- Email: [email protected]
Renal function assessment by texture analysis of R2 map
Abstracts/Physica Medica 32 (2016) e124–e134
Furthermore, the use of the SNRL parameter allows comparing the performance of different scanners in terms of SNR values regardless of technical specifications of each manufacturer. Reference [1] Jackson EF, et al. AAPM Report 100: Acceptance Testing and Quality Assurance Procedures for Magnetic Resonance Imaging Facilities, 2010. http://dx.doi.org/10.1016/j.ejmp.2016.01.430
E.423 RENAL FUNCTION ASSESSMENT BY TEXTURE ANALYSIS OF R2 MAP M. Biondi *,a, L. Pelliccia b, A. Bogi a, L.N. Mazzoni a, E. Vanzi a, G.M. Belmonte a, G. De Otto a, S.F. Carbone c, A. Guasti d, F. Banci Buonamici a. a Department of Medical Physics, University Hospital of Siena, Siena, Italy; b Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy; c Department of Diagnostic Imaging, University Hospital of Siena, Siena, Italy; d Department of Medical Physics, U.S.L. 7, Siena, Italy Introduction: Texture analysis is a new method for digital images investigation which provides a measure of intralesional heterogeneity. First order statistics include mean, standard deviation, skewness, kurtosis, uniformity, and entropy of the gray level histogram. The close relationship between the functional magnetic resonance imaging and renal function is well known in literature. In light of these backgrounds, the purpose of our work is to evaluate texture analysis of R2 maps (TAR2m) in the assessment of renal function. Materials and Methods: Axial multi-echo FGRE sequence on upper abdomen was acquired in 11 patients with renal parenchymal diseases at different renal function stages; 7 subjects without renal disease were used as controls. Serum Creatinine (sCr) of all the subjects was obtained and eGFR was calculated by MDRD formula. A hand-made ROI on central slice R2 map was used to sample renal parenchyma, including renal cortex and medulla, and finally mean, median, kurtosis, skewness, and density were calculated by using an open source texture analysis software. Results: sCr and skewness were found to have a significant relationship (p < 0.05). Significant differences were found between stage 1 and 2 for density (p = 0.04) and nearly significant between stage 2 and 3 for skewness (p = 0.07). Conclusions: TAR2m of kidney does not seem to be able to stratify renal impairment exception for the skewness and density. These latter parameters seem to be very promising and further studies on a large population are needed to best estimate the present preliminary data. http://dx.doi.org/10.1016/j.ejmp.2016.01.431
E.424 RECTAL CANCER TEXTURE ANALYSIS APPLIED ON ADC MAPS IN RESPONSE ASSESSMENT TO NEOADJUVANT THERAPY M. Biondi *,a, A. Bogi a, L.N. Mazzoni a, E. Vanzi a, G.M. Belmonte a, G. De Otto a, R. Martini b , E. Foderà b , S.F. Carbone c , L. Volterrani b , A. Guasti d , F. Banci Buonamici a. a Department of Medical Physics, University Hospital of Siena, Siena, Italy; b Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy; c Department of Diagnostic Imaging, University Hospital of Siena, Siena, Italy; d Department of Medical Physics, U.S.L. 7, Siena, Italy Introduction: The aim of this work was to evaluate feasibility of texture analysis of ADC maps in the assessment of response in neoadjuvant therapy of rectal cancer. With digital images texture analysis (TA) it is possible to measure ROI heterogeneity. This method consists in the evaluation of some gray level histogram parameters: mean (M), standard deviation (SD), skewness (SK), kurtosis (K), uniformity (U) and entropy (E). Materials and Methods: In this retrospective observational study, ten patients affected by rectal cancer underwent to MR imaging before and after neoadjuvant chemo-radiation therapy (CRT); in all the cases, post-surgical tumor regression grade (TRG) was obtained. ADC maps were calculated by diffusion-weighted imaging (b-values 0–800 s/mm2).
e125
Image analysis was made with a homemade ImageJ macro. Two blinded readers manually segmented hypontense areas corresponding to tumor ADC restriction. TA parameters for selected ROIs were evaluated applying a Laplacian of Gaussian bandpass filter between 0.5 and 2.5 to highlight different spatial scales. ANOVA was performed to evaluate differences among responder (R, TRG12) and non responder (NR, TRG 3) patients and longitudinal changes. Interreader variability was assessed by ICC. Results: Three patients were NR (TRG 3), while seven were considered R (TRG 1-2). M, E and U showed an ICC value > 0.75 while SK and K had an ICC value < 0.50. Among R and NR group, SD value was not significantly lower in R patients in pre-CRT ADC maps (p = 0.05); however, filter application seems to differentiate despite the small population considered. M, E and U values differed significantly after CRT (p < 0.05). Post-CRT changes were significant in R patients for M value (p < 0.01) while SD, E and U changed significantly both in R and NR patients. Conclusions: TA seems to be a promising approach, however, S and K showed a high inter-reader variability and further studies on large population are needed even with ADC maps with better spatial resolution. http://dx.doi.org/10.1016/j.ejmp.2016.01.432
E.424 bis A NEW REFINED ACOUSTIC AND THERMAL COUPLING MODEL FOR TEMPERATURE RISE IN MR-GUIDED HIFU C. Borrazzo *,a, M. Carnì b, E. Di Castro b, S. Pozzi c, B. Caccia c, G. Borasi d, A. Napoli e. a Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy; b Medical Physics unit, Policlinico Umberto I, Rome, Italy; c Department of Technology and Health, Istituto Superiore di Sanità and INFN, Rome, Italy; d Bicocca University of Milan, Milan, Italy; e Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy Introduction: The success of MRgFUS therapy relies on the accuracy of thermal mapping of sonication, which is obtained with guided MR. Unfortunately, inhomogeneities in the medium of propagation can cause significant distortion of the ultrasound beam, resulting in changes in focal-zone amplitude, location and shape. An adequate description of absorption, diffraction and nonlinear phenomena can be necessary to attenuate the limits of the technique. The aim of this study is to evaluate a quantitative comparison between MRgFUS experimentally measured thermal fields and that obtained using finite element method with Monte Carlo (MC). Materials and Methods: Essentially, the method employs MC integration to evaluate the solution of the nonlinear Khokhlov–Zabolotskaya–Kuznetsov (KZK) equation. The method can be used for complicated geometries, and it is well suited to parallelization. The method is validated against experimental temperature measurements on a homemade phantom gel, using fluorotopic thermometer. Results: The results of modeling obtained by both codes are compared with each other and with known experimental data, and are found to be in a good agreement. The analytical temperature solution used for temperature-based parameter estimations assumes a radial Gaussian heating pattern and that axial conduction and perfusion effect are negligible. The results demonstrated that the nonlinear model absorption should be taken into account in the evaluation of temperature rise for materials sonicated. Conclusion: The method is well suited to be used in applications where flexibility and rapid computation time are crucial, in particular clinical HIFU treatment planning. The development of optimized HIFU treatments is useful to control the ablation of the target tissues and to improve patient safety. It also potentially reduces the overall treatment duration and exposure to non-target tissues thanks to a better understanding of acoustic and thermal wave propagation. http://dx.doi.org/10.1016/j.ejmp.2016.01.433
Furthermore, the use of the SNRL parameter allows comparing the performance of different scanners in terms of SNR values regardless of technical specifications of each manufacturer. Reference [1] Jackson EF, et al. AAPM Report 100: Acceptance Testing and Quality Assurance Procedures for Magnetic Resonance Imaging Facilities, 2010. http://dx.doi.org/10.1016/j.ejmp.2016.01.430
E.423 RENAL FUNCTION ASSESSMENT BY TEXTURE ANALYSIS OF R2 MAP M. Biondi *,a, L. Pelliccia b, A. Bogi a, L.N. Mazzoni a, E. Vanzi a, G.M. Belmonte a, G. De Otto a, S.F. Carbone c, A. Guasti d, F. Banci Buonamici a. a Department of Medical Physics, University Hospital of Siena, Siena, Italy; b Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy; c Department of Diagnostic Imaging, University Hospital of Siena, Siena, Italy; d Department of Medical Physics, U.S.L. 7, Siena, Italy Introduction: Texture analysis is a new method for digital images investigation which provides a measure of intralesional heterogeneity. First order statistics include mean, standard deviation, skewness, kurtosis, uniformity, and entropy of the gray level histogram. The close relationship between the functional magnetic resonance imaging and renal function is well known in literature. In light of these backgrounds, the purpose of our work is to evaluate texture analysis of R2 maps (TAR2m) in the assessment of renal function. Materials and Methods: Axial multi-echo FGRE sequence on upper abdomen was acquired in 11 patients with renal parenchymal diseases at different renal function stages; 7 subjects without renal disease were used as controls. Serum Creatinine (sCr) of all the subjects was obtained and eGFR was calculated by MDRD formula. A hand-made ROI on central slice R2 map was used to sample renal parenchyma, including renal cortex and medulla, and finally mean, median, kurtosis, skewness, and density were calculated by using an open source texture analysis software. Results: sCr and skewness were found to have a significant relationship (p < 0.05). Significant differences were found between stage 1 and 2 for density (p = 0.04) and nearly significant between stage 2 and 3 for skewness (p = 0.07). Conclusions: TAR2m of kidney does not seem to be able to stratify renal impairment exception for the skewness and density. These latter parameters seem to be very promising and further studies on a large population are needed to best estimate the present preliminary data. http://dx.doi.org/10.1016/j.ejmp.2016.01.431
E.424 RECTAL CANCER TEXTURE ANALYSIS APPLIED ON ADC MAPS IN RESPONSE ASSESSMENT TO NEOADJUVANT THERAPY M. Biondi *,a, A. Bogi a, L.N. Mazzoni a, E. Vanzi a, G.M. Belmonte a, G. De Otto a, R. Martini b , E. Foderà b , S.F. Carbone c , L. Volterrani b , A. Guasti d , F. Banci Buonamici a. a Department of Medical Physics, University Hospital of Siena, Siena, Italy; b Department of Medical, Surgical and Neurosciences, University of Siena, Siena, Italy; c Department of Diagnostic Imaging, University Hospital of Siena, Siena, Italy; d Department of Medical Physics, U.S.L. 7, Siena, Italy Introduction: The aim of this work was to evaluate feasibility of texture analysis of ADC maps in the assessment of response in neoadjuvant therapy of rectal cancer. With digital images texture analysis (TA) it is possible to measure ROI heterogeneity. This method consists in the evaluation of some gray level histogram parameters: mean (M), standard deviation (SD), skewness (SK), kurtosis (K), uniformity (U) and entropy (E). Materials and Methods: In this retrospective observational study, ten patients affected by rectal cancer underwent to MR imaging before and after neoadjuvant chemo-radiation therapy (CRT); in all the cases, post-surgical tumor regression grade (TRG) was obtained. ADC maps were calculated by diffusion-weighted imaging (b-values 0–800 s/mm2).
e125
Image analysis was made with a homemade ImageJ macro. Two blinded readers manually segmented hypontense areas corresponding to tumor ADC restriction. TA parameters for selected ROIs were evaluated applying a Laplacian of Gaussian bandpass filter between 0.5 and 2.5 to highlight different spatial scales. ANOVA was performed to evaluate differences among responder (R, TRG12) and non responder (NR, TRG 3) patients and longitudinal changes. Interreader variability was assessed by ICC. Results: Three patients were NR (TRG 3), while seven were considered R (TRG 1-2). M, E and U showed an ICC value > 0.75 while SK and K had an ICC value < 0.50. Among R and NR group, SD value was not significantly lower in R patients in pre-CRT ADC maps (p = 0.05); however, filter application seems to differentiate despite the small population considered. M, E and U values differed significantly after CRT (p < 0.05). Post-CRT changes were significant in R patients for M value (p < 0.01) while SD, E and U changed significantly both in R and NR patients. Conclusions: TA seems to be a promising approach, however, S and K showed a high inter-reader variability and further studies on large population are needed even with ADC maps with better spatial resolution. http://dx.doi.org/10.1016/j.ejmp.2016.01.432
E.424 bis A NEW REFINED ACOUSTIC AND THERMAL COUPLING MODEL FOR TEMPERATURE RISE IN MR-GUIDED HIFU C. Borrazzo *,a, M. Carnì b, E. Di Castro b, S. Pozzi c, B. Caccia c, G. Borasi d, A. Napoli e. a Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy; b Medical Physics unit, Policlinico Umberto I, Rome, Italy; c Department of Technology and Health, Istituto Superiore di Sanità and INFN, Rome, Italy; d Bicocca University of Milan, Milan, Italy; e Department of Radiological Sciences, Oncology and Pathology, Sapienza University of Rome, Rome, Italy Introduction: The success of MRgFUS therapy relies on the accuracy of thermal mapping of sonication, which is obtained with guided MR. Unfortunately, inhomogeneities in the medium of propagation can cause significant distortion of the ultrasound beam, resulting in changes in focal-zone amplitude, location and shape. An adequate description of absorption, diffraction and nonlinear phenomena can be necessary to attenuate the limits of the technique. The aim of this study is to evaluate a quantitative comparison between MRgFUS experimentally measured thermal fields and that obtained using finite element method with Monte Carlo (MC). Materials and Methods: Essentially, the method employs MC integration to evaluate the solution of the nonlinear Khokhlov–Zabolotskaya–Kuznetsov (KZK) equation. The method can be used for complicated geometries, and it is well suited to parallelization. The method is validated against experimental temperature measurements on a homemade phantom gel, using fluorotopic thermometer. Results: The results of modeling obtained by both codes are compared with each other and with known experimental data, and are found to be in a good agreement. The analytical temperature solution used for temperature-based parameter estimations assumes a radial Gaussian heating pattern and that axial conduction and perfusion effect are negligible. The results demonstrated that the nonlinear model absorption should be taken into account in the evaluation of temperature rise for materials sonicated. Conclusion: The method is well suited to be used in applications where flexibility and rapid computation time are crucial, in particular clinical HIFU treatment planning. The development of optimized HIFU treatments is useful to control the ablation of the target tissues and to improve patient safety. It also potentially reduces the overall treatment duration and exposure to non-target tissues thanks to a better understanding of acoustic and thermal wave propagation. http://dx.doi.org/10.1016/j.ejmp.2016.01.433