scholarly journals Microstructural characterization and validation of a 3D printed axon-mimetic phantom for diffusion MRI

2020 ◽  
Author(s):  
Farah N. Mushtaha ◽  
Tristan K. Kuehn ◽  
Omar El-Deeb ◽  
Seyed A. Rohani ◽  
Luke W. Helpard ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Microstructural Characterization ◽  
Diffusion Kurtosis Imaging ◽  
Micro Ct ◽  
Variable Effect ◽  
3D Printed ◽  
The Stability ◽  
Diffusion Kurtosis ◽  
And Diffusion ◽  
Microscopy Images

AbstractPurposeTo introduce and characterize inexpensive and easily produced 3D-printed axon-mimetic (3AM) diffusion MRI (dMRI) phantoms in terms of pore geometry and diffusion kurtosis imaging (DKI) metrics.MethodsPhantoms were 3D-printed with a composite printing material that, after dissolution of the PVA, exhibits microscopic fibrous pores. Confocal microscopy and synchrotron phase contrast micro-CT imaging were performed to visualize and assess the pore sizes. dMRI scans of four identical phantoms and phantoms with varying print parameters in water were performed at 9.4T. DKI was fit to both datasets and used to assess the reproducibility between phantoms and effects of print parameters on DKI metrics. Identical scans were performed 25 and 76 days later to test their stability.ResultsSegmentation of pores in three microscopy images yielded a mean, median, and standard deviation of equivalent pore diameters of 7.57 μm, 3.51 μm, and 12.13 μm, respectively. Phantoms with identical parameters showed a low coefficient of variation (∼10%) in DKI metrics (D=1.38 ×10−3 mm2/s and K=0.52, T1= 3960 ms and T2=119 ms). Printing temperature and speed had a small effect on DKI metrics (<16%) while infill density had a larger and more variable effect (>16%). The stability analysis showed small changes over 2.5 months (<7%).Conclusion3AM phantoms can mimic the fibrous structure of axon bundles on a microscopic scale, serving as complex, anisotropic dMRI phantoms.

scholarly journals Preoperatively Grading Rectal Cancer with the Combination of Intravoxel Incoherent Motions Imaging and Diffusion Kurtosis Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhijun Geng ◽  
Yunfei Zhang ◽  
Shaohan Yin ◽  
Shanshan Lian ◽  
Haoqiang He ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Diagnostic Performance ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Diffusion Kurtosis Imaging ◽  
Low Grade ◽  
Characteristic Analysis ◽  
Microstructural Heterogeneity ◽  
Diffusion Kurtosis ◽  
And Diffusion

Purpose. To combine Intravoxel Incoherent Motions (IVIM) imaging and diffusion kurtosis imaging (DKI) which can aid in the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity to preoperatively grade rectal cancer. Methods. A total of 58 rectal patients were included into this prospective study. MRI was performed with a 3T scanner. Different combinations of IVIM-derived and DKI-derived parameters were performed to grade rectal cancer. Pearson correlation coefficients were applied to evaluate the correlations. Binary logistic regression models were established via integrating different DWI parameters for screening the most sensitive parameter. Receiver operating characteristic analysis was performed for evaluating the diagnostic performance. Results. For individual DWI-derived parameters, all parameters except the pseudodiffusion coefficient displayed the capability of grading rectal cancer ( p < 0.05 ). The better discrimination between high- and low-grade rectal cancer was achieved with the combination of different DWI-derived parameters. Similarly, ROC analysis suggested the combination of D (true diffusion coefficient), f (perfusion fraction), and Kapp (apparent kurtosis coefficient) yielded the best diagnostic performance (AUC = 0.953, p < 0.001 ). According to the result of binary logistic analysis, cellularity-related D was the most sensitive predictor (odds ratio: 9.350 ± 2.239) for grading rectal cancer. Conclusion. The combination of IVIM and DKI holds great potential in accurately grading rectal cancer as IVIM and DKI can provide the quantification of different biological inspirations including cellularity, vascularity, and microstructural heterogeneity.

scholarly journals Diagnostic evaluation of magnetization transfer and diffusion kurtosis imaging for prostate cancer detection in a re-biopsy population

2017 ◽  
Vol 28 (8) ◽  
pp. 3141-3150 ◽  
Author(s):  
Tristan Barrett ◽  
Mary McLean ◽  
Andrew N. Priest ◽  
Edward M. Lawrence ◽  
Andrew J. Patterson ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Detection ◽  
Magnetization Transfer ◽  
Diagnostic Evaluation ◽  
Diffusion Kurtosis Imaging ◽  
Prostate Cancer Detection ◽  
Diffusion Kurtosis ◽  
And Diffusion

scholarly journals Combined 18F-FET PET and diffusion kurtosis MRI in post-treatment glioblastoma: differentiation of true progression from treatment related changes

2021 ◽  
Author(s):  
Francesco D’Amore ◽  
Farida Grinberg ◽  
Jörg Mauler ◽  
Norbert Galldiks ◽  
Ganna Blazhenets ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Diagnostic Accuracy ◽  
Diffusion Mri ◽  
Tumour Progression ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Regions Of Interest ◽  
Fet Pet ◽  
Diffusion Kurtosis ◽  
And Diffusion

Abstract Background Radiological differentiation of tumour progression (TPR) from treatment-related changes (TRC) in pre-treated glioblastoma is crucial. This study aimed to explore the diagnostic value of diffusion kurtosis MRI combined with information derived from O-(2-[ 18F]-fluoroethyl)-L-tyrosine ( 18F-FET) PET for the differentiation of TPR from TRC in patients with pre-treated glioblastoma. Methods Thirty-two patients with histomolecularly defined and pre-treated glioblastoma suspected of having TPR were included in this retrospective study. Twenty-one patients were included in the TPR group, and 11 patients in the TRC group, as assessed by neuropathology or clinicoradiological follow-up. 3D regions-of-interest were generated based on increased 18F-FET uptake using a brain-to-tumour ratio of 1.6. Furthermore, diffusion MRI kurtosis maps were obtained from the same regions-of-interests using co-registered 18F-FET PET images, and an advanced histogram analysis of diffusion kurtosis map parameters was applied to generated 3D regions-of-interest. Diagnostic accuracy was analysed by receiver-operating characteristic curve analysis and combinations of PET and MRI parameters using multivariate logistic regression. Results Parameters derived from diffusion MRI kurtosis maps show high diagnostic accuracy, up to 88%, for differentiating between TPR and TRC. Logistic regression revealed that the highest diagnostic accuracy of 94% (area under the curve, 0.97; sensitivity, 94%; specificity, 91%) was achieved by combining the maximum tumour-to-brain ratio of 18F-FET uptake and diffusion MRI kurtosis metrics. Conclusions The combined use of 18F-FET PET and MRI diffusion kurtosis maps appears to be a promising approach to improve the differentiation of TPR from TRC in pre-treated glioblastoma and warrants further investigation.

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