Role of Computer Aided Diagnosis (CAD) in the detection of pulmonary nodules on 64 row multi detector computed tomography.

AIMS AND OBJECTIVES: To determine the overall performance of an existing CAD algorithm with thin-section computed tomography (CT) in the detection of pulmonary nodules and to evaluate detection sensitivity at a varying range of nodule density, size, and location. MATERIALS AND METHODS: A cross-sectional prospective study was conducted on 20 patients with 322 suspected nodules who underwent diagnostic chest imaging using 64-row multi-detector CT. The examinations were evaluated on reconstructed images of 1.4 mm thickness and 0.7 mm interval. Detection of pulmonary nodules, initially by a radiologist of 2 years experience (RAD) and later by CAD lung nodule software was assessed. Then, CAD nodule candidates were accepted or rejected accordingly. Detected nodules were classified based on their size, density, and location. The performance of the RAD and CAD system was compared with the gold standard that is true nodules confirmed by consensus of senior RAD and CAD together. The overall sensitivity and false-positive (FP) rate of CAD software was calculated. OBSERVATIONS AND RESULTS: Of the 322 suspected nodules, 221 were classified as true nodules on the consensus of senior RAD and CAD together. Of the true nodules, the RAD detected 206 (93.2%) and 202 (91.4%) by the CAD. CAD and RAD together picked up more number of nodules than either CAD or RAD alone. Overall sensitivity for nodule detection with the CAD program was 91.4%, and FP detection per patient was 5.5%. The CAD showed comparatively higher sensitivity for nodules of size 4-10 mm (93.4%) and nodules in hilar (100%) and central (96.5%) location when compared to RAD's performance. CONCLUSION: CAD performance was high in detecting pulmonary nodules including the small size and low-density nodules. CAD even with relatively high FP rate, assists and improves RAD's performance as a second reader, especially for nodules located in the central and hilar region and for small nodules by saving RADs time.

Primary mesenteric fibromatosis: a case report with brief review of literature.

Although fibromatosis of the mesentery is a very rare locally aggressive benign condition, the uncertain treatment modalities, the natural history of the disease, and the other common differential diagnosis of the condition along with inexperience of the general clinicians with this disease pose a challenge to the professionals. The prolonged periods of stability and even regression in size of the tumor offer a hope for treatment. Accounting for 0.03 % of all neoplasms, it is also known as deep fibromatosis and desmoid tumor. Here, we discuss one case of primary mesenteric fibromatosis in a young male patient who presented to us with chronic abdominal pain after he was treated for acid peptic disease for the same at a local hospital. This case shows how management of this disease can be delayed due to unfamiliarity among clinicians of this condition. In our patient, a palliative surgical management plan was undertaken due to symptomatic mass in the abdomen, owing to unresectability.

Estimation of missing data using windowed linear prediction in laterally truncated projections in cone-beam CT.

In this paper, we address the reconstruction problem from laterally truncated helical cone-beam projections. The reconstruction problem from lateral truncation, though similar to that of interior radon problem, is slightly different from it as well as the local (lambda) tomography and pseudo-local tomography in the sense that we aim to reconstruct the entire object being scanned from a region-of-interest (ROI) scan data. The method proposed in this paper is a projection data completion approach followed by the use of any standard accurate FBP type reconstruction algorithm. In particular, we explore a windowed linear prediction (WLP) approach for data completion and compare the quality of reconstruction with the linear prediction (LP) technique proposed earlier.

Extractive spectrophotometric determination of Cobalt(II) in synthetic and pharmaceutical samples using Cyanex 923.

Cyanex 923 has been proposed as a sensitive analytical reagent for the direct extractive spectrophotometric determination of cobalt(II). Cobalt(II) forms a blue-colored complex with Cyanex 923 in the organic phase. The maximum absorbance of the complex is measured at 635 nm. Beer's law was obeyed in the range 58.9 - 589.0 microg of cobalt. The molar absorptivitiy and Sandell's sensitivity of the complex was calculated to be 6.79 x 10920 l mol(-1) cm(-1) and 0.088 microg cm(-2), respectively. The nature of the extracted species was found to be Co(SCN)2 x 2S. An excellent linearity with a correlation coefficient value of 0.999 was obtained for the Co(II)-Cyanex 923 complex. Stability and regeneration of the reagent (Cyanex 923) for reuse is the main advantage of the present method. The method was successfully applied to the determination of cobalt in synthetic mixtures and pharmaceutical samples was found to give values close to the actual ones. Standard alloy samples, such as high-speed tool BCS 484 and 485, have been tested for the determination of cobalt for the purpose of validating the present method. The results of the proposed method are comparable with atomic absorption spectrometry and were found to be in good agreement.

Vector-extrapolated fast maximum likelihood estimation algorithms for emission tomography.

A new class of fast maximum-likelihood estimation (MLE) algorithms for emission computed tomography (ECT) is developed. In these cyclic iterative algorithms, vector extrapolation techniques are integrated with the iterations in gradient-based MLE algorithms, with the objective of accelerating the convergence of the base iterations. This results in a substantial reduction in the effective number of base iterations required for obtaining an emission density estimate of specified quality. The mathematical theory behind the minimal polynomial and reduced rank vector extrapolation techniques, in the context of emission tomography, is presented. These extrapolation techniques are implemented in a positron emission tomography system. The new algorithms are evaluated using computer experiments, with measurements taken from simulated phantoms. It is shown that, with minimal additional computations, the proposed approach results in substantial improvement in reconstruction.

Detection of edges from projections.

In a number of applications of computerized tomography, the ultimate goal is to detect and characterize objects within a cross section. Detection of edges of different contrast regions yields the required information. The problem of detecting edges from projection data is addressed. It is shown that the class of linear edge detection operators used on images can be used for detection of edges directly from projection data. This not only reduces the computational burden but also avoids the difficulties of postprocessing a reconstructed image. This is accomplished by a convolution backprojection operation. For example, with the Marr-Hildreth edge detection operator, the filtering function that is to be used on the projection data is the Radon transform of the Laplacian of the 2-D Gaussian function which is combined with the reconstruction filter. Simulation results showing the efficacy of the proposed method and a comparison with edges detected from the reconstructed image are presented.