Marko Pesola

Evaluation of different mathematical models for diffusion-weighted imaging of normal prostate and prostate cancer using high b-values: A repeatability study

To evaluate monoexponential, stretched exponential, kurtosis, and biexponential models for diffusion‐weighted imaging (DWI) of normal prostate and prostate cancer (PCa), using b‐values up to 2000 s/mm2, in terms of fitting quality and repeatability.

Mathematical models for diffusion-weighted imaging of prostate cancer using b values up to 2000 s/mm2: Correlation with Gleason score and repeatability of region of interest analysis

To evaluate four mathematical models for diffusion weighted imaging (DWI) of prostate cancer (PCa) in terms of PCa detection and characterization.

Optimization of b‐value distribution for biexponential diffusion‐weighted MR imaging of normal prostate

To determine the optimal b‐value distribution for biexponential diffusion‐weighted imaging (DWI) of normal prostate using both a computer modeling approach and in vivo measurements.

Fitting methods for intravoxel incoherent motion imaging of prostate cancer on region of interest level: Repeatability and gleason score prediction

To evaluate different fitting methods for intravoxel incoherent motion (IVIM) imaging of prostate cancer in the terms of repeatability and Gleason score prediction.

Diffusion-weighted imaging of prostate cancer: effect of b-value distribution on repeatability and cancer characterization.

PURPOSE To evaluate the effect of b-value distribution on the repeatability and Gleason score (GS) prediction of prostate cancer (PCa). METHODS Fifty PCa patients underwent two repeated 3T diffusion-weighted imaging (DWI) examinations using 12 b values in the range from 0 to 2000s/mm(2) and diffusion time of 20.3ms. Mean signal intensities of regions of interest, placed in PCa using whole mount prostatectomy sections as the reference, were fitted using monoexponential, kurtosis, stretched exponential, and biexponential models. In total, 4083 different b-value combinations consisting of 2 to 12 b values were evaluated. Repeatability was assessed by intraclass correlation coefficient, ICC(3,1), and coefficient of repeatability (CoR). Areas under receiver operating characteristic curve (AUCs) for PCa characterization were estimated while the correlation of the fitted values with GS groups (3+3, 3+4, >3+4) was evaluated by using the Spearman correlation coefficient (ρ). RESULTS The parameters of monoexponential, kurtosis, and stretched exponential models estimated using only 4-5, 5-7, 5-7 b values, respectively, had similar ICC(3,1), CoR, AUC, and ρ values as the parameters estimated using all 12 b values. Optimized b-value distributions demonstrated improved ICC(3,1) and CoR values but failed to improve AUC and ρ values. The parameters of biexponential model demonstrated the worst repeatability and diagnostic performance. CONCLUSION B-value distribution influences mainly the repeatability of DWI-derived parameters rather than the diagnostic performance.

Relaxation along fictitious field, diffusion‐weighted imaging, and T2 mapping of prostate cancer: Prediction of cancer aggressiveness

To evaluate the performance of relaxation along a fictitious field (RAFF) relaxation time (TRAFF), diffusion‐weighted imaging (DWI)‐derived parameters, and T2 relaxation time values for prostate cancer (PCa) detection and characterization.

MR signal-fat-fraction analysis and T2*weighted imaging measure BAT reliably on humans without cold exposure

OBJECTIVE Brown adipose tissue (BAT) is compositionally distinct from white adipose tissue (WAT) in terms of triglyceride and water content. In adult humans, the most significant BAT depot is localized in the supraclavicular area. Our aim is to differentiate brown adipose tissue from white adipose tissue using fat T2* relaxation time mapping and signal-fat-fraction (SFF) analysis based on a commercially available modified 2-point-Dixon (mDixon) water-fat separation method. We hypothesize that magnetic resonance (MR) imaging can reliably measure BAT regardless of the cold-induced metabolic activation, with BAT having a significantly higher water and iron content compared to WAT. MATERIAL AND METHODS The supraclavicular area of 13 volunteers was studied on 3T PET-MRI scanner using T2* relaxation time and SFF mapping both during cold exposure and at ambient temperature; and 18F-FDG PET during cold exposure. Volumes of interest (VOIs) were defined semiautomatically in the supraclavicular fat depot, subcutaneous WAT and muscle. RESULTS The supraclavicular fat depot (assumed to contain BAT) had a significantly lower SFF and fat T2* relaxation time compared to subcutaneous WAT. Cold exposure did not significantly affect MR-based measurements. SFF and T2* values measured during cold exposure and at ambient temperature correlated inversely with the glucose uptake measured by 18F-FDG PET. CONCLUSIONS Human BAT can be reliably and safely assessed using MRI without cold activation and PET-related radiation exposure.

Rotating frame relaxation imaging of prostate cancer: Repeatability, cancer detection, and Gleason score prediction

To investigate relaxation along a fictitious field (RAFF) and continuous wave (cw) T1ρ imaging of prostate cancer (PCa) in the terms of repeatability, PCa detection, and characterization.

Diffusion weighted imaging of prostate cancer: Prediction of cancer using texture features from parametric maps of the monoexponential and kurtosis functions

Computer aided diagnosis (CADx) systems for magnetic resonance imaging of prostate have shown potential to increase accuracy for detection of cancer. The purpose of this study is to introduce a method for CADx to detect prostate cancer based on texture features extracted from a grid placed on diffusion weighted imaging (DWI) parametric maps. Texture maps of DWI parametric maps (monoexponential: ADCm, kurtosis: ADCk and K) from 67 patients were obtained. Then the texture maps were divided in cubes, and median texture features were calculated for each cube. The features were used to train prediction models. Area under the curve (AUC) value was used to assess the prediction efficiency. In total, 875 texture features were extracted with Gabor filter, GLCM, LBP, Haar transform, and Hu moments. Statistical features were also calculated. The union of texture features from the ADCm ADCk and K parametric maps demonstrated high performance with AUC values of 0.81 to 0.85.