Biology subjects

Knutsson, L.

Publications and source records attributed to Knutsson, L..

3 recordsLinked to original sources

bioRxiv2025

Restriction-weighted q-space trajectory imaging (ResQ): Toward mapping diffusion time effects with tensor-valued diffusion encoding in human prostate cancer xenografts

PurposeTensor-valued diffusion encoding employs gradient waveforms that enable unique sensitivity to microstructural features of tissue, but the interpretation of signal and parameters may be confounded by diffusion-time dependence. We introduce a framework for restriction-weighted q-space trajectory imaging (ResQ) that incorporates diffusion-time effects via the restriction-weighting tensor, and we evaluate it in a longitudinal study of prostate cancer xenografts treated by external radiotherapy. MethodsWe proposed a novel gradient waveform design for tensor-valued encoding with controlled restriction weighting and applied a set of four waveforms at a 9.4 T preclinical MRI system. Mice were inoculated with human prostate cancer cells (LNCaP) and assigned to groups that were untreated controls or treated by external beam irradiation. ResQ produced parameters that describes the diffusion process in terms of the mean diffusivity (D), isotropic diffusional variance (VDi), and microscopic diffusion anisotropy (VDa) as well as their diffusion-time dependence ({Delta}D, {Delta}VDi, {Delta}VDa). Analyses were performed to characterize parameters longitudinally and across groups. To highlight the consequences of ignoring restriction effects, we compared ResQ to analogous parameters estimated by q-space trajectory imaging (QTI). ResultsResQ revealed clear diffusion-time dependence across all tumors, with significant longitudinal differences between treated and untreated groups, most prominent in D, {Delta}D, and VDi. The ResQ signal representation captured the signal dynamics, whereas QTI did not. Neglecting diffusion-time dependence in QTI led to substantial parameter bias, most notably a pronounced overestimation of microscopic diffusion anisotropy. ConclusionDiffusion-time effects are non-negligible in prostate cancer and must be considered when using tensor-valued diffusion encoding. The ResQ framework enables controlled restriction weighting and improved interpretability of diffusion MRI parameters compared to approaches that ignore the effects of restriction. This provides a more principled approach for tensor-valued diffusion encoding and may enable novel imaging biomarkers that disentangle diffusivity, isotropic diffusional variance, microscopic anisotropy, and their diffusion-time dependence.

biophysics↗

bioRxiv2022

Meningioma microstructure assessed by diffusion MRI: an investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology

BackgroundMean diffusivity (MD) and fractional anisotropy (FA) obtained with diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level. PurposeTo quantify the degree to which cell density (CD) and structure anisotropy (SA), as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether histological features other than cell density account for additional intra-tumor variability of MD. Materials and MethodsWe performed ex-vivo dMRI at 200 m isotropic resolution and histological imaging on 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FAIP). Histology images were analyzed in terms of cell nuclei density and structure anisotropy (obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FAIP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI maps from histology patches. The association between MRI and histology was analyzed in terms of coefficient of determination (R2). Regions showing unexplained variance (large residuals) were analyzed to identify features apart from cell density and structure anisotropy that could influence MD and FAIP. ResultsCell density assessed by histology poorly explained intra-tumor variability at the mesoscopic level (200 m) in MD (median R2 = 0.06, interquartile range 0.01 - 0.29) or FAIP (median R2 = 0.19, 0.09 - 0.29). Samples with low R2 for FAIP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, cell density and structure anisotropy were associated with MD (R2 = 0.58) and FAIP (R2 = 0.82), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction when solely CD was considered. Our results support that FAIP is high in the presence of elongated and aligned cell structures, but low otherwise. ConclusionCell density and structure anisotropy account for variability in MD and FAIP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD. HighlightsO_LICell density accounts for MD variability across but not within meningioma tumors. C_LIO_LIStructure anisotropy accounts for in-plane FA variability across and within tumors C_LIO_LIVascularization, psammoma bodies, and microcysts influence the MD. C_LIO_LIHigh and low meningioma tumor cell density can yield similar MD. C_LIO_LIFeatures beyond cell density need to be considered when interpreting MD. C_LI

neuroscience↗

bioRxiv2022

Concurrent measurement of perfusion parameters related to small blood and lymphatic vessels in the human brain using dynamic dual-spin-echo perfusion (DDSEP) MRI

PURPOSEAccumulating evidence from recent studies has indicated the importance of studying the interaction between the microvascular and lymphatic systems in the brain. To date, most imaging methods can only measure blood or lymphatic vessels separately, such as dynamic-susceptibility-contrast (DSC) MRI for blood vessels and DSC MRI in the CSF (cDSC MRI) for lymphatic vessels. An approach that can measure both blood and lymphatic vessels in a single scan will offer the advantages such as halved scan time and contrast dosage. Based on previous works on DSC and cDSC MRI, this study proposes an MRI approach for concurrent measurement of perfusion parameters related to small blood and lymphatic vessels in the brain within one single scan. METHODSBloch simulations were performed to optimize a dual-echo sequence for the measurement of gadolinium(Gd)-induced blood and CSF signal changes using a short and a long echo time, respectively. MRI experiments were performed in healthy subjects to evaluate the dual-echo approach by comparing it with existing separate methods. RESULTSThe proposed method showed consistent results in human brains as previous studies using separate methods. Signal changes from small blood vessels occurred faster than lymphatic vessels after intravenous Gd-injection. CONCLUSIONGd-induced signal changes in blood and CSF can be detected simultaneously in healthy subjects with the proposed sequence. To the best of our knowledge, this may be the first study in which the temporal difference in Gd-induced signal changes from small blood and lymphatic vessels after intravenous Gd-injection was measured in the same human subjects.

neuroscience↗