Yaniv Gal

Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means

This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. It is a novel variation on the nonlocal means (NLM) algorithm. The algorithm, called dynamic nonlocal means (DNLM), exploits the redundancy of information in the temporal sequence of images. Empirical evaluations of the performance of the DNLM algorithm relative to seven other denoising methods-simple Gaussian filtering, the original NLM algorithm, a trivial extension of NLM to include the temporal dimension, bilateral filtering, anisotropic diffusion filtering, wavelet adaptive multiscale products threshold, and traditional wavelet thresholding-are presented. The evaluations include quantitative evaluations using simulated data and real data (20 DCE-MRI data sets from routine clinical breast MRI examinations) as well as qualitative evaluations using the same real data (24 observers: 14 image/signal-processing specialists, 10 clinical breast MRI radiographers). The results of the quantitative evaluation using the simulated data show that the DNLM algorithm consistently yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the quantitative evaluation using the real data provide evidence, at the ¿ = 0.05 level of significance, that the DNLM algorithm yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the qualitative evaluation provide evidence, at the ¿ = 0.05 level of significance, that the DNLM algorithm performs visually better than all of the other algorithms. Collectively the qualitative and quantitative results suggest that the DNLM algorithm more effectively attenuates noise in DCE MR images than any of the other algorithms.

Correlation of MRI-derived apparent diffusion coefficients in newly diagnosed gliomas with [18F]-fluoro-L-dopa PET: what are we really measuring with minimum ADC?

BACKGROUND AND PURPOSE: There is significant interest in whether diffusion-weighted MR imaging indices, such as the minimum apparent diffusion coefficient, may be useful clinically for preoperative tumor grading and treatment planning. To help establish the pathologic correlate of minimum ADC, we undertook a study investigating the relationship between minimum ADC and maximum FDOPA PET uptake in patients with newly diagnosed glioblastoma multiforme. MATERIALS AND METHODS: MR imaging and FDOPA PET data were acquired preoperatively from 15 patients who were subsequently diagnosed with high-grade brain tumor (WHO grade III or IV) by histopathologic analysis. ADC and SUVR normalized FDOPA PET maps were registered to the corresponding CE MR imaging. Regions of minimum ADC within the FDOPA-defined tumor volume were anatomically correlated with areas of maximum FDOPA SUVR uptake. RESULTS: Minimal anatomic overlap was found between regions exhibiting minimum ADC (a putative marker of tumor cellularity) and maximum FDOPA SUVR uptake (a marker of tumor infiltration and proliferation). FDOPA SUVR measures for tumoral regions exhibiting minimum ADC (1.36 ± 0.22) were significantly reduced compared with those with maximum FDOPA uptake (2.45 ± 0.88, P = .0001). CONCLUSIONS: There was a poor correlation between minimum ADC and the most viable/aggressive component of high-grade gliomas. This study suggests that other factors, such as tissue compression and ischemia, may be contributing to restricted diffusion in GBM. Caution should be exercised in the clinical use of minimum ADC as a marker of tumor grade and the use of this index for guiding tumor biopsies preoperatively.

Hypoxia Imaging in Gliomas With 18F-Fluoromisonidazole PET: Toward Clinical Translation

There is significant interest in the development of improved image-guided therapy for neuro-oncology applications. Glioblastomas (GBM) in particular present a considerable challenge because of their pervasive nature, propensity for recurrence, and resistance to conventional therapies. MRI is routinely used as a guide for planning treatment strategies. However, this imaging modality is not able to provide images that clearly delineate tumor boundaries and affords only indirect information about key tumor pathophysiology. With the emergence of PET imaging with new oncology radiotracers, mapping of tumor infiltration and other important molecular events such as hypoxia is now feasible within the clinical setting. In particular, the importance of imaging hypoxia levels within the tumoral microenvironment is gathering interest, as hypoxia is known to play a central role in glioma pathogenesis and resistance to treatment. One of the hypoxia radiotracers known for its clinical utility is (18)F-fluoromisodazole ((18)F-FMISO). In this review, we highlight the typical causes of treatment failure in gliomas that may be linked to hypoxia and outline current methods for the detection of hypoxia. We also provide an overview of the growing body of studies focusing on the clinical translation of (18)F-FMISO PET imaging, strengthening the argument for the use of (18)F-FMISO hypoxia imaging to help optimize and guide treatment strategies for patients with glioblastoma.

Increasing feasibility and utility of 18F-FDOPA PET for the management of glioma

INTRODUCTION Despite radical treatment therapies, glioma continues to carry with it a uniformly poor prognosis. Patients diagnosed with WHO Grade IV glioma (glioblastomas; GBM) generally succumb within two years, even those with WHO Grade III anaplastic gliomas and WHO Grade II gliomas carry prognoses of 2-5 and 2 years, respectively. PET imaging with (18)F-FDOPA allows in vivo assessment of the metabolism of glioma relative to surrounding tissues. The high sensitivity of (18)F-DOPA imaging grants utility for a number of clinical applications. METHODS A collection of published work about (18)F-FDOPA PET was made and a critical review was discussed and written. RESULTS A number of research papers have been published demonstrating that in conjunction with MRI, (18)F-FDOPA PET provides greater sensitivity and specificity than these modalities in detection, grading, prognosis and validation of treatment success in both primary and recurrent gliomas. In further comparisons with (11)C-MET, (18)F-FLT, (18)F-FET and MRI, (18)F-FDOPA has shown similar or better efficacy. Recently synthesis cassettes have become available, making (18)F-FDOPA more accessible. CONCLUSIONS According to the available data, (18)F-FDOPA PET is a viable radiotracer for imaging and treatment planning of gliomas. ADVANCES IN KNOWLEDGE AND IMPLICATION FOR PATIENT CARE (18)F-FDOPA PET appears to be a viable radiopharmaceutical for the diagnosis and treatment planning of gliomas cases, improving on that of MRI and (18)F-FDG PET.

Validity and reliability of computerized measurement of lumbar intervertebral disc height and volume from magnetic resonance images.

BACKGROUND CONTEXT Magnetic resonance (MR) examinations of morphologic characteristics of intervertebral discs (IVDs) have been used extensively for biomechanical studies and clinical investigations of the lumbar spine. Traditionally, the morphologic measurements have been performed using time- and expertise-intensive manual segmentation techniques not well suited for analyses of large-scale studies.. PURPOSE The purpose of this study is to introduce and validate a semiautomated method for measuring IVD height and mean sagittal area (and volume) from MR images to determine if it can replace the manual assessment and enable analyses of large MR cohorts. STUDY DESIGN/SETTING This study compares semiautomated and manual measurements and assesses their reliability and agreement using data from repeated MR examinations. METHODS Seven healthy asymptomatic males underwent 1.5-T MR examinations of the lumbar spine involving sagittal T2-weighted fast spin-echo images obtained at baseline, pre-exercise, and postexercise conditions. Measures of the mean height and the mean sagittal area of lumbar IVDs (L1-L2 to L4-L5) were compared for two segmentation approaches: a conventional manual method (10-15 minutes to process one IVD) and a specifically developed semiautomated method (requiring only a few mouse clicks to process each subject). RESULTS Both methods showed strong test-retest reproducibility evaluated on baseline and pre-exercise examinations with strong intraclass correlations for the semiautomated and manual methods for mean IVD height (intraclass correlation coefficient [ICC]=0.99, 0.98) and mean IVD area (ICC=0.98, 0.99), respectively. A bias (average deviation) of 0.38 mm (4.1%, 95% confidence interval 0.18-0.59 mm) was observed between the manual and semiautomated methods for the IVD height, whereas there was no statistically significant difference for the mean IVD area (0.1%±3.5%). The semiautomated and manual methods both detected significant exercise-induced changes in IVD height (0.20 and 0.28 mm) and mean IVD area (5.7 and 8.3 mm(2)), respectively. CONCLUSIONS The presented semiautomated method provides an alternative to time- and expertise-intensive manual procedures for analysis of larger, cross-sectional, interventional, and longitudinal MR studies for morphometric analyses of lumbar IVDs.

Using the apparent diffusion coefficient to identifying MGMT promoter methylation status early in glioblastoma: importance of analytical method.

Accurate knowledge of O6‐methylguanine methyltransferase (MGMT) gene promoter subtype in patients with glioblastoma (GBM) is important for treatment. However, this test is not always available. Pre‐operative diffusion MRI (dMRI) can be used to probe tumour biology using the apparent diffusion coefficient (ADC); however, its ability to act as a surrogate to predict MGMT status has shown mixed results. We investigated whether this was due to variations in the method used to analyse ADC.

New Spatiotemporal Features for Improved Discrimination of Benign and Malignant Lesions in Dynamic Contrast-Enhanced-Magnetic Resonance Imaging of the Breast

Objectives: The objective of this study was to measure the efficacy of 7 new spatiotemporal features for discriminating between benign and malignant lesions in dynamic contrast-enhanced-magnetic resonance imaging (MRI) of the breast. Methods: A total of 48 breast lesions from 39 patients were used: 25 malignant and 23 benign. Lesions were acquired using 1.5-T MRI machines in 3 different protocols. Two experiments were performed: (i) selection of the most discriminatory subset of features drawn from the new features and features from the literature and (ii) validation of classification performance of the selected subset of features. Results: Results of the feature selection experiment show that the subset comprising 2 of the new features is the most useful for automatic classification of suspicious lesions in the breast: (i) gradient correlation of maximum intensity and (ii) mean wash-in rate. Results of the validation experiment show that using these 2 features, unseen data can be classified with an area under the receiver operating characteristic curve of 0.91 ± 0.06. Conclusions: Results of the experiments suggest that suspicious lesions in dynamic contrast-enhanced-MRI of the breast can be classified, with high accuracy, using only 2 of the proposed spatiotemporal features. The selected features indicate heterogeneity of enhancement and speed of enhancement in a tissue. High values of these indicators are likely to be correlated with malignancy.

Feature and Classifier Selection for Automatic Classification of Lesions in Dynamic Contrast-Enhanced MRI of the Breast

The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed and presents a preliminary study of the most discriminatory features for dynamic contrast-enhanced MRI of the breast. In particular the results of a feature/classifier selection experiment are presented based on 20 lesions (10 malignant and 10 benign) from 20 routine clinical breast MRI examinations. Each lesion was segmented manually by a clinical radiographer and its diagnostic status confirmed by cytopathology or histopathology. The results show that textural and kinetic, rather than morphometric, features are the most important for lesion classification. They also show that the SVM classifier with sigmoid kernel performs better than other well-known classifiers: Fishers linear discriminant function, Bayes linear classifier, logistic regression, and SVM with other kernels (distance, exponential, and radial).

The need for improved brain lesion segmentation techniques for children with cerebral palsy: A review.

Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. Accurate diagnosis and prognosis, in terms of motor type and severity, is difficult to obtain due to the heterogeneous appearance of brain injury and large anatomical distortions commonly observed in children with CP. There is a need to optimise treatment strategies for individual patients in order to lead to lifelong improvements in function and capabilities. Magnetic resonance imaging (MRI) is critical to non‐invasively visualizing brain lesions, and is currently used to assist the diagnosis and qualitative classification in CP patients. Although such qualitative approaches under‐utilise available data, the quantification of MRIs is not automated and therefore not widely performed in clinical assessment. Automated brain lesion segmentation techniques are necessary to provide valid and reproducible quantifications of injury. Such techniques have been used to study other neurological disorders, however the technical challenges unique to CP mean that existing algorithms require modification to be sufficiently reliable, and therefore have not been widely applied to MRIs of children with CP. In this paper, we present a review of a subset of available brain injury segmentation approaches that could be applied to CP, including the detection of cortical malformations, white and grey matter lesions and ventricular enlargement. Following a discussion of strengths and weaknesses, we suggest areas of future research in applying segmentation techniques to the MRI of children with CP. Specifically, we identify atlas‐based priors to be ineffective in regions of substantial malformations, instead propose relying on adaptive, spatially consistent algorithms, with fast initialisation mechanisms to provide additional robustness to injury. We also identify several cortical shape parameters that could be used to identify cortical injury, and shape modelling approaches to identify anatomical injury. The benefits of automatic segmentation in CP is important as it has the potential to elucidate the underlying relationship between image derived features and patient outcome, enabling better tailoring of therapy to individual patients.

Early prediction of treatment response in advanced gliomas with 18F-DOPA positron-emission tomography

The Editor Current Oncology August 27, 2013 Imaging markers that enable prediction of survival are of interest for aiding clinical decision-making for patients with advanced glioma. However, current imaging methods based on the use of contrast-enhanced magnetic resonance imaging (mri) and 18F-fludeoxyglucose positron-emission tomography (pet) applied in the early stages after treatment are not strongly correlated with patient outcome. In conjunction with mri, amino-acid pet tracers have shown promise for this application, including variations in l-[methyl-11C]-methionine uptake1 and 3′-dexoy-3′-[18F] fluorothymidine (flt)2 uptake, and volume variations of regions with high flt and 6-[18F] fluoro-l-dopa (18F-dopa)3 uptake. Uptake of flt is predictive of survival in recurrent advanced glioma, even when chemotherapy renders mri unreliable2. However, previous serial assessments have typically considered global uptake within the tumour1,2,4, even though treatment failure frequently occurs locally within areas of existing abnormality5. The hypothesis explored here is that poor patient survival is directly related to the extent of the most treatment-resistant cluster of malignant cells exhibiting persistent metabolic activity. Apart from the use of 18F-dopa as an early surrogate marker, a key difference between this study and previous work is the longitudinal comparison of metabolic activity within focal peritumoural regions. The study enrolled 9 patients (7 men; age range: 52–71 years), summarized in Table i, with histopathologically confirmed high-grade brain tumour (World Health Organization grade iv). The institutional ethics review board approved the study, and patients provided informed written consent. Patients received mri and 18F-dopa pet scans at two time points: a baseline immediately before tumour resection, and a follow-up at 12 weeks after resection. The post-resection interval consisted of 2 weeks’ recovery, a 6-week course of chemoradiotherapy (external-beam radiotherapy of 60 Gy in 30 fractions or 40 Gy in 15 fractions, with concurrent temozolomide), and 4 weeks’ recovery to minimize potential treatment-induced variations in metabolism. (Figure 1 provides a schematic outline.) The pet intensities were normalized to the ipsilateral cerebellum and reported as standardized uptake value ratios. One patient did not receive chemoradiotherapy. Table I Values extracted from each dataset Figure 1 Timing of magnetic resonance imaging (mri) and 18F-dopa positron-emission tomography (fdopa). Both modalities were used at two time points: once before surgery and once 12 weeks after surgery. Chemo-rt = chemoradiation therapy. After rigid registration, the most metabolically active voxel in each post-treatment tumour was selected from the pet image. Using nearby anatomic features from the fused mri, the corresponding location was selected on the pre-surgery image under the guidance of experienced specialists (PT, MF, RLJ, CW, AC) blinded to the pre-treatment pet image and patient outcome. The mean 18F-dopa uptake within a 1-cm radius sphere centred on each landmark was calculated before computing the difference between baseline and follow-up. The 1-cm radius traded off the typical statistical variation in pet intensities and potential inaccuracies in spatial correspondences with the size of metabolically active regions. A Cox proportional hazards analysis model was used to evaluate the relationship between variations in 18Fdopa uptake and survival. Survival times, plotted in Figure 2 as a function of change in 18F-dopa uptake, ranged from less than 30 weeks to more than 110 weeks. At last report, 3 patients remained alive. Variations in 18F-dopa uptake ranged from less than −50% to more than +20%. The results, summarized in Table ii, demonstrate that a decrease in 18F-dopa uptake is a predictor of extended survival. For interest, the selected regions in each patient are shown in Figures 3–5. A Cox proportional hazards model fitted the data closely (r = 0.65), and showed that the hazard to the patient declined by 10.3% with each 1% decrease in local 18F-dopa uptake. The null hypothesis—that changes in 18F-dopa are not related to survival—was rejected with some significance (p < 0.032). Figure 2 Plot of survival versus local change in the spherical region centred on the point of greatest metabolism after treatment. A value of 0% (dotted line) indicates no change in 18F-dopa uptake. Chemo-rt = chemoradiation therapy. aPatient was alive at last ... Table II Statistics extracted from all patients Figure 3 The spherical region of interest (green circles) superimposed on the 18F-dopa positron-emission tomography (pet) and contrast-enhanced magnetic resonance imaging (mri) images for patients 1–3 at baseline and follow-up. Baseline images were acquired ... Figure 5 The spherical region of interest (green circles) superimposed on the 18F-dopa positron-emission tomography (pet) and contrast-enhanced magnetic resonance (mr) images for patients 7–9 at baseline and follow-up. Baseline images were acquired before ... Figure 4 The spherical region of interest (green circles) superimposed on the 18F-dopa positron-emission tomography (pet) and contrast-enhanced magnetic resonance (mr) images for patients 4–6 at baseline and follow-up. Baseline images were acquired before ... A second model, incorporating age and sex, was compared with the first using analysis of variance (Table iii). The comparison showed that the models largely overlapped (p > 0.7), and consecutive increases for log likelihood (4.74 for change in local 18F-dopa, 0.24 for age, and 0.23 for sex) indicated that age and sex had a more limited influence on the results. Finally, global 18F-dopa uptake (mean over the entire tumour) was compared with survival. Compared with local 18F-dopa, global 18F-dopa was less correlated with survival (5.2% decline in hazard for 1% decrease in uptake), had a poorer fit (r = 0.33), and was less significant (p < 0.1). Table III Crudea analysis of variance that considers additional effects of age and sex The more significant correlation between 18F-dopa variations locally within tumours (rather than globally), supports the hypothesis that patient survival might be linked to focal points of treatment failure within peritumoural colonies of malignant cells that exhibit enhanced amino-acid uptake after therapy. Hence, variations in 18F-dopa are potentially indicative of a genetic predisposition in certain tumours toward treatment sensitivity, with the resulting delay before disease progression leading to extended survival. These results motivate for the use of 18F-dopa–based response criteria as endpoint markers and extend the concept of using 18F-dopa–based response criteria to as early as 12 weeks after surgery. Earlier prediction of response could potentially enable early transfer to palliative care if appropriate, early enrolment of patients into therapeutic trials for recurrent tumour, and rapid screening of new therapeutic agents.