Eric E. Sauerbrei

The association between preterm newborn hypotension and hypoxemia and outcome during the first year

Ninety‐eight newborn infants, less than 34 weeks at birth, were studied to examine the relationship between newborn hypotension and hypoxemia and brain damage. Heart rate, blood pressure and oxygen tension were recorded continuously during the 96 h following delivery. Outcome measures included neuropathology in children who died, and motor and cognitive development at one year corrected age in children who survived. There were 22 children with a minor and 27 with a major abnormal outcome. There was a relationship between newborn hypotension, newborn hypoxemia and low birth weight, and a major abnormal outcome. The probability of a major abnormal outcome increased from 8% in newborns with no hypotension or hypoxemia, to 53% in children with both hypotension and hypoxemia. These findings support the contention that combinations of sustained newborn hypotension and hypoxemia are important factors in the development of brain damage, accounting for a major abnormal outcome.

Augmenting Detection of Prostate Cancer in Transrectal Ultrasound Images Using SVM and RF Time Series

We propose a novel and accurate method based on ultrasound RF time series analysis and an extended version of support vector machine classification for generating probabilistic cancer maps that can augment ultrasound images of prostate and enhance the biopsy process. To form the RF time series, we record sequential ultrasound RF echoes backscattered from tissue while the imaging probe and the tissue are stationary in position. We show that RF time series acquired from agar-gelatin-based tissue mimicking phantoms, with difference only in the size of cell-mimicking microscopic glass beads, are distinguishable with statistically reliable accuracies up to 80.5%. This fact indicates that the differences in tissue microstructures affect the ultrasound RF time series features. Based on this phenomenon, in an ex vivo study involving 35 prostate specimens, we show that the features extracted from RF time series are significantly more accurate and sensitive compared to two other established categories of ultrasound-based tissue typing methods. We report an area under receiver operating characteristic curve of 0.95 in tenfold cross validation and 0.82 in leave-one-patient-out cross validation for detection of prostate cancer.

Maternal, fetal, and newborn complications associated with newborn intracranial hemorrhage

Two hundred twenty newborn infants with one or more fetal or newborn complications and 54 newborn infants without fetal or newborn complications were prospectively studied to assess the relationship between maternal, obstetric, fetal, and newborn complications and intracranial hemorrhage. Intracranial hemorrhage occurred in 47 newborn infants with fetal or newborn complications (21%) and in one infant with no fetal or newborn complications (2%). Maternal and obstetric complications, duration of labor, and mode of delivery were not associated with intracranial hemorrhage. Newborn immaturity at delivery is an important factor in the occurrence of intracranial hemorrhage. There is little evidence that fetal hypoxia is a contributing factor. Severe respiratory complications and major infections are newborn complications associated with intracranial hemorrhage.

Sealed rupture of abdominal aortic aneurysm imitating metastatic carcinoma

We report the case of a 53-year-old man in whom rupture of an abdominal aortic aneurysm associated with a large retroperitoneal hematoma caused pressure erosion and destruction of lumbar vertebrae. The bone destruction was thought to represent metastasis from a bronchogenic carcinoma. Only after 18 months of gradual clinical deterioration with presumed metastatic cancer to the lumbar spine was the true nature of his disease recognized. After the ruptured aortic aneurysm was repaired, rapid recovery occurred and the retroperitoneal hematoma gradually was resorbed.

Motor and cognitive development of infants with intraventricular hemorrhage, ventriculomegaly, or periventricular parenchymal lesions

Two hundred twenty-six moderate- or high-risk newborn infants were studied to examine the relationship between ultrasound findings in the newborn period and at 6 months and motor and cognitive deficits at 1 year. A three-part classification of abnormal ultrasound findings was used to grade intraventricular hemorrhage, ventriculomegaly, and parenchymal lesions. Abnormal ultrasound findings were observed in 48 infants, of whom 21 had intraventricular hemorrhage, 18 persistent ventriculomegaly, and nine parenchymal lesions. The incidence of deficits was as follows: normal ultrasound examination, 20%; intraventricular hemorrhage, 33%; persistent ventriculomegaly, 67%; and parenchymal lesions, 89%. The present study indicates that serial ultrasound examinations are indicated in preterm newborn infants less than 1500 gm and in selected newborn infants at risk and greater than 1500 gm at birth. The three-part classification of abnormal ultrasound findings should be used because of the predictive significance of persistent ventriculomegaly and parenchymal lesions for motor and cognitive deficits at 1 year of age.

Detection of Prostate Cancer from RF Ultrasound Echo Signals Using Fractal Analysis

In this paper we propose a new feature, average Higuchi dimension of RF time series (AHDRFT), for detection of prostate cancer using ultrasound data. The proposed feature is extracted from RF echo signals acquired from prostate tissue in an in vitro setting and is used in combination with texture features extracted from the corresponding B-scan images. In a novel approach towards RF data collection, we continuously recorded backscattered echoes from the prostate tissue to acquire time series of the RF signals. We also collected B-scan images and performed a detailed histopathologic analysis on the tissue. To compute AHDRFT, the Higuchi fractal dimensions of the RF time series were averaged over a region of interest. AHDRFT and texture features extracted from corresponding B-scan images were used to classify regions of interest, as small as 0.028 cm of the prostate tissue in cancerous and normal classes. We validated the results based on our histopathologic maps. A combination of image statistical moments and features extracted from co-occurrence matrices of the B-scan images resulted in classification accuracy of around 87%. When AHDRFT was added to the feature vectors, the classification accuracy was consistently over 95% with best results of over 99% accuracy. Our results show that the RF time series backscattered from prostate tissues contain information that can be used for detection of prostate cancer

Evaluation of techniques for induction of ovulation in outpatients employing pulsatile gonadotropin releasing hormone

The efficacy, safety, and patients acceptance of intravenous and subcutaneous therapy with gonadotropin releasing hormone (GnRH) with the use of either an autoinfusion pump or a smaller manual pen pump delivery system have been evaluated during the induction of ovulation in outpatients with hypothalamic hypogonadotropic amenorrhea. An ovulatory response to intravenous GnRH was highly reproducible, even at low doses, and complications associated with the intravenous route of delivery were infrequent and readily treated. Although no complications were observed during subcutaneous GnRH therapy, the response to the subcutaneous route of delivery was unpredictable over a wide range of dosages and delivery schedules. The pen pump was rated more highly by subjects than the autoinfusion pump, because the pen pump was both light and inconspicuous.

Discrete Fourier Analysis of Ultrasound RF Time Series for Detection of Prostate Cancer

In this paper, we demonstrate that a set of six features extracted from the discrete Fourier transform of ultrasound radio-frequency (RF) time series can be used to detect prostate cancer with high sensitivity and specificity. Ultrasound RF time series refer to a series of echoes received from one spatial location of tissue while the imaging probe and the tissue are fixed in position. Our previous investigations have shown that at least one feature, fractal dimension, of these signals demonstrates strong correlation with the tissue microstructure. In the current paper, six new features that represent the frequency spectrum of the RF time series have been used, in conjunction with a neural network classification approach, to detect prostate cancer in regions of tissue as small as 0.03 cm2. Based on pathology results used as gold standard, we have acquired mean accuracy of 91%, mean sensitivity of 92% and mean specificity of 90% on seven human prostates.

Automated detection of prostate cancer using wavelet transform features of ultrasound RF time series

The aim of this research was to investigate the performance of wavelet transform based features of ultrasound radiofrequency (RF) time series for automated detection of prostate cancer tumors in transrectal ultrasound images. Sequential frames of RF echo signals from 35 extracted prostate specimens were recorded in parallel planes, while the ultrasound probe and the tissue were fixed in position in each imaging plane. The sequence of RF echo signal samples corresponding to a particular spot in tissue imaging plane constitutes one RF time series. Each region of interest (ROI) of ultrasound image was represented by three groups of features of its time series, namely, wavelet, spectral and fractal features. Wavelet transform approximation and detail sequences of each ROI were averaged and used as wavelet features. The average value of the normalized spectrum in four quarters of the frequency range along with the intercept and slope of a regression line fitted to the values of the spectrum versus normalized frequency plot formed six spectral features. Fractal dimension (FD) of the RF time series were computed based on the Higuchis approach. A support vector machine (SVM) classifier was used to classify the ROIs. The results indicate that combining wavelet coefficient based features with previously proposed spectral and fractal features of RF time series data would increase the area under ROC curve from 93.1% to 95.0%, respectively. Furthermore, the accuracy, sensitivity, and specificity increases to 91.7%, 86.6%, and 94.7%, from 85.7%, 85.2%, and 86.1%, respectively, using only spectral and fractal features.

A new approach to analysis of RF ultrasound echo signals for tissue characterization: animal studies

We present the results of an animal tissue characterization study to demonstrate the effectiveness of a novel approach in collecting and analyzing ultrasound echo signals. In this approach, we continuously record RF echo signals backscattered from a tissue sample, while the imaging probe and the tissue are fixed in position. The continuously recorded RF data generates a time series of RF signal samples. The Higuchi fractal dimension of the resulting time series at each spatial coordinate of the RF frame, averaged over a region of interest, serves as our tissue characterizing feature. The proposed feature is used along with Bayesian classifiers and feed-forward neural networks to distinguish different types of animal tissue. Pairwise classification of four different types of animal tissue are performed. Accuracies are in the range of 68%-96% and are significantly higher than the natural split of the data. The promising results of this study show that analysis of RF time series as proposed here, can potentially give rise to effective measures for ultrasound-based tissue characterization.