions in Acta Radiologica

For this historical review, we searched a database containing all the articles published in Acta Radiologica during its 100-year history to find those on the use of information technology (IT) in radiology. After reading the full texts, we selected the presented articles according to major radiology IT domains such as teleradiology, picture archiving and communication systems, image processing, image analysis, and computer-aided diagnostics in order to describe the development as it appeared in the journal. Publications generally follow IT megatrends, but because the contents of Acta Radiologica are mainly clinically oriented, some technology achievements appear later than they do in journals discussing mainly imaging informatics topics.

Diagnostic performance evaluation of a computer-aided simple triage system for coronary CT angiography in patients with intermediate risk for acute coronary syndrome

RATIONALE AND OBJECTIVES

Given the significance of coronary artery disease as the most important socioeconomic health care problem in the Western World, the application of computer-aided simple triage (CAST) systems to this disease would be desirable.

MATERIALS AND METHODS

In total, 93 patients with acute chest pain and an intermediate risk score for acute coronary syndrome underwent coronary computed tomography angiography (cCTA). Among those, 74 were of adequate image quality for automated analysis by a commercially available CAST system (COR Analyzer, RCADIA, Haifa, Israel). CAST findings were compared to human expert interpretation for the detection of significant stenosis (≥50%) in the left main, left anterior descending, circumflex, right coronary artery, or arterial branches. Further, one inexperienced observer evaluated all studies for significant stenoses alone and after 1 month guided by a CAST system as an initial read.

RESULTS

Human expert interpretation identified 37/74 patients with stenosis ≥50%, whereas the CAST detected 45 patients. The CAST system demonstrated a sensitivity of 100%/79% and a specificity of 78%/89% on a per-patient/per-vessel level, respectively. With CAST, the inexperienced readers' per-vessel sensitivity and positive predictive values significantly improved (P = .011, P = .009) from 69% and 41% to 91% and 74%, respectively.

CONCLUSIONS

The investigated CAST system for automatic stenosis detection can accurately identify patients with coronary artery stenosis ≥50% and may be of use as initial interpretation and triage of cCTA studies as well as a second reader for inexperienced readers, in absence of expert readers.

Automated computer-aided stenosis detection at coronary CT angiography: initial experience

OBJECTIVE

To evaluate the performance of a computer-aided algorithm for automated stenosis detection at coronary CT angiography (cCTA).

METHODS

We investigated 59 patients (38 men, mean age 58 +/- 12 years) who underwent cCTA and quantitative coronary angiography (QCA). All cCTA data sets were analyzed using a software algorithm for automated, without human interaction, detection of coronary artery stenosis. The performance of the algorithm for detection of stenosis of 50% or more was compared with QCA.

RESULTS

QCA revealed a total of 38 stenoses of 50% or more of which the algorithm correctly identified 28 (74%). Overall, the automated detection algorithm had 74%/100% sensitivity, 83%/65% specificity, 46%/58% positive predictive value, and 94%/100% negative predictive value for diagnosing stenosis of 50% or more on per-vessel/per-patient analysis, respectively. There were 33 false positive detection marks (average 0.56/patient), of which 19 were associated with stenotic lesions of less than 50% on QCA and 14 were not associated with an atherosclerotic surrogate.

CONCLUSION

Compared with QCA, the automated detection algorithm evaluated has relatively high accuracy for diagnosing significant coronary artery stenosis at cCTA. If used as a second reader, the high negative predictive value may further enhance the confidence of excluding significant stenosis based on a normal or near-normal cCTA study.

A new method for automatic identification of coronary arteries in standard biplane angiograms

A new computerized method for analyzing biplane coronary arteriograms was proposed. The method extracted the vessels in an image, detected branching points, and identified vessel segments according to their anatomical names. The method was applied to a pair of orthogonal projection images acquired in the right anterior oblique and left anterior oblique views. It recognized the corresponding vessel segments in the images and labeled them correctly by their anatomical names. For diameter measurement, a combined first and second derivative filter process was applied to the images along the skeleton lines of the extracted vessel segments. We also designed a method of representation in which vessel diameters measured in the two projection images could be displayed in a single image.