Lizann Bolinger

DYNAMIC MAGNETIC RESONANCE IMAGING OF THE FEMALE PELVIS: THE RELATIONSHIP WITH THE PELVIC ORGAN PROLAPSE QUANTIFICATION STAGING SYSTEM

PURPOSEnMagnetic resonance imaging (MRI) was performed to determine anatomical correlations with respect to physical examination using the Pelvic Organ Prolapse (POP) staging system. In addition, the standard POP staging system was analyzed to obtain normative data and determine any risk factors for prolapse.nnnMATERIALS AND METHODSnA total of 52 continent women 19 to 67 years old participated in our study. Pelvic MRI was performed at 1.5 Tesla. The vagina, bladder and rectum were opacified. Subjects performed pelvic floor contraction, relaxation and straining maneuvers for T1-weighted imaging. One-way analysis of variance, Fishers exact test and multinomial logistic regression were used to analyze the data.nnnRESULTSnPOP stage is quantified from 0 to IV. Stage was 0 to II in 56%, 27% and 17% of cases. POP stage was not significantly influenced by the number of cesarean sections (p = 0.64) or smoking (p = 0.91) but the number of vaginal deliveries significantly correlated with stage. Women with 1 vaginal delivery were at increased risk for a stage I condition (p = 0.018), whereas those with more than 1 were at increased risk for stage II (p = 0.013). On MRI stages 0 versus I or II differed significantly in regard to bladder descent (p = 0.01 and <0.0001, respectively), while stages 0 versus I differed in regard to levator angle (p = 0.007). No significant staging differences were observed in regard to the posterior urethrovesical angle or stages I versus II with respect to all 3 MRI measurements.nnnCONCLUSIONSnMRI appears to detect anatomically measurable changes in POP stage 0 versus other stages in regard to bladder descent and the levator angle and yet it is not sensitive enough to detect differences in stages I and II. It is not unusual for continent women to have a moderate degree of pelvic prolapse and previous vaginal delivery appears to increase this risk.

Segmentation of wall and plaque in in vitro vascular MR images.

Atherosclerosis leads to heart attack and stroke, which are major killers in the western world. These cardiovascular events frequently result from local rupture of vulnerable atherosclerotic plaque. Non-invasive assessment of plaque vulnerability would dramatically change the way in which atherosclerotic disease is diagnosed, monitored, and treated. In this paper, we report a computerized method for segmentation of arterial wall layers and plaque from high-resolution volumetric MR images. The method uses dynamic programming to detect optimal borders in each MRI frame. The accuracy of the results was tested in 62 T1-weighted MR images from six vessel specimens in comparison to borders manually determined by an expert observer. The mean signed border positioning errors for the lumen, internal elastic lamina, and external elastic lamina borders were −0.1 ± 0.1, 0.0 ± 0.1, and −0.1 ± 0.1 mm, respectively. The presented wall layer segmentation approach is one of the first steps towards non-invasive assessment of plaque vulnerability in atherosclerotic subjects.

Discrimination of MR images of breast masses with fractal-interpolation function models*

RATIONALE AND OBJECTIVESnThe authors evaluated the feasibility of using statistical fractal-dimension features to improve discrimination between benign and malignant breast masses at magnetic resonance (MR) imaging.nnnMATERIALS AND METHODSnThe study evaluated MR images of 32 malignant and 20 benign breast masses from archived data at the University of Pennsylvania Medical Center. The test set included four cases that were difficult to evaluate on the basis of border characteristics. All diagnoses had been confirmed at excisional biopsy. The fractal-dimension feature was computed as the mean of a sample space of fractal-dimension estimates derived from fractal interpolation function models. To evaluate the performance of the fractal-dimension feature, the classification effectiveness of five expert-observer architectural features was compared with that of the fractal dimension combined with four expert-observer features. Feature sets were evaluated with receiver operating characteristic analysis. Discrimination analysis used artificial neural networks and logistic regression. Robustness of the fractal-dimension feature was evaluated by determining changes in discrimination when the algorithm parameters were perturbed.nnnRESULTSnThe combination of fractal-dimension and expert-observer features provided a statistically significant improvement in discrimination over that achieved with expert-observer features alone. Perturbing selected parameters in the fractal-dimension algorithm had little effect on discrimination.nnnCONCLUSIONnA statistical fractal-dimension feature appears to be useful in distinguishing MR images of benign and malignant breast masses in cases where expert radiologists may have difficulty. The statistical approach to estimating the fractal dimension appears to be more robust than other fractal measurements on data-limited medical images.

Real-time multiple linear regression for fMRI supported by time-aware acquisition and processing

Real‐time parametric statistical analysis of functional MRI (fMRI) data would potentially enlarge the scope of experimentation and facilitate its application to clinical populations. A system is described that addresses the need for rapid analysis of fMRI data and lays the foundation for dealing with problems that impede the application of fMRI to clinical populations. The system, I/OWA (Input/Output time‐aWare Architecture), combines a general architecture for sampling and time‐stamping relevant information channels in fMRI (image acquisition, stimulation, subject responses, cardiac and respiratory monitors, etc.) and an efficient approach to manipulating these data, featuring incremental subsecond multiple linear regression. The advantages of the system are the simplification of event timing and efficient and unified data formatting. Substantial parametric analysis can be performed and displayed in real‐time. Immediate (replay) and delayed off‐line analysis can also be performed with the same interface. The capabilities of the system are demonstrated in normal subjects using a polar visual angle phase mapping paradigm. The system provides a time‐accounting infrastructure that readily supports standard and innovative approaches to fMRI. Magn Reson Med 45:289–298, 2001.

Analysis of speech-related variance in rapid event-related fMRI using a time-aware acquisition system

Speech production introduces signal changes in fMRI data that can mimic or mask the task-induced BOLD response. Rapid event-related designs with variable ISIs address these concerns by minimizing the correlation of task and speech-related signal changes without sacrificing efficiency; however, the increase in residual variance due to speech still decreases statistical power and must be explicitly addressed primarily through post-processing techniques. We investigated the timing, magnitude, and location of speech-related variance in an overt picture naming fMRI study with a rapid event-related design, using a data acquisition system that time-stamped image acquisitions, speech, and a pneumatic belt signal on the same clock. Using a spectral subtraction algorithm to remove scanner gradient noise from recorded speech, we related the timing of speech, stimulus presentation, chest wall movement, and image acquisition. We explored the relationship of an extended speech event time course and respiration on signal variance by performing a series of voxelwise regression analyses. Our results demonstrate that these effects are spatially heterogeneous, but their anatomic locations converge across subjects. Affected locations included basal areas (orbitofrontal, mesial temporal, brainstem), areas adjacent to CSF spaces, and lateral frontal areas. If left unmodeled, speech-related variance can result in regional detection bias that affects some areas critically implicated in language function. The results establish the feasibility of detecting and mitigating speech-related variance in rapid event-related fMRI experiments with single word utterances. They further demonstrate the utility of precise timing information about speech and respiration for this purpose.

Model assessment and model building in fMRI

Model quality is rarely assessed in fMRI data analyses and less often reported. This may have contributed to several shortcomings in the current fMRI data analyses, including: (1) Model mis‐specification, leading to incorrect inference about the activation‐maps, SPM{t} and SPM{F}; (2) Improper model selection based on the number of activated voxels, rather than on model quality; (3) Under‐utilization of systematic model building, resulting in the common but suboptimal practice of using only a single, pre‐specified, usually over‐simplified model; (4) Spatially homogenous modeling, neglecting the spatial heterogeneity of fMRI signal fluctuations; and (5) Lack of standards for formal model comparison, contributing to the high variability of fMRI results across studies and centers. To overcome these shortcomings, it is essential to assess and report the quality of the models used in the analysis. In this study, we applied images of the Durbin‐Watson statistic (DW‐map) and the coefficient of multiple determination (R2‐map) as complementary tools to assess the validity as well as goodness of fit, i.e., quality, of models in fMRI data analysis. Higher quality models were built upon reduced models using classic model building. While inclusion of an appropriate variable in the model improved the quality of the model, inclusion of an inappropriate variable, i.e., model mis‐specification, adversely affected it. Higher quality models, however, occasionally decreased the number of activated voxels, whereas lower quality or inappropriate models occasionally increased the number of activated voxels, indicating that the conventional approach to fMRI data analysis may yield sub‐optimal or incorrect results. We propose that model quality maps become part of a broader package of maps for quality assessment in fMRI, facilitating validation, optimization, and standardization of fMRI result across studies and centers. Hum. Brain Mapping 20:227–238, 2003.

Source of low-frequency fluctuations in functional MRI signal

To investigate the source of native low‐frequency fluctuations (LFF) in functional MRI (fMRI) signal.

Magnetization transfer imaging of the brain: A quantitative comparison of results obtained at 1.5 and 4.0 t

The preliminary results of magnetization transfer (MT) imaging on a whole body 4.0 T system are presented. Cooked egg phantoms and several volunteers were imaged on 1.5 and 4.0 T magnets interfaced to GE Signa scanners. The MT ratio (MTR), signal difference to noise ratio (SDNR), and contrast parameters were measured at both fields and compared. Furthermore, single‐shot Z‐spectroscopy was used to characterize the frequency dependence of the MT phenomenon. The results show that MT imaging can be safely performed at 4.0 T without exceeding limitations of radio frequency power. The MT effect is more pronounced at the higher field, leading to better quality images with higher contrast and SDNR. The Z‐spectra are not markedly different at the higher field although the MTR is greater. The potential applications of this technique to study neurodegenerative diseases, as well as, perfusion imaging and angiography are discussed. J. Magn. Reson. Imaging 1999;10:527–532.

Fractal discrimination of MRI breast masses using multiple segmentations

Fractal dimension (fd) of lesion borders has been proposed as a feature to discriminate between malignant and benign masses on MR breast images. The fd value is computed using a sample space of fractal models, an approach that reduces sensitivity to signal noise and image variability. The user specifies a rectangular region of interest (ROI) around the mass and the algorithm generates a segmentation zone from the ROI. Fractal models are constructed on multiple threshold intensity contours within the segmentation zone. Preliminary results show that the combination of statistical fd feature and expert-observer interpretations improves separation of benign from malignant breast masses when compared to expert-observer interpretations alone. The statistical fd feature has been incorporated into a prototype computer-aided-diagnosis (CAD) system that outputs the following to assist the diagnostician in determining clinical action: (1) A likelihood-of-cancer measure computed from fd and reader interpretations, (2) A binary categorical value indicating whether a test case is fd- highly suspicious or fd-inconclusive, (3) The ROI with portions of the mass border with the most cancer-like fractal characteristics highlighted.

Adaptive pacing of visual stimulation for fMRI studies involving overt speech

We report the development of an interactive approach to single-word language production studies in fMRI. The approach, adaptive pacing, involves real-time adjustment of stimulus presentation times based on individual subject performance timing and content. At the same time, it maintains a stochastic distribution of interstimulus intervals to avoid confounding task covariates with speech-related signal variance. Adaptive pacing of overt speech production is an example of a new class of paradigms that require an observational approach to data acquisition and benefit from a time-aware acquisition and processing environment. The advantages of adaptive pacing in fMRI of impaired subjects are expected to be the acquisition of more informative data per unit time, less contamination of data by correlates of non-language processes such as emotion, and facilitation of experiments that combine normal and impaired subjects.