A mathematical algorithm that computes breast cancer sizes and doubling times detected by screening.

This paper presents a mathematical algorithm that computes the sizes and growth rates of breast cancer detected in a hypothetical population that is screened for the disease. The algorithm works by simulating the outcomes of the hypothetical population twice, first without screening and then with screening. The simulation without screening relies on an underlying model of the natural history of the disease. The simulation with screening uses this natural history model to track the growth of breast tumors backwards in the time starting from the time they would have been detected without screening. The method of tracking tumor growth backward in time is different from methods that track tumor growth forward in time by starting from an estimated time of tumor onset. The screening algorithm combines the natural history model, the method tracking of tumor growth backward in time, the age group, the interval between screening exams, and the detection threshold of the screening exam to compute the joint distribution of tumor size and growth rate among screen-detected and interval patients. The algorithm also computes the sensitivity and leadtime distribution. It allows for arbitrary age groups, detection thresholds and screening intervals and may contribute to the design of future screening trials.

Digital storage phosphor chest radiography: an ROC study of the effect of 2K versus 4K matrix size on observer performance.

PURPOSE: To compare observer performance in the detection of abnormalities on 1,760 x 2,140 matrix (2K) and 3,520 x 4,280 matrix (4K) digital storage phosphor chest radiographs. MATERIALS AND METHODS: One hundred sixty patients who underwent dedicated computed tomography (CT) of the thorax were prospectively recruited into the study. Posteroanterior and lateral computed radiographs of the chest were acquired in each patient and printed in 2K and 4K formats. Six radiologists independently analyzed the hard-copy images and scored the presence of parenchymal (opacities 2 cm, and subtle interstitial), mediastinal, and pleural abnormalities on a five-point confidence scale. With CT as the reference standard, observer performance tests were carried out by using receiver operating characteristic (ROC) analysis. RESULTS: Analysis of averaged observer performance showed 2K and 4K images were equally effective in detection of all three groups of abnormalities. In the detection of the three subtypes of parenchymal abnormalities, there were no significant differences in averaged performance between the 2K and 4K formats (area below ROC curve [A(z)] values: opacities 2 cm, 0.86 +/-.025 and 0.85 +/- 0.030; subtle interstitial abnormalities, 0.73 +/- 0.041 and 0.72 +/- 0.041). Averaged performance in detection of mediastinal and pleural abnormalities was equivalent (A(z) values: mediastinal, 0.70 +/- 0.046 and 0.73 +/- 0.033; pleural, 0.85 +/- 0.032 and 0.86 +/- 0.033). CONCLUSION: Observer performance in detection of parenchymal, mediastinal, and pleural abnormalities was not significantly different on 2K and 4K storage phosphor chest radiographs.

Breast disease: dynamic spiral MR imaging.

PURPOSE: To compare various subjective, empiric, and pharmacokinetic methods for interpreting findings at dynamic magnetic resonance (MR) imaging of the breast. MATERIALS AND METHODS: Dynamic spiral breast MR imaging was performed in 52 women suspected of having or with known breast disease. Gadolinium-enhanced images were obtained at 12 locations through the whole breast every 7.8 seconds for 8.5 minutes after bolus injection of contrast material. Time-signal intensity curves from regions of interest corresponding to 57 pathologically proved lesions were analyzed by means of a two-compartment pharmacokinetic model, and the diagnostic performance of various parameters was analyzed. RESULTS: Findings included invasive carcinoma in 17 patients, isolated ductal carcinoma in situ (DCIS) in six, and benign lesions in 34. Although some overlap between carcinomas and benign diagnoses was noted for all parameters, receiver operating characteristic analysis indicated that the exchange rate constant had the greatest overall ability to discriminate benign and malignant disease. The elimination rate constant and washout were the most specific parameters. The exchange rate constant, wash-in, and extrapolation point were the most sensitive parameters. DCIS was not consistently distinguished from benign disease with any method. CONCLUSION: Dynamic spiral breast MR imaging proved an excellent method with which to collect contrast enhancement data rapidly enough that accurate comparisons can be made between many analytic methods.

MRS imaging using anatomically based k-space sampling and extrapolation.

A comprehensive strategy for the acquisition, reconstruction, and postprocessing of MR spectroscopic images is presented. The reconstruction algorithm is the most critical component of this strategy. It is assumes that the desired image is spatially bounded, meaning that the desired image contains an object that is surrounded by a background of zeros. The reconstruction algorithm relies on prior knowledge of the background zeros for k-space extrapolation. This algorithm is a good candidate for proton MR spectroscopic image reconstruction because these images are often spatially bounded and prior knowledge of the zeros is easily obtained from a rapidly acquired high resolution conventional MRI. Although the reconstruction algorithm can be used with the standard 3DFT k-space distribution, a distribution that relies on anatomical features that are likely to occur in the spectroscopic image can produce better results. Prior knowledge of these anatomical features is also obtained from a conventional MRI. Finally, the postprocessing component of this strategy is valuable for reducing subcutaneous lipid contamination. Overall, the comprehensive approach presented here produces images that are better resolved than standard approaches without increasing acquisition time or reducing SNR. Examples using NAA data are provided.

Spectral extrapolation of spatially bounded images [MRI application].

A spectral extrapolation algorithm for spatially bounded images is presented. An image is said to be spatially bounded when it is confined to a closed region and is surrounded by a background of zeros. With prior knowledge of the spatial domain zeros, the extrapolation algorithm extends the image's spectrum beyond a known interval of low-frequency components. The result, which is referred to as the finite support solution, has space variant resolution; features near the edge of the support region are better resolved than those in the center. The resolution of the finite support solution is discussed as a function of the number of known spatial zeros and known spectral components. A regularized version of the finite support solution is included for handling the case where the known spectral components are noisy. For both the noiseless and noisy cases, the resolution of the finite support solution is measured in terms of its impulse response characteristics, and compared to the resolution of the zerofilled and Nyquist solutions. The finite support solution is superior to the zerofilled solution for both the noisy and noiseless data cases. When compared to the Nyquist solution, the finite support solution may be preferred in the noisy data case. Examples using medical image data are provided.