Stratification of prostate cancer patients into low- and high-grade groups using multiparametric magnetic resonance radiomics with dynamic contrast-enhanced image joint histograms.

PURPOSE: This study aimed to investigate the potential of stratification of prostate cancer patients into low- and high-grade groups (GGs) using multiparametric magnetic resonance (mpMR) radiomics in conjunction with two-dimensional (2D) joint histograms computed with dynamic contrast-enhanced (DCE) images. METHODS: A total of 101 prostate cancer regions extracted from the MR images of 44 patients were identified and divided into training (n = 31 with 72 cancer regions) and test datasets (n = 13 with 29 cancer regions). Each dataset included low-grade tumors (International Society of Urological Pathology [ISUP] GG ≤ 2) and high-grade tumors (ISUP GG ≥ 3). A total of 137,970 features consisted of mpMR image (16 types of images in four sequences)-based and joint histogram (DCE images at 10 phases)-based features for each cancer region. Joint histogram features can visualize temporally changing perfusion patterns in prostate cancer based on the joint histograms between different phases or subtraction phases of DCE images. Nine signatures (a set of significant features related to GGs) were determined using the best combinations of features selected using the least absolute shrinkage and selection operator. Further, support vector machine models with the nine signatures were built based on a leave-one-out cross-validation for the training dataset and evaluated with receiver operating characteristic (ROC) curve analysis. RESULTS: The signature showing the best performance was constructed using six features derived from the joint histograms, DCE original images, and apparent diffusion coefficient maps. The areas under the ROC curves for the training and test datasets were 1.00 and 0.985, respectively. CONCLUSION: This study suggests that the proposed approach with mpMR radiomics in conjunction with 2D joint histogram computed with DCE images could have the potential to stratify prostate cancer patients into low- and high-GGs.

A feasibility study to estimate optimal rigid-body registration using combinatorial rigid registration optimization (CORRO).

PURPOSE: Clinical image pairs provide the most realistic test data for image registration evaluation. However, the optimal registration is unknown. Using combinatorial rigid registration optimization (CORRO) we demonstrate a method to estimate the optimal alignment for rigid-registration of clinical image pairs. METHODS: Expert selected landmark pairs were selected for each CT/CBCT image pair for six cases representing head and neck, thoracic, and pelvic anatomic regions. Combination subsets of a k number of landmark pairs (k-combination set) were generated without repeat to form a large set of k-combination sets (k-set) for k = 4,8,12. The rigid transformation between the image pairs was calculated for each k-combination set. The mean and standard deviation of these transformations were used to derive final registration for each k-set. RESULTS: The standard deviation of registration output decreased as the k-size increased for all cases. The joint entropy evaluated for each k-set of each case was smaller than those from two commercially available registration programs indicating a stronger correlation between the image pair after CORRO was used. A joint histogram plot of all three algorithms showed high correlation between them. As further proof of the efficacy of CORRO the joint entropy of each member of 30 000 k-combination sets in k = 4 were calculated for one of the thoracic cases. The minimum joint entropy was found to exist at the estimated mean of registration indicating CORRO converges to the optimal rigid-registration results. CONCLUSIONS: We have developed a methodology called CORRO that allows us to estimate optimal alignment for rigid-registration of clinical image pairs using a large set landmark point. The results for the rigid-body registration have been shown to be comparable to results from commercially available algorithms for all six cases. CORRO can serve as an excellent tool that can be used to test and validate rigid registration algorithms.

Extending two-dimensional histology into the third dimension through conventional micro computed tomography.

Histological examination achieves sub-micrometer resolution laterally. In the third dimension, however, resolution is limited to section thickness. In addition, histological sectioning and mounting sections on glass slides introduce tissue-dependent stress and strain. In contrast, state-of-the-art hard X-ray micro computed tomography (µCT) systems provide isotropic sub-micrometer resolution and avoid sectioning artefacts. The drawback of µCT in the absorption contrast mode for visualising physically soft tissue is a low attenuation difference between anatomical features. In this communication, we demonstrate that formalin-fixed paraffin-embedded human cerebellum yields appropriate absorption contrast in laboratory-based µCT data, comparable to conventional histological sections. Purkinje cells, for example, are readily visible. In order to investigate the pros and cons of complementary approaches, two- and three-dimensional data were manually and automatically registered. The joint histogram of histology and the related µCT slice allows for a detailed discussion on how to integrate two-dimensional information from histology into a three-dimensional tomography dataset. This methodology is not only rewarding for the analysis of the human cerebellum, but it also has relevance for investigations of tissue biopsies and post-mortem applications. Our data indicate that laboratory-based µCT as a modality can fill the gap between synchrotron radiation-based µCT and histology for a variety of tissues. As the information from haematoxylin and eosin (H&E) stained sections and µCT data is related, one can colourise local X-ray absorption values according to the H&E stain. Hence, µCT data can correlate and virtually extend two-dimensional (2D) histology data into the third dimension.

Dual modality neutron and x-ray tomography for enhanced image analysis of the bone-metal interface.

The bone tissue formed at the contact interface with metallic implants, particularly its 3D microstructure, plays a pivotal role for the structural integrity of implant fixation. X-ray tomography is the classical imaging technique used for accessing microstructural information from bone tissue. However, neutron tomography has shown promise for visualising the immediate bone-metal implant interface, something which is highly challenging with x-rays due to large differences in attenuation between metal and biological tissue causing image artefacts. To highlight and explore the complementary nature of neutron and x-ray tomography, proximal rat tibiae with titanium-based implants were imaged with both modalities. The two techniques were compared in terms of visualisation of different material phases and by comparing the properties of the individual images, such as the contrast-to-noise ratio. After superimposing the images using a dedicated image registration algorithm, the complementarity was further investigated via analysis of the dual modality histogram, joining the neutron and x-ray data. From these joint histograms, peaks with well-defined grey value intervals corresponding to the different material phases observed in the specimens were identified and compared. The results highlight differences in how neutrons and x-rays interact with biological tissues and metallic implants, as well as the benefits of combining both modalities. Future refinement of the joint histogram analysis could improve the segmentation of structures and tissues, and yield novel information about specimen-specific properties such as moisture content.

Mineralization of Early Stage Carious Lesions In Vitro-A Quantitative Approach.

Micro computed tomography has been combined with dedicated data analysis for the in vitro quantification of sub-surface enamel lesion mineralization. Two artificial white spot lesions, generated on a human molar crown in vitro, were examined. One lesion was treated with a self-assembling peptide intended to trigger nucleation of hydroxyapatite crystals. We non-destructively determined the local X-ray attenuation within the specimens before and after treatment. The three-dimensional data was rigidly registered. Three interpolation methods, i.e., nearest neighbor, tri-linear, and tri-cubic interpolation were evaluated. The mineralization of the affected regions was quantified via joint histogram analysis, i.e., a voxel-by-voxel comparison of the tomography data before and after mineralization. After ten days incubation, the mean mineralization coefficient reached 35.5% for the peptide-treated specimen compared to 11.5% for the control. This pilot study does not give any evidence for the efficacy of peptide treatment nor allows estimating the necessary number of specimens to achieve significance, but shows a sound methodological approach on the basis of the joint histogram analysis.