Rakesh Mullick

Comparison of 4-class and continuous fat/water methods for whole-body, MR-based PET attenuation correction

The goal of this study was to compare two approaches for MR-based PET patient attenuation correction (AC) in whole-body FDG-PET imaging using a tri-modality PET/CT & MR setup. Sixteen clinical whole-body FDG patients were included in this study. Mean standard uptake values (SUV) were measured for liver and lung volumes-of-interest for comparison. Maximum SUV values were measured in 18 FDGavid features in ten of the patients. The AC methods compared to gold-standard CT-based AC were segmentation of the CT (air, lung, fat, water), MR image segmentation with 4 tissue classes (air, lung, fat, water) and segmentation with air, lung and a continuous fat/water method. Results: The magnitude of uptake value differences induced by CT-based image segmentation were similar but lower on average than those found using the MRderived AC methods. The average liver SUV difference with that found using CTAC was 1.3%, 10.4% and 5.7% for 4-class segmented CT, 4-class MRAC and continuous fat/water MRAC methods, respectively. The average FDG-avid feature SUV max difference was -0.5%,1.7% and -1.6% for 4-class segmented CT, 4-class MRAC and continuous fat/water MRAC methods, respectively. Conclusion: The results demonstrated that both 4class and continuous fat/water AC methods provided adequate quantitation in the body, and that the continuous fat/water method was within 5.7% on average for SUV mean in liver and 1.6% on average for SUV max for FDG-avid features.

A framework for automated tumor detection in thoracic FDG pet images using texture-based features

This paper proposes a novel framework for tumor detection in Positron Emission Tomography (PET) images. A set of 8 second-order texture features obtained from the gray level co-occurrence matrix (GLCM) across 26 offsets, together with uptake value was used to construct a feature vector at each voxel in the data. Volume of Interest (VOI) samples from 42 images (7 patients with 6 gates each), marked by a radiologist, representing 5 distinct anatomy types and pathology were used to train a logit boost classifier. A ten-fold cross-validation showed a true positive rate of 96%and a false positive rate of 8% for tumor classification. The test dataset consisted of 50 × 50 × 40 representative VOIs from gated PET images of 3 patients. The classifier was run on the test data, followed by an SUV-based thresholding and elimination of noise using connected component analysis. The method detected 10/12 (83%) tumors while detecting an average of 20 false positive structures.

Automatic partitioning of head CTA for enabling segmentation

Radiologists perform a CT Angiography procedure to examine vascular structures and associated pathologies such as aneurysms. Volume rendering is used to exploit volumetric capabilities of CT that provides complete interactive 3-D visualization. However, bone forms an occluding structure and must be segmented out. The anatomical complexity of the head creates a major challenge in the segmentation of bone and vessel. An analysis of the head volume reveals varying spatial relationships between vessel and bone that can be separated into three sub-volumes: “proximal”, “middle”, and “distal”. The “proximal” and “distal” sub-volumes contain good spatial separation between bone and vessel (carotid referenced here). Bone and vessel appear contiguous in the “middle” partition that remains the most challenging region for segmentation. The partition algorithm is used to automatically identify these partition locations so that different segmentation methods can be developed for each sub-volume. The partition locations are computed using bone, image entropy, and sinus profiles along with a rule-based method. The algorithm is validated on 21 cases (varying volume sizes, resolution, clinical sites, pathologies) using ground truth identified visually. The algorithm is also computationally efficient, processing a 500+ slice volume in 6 seconds (an impressive 0.01 seconds / slice) that makes it an attractive algorithm for pre-processing large volumes. The partition algorithm is integrated into the segmentation workflow. Fast and simple algorithms are implemented for processing the “proximal” and “distal” partitions. Complex methods are restricted to only the “middle” partition. The partitionenabled segmentation has been successfully tested and results are shown from multiple cases.

Simulations of Virtual PET/CT 3-D Bronchoscopy Imaging Using a Physical Porcine Lung–Heart Phantom

PurposeWe present a systematic approach for studying positron emission tomography–computed tomography (PET/CT) 3-D virtual fly-through endoscopy and for assessing the accuracy of this technology for visualizing and detecting endobronchial lesions as a function of focal lesion morphology and activity.ProceduresCapsules designed to simulate endobronchial lesions were filled with activity and introduced into a porcine lung–heart phantom. PET/CT images were acquired, reconstructed, and volume rendered as 3-D fly-through and fly-around visualizations. Anatomical positioning of lesions seen on the 3-D-volume-rendered PET/CT images was compared to the actual position of the capsules.ResultsLesion size was observed to be highly sensitive to PET threshold parameter settings and careful opacity and color transfer function parameter assignment.ConclusionWe have demonstrated a phantom model for studies of PET/CT 3-D virtual fly-through bronchoscopy and have applied this model for understanding the effect of PET thresholding on the visualization and detection of lesions.

Deformable registration with spatially varying degrees of freedom constraints

Intra-subject deformable registration applications, such as longitudinal analysis and multi-modal imaging, use a high degree freedom deformation to accurately align soft tissue. However, smoothness constraints applied to the deformation and insufficient degrees of freedom in the deformation may distort the more rigid tissue types such as bone. In this paper, we present a technique that aligns rigid structures using rigid constraints while aligning soft tissue with a high degree of freedom deformation.

Recursive feature elimination for biomarker discovery in resting-state functional connectivity

Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate analysis suffers from the problem of multiple comparisons. Here, we adopt an alternative data-driven method for identifying population differences in functional connectivity. We propose a machine-learning approach to down-select functional connectivity features associated with symptom severity in mild traumatic brain injury (mTBI). Using this approach, we identified functional regions with altered connectivity in mTBI. including the executive control, visual and precuneus networks. We compared functional connections at multiple resolutions to determine which scale would be more sensitive to changes related to patient recovery. These modular network-level features can be used as diagnostic tools for predicting disease severity and recovery profiles.

Topological texture-based method for mass detection in breast ultrasound image

Texture analysis plays an important role in many image processing tasks. In this work, we present a texture descriptor based on the topology of excursion sets, derived from the concept of Minkowski functionals, and evaluate their usefulness in the detection of breast masses in 2D breast ultrasound images. The application includes three major stages: preprocessing, including candidate generation through computation of gradient concentration under a Fisher-Tippet noise model (in itself another contribution of the paper); texture feature extraction; and region classification using a Random Forests classifier. Performance of the proposed method is evaluated on 135 2D BUS images with 139 masses. Our method reaches 91% sensitivity with an averaged 1.19 false detections, and the proposed texture feature compares favorably against the often-used grey level co-occurrence matrices on the exact the same task.

Volume rendering segmented data using 3D textures: a practical approach for intra-operative visualization

Volume rendering has high utility in visualization of segmented datasets. However, volume rendering of the segmented labels along with the original data causes undesirable intermixing/bleeding artifacts arising from interpolation at the sharp boundaries. This issue is further amplified in 3D textures based volume rendering due to the inaccessibility of the interpolation stage. We present an approach which helps minimize intermixing artifacts while maintaining the high performance of 3D texture based volume rendering - both of which are critical for intra-operative visualization. Our approach uses a 2D transfer function based classification scheme where label distinction is achieved through an encoding that generates unique gradient values for labels. This helps ensure that labelled voxels always map to distinct regions in the 2D transfer function, irrespective of interpolation. In contrast to previously reported algorithms, our algorithm does not require multiple passes for rendering and supports greater than 4 masks. It also allows for real-time modification of the colors/opacities of the segmented structures along with the original data. Additionally, these capabilities are available with minimal texture memory requirements amongst comparable algorithms. Results are presented on clinical and phantom data.

Implicit function-based phantoms for evaluation of registration algorithms

Medical image fusion is increasingly enhancing diagnostic accuracy by synergizing information from multiple images, obtained by the same modality at different times or from complementary modalities such as structural information from CT and functional from PET. An active, crucial research topic in fusion is validation of the registration (point-to-point correspondence) used. Phantoms and other simulated studies are useful in the absence of, or as a preliminary to, definitive clinical tests. Software phantoms in specific have the added advantage of robustness, repeatability and reproducibility. Our virtual-lung-phantom-based scheme can test the accuracy of any registration algorithm and is flexible enough for added levels of complexity (addition of blur/anti-alias, rotate/warp, and modality-associated noise) to help evaluate the robustness of an image registration/fusion methodology. Such a framework extends easily to different anatomies. The feature of adding software-based fiducials both within and outside simulated anatomies prove more beneficial when compared to experiments using data from external fiducials on a patient. It would help the diagnosing clinician make a prudent choice of registration algorithm.

Textural content in 3T MR: an image-based marker for Alzheimer's disease

In this paper, we propose a study, which investigates the first-order and second-order distributions of T2 images from a magnetic resonance (MR) scan for an age-matched data set of 24 Alzheimers disease and 17 normal patients. The study is motivated by the desire to analyze the brain iron uptake in the hippocampus of Alzheimers patients, which is captured by low T2 values. Since, excess iron deposition occurs locally in certain regions of the brain, we are motivated to investigate the spatial distribution of T2, which is captured by higher-order statistics. Based on the first-order and second-order distributions (involving gray level co-occurrence matrix) of T2, we show that the second-order statistics provide features with sensitivity >90% (at 80% specificity), which in turn capture the textural content in T2 data. Hence, we argue that different texture characteristics of T2 in the hippocampus for Alzheimers and normal patients could be used as an early indicator of Alzheimers disease.