Bifurcated Topological Optimization for IVIM

In this work, we shed light on the issue of estimating Intravoxel Incoherent Motion (IVIM) for diffusion and perfusion estimation by characterizing the objective function using simplicial homology tools. We provide a robust solution via topological optimization of this model so that the estimates are more reliable and accurate. Estimating the tissue microstructure from diffusion MRI is in itself an ill-posed and a non-linear inverse problem. Using variable projection functional (VarPro) to fit the standard bi-exponential IVIM model we perform the optimization using simplicial homology based global optimization to better understand the topology of objective function surface. We theoretically show how the proposed methodology can recover the model parameters more accurately and consistently by casting it in a reduced subspace given by VarPro. Additionally we demonstrate that the IVIM model parameters cannot be accurately reconstructed using conventional numerical optimization methods due to the presence of infinite solutions in subspaces. The proposed method helps uncover multiple global minima by analyzing the local geometry of the model enabling the generation of reliable estimates of model parameters.

BRMP-04. AI-based biomarker of the peritumoral region using tissue microstructure

PURPOSE Glioblastomas, the most common malignant brain tumor [BS1], infiltrate into peritumoral brain structures, making clinical management challenging. An unmet need is to develop a biomarker that reliably characterize infiltration in the peritumoral region, where surgical biopsy or resection may be hazardous. Diffusion tensor imaging (DTI) with multicompartment modeling can characterize extracellular free water, providing unique information of the tissue microstructure that is able to capture this heterogeneity. We propose a novel biomarker based on peritumoral tissue microstructure, using deep-learning on DTI-based free water map. METHOD Peritumoral regions were automatically segmented for 136 patients with brain tumors (86 glioblastomas and 50 metastases, ages 23–87 years, 65 females). We trained a Convolutional Neural Network (CNN) on free-water maps using automatically defined patches in the peritumoral area from glioblastomas and metastases, labeled as low free-water and high free-water to extract a microstructural index for each voxel. To extract the biomarker, we grouped peritumoral voxels into connected components (CCs) where adjacent voxels have high (>0.9) microstructural index values. Two independent test sets related to two clinically significant problems were evaluated: i) metastases vs. glioblastomas; ii) glioma patients categorized into short and long survival groups and the number of CCs were statistically compared. RESULT Two-sample t-tests showed significant group difference in the number of CCs between metastases and glioblastomas (p< 0.05), and short and long-survivals (p<0.05) with higher number of CCs in metastases and long-survivals, which suggests smaller number of voxels in CCs. CONCLUSION The proposed biomarker based on CCs of microstructural index captures the differences in infiltration of the peritumoral region, showing larger CCs in glioblastomas and short-survivals corresponding to higher infiltration. CLINICAL IMPORTANCE The proposed biomarker provides a novel insight into the peritumoral microenvironment and can be derived from clinically feasible DTI data, providing new possibilities for the diagnosis and treatment of glioblastoma.

Effect of High Hydrostatic Pressure on the Extractability and Bioaccessibility of Carotenoids and Their Esters from Papaya (Carica papaya L.) and Its Impact on Tissue Microstructure

High hydrostatic pressure (HHP) is a non-thermal technology widely used in the industry to extend food shelf-life and it has been proven to enhance the extractability of secondary metabolites, such as carotenoids, in plant foods. In this study, fresh-cut papaya pulp of varieties (Sweet Mary, Alicia and Eksotika) from the Canary Islands (Spain) were submitted to the HHP process (pressure: 100, 350 and 600 MPa; time: come-up time (CUT) and 5 min) to evaluate, for the first time, individual carotenoid and carotenoid ester extractability and to assess their bioaccessibility using an in vitro simulated gastrointestinal digestion assay, following the standardized INFOGEST® methodology. In addition, changes in papaya pulp microstructure after HHP treatments and during the different phases of the in vitro digestion were evaluated with optical light microscopy. HPLC-DAD (LC-MS/MS (APCI+)) analyses revealed that HHP treatments increased the carotenoid content, obtaining the highest extractability in pulp of the Sweet Mary papaya variety treated at 350 MPa during 5 min (4469 ± 124 μg/100 g fresh weight) which was an increase of 269% in respect to the HHP-untreated control sample. The highest carotenoid extraction value within each papaya variety among all HHP treatments was observed for (all-E)-lycopene, in a range of 98–1302 μg/100 g fresh weight (23–344%). Light micrographs of HHP-treated pulps showed many microstructural changes associated to carotenoid release related to the observed increase in their content. Carotenoids and carotenoid esters of papaya pulp submitted to in vitro digestion showed great stability; however, their bioaccessibility was very low due to the low content of fatty acids in papaya pulp necessary for the micellarization process. Further studies will be required to improve papaya carotenoid and carotenoid ester bioaccessibility.

Soma and Neurite Density MRI (SANDI) of the in-vivo mouse brain

Diffusion MRI (dMRI) provides unique insights into the neural tissue milieu by probing interaction of diffusing molecules and tissue microstructure. Most dMRI techniques focus on white matter tissues (WM) due to the relatively simpler modelling of diffusion in the more organized tracts; however, interest is growing in gray matter characterisations. The Soma and Neurite Density MRI (SANDI) methodology harnesses a model incorporating water diffusion in spherical objects (assumed to be associated with cell bodies) and in impermeable 'sticks' (representing neurites), which potentially enables the characterisation of cellular and neurite densities. Recognising the importance of rodents in animal models of development, aging, plasticity, and disease, we here sought to develop SANDI for preclinical imaging and provide a validation of the methodology by comparing its metrics with the Allen mouse brain atlas. SANDI was implemented on a 9.4T scanner equipped with a cryogenic coil, and experiments were carried out on N=6 mice. Pixelwise, ROI-based, and atlas comparisons were performed, and results were also compared to more standard Diffusion Kurtosis MRI (DKI) metrics. We further investigated effects of different pre-processing pipelines, specifically the comparison of magnitude and real-valued data, as well as different acceleration factors. Our findings reveal excellent reproducibility of the SANDI parameters, including the sphere and stick fraction as well as sphere size. More strikingly, we find a very good rank correlation between SANDI-driven soma fraction and Allen brain atlas contrast (which represents the cellular density in the mouse brain). Although some DKI parameters (FA, MD) correlated with some SANDI parameters in some ROIs, they did not correlate nearly as well as SANDI parameters with the Allen atlas, suggesting a much more specific nature of the SANDI parameters. We conclude that SANDI is a viable preclinical MRI technique that can greatly contribute to research on brain tissue microstructure.

Half Way There: Theoretical Considerations for Power Laws and Sticks in Diffusion MRI for Tissue Microstructure

In this article, we consider how differing approaches that characterize biological microstructure with diffusion weighted magnetic resonance imaging intersect. Without geometrical boundary assumptions, there are techniques that make use of power law behavior which can be derived from a generalized diffusion equation or intuited heuristically as a time dependent diffusion process. Alternatively, by treating biological microstructure (e.g., myelinated axons) as an amalgam of stick-like geometrical entities, there are approaches that can be derived utilizing convolution-based methods, such as the spherical means technique. Since data acquisition requires that multiple diffusion weighting sensitization conditions or b-values are sampled, this suggests that implicit mutual information may be contained within each technique. The information intersection becomes most apparent when the power law exponent approaches a value of 12, whereby the functional form of the power law converges with the explicit stick-like geometric structure by way of confluent hypergeometric functions. While a value of 12 is useful for the case of solely impermeable fibers, values that diverge from 12 may also reveal deep connections between approaches, and potentially provide insight into the presence of compartmentation, exchange, and permeability within heterogeneous biological microstructures. All together, these disparate approaches provide a unique opportunity to more completely characterize the biological origins of observed changes to the diffusion attenuated signal.