A method for pharmacokinetic modelling of dynamic contrast enhanced MRI studies of rapidly enhancing lesions acquired in a clinical setting

Abnormal microcirculation is a feature of many neoplastic and non-neoplastic diseases. Physiological variables that characterize tissue microcirculation (capillary permeability and the volume of the extravascular extracellular fluid) are altered in pathological states. Pharmacokinetic analysis of dynamic contrast enhanced MRI (DCE-MRI) has found a widespread use in the assessment of abnormal microcirculation due to the direct link between the contrast agent kinetics and underlying microcirculatory properties. A representation of temporal variation of contrast agent concentration in blood plasma (C(p)(t)) is central to this analysis. In clinical applications of DCE-MRI, signal intensity curves derived from rapidly enhancing lesions often display a sigmoid shape during the initial phase of contrast uptake and rapid arrival at the equilibrium phase. In this work, the features of two principal methods for pharmacokinetic analysis of DCE-MRI which allow for theoretical representation of C(p)(t) are examined and combined to improve analysis of this particular class of DCE-MRI curves. The proposed method allows the representation of the initial sigmoid part of the enhancement profiles whilst retaining a realistic representation of C(p)(t) based on previously published measurements obtained in healthy volunteers. The results of the computer simulations indicate that in rapidly enhancing lesions, with the transfer constant K(trans) greater than 0.1 min(-1), the DCE-MRI acquisition can be restricted to 5 min post-injection and a mono-exponential representation of C(p)(t) decay is sufficient. Furthermore, non-ideal bolus delivery can be represented as a short constant rate infusion when the tissue under investigation exhibits a sigmoid pattern of contrast uptake.     

Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is being used increasingly in the detection and diagnosis of breast cancer as a complementary modality to mammography and sonography. Although the potential diagnostic value of kinetic curves in DCE-MRI is established, the method for generating kinetic curves is not standardized. The inherent reason that curve identification is needed is that the uptake of contrast agent in a breast lesion is often heterogeneous, especially in malignant lesions. It is accepted that manual region of interest selection in 4D breast magnetic resonance (MR) images to generate the kinetic curve is a time-consuming process and suffers from significant inter- and intraobserver variability. We investigated and developed a fuzzy c-means (FCM) clustering-based technique for automatically identifying characteristic kinetic curves from breast lesions in DCE-MRI of the breast. Dynamic contrast-enhanced MR images were obtained using a T1-weighted 3D spoiled gradient echo sequence with Gd-DTPA dose of 0.2 mmol/kg and temporal resolution of 69 s. FCM clustering was applied to automatically partition the signal-time curves in a segmented 3D breast lesion into a number of classes (i.e., prototypic curves). The prototypic curve with the highest initial enhancement was selected as the representative characteristic kinetic curve (CKC) of the lesion. Four features were then extracted from each characteristic kinetic curve to depict the maximum contrast enhancement, time to peak, uptake rate, and washout rate of the lesion kinetics. The performance of the kinetic features in the task of distinguishing between benign and malignant lesions was assessed by receiver operating characteristic analysis. With a database of 121 breast lesions (77 malignant and 44 benign cases), the classification performance of the FCM-identified CKCs was found to be better than that from the curves obtained by averaging over the entire lesion and similar to kinetic curves generated from regions drawn within the lesion by a radiologist experienced in breast MRI.     

The effect of motion correction on pharmacokinetic parameter estimation in dynamic-contrast-enhanced MRI

A dynamic-contrast-enhanced magnetic resonance imaging (DCE-MRI) dataset consists of many imaging frames, often acquired both before and after contrast injection. Due to the length of time spent acquiring images, patient motion is likely and image re-alignment or registration is required before further analysis such as pharmacokinetic model fitting. Non-rigid image registration procedures may be used to correct motion artefacts; however, a careful choice of registration strategy is required to reduce misregistration artefacts associated with enhancing features. This work investigates the effect of registration on the results of model-fitting algorithms for 52 DCE-MR mammography cases for 14 patients. Results are divided into two sections: a comparison of registration strategies in which a DCE-MRI-specific algorithm is preferred in 50% of cases, followed by an investigation of parameter changes with known applied deformations, inspecting the effect of magnitude and timing of motion artefacts. Increased motion magnitude correlates with increased model-fit residual and is seen to have a strong influence on the visibility of strongly enhancing features. Motion artefacts in images close to the contrast agent arrival have a disproportionate effect on discrepancies in parameter estimation. The choice of algorithm, magnitude of motion and timing of the motion are each shown to influence estimated pharmacokinetic parameters even when motion magnitude is small.     

Intensity based image registration by minimizing exponential function weighted residual complexity

In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results. By introducing a specifically designed exponential weighting function to the residual term in residual complexity, the proposed similarity measure performed well due to automatically weighting the residual image between the reference image and the warped floating image. We utilized local variance of the reference image to model the exponential weighting function. The proposed technique was applied to brain magnetic resonance images, dynamic contrast enhanced magnetic resonance images (DCE-MRI) of breasts and contrast enhanced 3D CT liver images. The experimental results clearly indicated that the proposed approach has achieved more accurate and robust performance than mutual information, residual complexity and Jensen–Tsallis.     

Evaluation of deformable image registration and a motion model in CT images with limited features

Deformable image registration (DIR) is increasingly used in radiotherapy applications and provides the basis for a previously described model of patient-specific respiratory motion. We examine the accuracy of a DIR algorithm and a motion model with respiration-correlated CT (RCCT) images of software phantom with known displacement fields, physical deformable abdominal phantom with implanted fiducials in the liver and small liver structures in patient images. The motion model is derived from a principal component analysis that relates volumetric deformations with the motion of the diaphragm or fiducials in the RCCT. Patient data analysis compares DIR with rigid registration as ground truth: the mean ± standard deviation 3D discrepancy of liver structure centroid positions is 2.0 ± 2.2 mm. DIR discrepancy in the software phantom is 3.8 ± 2.0 mm in lung and 3.7 ± 1.8 mm in abdomen; discrepancies near the chest wall are larger than indicated by image feature matching. Marker's 3D discrepancy in the physical phantom is 3.6 ± 2.8 mm. The results indicate that visible features in the images are important for guiding the DIR algorithm. Motion model accuracy is comparable to DIR, indicating that two principal components are sufficient to describe DIR-derived deformation in these datasets.     

Computer-Aided Diagnosis of Breast DCE-MRI Images Using Bilateral Asymmetry of Contrast Enhancement Between Two Breasts

Dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) of breasts is an important imaging modality in breast cancer diagnosis with higher sensitivity but relatively lower specificity. The objective of this study is to investigate a new approach to help improve diagnostic performance of DCE-MRI examinations based on the automated detection and analysis of bilateral asymmetry of characteristic kinetic features between the left and right breast. An image dataset involving 130 DCE-MRI examinations was assembled and used in which 80 were biopsy-proved malignant and 50 were benign. A computer-aided diagnosis (CAD) scheme was developed to segment breast areas depicted on each MR image, register images acquired from the sequential MR image scan series, compute average contrast enhancement of all pixels in one breast, and a set of kinetic features related to the difference of contrast enhancement between the left and right breast, and then use a multi-feature based Bayesian belief network to classify between malignant and benign cases. A leave-one-case-out validation method was applied to test CAD performance. The computed area under a receiver operating characteristic (ROC) curve is 0.78 ± 0.04. The positive and negative predictive values are 0.77 and 0.64, respectively. The study indicates that bilateral asymmetry of kinetic features between the left and right breasts is a potentially useful image biomarker to enhance the detection of angiogenesis associated with malignancy. It also demonstrates the feasibility of applying a simple CAD approach to classify between malignant and benign DCE-MRI examinations based on this new image biomarker.     

A patient-specific respiratory model of anatomical motion for radiation treatment planning

The modeling of respiratory motion is important for a more accurate understanding and accounting of its effect on dose to cancers in the thorax and abdomen by radiotherapy. We have developed a model of respiration-induced organ motion in the thorax without the commonly adopted assumption of repeatable breath cycles. The model describes the motion of a volume of interest within the patient based on a reference three-dimensional (3D) image (at end expiration) and the diaphragm positions at different time points. The input data are respiration-correlated CT (RCCT) images of patients treated for non-small- cell lung cancer, consisting of 3D images, including the diaphragm positions, at ten phases of the respiratory cycle. A deformable image registration algorithm calculates the deformation field that maps each 3D image to the reference 3D image. A principal component analysis is performed to parameterize the 3D deformation field in terms of the diaphragm motion. We show that the first two principal components are adequate to accurately and completely describe the organ motion in the data of four patients. Artifacts in the RCCT images that commonly occur at the mid-respiration states are reduced in the model-generated images. Further validation of the model is demonstrated in the successful application of the parameterized 3D deformation field to RCCT data of the same patient but acquired several days later. We have developed a method for predicting respiration-induced organ motion in patients that has potential for improving the accuracy of dose calculation in radiotherapy. Possible limitations of the model are cases where the correlation between lung tumor and diaphragm position is less reliable such as superiorly situated tumors and interfraction changes in tumor-diaphragm correlation. The limited number of clinical cases examined suggests, but does not confirm, the model's applicability to a wide range of patients.     

DCE-MRI及DWI影像特征对乳腺癌病理组织学分级及Ki-67表达的预测研究

联合动态增强磁共振成像(DCE-MRI)以及弥散加权成像(DWI)的影像特征,通过建立模型,分别对乳腺癌的组织学分级以及Ki-67的表达进行预测。对144例未经过任何手术或化疗的乳腺浸润性导管癌患者的数据进行回顾性分析,这些患者均采用3T 扫描仪进行术前乳腺 MRI 检查,从中获取DCE-MRI以及DWI影像,并从 DWI 中计算得到表观扩散系数 (ADC)。对不同参数磁共振影像进行肿瘤分割,并分别从整个肿瘤区域中提取纹理特征、统计特征、形态特征等。采用无监督判别特征选择方法(UDFS)和Fisher Score算法进行特征选择,将分类模型分别应用于DCE-MRI及DWI图像数据,将得到的不同分类器进行多分类器模型融合,最终得到多参数图像的联合预测结果。为了评估所建立模型的分类性能, 通过留一法交叉验证 (LOOCV) 的方法计算ROC曲线下的面积(AUC)。对于分级任务,DCE-MRI的第二增强序列达到0.780的最优AUC,(特异度为0.647,灵敏度为0.934);对于Ki-67预测任务,DWI序列达到0.756的最优AUC(特异度为0.806,灵敏度为0.695)。经过融合,分级的预测结果提高到AUC为0.808(特异度为0.706,灵敏度为0.895),Ki-67的预测结果提高到AUC为0.783(特异度为0.778,灵敏度为0.722)。结果表明,相比采用单一参数的磁共振图像数据,DCE-MRI和DWI的影像特征联合可以提高分类器的性能。     

Assessing changes in tumour vascular function using dynamic contrast-enhanced magnetic resonance imaging

Dynamic contrast-enhanced MRI (DCE-MRI) is widely used in the diagnosis and staging of cancer and is emerging as a promising method for monitoring tumour response to treatment. However, DCE-MR imaging techniques are still evolving and methods of image analysis remain variable and non-standard, and range from relative changes in the pattern of enhancement to pharmacokinetic modelling of contrast agent uptake. The combination of results from different institutions is therefore difficult and the sensitivities of different methods have not been compared. The purpose of this study is to investigate correlations between qualitative and quantitative methods of analysis for DCE-MR images from breast cancer patients undergoing neo-adjuvant chemotherapy. Fifteen patients underwent DCE-MRI examinations before and after one course of chemotherapy. Changes in the temporal pattern of signal enhancement, the rate and amplitude of enhancement and the volume transfer constant of contrast agent between the blood plasma and the extravascular extracellular space (EES), K(trans), and the EES fractional volume, nu(e), were determined. In addition, whole tumour region-of-interest analysis was compared with histogram analysis to investigate the extent of tumour heterogeneity. It was found that changes in the rate of enhancement correlated strongly with changes in K(trans) values (Kendall's tau = 0.68, P < 0.001). Furthermore, it was found that the shape of the signal enhancement curve only changed when the K(trans) values changed by 50% or more. Median K(trans) values determined following histogram analysis of pixel maps of K(trans) were approximately equal to those determined by whole tumour region-of-interest analysis. The absolute change in the K(trans) values correlated negatively with the pre-treatment values, particularly for responding patients. Thus, for higher pre-treatment K(trans) values, a greater decrease was observed. Greater changes were observed in the upper extremes of the K(trans) histogram than in the median values after one course of treatment.     

Registration of low resolution medical images.

There are several situations in which the registration of two medical images is desirable. One example is the registration of two images of the same organ taken using different radionuclide tracers, for example the ventilation and perfusion components of a V/Q lung scan, where the aim is to compare the regional uptake of the two tracers. Another example is the registration of images of an organ belonging to a single patient but taken at different times, where the aim is to follow changes in tracer uptake. Such techniques require a reliable method of registering images. One image is usually brought into registration with another image using a coordinate transfer function (CTF) and the central problem in image registration is the determination of the appropriate CTF. A new semi-automatic approach to the problem of finding the CTF for similar images is described which is especially applicable to low resolution images.     

基于纵向时间深度网络融合的乳腺癌新辅助化疗疗效预测

新辅助化疗提高了乳腺癌的治愈率,但并不是对所有患者都有效,准确预测化疗疗效可以为患者治疗方案的制定提供参考价值。本研究使用深度学习的方法,融合纵向时间的动态增强磁共振成像(DCE-MRI)的影像特征对新辅助化疗疗效进行预测。分析164例进行了乳腺癌新辅助化疗患者的DCE-MRI影像,从每例患者影像数据集中挑选肿瘤最大径及上下2张切片以扩充数据量至442例,并随机划分为训练集312例,测试集130例。DCE-MRI影像共6个序列,分割每个序列的乳房区域,去除皮肤和胸腔,使用深度学习模型分别根据化疗前影像、2个疗程化疗后影像、化疗前和2个疗程化疗后影像相融合对新辅助化疗疗效进行预测,并绘制预测结果的ROC曲线,计算对应曲线下面积(AUC)评估模型的分类性能。深度学习模型对化疗前影像、2个疗程化疗后影像的疗效预测的最佳AUC分别为0.775和0.808,融合化疗前和2个疗程化疗后影像对疗效进行预测的最佳AUC为0.863,预测效果优于仅使用化疗前的影像。实验结果表明,相较于单独使用化疗前影像,融合使用纵向时间的影像可以提高对新辅助化疗疗效的预测性能。     

Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses on Breast DCE-MRI Images Using Deep Convolutional Neural Network with Bayesian Optimization

Although magnetic resonance imaging (MRI) has a higher sensitivity of early breast cancer than mammography, the specificity is lower. The purpose of this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses on dynamic contrast material-enhanced MRI (DCE-MRI) by using a deep convolutional neural network (DCNN) with Bayesian optimization. Our database consisted of 56 DCE-MRI examinations for 56 patients, each of which contained five sequential phase images. It included 26 benign and 30 malignant masses. In this study, we first determined a baseline DCNN model from well-known DCNN models in terms of classification performance. The optimum architecture of the DCNN model was determined by changing the hyperparameters of the baseline DCNN model such as the number of layers, the filter size, and the number of filters using Bayesian optimization. As the input of the proposed DCNN model, rectangular regions of interest which include an entire mass were selected from each of DCE-MRI images by an experienced radiologist. Three-fold cross validation method was used for training and testing of the proposed DCNN model. The classification accuracy, the sensitivity, the specificity, the positive predictive value, and the negative predictive value were 92.9% (52/56), 93.3% (28/30), 92.3% (24/26), 93.3% (28/30), and 92.3% (24/26), respectively. These results were substantially greater than those with the conventional method based on handcrafted features and a classifier. The proposed DCNN model achieved high classification performance and would be useful in differential diagnoses of masses in breast DCE-MRI images as a diagnostic aid.     

A comparison of image registration techniques for the correlation of radiolabelled antibody distribution with tumour morphology.

Image registration is a powerful tool for correlating functional images with images of anatomical structure. This facilitates more accurate quantitation of regional radiopharmaceutical uptake. Similarly, registration of images of radiolabelled antibody distribution, in tissue sections, with the equivalent histological images allows the comparison and measurement of radiopharmaceutical distribution with morphological structure. The images used were obtained by storage phosphor plate technology, for the radiopharmaceutical distribution, and by digitization of the stained histological sections. Here we compare four fully automatic registration techniques and one manual technique in terms of their spatial accuracy. We have found that there was no difference in accuracy between cross-correlation, minimization of variance and mutual information. These techniques were more accurate than principal axes and the manual technique. However, minimization of variance and mutual information were more time-consuming than the other methods. Consequently, cross-correlation is the method of choice for automatic registration of large numbers of these image pairs.     

PTK787/ZK222584, a tyrosine kinase inhibitor of vascular endothelial growth factor receptor, reduces uptake of the contrast agent GdDOTA by murine orthotopic B16/BL6 melanoma tumours and inhibits their growth in vivo

Assessment of tumour vascularity may characterize malignancy as well as predict responsiveness to anti-angiogenic therapy. Non-invasive measurement of tumour perfusion and blood vessel permeability assessed as the transfer constant, K(trans), can be provided by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Using the orthotopic murine tumour model B16/BL6 melanoma, the small contrast agent GdDOTA (DOTAREM(R); Guerbet, Paris) was applied to assess the vascular transfer constant, K(trans), and interstitial leakage space, whereas intravascular iron oxide nanoparticles (Endorem(R); Guerbet, Paris) were used to detect relative tumour blood volume (rTBV), and in one experiment blood flow index (BFI). No correlations were observed between these four parameters (r(2) always <0.05). The B16/BL6 primary tumour and lymph-node cervical (neck) metastases produced high levels of the permeability/growth factor, VEGF. To probe the model, the novel VEGF receptor (VEGF-R) tyrosine kinase inhibitor, PTK787/ZK222584 (PTK/ZK) was tested for anti-tumour efficacy and its effects on DCE-MRI measured parameters of tumour vascularity. Data from the non-invasive measure of tumour vascularity were compared with a histological measurement of vasculature using the DNA-staining dye H33342. PTK/ZK inhibited growth of the primary and, particularly, cervical tumour metastases following chronic treatment for 2 weeks (50 or 100 mg/kg daily) of 1-week-old tumours, or with 1 week of treatment against more established (2-week-old) tumours. After chronic treatment with PTK/ZK, DCE-MRI detected significant decreases in K(trans) and interstitial leakage space, but not rTBV of both primary tumours and cervical metastases. Histological data at this time-point showed a significant decrease in blood vessel density of the cervical metastases but not the primary tumours. However, in the cervical metastases, the mean blood vessel width was increased by 38%, suggesting overall no marked change in blood volume. After acute (2-4 day) treatment, DCE-MRI of the cervical metastases demonstrated a significant decrease in K(trans) and interstitial leakage space and also in the initial area under the enhancement curve for GdDOTA (IAUC), but no change in the rTBV or BFI. Thus, significant changes could be detected in the DCE-MRI measurement of tumour uptake of a small contrast agent prior to changes in tumour size, which suggests that DCE-MRI could be applied in the clinic as a rapid and sensitive biomarker for the effects of VEGF-R inhibition on tumour blood vessel permeability and thus may provide an early marker for eventual tumour response.     

A Method to Recognize Anatomical Site and Image Acquisition View in X-ray Images

A method was developed to recognize anatomical site and image acquisition view automatically in 2D X-ray images that are used in image-guided radiation therapy. The purpose is to enable site and view dependent automation and optimization in the image processing tasks including 2D-2D image registration, 2D image contrast enhancement, and independent treatment site confirmation. The X-ray images for 180 patients of six disease sites (the brain, head-neck, breast, lung, abdomen, and pelvis) were included in this study with 30 patients each site and two images of orthogonal views each patient. A hierarchical multiclass recognition model was developed to recognize general site first and then specific site. Each node of the hierarchical model recognized the images using a feature extraction step based on principal component analysis followed by a binary classification step based on support vector machine. Given two images in known orthogonal views, the site recognition model achieved a 99% average F1 score across the six sites. If the views were unknown in the images, the average F1 score was 97%. If only one image was taken either with or without view information, the average F1 score was 94%. The accuracy of the site-specific view recognition models was 100%.     

Three-Dimensional Registration of Magnetic Resonance Image Data to Histological Sections with Model-Based Evaluation

We developed a three-dimensional (3D) registration method to align medical scanner data with histological sections. After acquiring 3D medical scanner images, we sliced and photographed the tissue using, a custom apparatus, to obtain a volume of tissue section images. Histological samples from the sections were digitized using a video microscopy system. We aligned the histology and medical images to the reference tissue images using our 3D registration method. We applied the method to correlate in vivo magnetic resonance (MR) and histological measurements for radio-frequency thermal ablation lesions in rabbit thighs. For registration evaluation, we used an ellipsoid model to describe the lesion surfaces. The model surface closely fit the inner (M1) and outer (M2) boundaries of the hyperintense region in MR lesion images, and the boundary of necrosis (H1) in registered histology images. We used the distance between the model surfaces to indicate the 3D registration error. For four experiments, we measured a registration accuracy of 0.96± 0.13 mm (mean±SD) from the absolute distance between the M2 and H1 model surfaces, which compares favorably to the 0.70 mm in-plane MR voxel dimension. This suggests that our registration method provides sufficient spatial correspondence to correlate 3D medical scanner and histology data.     

Automatic assessment of dynamic contrast-enhanced MRI in an ischemic rat hindlimb model: an exploratory study of transplanted multipotent progenitor cells

This study presents computerized automatic image analysis for quantitatively evaluating dynamic contrast-enhanced MRI in an ischemic rat hindlimb model. MRI at 7 T was performed on animals in a blinded placebo-controlled experiment comparing multipotent adult progenitor cell-derived progenitor cell (MDPC)-treated, phosphate buffered saline (PBS)-injected, and sham-operated rats. Ischemic and non-ischemic limb regions of interest were automatically segmented from time-series images for detecting changes in perfusion and late enhancement. In correlation analysis of the time-signal intensity histograms, the MDPC-treated limbs correlated well with their corresponding non-ischemic limbs. However, the correlation coefficient of the PBS control group was significantly lower than that of the MDPC-treated and sham-operated groups. In semi-quantitative parametric maps of contrast enhancement, there was no significant difference in hypo-enhanced area between the MDPC and PBS groups at early perfusion-dependent time frames. However, the late-enhancement area was significantly larger in the PBS than the MDPC group. The results of this exploratory study show that MDPC-treated rats could be objectively distinguished from PBS controls. The differences were primarily determined by late contrast enhancement of PBS-treated limbs. These computerized methods appear promising for assessing perfusion and late enhancement in dynamic contrast-enhanced MRI.     

Deformable registration of the planning image (kVCT) and the daily images (MVCT) for adaptive radiation therapy

The incorporation of daily images into the radiotherapy process leads to adaptive radiation therapy (ART), in which the treatment is evaluated periodically and the plan is adaptively modified for the remaining course of radiotherapy. Deformable registration between the planning image and the daily images is a key component of ART. In this paper, we report our researches on deformable registration between the planning kVCT and the daily MVCT image sets. The method is based on a fast intensity-based free-form deformable registration technique. Considering the noise and contrast resolution differences between the kVCT and the MVCT, an 'edge-preserving smoothing' is applied to the MVCT image prior to the deformable registration process. We retrospectively studied daily MVCT images from commercial TomoTherapy machines from different clinical centers. The data set includes five head-neck cases, one pelvis case, two lung cases and one prostate case. Each case has one kVCT image and 20-40 MVCT images. We registered the MVCT images with their corresponding kVCT image. The similarity measures and visual inspections of contour matches by physicians validated this technique. The applications of deformable registration in ART, including 'deformable dose accumulation', 'automatic re-contouring' and 'tumour growth/regression evaluation' throughout the course of radiotherapy are also studied.     

A 3D MRI sequence for computer assisted surgery of the lumbar spine

The aim of this research was to develop a magnetic resonance (MR) sequence capable of producing images suitable for use with computer assisted surgery (CAS) of the lumbar spine. These images needed good tissue contrast between bone and soft tissue to allow for image segmentation and generation of a 3D-surface model of the bone for surface registration. A 3D double echo fast gradient echo sequence was designed. Images were filtered for noise and non-uniformity and combined into a single data set. Registration experiments were carried out to directly compare segmentation of MR and computed tomography (CT) images using a physical model of a spine. These experiments showed the MR data produced adequate surface registration in 90% of the experiments compared to 100% with CT data. The MR images acquired using the sequence and processing described in this article are suitable to be used with CAS of the spine.     

Prediction of chemotherapeutic response of colorectal liver metastases with dynamic gadolinium-DTPA-enhanced MRI and localized 19F MRS pharmacokinetic studies of 5-fluorouracil

Systemic chemotherapy is effective in only a subset of patients with metastasized colorectal cancer. Therefore, early selection of patients who are most likely to benefit from chemotherapy is desirable. Response to treatment may be determined by the delivery of the drug to the tumor, retention of the drug in the tumor and by the amount of intracellular uptake, metabolic activation and catabolism, as well as other factors. The first aim of this study was to investigate the predictive value of DCE-MRI with the contrast agent Gd-DTPA for tumor response to first-line chemotherapy in patients with liver metastases of colorectal cancer. The second aim was to investigate the predictive value of 5-fluorouracil (FU) uptake, retention and catabolism as measured by localized (19)F MRS for tumor response to FU therapy. Since FU uptake, retention and metabolism may depend on tumor vascularization, the relationship between (19)F MRS and the DCE-MRI parameters k(ep), K(trans) and v(e) was also examined (1). In this study, 37 patients were included. The kinetic parameters of DCE-MRI, k(ep), K(trans) and v(e), before start of treatment did not predict tumor response after 2 months, suggesting that the delivery of chemotherapy by tumor vasculature is not a major factor determining response in first-line treatment. No evident correlations between (19)F MRS parameters and tumor response were found. This suggests that in liver metastases that are not selected on the basis of their tumor diameter, FU uptake and catabolism are not limiting factors for response. The transfer constant K(trans), as measured by DCE-MRI before start of treatment, was negatively correlated with FU half-life in the liver metastases, which suggests that, in metastases with a larger tumor blood flow or permeability surface area product, FU is rapidly washed out from the tumor.