Anna Celler

Visualization and exploration of time-varying medical image data sets

In this work, we propose and compare several methods for the visualization and exploration of time-varying volumetric medical images based on the temporal characteristics of the data. The principle idea is to consider a time-varying data set as a 3D array where each voxel contains a time-activity curve (TAC). We define and appraise three different TAC similarity measures. Based on these measures we introduce three methods to analyze and visualize time-varying data. The first method relates the whole data set to one template TAC and creates a 1D histogram. The second method extends the 1D histogram into a 2D histogram by taking the Euclidean distance between voxels into account. The third method does not rely on a template TAC but rather creates a 2D scatter-plot of all TAC data points via multi-dimensional scaling. These methods allow the user to specify transfer functions on the 1D and 2D histograms and on the scatter plot, respectively. We validate these methods on synthetic dynamic SPECT and PET data sets and a dynamic planar Gamma camera image of a patient. These techniques are designed to offer researchers and health care professionals a new tool to study the time-varying medical imaging data sets.

Complexity and accuracy of image registration methods in SPECT-guided radiation therapy.

The use of functional imaging in radiotherapy treatment (RT) planning requires accurate co-registration of functional imaging scans to CT scans. We evaluated six methods of image registration for use in SPECT-guided radiotherapy treatment planning. Methods varied in complexity from 3D affine transform based on control points to diffeomorphic demons and level set non-rigid registration. Ten lung cancer patients underwent perfusion SPECT-scans prior to their radiotherapy. CT images from a hybrid SPECT/CT scanner were registered to a planning CT, and then the same transformation was applied to the SPECT images. According to registration evaluation measures computed based on the intensity difference between the registered CT images or based on target registration error, non-rigid registrations provided a higher degree of accuracy than rigid methods. However, due to the irregularities in some of the obtained deformation fields, warping the SPECT using these fields may result in unacceptable changes to the SPECT intensity distribution that would preclude use in RT planning. Moreover, the differences between intensity histograms in the original and registered SPECT image sets were the largest for diffeomorphic demons and level set methods. In conclusion, the use of intensity-based validation measures alone is not sufficient for SPECT/CT registration for RTTP. It was also found that the proper evaluation of image registration requires the use of several accuracy metrics.

Co-registration of Bone CT and SPECT Images Using Mutual Information

We present an automatic and accurate technique for 3D co-registration of SPECT and CT. The method allows the attenuation correction of SPECT images and fusion of the anatomic details from CT and the functional information from SPECT. Registration was achieved by optimizing the mutual information metric over the parameter space defined by the translation and rotation parameters. To improve the robustness and accuracy of the algorithm, registration was performed in a coarse-to-fine manner. We applied the algorithm on three clinical data sets originating from 1 pelvic and 2 thoracic studies. Validation was done by inspecting the 2D and 3D fusion of the registered images and by observing the convergence in the metric and the transformation parameters. We also evaluated quantitatively the effects of the choice of the parameters, the number of multiresolution levels, and initial misalignment of the paired volumes. Registration of both studies converged close to a final alignment with a maximum translational error of 1.41 mm plusmn 0.78 mm and rotational error of 1.21deg plusmn 0.46deg for the thoracic study and a maximum translational error of 1.96 mm plusmn 1.27 mm and rotational error of 0.57deg plusmn 0.34deg for the pelvic studies. The average computation time on a 3.0 GHz PC was < 4 minutes for the entire registration procedure. We conclude that the algorithm had successfully co-registered the CT and SPECT images

Validation of mutual information-based registration of CT and bone SPECT images in dual-isotope studies

The registration of computed tomography (CT) and nuclear medicine (NM) images can substantially enhance patient diagnosis as it allows for the fusion of anatomical and functional information, as well as the attenuation correction of NM images. However, irrespective of the method used, registration accuracy depends heavily on the characteristics of the images that are registered and the degree of similarity between them. This poses a challenge for registering CT and NM images as they have very different characteristics and content. To address the particular problem of registering single photon emission computed tomography (SPECT) oncology studies with corresponding CT, we have proposed to perform a dual-isotope study with simultaneous injection of a tumor tracer and a bone imaging agent to obtain a tumor SPECT and a bone SPECT image that are inherently registered. As bone structures are generally visible in both CT and bone SPECT, performing registration of these images will be more easily attainable than registration of CT and tumor SPECT. By subsequently applying the spatial transformation determined from this registration to the tumor SPECT acquired from the same dual-isotope study, the optimal alignment between the CT and tumor SPECT images can be obtained. In this paper, we present the proof-of-concept of the proposed approach, the MI-based algorithm employed, and the techniques used to select the algorithms parameters. Our objectives are to show the feasibility of CT and bone SPECT registration using this algorithm and to validate quantitatively the results generated using clinical data.

Scatter segmentation in dynamic SPECT images using principal component analysis

Dynamic single photon emission computed tomography (dSPECT) provides time-varying spatial information about changes of tracer distribution in the body from data acquired using a standard (single slow rotation) protocol. Variations of tracer distribution observed in the images might be due to physiological processes in the body, but may also stem from reconstruction artefacts. These two possibilities are not easily separated because of the highly underdetermined nature of the dynamic reconstruction problem. Since it is expected that temporal changes in tracer distribution may carry important diagnostic information, the analysis of dynamic SPECT images should consider and use this additional information. In this paper we present a segmentation scheme for aggregating voxels with similar time activity curves (TACs). Voxel aggregates are created through region merging based on a similarity criterion on a reduced set of features, which is derived after transformation into eigenspace. Region merging was carried out on dSPECT images from simulated and patient myocardial perfusion studies using various stopping criteria and ranges of accumulated variances in eigenspace. Results indicate that segmentation clearly separates heart and liver tissues from the background. The segmentation quality did not change significantly if more than 99% of the variance was incorporated into the feature vector. The heart behaviour followed an expected exponential decay curve while some variation of time behaviour in liver was observed. Scatter artefacts from photons originating from liver could be identified in long as well as in short studies.

Monte Carlo simulation in SPECT: a comparison of two approaches

Monte Carlo methods play an important role in medical imaging research. Direct analog Monte Carlo simulations can be very accurate but require considerable computational resources. Variance reduction techniques may offer a solution to this problem. In this paper we present a comparison of expected values of standard quantities of interest for SPECT using these two simulation methods. The effect of variance reduction on the statistical characteristics of the simulated data is also investigated.

Segmentation-Based Regularization of Dynamic SPECT Reconstructions

Dynamic SPECT reconstruction using a single slow camera rotation is a highly underdetermined problem, which requires the use of regularization techniques to obtain useful results. The dSPECT algorithm (Farncombe et al. 1999) provides temporal but not spatial regularization, resulting in poor contrast and low activity levels in organs of interest, due mostly to blurring. In this paper we incorporate a user-assisted segmentation algorithm (Saad et al. 2008) into the reconstruction process to improve the results. Following an initial reconstruction using the existing dSPECT technique, a user places seeds in the image to indicate regions of interest (ROIs). A random-walk based automatic segmentation algorithm then assigns every voxel in the image to one of the ROIs, based on its proximity to the seeds as well as the similarity between time activity curves (TACs). The user is then able to visualize the segmentation and improve it if necessary. Average TACs are extracted from each ROI and assigned to every voxel in the ROI, giving an image with a spatially uniform TAC in each ROI. This image is then used as initial input to a second run of dSPECT, in order to adjust the dynamic image to better fit the projection data. We test this approach with a digital phantom simulating the kinetics of Tc99m-DTPA in the renal system, including healthy and unhealthy behaviour. Summed TACs for each kidney and the bladder were calculated for the spatially regularized and non-regularized reconstructions, and compared to the true values. The TACs for the two kidneys were noticeably improved in every case, while TACs for the smaller bladder region were unchanged. Furthermore, in two cases where the segmentation was intentionally done incorrectly, the spatially regularized reconstructions were still as good as the non-regularized ones. In general, the segmentation-based regularization improves TAC quality within ROIs, as well as image contrast.

Local identification and removal of scatter artefacts based on the temporal information in dynamic SPECT images

Scatter in SPECT images may mask decreased uptake of radiopharmaceuticals in the left ventricle of the heart, which can alter the diagnostic outcome of the study. The newly developed dynamic SPECT (dSPECT) method, which reconstructs 4D images from a standard acquisition protocol, provides additional temporal information, which may be helpful to recognise such artefacts. Each voxel carries a time signature, which is different for different organs. In this paper, we investigate whether this signature can be used to detect and remove scatter. Time activity curves (TACs) from segmented data are tested for their potential to locally identify scatter according to a simple model. The investigation is carried out on artificial artefacts in real patient data as well as on existing scatter. Tests on the artificial artefacts showed that scatter can indeed be detected and removed while tests on real data revealed that the simplified model may suffice to remove the majority of local scatter.

Segmentierung des linken Ventrikels in 4d-dSPECT-Daten mittels Frei-Form-Deformation von Superellipsoiden

Die 4D-dSPECT-Technik ist eine neue Moglichkeit zur Erkennung und Beurteilung von Herzerkrankungen. Die Auswertung der Daten erfolgte dabei in einem mehrstufigen Vorgang. So muss zuerst wegen der schlechten Bildqualitat eine Reduktion von Rauschartefakten in den Daten vorgenommen werden. Zur eigentlichen Segmentierung des linken Ventrikels wurde eine modellbasierte Segmentierungsmethode entwickelt. Diese besteht in der Anpassung eines Gestaltmodells mittels Frei-Form-Deformation an die Daten. Zur Abschatzung der Gute dieser Segmentierung wurde ein Vergleich mit einer manuellen Segmentierung durchgefuhrt.

Segmentation-free direct tumor volume and metabolic activity estimation from PET scans

Tumor volume and metabolic activity are two robust imaging biomarkers for predicting early therapy response in F-fluorodeoxyglucose (FDG) positron emission tomography (PET), which is a modality to image the distribution of radiotracers and thereby observe functional processes in the body. To date, estimation of these two biomarkers requires a lesion segmentation step. While the segmentation methods requiring extensive user interaction have obvious limitations in terms of time and reproducibility, automatically estimating activity from segmentation, which involves integrating intensity values over the volume is also suboptimal, since PET is an inherently noisy modality. Although many semi-automatic segmentation based methods have been developed, in this paper, we introduce a method which completely eliminates the segmentation step and directly estimates the volume and activity of the lesions. We trained two parallel ensemble models using locally extracted 3D patches from phantom images to estimate the activity and volume, which are derivatives of other important quantification metrics such as standardized uptake value (SUV) and total lesion glycolysis (TLG). For validation, we used 54 clinical images from the QIN Head and Neck collection on The Cancer Imaging Archive, as well as a set of 55 PET scans of the Elliptical Lung-Spine Body Phantom™with different levels of noise, four different reconstruction methods, and three different background activities, namely; air, water, and hot background. In the validation on phantom images, we achieved relative absolute error (RAE) of 5.11u202f%u202f±3.5% and 5.7u202f%u202f±5.25% for volume and activity estimation, respectively, which represents improvements of over 20% and 6% respectively, compared with the best competing methods. From the validation performed using clinical images, we found that the proposed method is capable of obtaining almost the same level of agreement with a group of trained experts, as a single trained expert is, indicating that the method has the potential to be a useful tool in clinical practice.