Assessment of early response to concurrent chemoradiotherapy in cervical cancer: value of diffusion-weighted and dynamic contrast-enhanced MR imaging.

PURPOSE: To investigate diffusion-weighted (DWI) and dynamic contrast-enhanced MR imaging (DCE-MRI) as early response predictors in cervical cancer patients who received concurrent chemoradiotherapy (CCRT). MATERIALS AND METHODS: Sixteen patients with cervical cancer underwent DWI and DCE-MRI before CCRT (preTx), at 1week (postT1) and 4weeks (postT2) after initiating treatment, and 1month after the end of treatment (postT3). At each point, apparent diffusion coefficient (ADC) and DCE-MRI parameters were measured in tumors and gluteus muscles (GM). Tumor response was correlated with imaging parameters or changes in imaging parameters at each point. RESULTS: At each point, ADC, K(trans) and Ve in tumors showed significant changes (P<0.05), as compared with those of GM (P>0.05). PostT1 tumor ADCs showed a significant correlation with tumor size response at postT2 (P=0.041), and changes in tumor ADCs at postT1 had a significant correlation with tumor size (P=0.04) and volume response (P=0.003) at postT2. In tumors, preTx K(trans) and Ve showed significant correlations with tumor size at postT3 (P=0.011) and tumor size response at postT2 (P=0.019), respectively. CONCLUSION: DWI and DCE-MRI, as early biomarkers, have the potential to evaluate therapeutic responses to CCRT in cervical cancers.

The principal of dynamic contrast enhanced MRI, the method of pharmacokinetic analysis, and its application in the head and neck region.

Many researchers have established the utility of the dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in the differential diagnosis in the head and neck region, especially in the salivary gland tumors. The subjective assessment of the pattern of the time-intensity curve (TIC) or the simple quantification of the TIC, such as the time to peak enhancement (T(peak)) and the wash-out ratio (WR), is commonly used. Although the semiquantitative evaluations described above have been widely applied, they do not provide information on the underlying pharmacokinetic analysis in tissue. The quantification of DCE-MRI is preferable; therefore, many compartment model analyses have been proposed. The Toft and Kermode (TK) model is one of the most popular compartment models, which provide information about the influx forward volume transfer constant from plasma into the extravascular-extracellular space (EES) and the fractional volume of EES per unit volume of tissue is used in many clinical studies. This paper will introduce the method of pharmacokinetic analysis and also describe the clinical application of this technique in the head and neck region.

Pharmacokinetic analysis based on dynamic contrast-enhanced MRI for evaluating tumor response to preoperative therapy for oral cancer.

PURPOSE: To evaluate whether a pharmacokinetic analysis is useful for monitoring the response of oral cancer to chemoradiotherapy (CRT). MATERIALS AND METHODS: Twenty-nine patients were included. They underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) before and after CRT. The DCE-MRI data were analyzed using a Tofts and Kermode (TK) model. The histological evaluation of the effects of CRT was performed according to Ohboshi and Shimosato's classification. RESULTS: None of the pre-CRT parameters were significantly different between the responders and nonresponders. The post-CRT volume of the extravascular extracellular space (EES) per unit volume of tissue (v(e) ) of responders (0.397 ± 0.080) was higher than that of nonresponders (0.281 ± 0.076) (P = 0.01). The change of the v(e) between the pre- and post-CRT of the responders (0.154 ± 0.093) was larger than that of the nonresponders (0.033 ± 0.073) (P = 0.001). Therefore, the increase in the v(e) strongly suggested a good tumor response to CRT, which reflected an increase of the EES secondary to the destruction of the cancer nest. The changes in the volume transfer constant (K(trans) ) were significantly different between the responders and nonresponders (P = 0.018). CONCLUSION: Both the increase of the v(e) and the elevation of permeability (K(trans) ) were indicative of a good tumor response to CRT. The pharmacokinetic analysis had potential for monitoring the histopathological response to CRT.

Tumour hyperthermia and ablation in rats using a clinical MR-HIFU system equipped with a dedicated small animal set-up.

PURPOSE: We report on the design, performance, and specifications of a dedicated set-up for the treatment of rats on a clinical magnetic resonance high intensity focused ultrasound (MR-HIFU) system. MATERIALS AND METHODS: The small animal HIFU-compatible 4-channel MR receiver volume coil and animal support were designed as add-on to a clinical 3T Philips Sonalleve MR-HIFU system. Prolonged hyperthermia (T ≈ 42°C, 15 min) and thermal ablation (T = 65°C) was performed in vivo on subcutaneous rat tumours using 1.44 MHz acoustic frequency. The direct treatment effect was assessed with T(2)-weighted imaging and dynamic contrast enhanced (DCE-) MRI as well as histology. RESULTS: The developed HIFU-compatible coil provided an image quality that was comparable to conventional small animal volume coils (i.e. without acoustic window), and a SNR increase by a factor of 10 as compared to the coil set-up used for clinical MR-HIFU therapy. The use of an animal support minimised far field heating and allowed precise regulation of the animal body core temperature, which varied <1°C during treatment. CONCLUSIONS: The results demonstrated that, by using a designated set-up, both controlled hyperthermia and thermal ablation treatment of malignant tumours in rodents can be performed on a clinical MR-HIFU system. This approach provides all the advantages of clinical MR-HIFU, such as volumetric heating, temperature feedback control and a clinical software interface for use in rodent treatment. The use of a clinical system moreover facilitates a rapid translation of the developed protocols into the clinic.

Nosologic imaging of the brain: segmentation and classification using MRI and MRSI.

A new technique is presented to create nosologic images of the brain based on magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI). A nosologic image summarizes the presence of different tissues and lesions in a single image by color coding each voxel or pixel according to the histopathological class it is assigned to. The proposed technique applies advanced methods from image processing as well as pattern recognition to segment and classify brain tumors. First, a registered brain atlas and a subject-specific abnormal tissue prior, obtained from MRSI data, are used for the segmentation. Next, the detected abnormal tissue is classified based on supervised pattern recognition methods. Class probabilities are also calculated for the segmented abnormal region. Compared to previous approaches, the new framework is more flexible and able to better exploit spatial information leading to improved nosologic images. The combined scheme offers a new way to produce high-resolution nosologic images, representing tumor heterogeneity and class probabilities, which may help clinicians in decision making.

Automated planning of scan geometries in spine MRI scans.

Consistency of MR scan planning is very important for diagnosis, especially in multi-site trials and follow-up studies, where disease progress or response to treatment is evaluated. Accurate manual scan planning is tedious and requires skillful operators. On the other hand, automated scan planning is difficult due to relatively low quality of survey images ("scouts") and strict processing time constraints. This paper presents a novel method for automated planning of MRI scans of the spine. Lumbar and cervical examinations are considered, although the proposed method is extendible to other types of spine examinations, such as thoracic or total spine imaging. The automated scan planning (ASP) system consists of an anatomy recognition part, which is able to automatically detect and label the spine anatomy in the scout scan, and a planning part, which performs scan geometry planning based on recognized anatomical landmarks. A validation study demonstrates the robustness of the proposed method and its feasibility for clinical use.

An automated quantitation of short echo time MRS spectra in an open source software environment: AQSES.

This paper describes a new quantitation method called AQSES for short echo time magnetic resonance spectra. This method is embedded in a software package available online from www.esat.kuleuven.be/sista/members/biomed/new/ with a graphical user interface, under an open source license, which means that the source code is freely available and easy to adapt to specific needs of the user. The quantitation problem is mathematically formulated as a separable nonlinear least-squares fitting problem, which is numerically solved using a modified variable-projection procedure. A macromolecular baseline is incorporated into the fit via nonparametric modelling, efficiently implemented using penalized splines. Unwanted components such as residual water are removed with a maximum-phase FIR filter. Constraints on the phases, dampings and frequencies of the metabolites can be imposed. AQSES has been tested on simulated MR spectra with several types of disturbance and on short echo time in vivo proton MR spectra. Results show that AQSES is robust, easy to use and very flexible.

A low temperature embedding and section registration strategy for 3D image reconstruction of the rat brain from autoradiographic sections.

In studies on animal models of human brain pathologies, three-dimensional reconstruction from histological sections is particularly useful when assessing the morphologic, functional and biochemical changes induced by pathology. It allows assessing lesion heterogeneity in planes different from the cutting plane and allows correlating the histology with images obtained in vivo, such as by means of magnetic resonance imaging. To create a 3D volume from autoradiographic sections with minimal distortion, both cryosectioning as well as section registration need to be optimal. This paper describes a strategy whereby four external fiducial markers are positioned outside the rat brain with the use of a low temperature brain embedding procedure. The fiducial markers proposed here can be rapidly added to any frozen tissue block with no impact on the subsequent histological operations. Since embedding is performed at a low temperature, no tissue degradation occurs due to sample heating. The markers enable robust and almost error free registration, even in the presence of missing sections and poor image quality. Furthermore, the markers may be used to partially correct for global distortions.

Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra.

A computer-based decision support system to assist radiologists in diagnosing and grading brain tumours has been developed by the multi-centre INTERPRET project. Spectra from a database of 1H single-voxel spectra of different types of brain tumours, acquired in vivo from 334 patients at four different centres, are clustered according to their pathology, using automated pattern recognition techniques and the results are presented as a two-dimensional scatterplot using an intuitive graphical user interface (GUI). Formal quality control procedures were performed to standardize the performance of the instruments and check each spectrum, and teams of expert neuroradiologists, neurosurgeons, neurologists and neuropathologists clinically validated each case. The prototype decision support system (DSS) successfully classified 89% of the cases in an independent test set of 91 cases of the most frequent tumour types (meningiomas, low-grade gliomas and high-grade malignant tumours--glioblastomas and metastases). It also helps to resolve diagnostic difficulty in borderline cases. When the prototype was tested by radiologists and other clinicians it was favourably received. Results of the preliminary clinical analysis of the added value of using the DSS for brain tumour diagnosis with MRS showed a small but significant improvement over MRI used alone. In the comparison of individual pathologies, PNETs were significantly better diagnosed with the DSS than with MRI alone.

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification.

The purpose of this paper is to evaluate the effect of the combination of magnetic resonance spectroscopic imaging (MRSI) data and magnetic resonance imaging (MRI) data on the classification result of four brain tumor classes. Suppressed and unsuppressed short echo time MRSI and MRI were performed on 24 patients with a brain tumor and four volunteers. Four different feature reduction procedures were applied to the MRSI data: simple quantitation, principal component analysis, independent component analysis and LCModel. Water intensities were calculated from the unsuppressed MRSI data. Features were extracted from the MR images which were acquired with four different contrasts to comply with the spatial resolution of the MRSI. Evaluation was performed by investigating different combinations of the MRSI features, the MRI features and the water intensities. For each data set, the isolation in feature space of the tumor classes, healthy brain tissue and cerebrospinal fluid was calculated and visualized. A test set was used to calculate classification results for each data set. Finally, the effect of the selected feature reduction procedures on the MRSI data was investigated to ascertain whether it was more important than the addition of MRI information. Conclusions are that the combination of features from MRSI data and MRI data improves the classification result considerably when compared with features obtained from MRSI data alone. This effect is larger than the effect of specific feature reduction procedures on the MRSI data. The addition of water intensities to the data set also increases the classification result, although not significantly. We show that the combination of data from different MR investigations can be very important for brain tumor classification, particularly if a large number of tumors are to be classified simultaneously.

A chemometric approach for brain tumor classification using magnetic resonance imaging and spectroscopy.

A new classification approach was developed to improve the noninvasive diagnosis of brain tumors. Within this approach, information is extracted from magnetic resonance imaging and spectroscopy data, from which the relative location and distribution of selected tumor classes in feature space can be calculated. This relative location and distribution is used to select the best information extraction procedure, to identify overlapping tumor classes, and to calculate probabilities of class membership. These probabilities are very important, since they provide information about the reliability of classification and might provide information about the heterogeneity of the tissue. Classification boundaries were calculated by setting thresholds for each investigated tumor class, which enabled the classification of new objects. Results on histopathologically determined tumors are excellent, demonstrated by spatial maps showing a high probability for the correctly identified tumor class and, moreover, low probabilities for other tumor classes.