Murray's law has been generalized to provide morphometric relationships among various subtrees as well as between a feeding segment and the subtree it perfuses. The equivalent resistance of each subtree is empirically determined to be proportional to the cube of a subtree's cumulative arterial length (L) and inversely proportional to a subtree's arterial volume (V) raised to a power of approximately 2.6. This relationship, along with a minimization of a cost function, and a linearity assumption between flow and cumulative arterial length, provides a power law relationship between V and L. These results, in conjunction with conservation of energy, yield relationships between the diameter of a segment and the length of its distal subtree. The relationships were tested based on a complete set of anatomical data of the coronary arterial trees using two models. The first model, called the truncated tree model, is an actual reconstruction of the coronary arterial tree down to 500 microm in diameter. The second model, called the symmetric tree model, satisfies all mean anatomical data down to the capillary vessels. Our results show very good agreement between the theoretical formulation and the measured anatomical data, which may provide insight into the design of the coronary arterial tree. Furthermore, the established relationships between the various morphometric parameters of the truncated tree model may provide a basis for assessing the extent of diffuse coronary artery disease.
Coronary artery disease is a major cause of death in women. Breast arterial calcifications (BACs), detected in mam-mograms, can be useful risk markers associated with the disease. We investigate the feasibility of automated and accurate detection of BACs in mammograms for risk assessment of coronary artery disease. We develop a twelve-layer convolutional neural network to discriminate BAC from non-BAC and apply a pixel-wise, patch-based procedure for BAC detection. To assess the performance of the system, we conduct a reader study to provide ground-truth information using the consensus of human expert radiologists. We evaluate the performance using a set of 840 full-field digital mammograms from 210 cases, using both free-response receiver operating characteristic (FROC) analysis and calcium mass quantification analysis. The FROC analysis shows that the deep learning approach achieves a level of detection similar to the human experts. The calcium mass quantification analysis shows that the inferred calcium mass is close to the ground truth, with a linear regression between them yielding a coefficient of determination of 96.24%. Taken together, these results suggest that deep learning can be used effectively to develop an automated system for BAC detection in mammograms to help identify and assess patients with cardiovascular risks.
Low doses (< 1 cGy) of 60 kVp X-rays protect HeLa x skin fibroblast human hybrid cells against neoplastic transformation in vitro.
The induction of neoplastic transformation in vitro after exposure of HeLa x skin fibroblast hybrid cells to low doses of mammography-energy (28 kVp) X rays has been studied. The data indicate no evidence of an increase in transformation frequency over the range 0.05 to 22 cGy, and doses in the range 0.05 to 1.1 cGy may result in suppression of transformation frequencies to levels below that seen spontaneously. This finding is not consistent with a linear, no-threshold dose- response curve. The dose range at which possible suppression is evident includes doses typically experienced in mammographic examination of the human breast. Experiments are described that attempt to elucidate any possible role of bystander effects in modulating this low-dose radiation response. Not unexpectedly, inhibition of gap junction intercellular communication (GJIC) with the inhibitor lindane did not result in any significant alteration of transformation frequencies seen at doses of 0.27 or 5.4 cGy in these subconfluent cultures. Furthermore, no evidence of a bystander effect associated with factors secreted into the extracellular medium was seen in medium transfer experiments. Thus, in this system and under the experimental conditions used, bystander effects would not appear to be playing a major role in modulating the shape of the dose-response curve.
Purpose To retrospectively validate a first-pass analysis (FPA) technique that combines computed tomographic (CT) angiography and dynamic CT perfusion measurement into one low-dose examination. Materials and Methods The study was approved by the animal care committee. The FPA technique was retrospectively validated in six swine (mean weight, 37.3 kg ± 7.5 [standard deviation]) between April 2015 and October 2016. Four to five intermediate-severity stenoses were generated in the left anterior descending artery (LAD), and 20 contrast material-enhanced volume scans were acquired per stenosis. All volume scans were used for maximum slope model (MSM) perfusion measurement, but only two volume scans were used for FPA perfusion measurement. Perfusion measurements in the LAD, left circumflex artery (LCx), right coronary artery, and all three coronary arteries combined were compared with microsphere perfusion measurements by using regression, root-mean-square error, root-mean-square deviation, Lin concordance correlation, and diagnostic outcomes analysis. The CT dose index and size-specific dose estimate per two-volume FPA perfusion measurement were also determined. Results FPA and MSM perfusion measurements (P and P) in all three coronary arteries combined were related to reference standard microsphere perfusion measurements (P), as follows: P = 1.02 P + 0.11 (r = 0.96) and P = 0.28 P + 0.23 (r = 0.89). The CT dose index and size-specific dose estimate per two-volume FPA perfusion measurement were 10.8 and 17.8 mGy, respectively. Conclusion The FPA technique was retrospectively validated in a swine model and has the potential to be used for accurate, low-dose vessel-specific morphologic and physiologic assessment of coronary artery disease. RSNA, 2017.
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