Artem Khmelinskii

Brain maturation of the adolescent rat cortex and striatum: Changes in volume and myelination☆

Longitudinal studies on brain pathology and assessment of therapeutic strategies rely on a fully mature adult brain to exclude confounds of cerebral developmental changes. Thus, knowledge about onset of adulthood is indispensable for discrimination of developmental phase and adulthood. We have performed a high-resolution longitudinal MRI study at 11.7T of male Wistar rats between 21days and six months of age, characterizing cerebral volume changes and tissue-specific myelination as a function of age. Cortical thickness reaches final value at 1month, while volume increases of cortex, striatum and whole brain end only after two months. Myelin accretion is pronounced until the end of the third postnatal month. After this time, continuing myelination increases in cortex are still seen on histological analysis but are no longer reliably detectable with diffusion-weighted MRI due to parallel tissue restructuring processes. In conclusion, cerebral development continues over the first three months of age. This is of relevance for future studies on brain disease models which should not start before the end of month 3 to exclude serious confounds of continuing tissue development.

Articulated Whole-Body Atlases for Small Animal Image Analysis: Construction and Applications

PurposeUsing three publicly available small-animal atlases (Sprague–Dawley rat, MOBY, and Digimouse), we built three articulated atlases and present several applications in the scope of molecular imaging.ProceduresMajor bones/bone groups were manually segmented for each atlas skeleton. Then, a kinematic model for each atlas was built: each joint position was identified and the corresponding degrees of freedom were specified.ResultsThe articulated atlases enable automated registration into a common coordinate frame of multimodal small-animal imaging data. This eliminates the postural variability (e.g., of the head, back, and front limbs) that occurs in different time steps and due to modality differences and nonstandardized acquisition protocols.ConclusionsThe articulated atlas proves to be a useful tool for multimodality image combination, follow-up studies, and image processing in the scope of molecular imaging. The proposed models were made publicly available.

Optical advances in skeletal imaging applied to bone metastases

Optical Imaging has evolved into one of the standard molecular imaging modalities used in pre-clinical cancer research. Bone research however, strongly depends on other imaging modalities such as SPECT, PET, x-ray and μCT. Each imaging modality has its own specific strengths and weaknesses concerning spatial resolution, sensitivity and the possibility to quantify the signal. An increasing number of bone specific optical imaging models and probes have been developed over the past years. This review gives an overview of optical imaging modalities, models and probes that can be used to study skeletal complications of cancer in small laboratory animals.

Precise Anatomic Localization of Accumulated Lipids in Mfp2 Deficient Murine Brains Through Automated Registration of SIMS Images to the Allen Brain Atlas.

AbstractMass spectrometry imaging (MSI) is a powerful tool for the molecular characterization of specific tissue regions. Histochemical staining provides anatomic information complementary to MSI data. The combination of both modalities has been proven to be beneficial. However, direct comparison of histology based and mass spectrometry-based molecular images can become problematic because of potential tissue damages or changes caused by different sample preparation. Curated atlases such as the Allen Brain Atlas (ABA) offer a collection of highly detailed and standardized anatomic information. Direct comparison of MSI brain data to the ABA allows for conclusions to be drawn on precise anatomic localization of the molecular signal. Here we applied secondary ion mass spectrometry imaging at high spatial resolution to study brains of knock-out mouse models with impaired peroxisomal β-oxidation. Murine models were lacking D-multifunctional protein (MFP2), which is involved in degradation of very long chain fatty acids. SIMS imaging revealed deposits of fatty acids within distinct brain regions. Manual comparison of the MSI data with the histologic stains did not allow for an unequivocal anatomic identification of the fatty acids rich regions. We further employed an automated pipeline for co-registration of the SIMS data to the ABA. The registration enabled precise anatomic annotation of the brain structures with the revealed lipid deposits. The precise anatomic localization allowed for a deeper insight into the pathology of Mfp2 deficient mouse models. Graphical Abstractᅟ

Atlas-based organ & bone approximation for ex-vivo μMRI mouse data: A pilot study

In this paper we propose a novel semi-automated atlas-based approach for organ and bone approximation for micro-Magnetic Resonance Imaging (μMRI) data of mice. Based on a set of 18 manually indicated landmarks at specific joint & bone locations, individual atlas bones (pelvis, limb bones and sternum) are mapped to the target in a first step and a sparse set of corresponding landmarks on a skin surface representation is determined in a second step. Subsequently, this sparse set on the skin is used to derive a dense set of correspondences relying on matching spectra of local geodesic distances. Finally, determined by the skin correspondence, a Thin-Plate-Spline (TPS) approximation of major organs (heart, lungs, liver, spleen, stomach, kidneys) is performed. The method was tested using 3 µMRI mouse datasets and the MOBY atlas. The performance of the organ approximation algorithm was estimated using manual segmentations of 6 organs for each MRI dataset and calculating Dice indices of organ-volume overlap for each dataset and the atlas. The obtained results indicate excellent fitting of heart and kidneys and moderate fitting of spleen, lungs, liver and stomach. These initial results are satisfactory and comparable to other organ mapping studies using different approaches and μComputed Tomography (CT) mouse data.

Segmentation and Visual Analysis of Whole-Body Mouse Skeleton microSPECT

Whole-body SPECT small animal imaging is used to study cancer, and plays an important role in the development of new drugs. Comparing and exploring whole-body datasets can be a difficult and time-consuming task due to the inherent heterogeneity of the data (high volume/throughput, multi-modality, postural and positioning variability). The goal of this study was to provide a method to align and compare side-by-side multiple whole-body skeleton SPECT datasets in a common reference, thus eliminating acquisition variability that exists between the subjects in cross-sectional and multi-modal studies. Six whole-body SPECT/CT datasets of BALB/c mice injected with bone targeting tracers 99mTc-methylene diphosphonate (99mTc-MDP) and 99mTc-hydroxymethane diphosphonate (99mTc-HDP) were used to evaluate the proposed method. An articulated version of the MOBY whole-body mouse atlas was used as a common reference. Its individual bones were registered one-by-one to the skeleton extracted from the acquired SPECT data following an anatomical hierarchical tree. Sequential registration was used while constraining the local degrees of freedom (DoFs) of each bone in accordance to the type of joint and its range of motion. The Articulated Planar Reformation (APR) algorithm was applied to the segmented data for side-by-side change visualization and comparison of data. To quantitatively evaluate the proposed algorithm, bone segmentations of extracted skeletons from the correspondent CT datasets were used. Euclidean point to surface distances between each dataset and the MOBY atlas were calculated. The obtained results indicate that after registration, the mean Euclidean distance decreased from 11.5±12.1 to 2.6±2.1 voxels. The proposed approach yielded satisfactory segmentation results with minimal user intervention. It proved to be robust for “incomplete” data (large chunks of skeleton missing) and for an intuitive exploration and comparison of multi-modal SPECT/CT cross-sectional mouse data.

Super-resolution reconstruction of whole-body MRI mouse data: An interactive approach

Super-resolution reconstruction (SRR) is a post-acquisition method for producing a high-resolution (HR) image from a set of low-resolution (LR) images. However, for large volumes of data, this technique is computationally very demanding and time consuming. In this study we focus on the specific case of whole-body mouse data and present a novel, integrated, end-to-end approach to overcome this problem. We combine articulated atlas-based segmentation and planar reformation techniques with state-of-the-art in SRR to produce high resolution, interactively selected, localized isotropic volumes-of-interest in whole-body mouse MRI. With this method we overcome time and memory related limitations when applying the SRR algorithm to the entire dataset, enabling interactive visualization and exploration of anatomical structures of interest in whole-body MRI mouse data on a normal desktop PC.

Atlas-based articulated skeleton segmentation of μSPECT mouse data

In this paper we propose an automated articulated atlas-based approach for bone segmentation in whole-body μSPECT data of mice, obtained by injecting the 99mTc-methylene diphosphonate (99mTc - MDP). This is a difficult task, since SPECT data is usually noisy and low resolution, and the skeleton image is incomplete with several portions missing (e.g.: in limbs and skull). For this purpose the articulated version of the MOBY atlas skeleton with a correspondent hierarchical tree description is used. Iterative Closest Point registration is used, while constraining the local degrees of freedom (DoFs) in accordance to the type of joint and its range of motion. The method was tested using 3 whole-body μSPECT mouse datasets acquired using a NanoSPECT/CT scanner for small animals and the MOBY atlas. To evaluate the proposed algorithm, manual bone segmentations of extracted skeletons from the correspondent CT datasets were used. Euclidean point to surface distances for each dataset and the MOBY atlas were calculated. The obtained results indicate that after registration, the mean Euclidean distance descreased from 8.37 ± 8.70 to 2.27 ± 2.06 voxels. The results were presented using a novel method for change visualization in small animal imaging (Articulated Planar Reformation).

Automated Ischemic Lesion Segmentation in MRI Mouse Brain Data after Transient Middle Cerebral Artery Occlusion

Magnetic resonance imaging (MRI) has become increasingly important in ischemic stroke experiments in mice, especially because it enables longitudinal studies. Still, quantitative analysis of MRI data remains challenging mainly because segmentation of mouse brain lesions in MRI data heavily relies on time-consuming manual tracing and thresholding techniques. Therefore, in the present study, a fully automated approach was developed to analyze longitudinal MRI data for quantification of ischemic lesion volume progression in the mouse brain. We present a level-set-based lesion segmentation algorithm that is built using a minimal set of assumptions and requires only one MRI sequence (T2) as input. To validate our algorithm we used a heterogeneous data set consisting of 121 mouse brain scans of various age groups and time points after infarct induction and obtained using different MRI hardware and acquisition parameters. We evaluated the volumetric accuracy and regional overlap of ischemic lesions segmented by our automated method against the ground truth obtained in a semi-automated fashion that includes a highly time-consuming manual correction step. Our method shows good agreement with human observations and is accurate on heterogeneous data, whilst requiring much shorter average execution time. The algorithm developed here was compiled into a toolbox and made publically available, as well as all the data sets.

Interactive local super-resolution reconstruction of whole-body MRI mouse data: A pilot study with applications to bone and kidney metastases

In small animal imaging studies, when the locations of the micro-structures of interest are unknown a priori, there is a simultaneous need for full-body coverage and high resolution. In MRI, additional requirements to image contrast and acquisition time will often make it impossible to acquire such images directly. Recently, a resolution enhancing post-processing technique called super-resolution reconstruction (SRR) has been demonstrated to improve visualization and localization of micro-structures in small animal MRI by combining multiple low-resolution acquisitions. However, when the field-of-view is large relative to the desired voxel size, solving the SRR problem becomes very expensive, in terms of both memory requirements and computation time. In this paper we introduce a novel local approach to SRR that aims to overcome the computational problems and allow researchers to efficiently explore both global and local characteristics in whole-body small animal MRI. The method integrates state-of-the-art image processing techniques from the areas of articulated atlas-based segmentation, planar reformation, and SRR. A proof-of-concept is provided with two case studies involving CT, BLI, and MRI data of bone and kidney tumors in a mouse model. We show that local SRR-MRI is a computationally efficient complementary imaging modality for the precise characterization of tumor metastases, and that the method provides a feasible high-resolution alternative to conventional MRI.