Johannes Buurman

The Influence of Temporal Resolution in Determining Pharmacokinetic Parameters From DCE-MRI Data

We investigated the influence of the temporal resolution of dynamic contrast‐enhanced MRI data on pharmacokinetic parameter estimation. Dynamic Gd‐DTPA (Gadolinium‐diethylene triamine pentaacetic acid) enhanced MRI data of implanted prostate tumors on rat hind limb were acquired at 4.7 T, with a temporal resolution of ∼5 sec. The data were subsequently downsampled to temporal resolutions in the range of 15 sec to 85 sec, using a strategy that involves a recombination of k‐space data. A basic two‐compartment model was fit to the contrast agent uptake curves. The results demonstrated that as temporal resolution decreases, the volume transfer constant (Ktrans) is progressively underestimated (∼4% to ∼25%), and the fractional extravascular extracellular space (ve) is progressively overestimated (∼1% to ∼10%). The proposed downsampling strategy simulates the influence of temporal resolution more realistically than simply downsampling by removing samples. Magn Reson Med 63:811–816, 2010.

Clinical introduction of an adjustable rigid instrument holder for frameless stereotactic interventions.

Interactive image guidance is now in routine use for open neurosurgical procedures and has demonstrated patient benefits. However, freehand interactive guidance is not an appropriate replacement for the traditional frame-based stereotactic procedures of biopsy, electrode placement, and functional lesioning. These point-based procedures require precise target localization and direct instrument guidance to avoid collateral brain injury. To perform true frameless stereotactic procedures requires a guide that is also adjustable for positioning, lockable, and adaptable to multiple instruments. We describe such a device, which is employed for the guidance of biopsy needles, shunts, electrodes, and endoscopes during neuronavigation. The method of frameless stereotactic biopsy retrieval with an infrared-based neuronavigation system is described, clinical results are given, and further areas of application discussed.

The use of a reference tissue arterial input function with low-temporal-resolution DCE-MRI data

Pharmacokinetic modeling is a promising quantitative analysis technique for cancer diagnosis. However, diagnostic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is commonly performed with low temporal resolution. This limits its clinical utility. We investigated for a range of temporal resolutions whether pharmacokinetic parameter estimation is impacted by the use of data-derived arterial input functions (AIFs), obtained via analysis of dynamic data from a reference tissue, as opposed to the use of a standard AIF, often obtained from the literature. We hypothesized that the first method allows the use of data at lower temporal resolutions than the second method. Test data were obtained by downsampling high-temporal-resolution rodent data via a k-space-based strategy. To fit the basic Tofts model, either the data-derived or the standard AIF was used. The resulting estimates of K(trans) and v(e) were compared with the standard estimates obtained by using the original data. The deviations in K(trans) and v(e), introduced when lowering temporal resolution, were more modest using data-derived AIFs compared with using a standard AIF. Specifically, lowering the resolution from 5 to 60 s, the respective changes in K(trans) were 2% (non-significant) and 18% (significant). Extracting the AIF from a reference tissue enables accurate pharmacokinetic parameter estimation for low-temporal-resolution data.

Robust segmentation of mass-lesions in contrast-enhanced dynamic breast MR images.

To develop and evaluate a computerized segmentation method for breast MRI (BMRI) mass‐lesions.

Anatomical landmark image registration: validation and comparison

This paper describes a validation approach for (interactive) anatomical landmark registration of CT and MR images. Eleven MR-CT image pairs were used, four of which had been scanned with a Leksell stereotactic frame present. Four observers each selected twelve pairs of anatomical landmarks per image pair. The images were then registered using a least squares minimization technique, only taking six transformation parameters (translation and rotation) into account. Each observer processed all twelve image pairs five times. Secondly we describe an algorithm to automatically detect a Leksell stereotactic frame from CT and MR images, scanned according to Leksell protocol. Results have been assessed by examining observer variations in the six parameters. Observer performance is differentiated by comparing median distances (and mean deviations) of identical points in different registrations. Comparison of all results show that intra- and interobserver variations are of the same magnitude as the difference between the automatic frame registration and the average observer registration.

Pharmacokinetic models in clinical practice: What model to use for DCE-MRI of the breast?

Pharmacokinetic modeling is increasingly used in DCE-MRI high risk breast cancer screening. Several models are available. The most common models are the standard and extended Tofts, the shutter-speed, and the Brix model. Each model and the meaning of its parameters is explained. It was investigated which models can be used in a clinical setting by simulating a range of sampling rates and noise levels representing different MRI acquisition schemes. In addition, an investigation was performed on the errors introduced in the estimates of the pharmacokinetic parameters when using a physiologically less complex model, i.e. the standard Tofts model, to fit curves generated with more complex models. It was found that the standard Tofts model is the only model that performs within an error margin of 20% on parameter estimates over a range of sampling rates and noise levels. This still holds when small complex physiological effects are present.

Detecting Changes in Process Behavior Using Comparative Case Clustering

Real-life business processes are complex and often exhibit a high degree of variability. Additionally, due to changing conditions and circumstances, these processes continuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Case data (e.g. treating physician, patient age) also influence how processes are executed. Existing process mining techniques assume processes to be static and therefore are less suited for the analysis of contemporary, flexible business processes. This paper presents a novel comparative case clustering approach that is able to expose changes in behavior. Valuable insights can be gained and process improvements can be made by finding those points in time where behavior changed and the reasons why. Evaluation using both synthetic and real-life event data shows our technique can provide these insights.

Using Domain Knowledge to Enhance Process Mining Results

Process discovery algorithms typically aim at discovering process models from event logs. Most algorithms achieve this by solely using an event log, without allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. First, we present a verification algorithm to verify the presence of certain constraints in a process model. Then, we present three modification algorithms to modify the process model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining.

Cross-Sectional Relatedness Between Sentences in Breast Radiology Reports: Development of an SVM Classifier and Evaluation Against Annotations of Five Breast Radiologists

Introduce the notion of cross-sectional relatedness as an informational dependence relation between sentences in the conclusion section of a breast radiology report and sentences in the findings section of the same report. Assess inter-rater agreement of breast radiologists. Develop and evaluate a support vector machine (SVM) classifier for automatically detecting cross-sectional relatedness. A standard reference is manually created from 444 breast radiology reports by the first author. A subset of 37 reports is annotated by five breast radiologists. Inter-rater agreement is computed among their annotations and standard reference. Thirteen numerical features are developed to characterize pairs of sentences; the optimal feature set is sought through forward selection. Inter-rater agreement is F-measure 0.623. SVM classifier has F-measure of 0.699 in the 12-fold cross-validation protocol against standard reference. Report length does not correlate with the classifier’s performance (correlation coefficient = −0.073). SVM classifier has average F-measure of 0.505 against annotations by breast radiologists. Mediocre inter-rater agreement is possibly caused by: (1) definition is insufficiently actionable, (2) fine-grained nature of cross-sectional relatedness on sentence level, instead of, for instance, on paragraph level, and (3) higher-than-average complexity of 37-report sample. SVM classifier performs better against standard reference than against breast radiologists’s annotations. This is supportive of (3). SVM’s performance on standard reference is satisfactory. Since optimal feature set is not breast specific, results may transfer to non-breast anatomies. Applications include a smart report viewing environment and data mining.