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Biomedical subjects

Gordon Gamsu

Publications and source records attributed to Gordon Gamsu.

5 recordsLinked to original sources
PubMed2004

Reduced radiation for adult thoracic CT: a practical approach.

PURPOSE: To determine whether radiation dose to patients can be reduced for clinical thoracic CT scans without loss of diagnostic information. MATERIALS AND METHODS: One hundred consecutive patients having clinical CT examinations of the thorax were included. The patients were divided into 4 groups, and the mAs setting determined from the patient's weight as follows (max. 300 and min. 100 mAs): group 1: mAs = weight in lbs. rounded to the next multiple of 10, plus 30 (mAs = wtR + 30); group 2: mAs = weight rounded to the next multiple of 10 (mAs = wtR); group 3: mAs = weight rounded to the next multiple of 10, minus 20 (mAs = wtR - 20); group 4: mAs = weight rounded to the next multiple of 10, minus 30 (mAs = wtR - 30). The neck, mediastinum, lungs, and upper abdomen were assessed for quality on a 4 point scale. RESULTS: Only at the lowest mAs levels did we see some degradation of image quality in the lower neck with an increase in streak artifacts. The other body regions all showed good or excellent image quality. CONCLUSIONS: Radiation dosage can be reduced by more than half without loss of diagnostic quality, using this simple formula: mAs = weight in lbs. rounded to the next multiple of 10 minus 30 (mAs = wtR - 30).

Analysis of Variance↗
PubMed2003

Picture archiving and communication systems (PACS).

Over the past 2 decades, groups of computer scientists, electronic design engineers, and physicians, in universities and industry, have worked to achieve an electronic environment for the practice of medicine and radiology. The radiology component of this revolution is often called PACS (picture archiving and communication systems). More recently it has become evident that the efficiencies and cost savings of PACS are realized when they are part of an enterprise-wide electronic medical record. The installation of PACS requires careful planning by all the various stakeholds over many months prior to installation. All of the users must be aware of the initial disruption that will occur as they become familiar with the systems. Modern fourth generation PACS is linked to radiology and hospital information systems. The PACS consist of electronic acquisition sites-a robust network intelligently managed by a server, multiple viewing sites, and an archive. The details of how these are linked and their workflow analysis determines the success of PACS. PACS evolves over time, components are frequently replaced, and so the users must expect continuous learning about new updates and improved functionality. The digital medical revolution is rapidly being adopted in many medical centers, improving patient care and the success of the institution.

Radiology Information Systems↗
PubMed2003

Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm.

Increasingly, computed tomography (CT) offers higher resolution and faster acquisition times. This has resulted in the opportunity to detect small lung nodules, which may represent lung cancers at earlier and potentially more curable stages. However, in the current clinical practice, hundreds of such thin-sectional CT images are generated for each patient and are evaluated by a radiologist in the traditional sense of looking at each image in the axial mode. This results in the potential to miss small nodules and thus potentially miss a cancer. In this paper, we present a computerized method for automated identification of small lung nodules on multislice CT (MSCT) images. The method consists of three steps: (i) separation of the lungs from the other anatomic structures, (ii) detection of nodule candidates in the extracted lungs, and (iii) reduction of false-positives among the detected nodule candidates. A three-dimensional lung mask can be extracted by analyzing density histogram of volumetric chest images followed by a morphological operation. Higher density structures including nodules scattered throughout the lungs can be identified by using a local density maximum algorithm. Information about nodules such as size and compact shape are then incorporated into the algorithm to reduce the detected nodule candidates which are not likely to be nodules. The method was applied to the detection of computer simulated small lung nodules (2 to 7 mm in diameter) and achieved a sensitivity of 84.2% with, on average, five false-positive results per scan. The preliminary results demonstrate the potential of this technique for assisting the detection of small nodules from chest MSCT images.

Algorithms↗
PubMedDate not supplied

Lung nodule detection on chest CT: evaluation of a computer-aided detection (CAD) system.

OBJECTIVE: To evaluate the capacity of a computer-aided detection (CAD) system to detect lung nodules in clinical chest CT. MATERIALS AND METHODS: A total of 210 consecutive clinical chest CT scans and their reports were reviewed by two chest radiologists and 70 were selected (33 without nodules and 37 with 1-6 nodules, 4-15.4 mm in diameter). The CAD system (ImageChecker CT LN-1000) developed by R2 Technology, Inc. (Sunnyvale, CA) was used. Its algorithm was designed to detect nodules with a diameter of 4-20 mm. The two chest radiologists working with the CAD system detected a total of 78 nodules. These 78 nodules form the database for this study. Four independent observers interpreted the studies with and without the CAD system. RESULTS: The detection rates of the four independent observers without CAD were 81% (63/78), 85% (66/78), 83% (65/78), and 83% (65/78), respectively. With CAD their rates were 87% (68/78), 85% (66/78), 86% (67/78), and 85% (66/78), respectively. The differences between these two sets of detection rates did not reach statistical significance. In addition, CAD detected eight nodules that were not mentioned in the original clinical radiology reports. The CAD system produced 1.56 false-positive nodules per CT study. The four test observers had 0, 0.1, 0.17, and 0.26 false-positive results per study without CAD and 0.07, 0.2, 0.23, and 0.39 with CAD, respectively. CONCLUSION: The CAD system can assist radiologists in detecting pulmonary nodules in chest CT, but with a potential increase in their false positive rates. Technological improvements to the system could increase the sensitivity and specificity for the detection of pulmonary nodules and reduce these false-positive results.

Diagnosis, Computer-Assisted↗