High-Level and Hierarchical Test Sequence Generation

Test generation at the gate-level produces high-quality tests but is computationally expensive in the case of large systems. Recently, several research efforts have investigated the possibility of devising test generation methods and tools to work on high-level descriptions. The goal of these methods is to provide the designers with testability information and test sequences in the early design stages. The cost for generating test sequences in the high abstraction levels is often lower than that for generating test sequences at the gate-level, with comparable or even higher fault coverage. This paper first analyses several high-level fault models in order to select the most suitable one for estimating the testability of circuits by reasoning on their behavioral descriptions and for guiding the test generation process at the behavioral level. We assess then the effectiveness of high-level test generation with a simple ATPG algorithm, and present a novel highlevel hierarchical test generation approach to improve the results obtained by a pure high-level test generator

Testability in Co-design Environments

This document describes the process we followed for assessing the feasibility and effectiveness of high-level test vector generation. An experimental analysis of the available high-level fault models is first reported, whose purpose is to identify a reference fault model that could be fruitfully used for evaluating testability of circuits by reasoning on their behavior, only. A prototypical high-level fault simulation tool is also described, whose purpose is to support the fault models analysis. Finally, a test generation algorithm is presented that generates high quality test vectors by exploiting the selected fault model and the described high-level fault simulator

Breast magnetic resonance imaging (MRI) surveillance in breast cancer survivors

Purpose: As the breast cancer survivor population increases, the topic of screening these women for recurrences is increasingly relevant. In our institution, we use both breast MRI and mammography in the surveillance of breast cancer survivors, although little data exists on the use of MRI in this setting. We present a retrospective analysis of our experience and compare the sensitivity and specificity of MRI vs. mammography in this setting. Methods: We identified women under 65 with a history of breast cancer and at least one follow-up MRI performed along with a mammogram done within 6 months of the MRI. We compared the outcomes of MRI and mammography in terms of biopsies performed as well as in detection of new cancers. Results: Of 617 charts reviewed, 249 patients met inclusion criteria, with 571 paired MRI/mammogram results. There were 27 biopsies performed due to MRI findings alone, 10 done due to mammographic findings alone, and 15 done based on abnormalities seen on both imaging modalities. There were 8 malignancies identified based on an abnormal MRI, 3 detected on both MRI and mammography, and none identified via mammography alone. Overall, MRI had a sensitivity of 84.6% (the 95% CI 54.6-98.1) and a specificity of 95.3% (the 95% CI 93.3-96.9); mammography a sensitivity of 23.1% (the 95% CI 5.0-53.8), and a specificity of 96.4% (the 95% CI 94.5-97.8). Conclusions: Breast MRI is a useful surveillance modality in breast cancer survivors and may be more sensitive at detecting recurrences than mammography alone in this population