Quantitative tissue motion analysis of digitized m-mode images: Gestational differences of fetal lung

Quantitative analysis of transmitted cardiac motion in fetal lung is evaluated by applying correlation techniques to digitized M-mode images in 21 patients, subdivided into two subgroups by gestational age: (1) 25-30 weeks (11 patients), and (11) >=35 weeks (10 patients). The corresponding numbers of M-mode images analyzed for each group are 23 and 18, respectively. This partition is expected to reflect functionally "immature" and "mature" lungs. The estimated maximum mean radial deformation per unit epicardial excursion, r I = 0.79 +/- 0.11 (sem) and r II = 0.62 +/- 0.13 (sem). The analysis presented, albeit in a limited population, is indicative of a trend in accordance with qualitative observations of Birnholz and Farrell (1985). M-mode analysis, as indicated by Adler et al. (1989) is a potentially useful technique to quantify such tissue motion.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28898/1/0000735.pd

Quantitative assessment of cartilage surface roughness in osteoarthritis using high frequency ultrasound

Osteoarthritis (OA) is a common disease which affects nearly 50% of people over age 60. Histologic evaluation suggests that fibrillations ~20-150 [mu]m are among the earliest changes in the articular cartilage. We propose a technique to quantify these surface fibrillatory changes in osteoarthritic articular cartilage by considering the angular distribution of the envelope-detected backscattered pressure field from an incident 30-MHz focused transducer. The angular distribution of the scattered acoustic field from an insonifying source will directly relate to the distribution of surface fibrillatory changes. Data are presented for three different grades (400, 500 and 600 grit) of commercially available emory paper and three samples of osteoarthritic femoral head articular cartilage, which were visually assessed as having smooth, intermediate and rough surfaces, respectively. Our preliminary results indicate a probable monotonic relationship between articular cartilage roughening and the degree of broadening in the angle-dependent pressure amplitude. When applied to the emory paper, the technique indicates sensitivity to differences as small as ~5-10 [mu]m in mean roughness. This procedure may provide an extremely sensitive and reproducible means of quantifying and following the cartilage changes observed in early osteoarthritis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30279/1/0000680.pd

The Lung Image Database Consortium (LIDC):ensuring the integrity of expert-defined "truth"

RATIONALE AND OBJECTIVES: Computer-aided diagnostic (CAD) systems fundamentally require the opinions of expert human observers to establish “truth” for algorithm development, training, and testing. The integrity of this “truth,” however, must be established before investigators commit to this “gold standard” as the basis for their research. The purpose of this study was to develop a quality assurance (QA) model as an integral component of the “truth” collection process concerning the location and spatial extent of lung nodules observed on computed tomography (CT) scans to be included in the Lung Image Database Consortium (LIDC) public database. MATERIALS AND METHODS: One hundred CT scans were interpreted by four radiologists through a two-phase process. For the first of these reads (the “blinded read phase”), radiologists independently identified and annotated lesions, assigning each to one of three categories: “nodule ≥ 3mm,” “nodule < 3mm,” or “non-nodule ≥ 3mm.” For the second read (the “unblinded read phase”), the same radiologists independently evaluated the same CT scans but with all of the annotations from the previously performed blinded reads presented; each radiologist could add marks, edit or delete their own marks, change the lesion category of their own marks, or leave their marks unchanged. The post-unblinded-read set of marks was grouped into discrete nodules and subjected to the QA process, which consisted of (1) identification of potential errors introduced during the complete image annotation process (such as two marks on what appears to be a single lesion or an incomplete nodule contour) and (2) correction of those errors. Seven categories of potential error were defined; any nodule with a mark that satisfied the criterion for one of these categories was referred to the radiologist who assigned that mark for either correction or confirmation that the mark was intentional. RESULTS: A total of 105 QA issues were identified across 45 (45.0%) of the 100 CT scans. Radiologist review resulted in modifications to 101 (96.2%) of these potential errors. Twenty-one lesions erroneously marked as lung nodules after the unblinded reads had this designation removed through the QA process. CONCLUSION: The establishment of “truth” must incorporate a QA process to guarantee the integrity of the datasets that will provide the basis for the development, training, and testing of CAD systems

Evaluation of lung MDCT nodule annotation across radiologists and methods

RATIONALE AND OBJECTIVES: Integral to the mission of the National Institutes of Health–sponsored Lung Imaging Database Consortium is the accurate definition of the spatial location of pulmonary nodules. Because the majority of small lung nodules are not resected, a reference standard from histopathology is generally unavailable. Thus assessing the source of variability in defining the spatial location of lung nodules by expert radiologists using different software tools as an alternative form of truth is necessary. MATERIALS AND METHODS: The relative differences in performance of six radiologists each applying three annotation methods to the task of defining the spatial extent of 23 different lung nodules were evaluated. The variability of radiologists’ spatial definitions for a nodule was measured using both volumes and probability maps (p-map). Results were analyzed using a linear mixed-effects model that included nested random effects. RESULTS: Across the combination of all nodules, volume and p-map model parameters were found to be significant at P < .05 for all methods, all radiologists, and all second-order interactions except one. The radiologist and methods variables accounted for 15% and 3.5% of the total p-map variance, respectively, and 40.4% and 31.1% of the total volume variance, respectively. CONCLUSION: Radiologists represent the major source of variance as compared with drawing tools independent of drawing metric used. Although the random noise component is larger for the p-map analysis than for volume estimation, the p-map analysis appears to have more power to detect differences in radiologist-method combinations. The standard deviation of the volume measurement task appears to be proportional to nodule volume

Robust three‐dimensional object definition in CT and MRI

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134836/1/mp7686.pd