Optimized non-invasive MRI protocols for characterizing tissue microstructures: applications in humans to prostate cancer and fetal brain development

This PhD project was aimed to optimize MRI protocols for pelvis imaging, in particular for the diagnosis of prostate cancer (PCa) and for the fetal brain development. Different non-invasive MRI techniques were employed to investigate biological tissues, with the purpose to obtain information on microstructures and potentially metabolism. Prostate cancer is the second most common malignancy and the fifth leading cause of death in men worldwide. Due to the high incidence of PCa and the limitations of current diagnostic methods, the primary goal of this work was to develop an MRI protocol able to improve the sensitivity of the diagnostic. The investigation of prostate cancer started with ex-vivo experiments conducted on specimens of human prostate gland, obtained after radical prostatectomy, with the 9.4T scanner at the NMR and Medical Physics Laboratory of CNR-ISC (Sapienza). Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI) were performed at high-resolution (70x70 micrometers in plane) to evaluate diffusion metrics in the different prostate compartment and directly compare measurements with the histopathology results. This study proceeded with in-vivo experiments with a 3T clinical MR scanner (Philips Achieva at Policlinico Tor Vergata) on subjects with diagnosed PCa. DTI was performed with the purpose to assess its diagnostic ability in individuating and classifying PCa with different ranges of diffusion weightings, i.e. b-values. Our results showing that the diagnostic accuracy of DTI is improved with high diffusion weightings motivated our interest in performing DKI, a technique that captures water diffusion features when high b-values are employed, providing additional information on tissue microstructures, inaccessible to DTI technique. The second part of this PhD project was conducted at the Center for Magnetic Resonance Research (CMRR) in Minneapolis and was funded by the European Union's Horizon 2020 research and innovation program under the Marie Curie grant agreement No 691110 (MICROBRADAM). The study was dedicated to perform prostate cancer imaging with new contrast mechanisms, based on T1rho and T2rho relaxation times. T1rho and T2rho characterize the relaxation of the nuclear magnetization in the rotating frame and they are sensitive to molecular dynamics occurring at frequencies in the range of kHz, characteristic of several in-vivo processes, enabling the access to important information on tissue microenvironment. T1rho and T2rho imaging is limited by the intensive energy deposited by the acquisition sequence, which it is usually overcome by increasing the acquisition time, preventing the possibility of diagnostic applications. Therefore the aim of this work was to develop a new approach to perform imaging in the rotating frame with a three-dimensional acquisition method, recently developed at the CMRR, in order to address the aforementioned shortcomings. Given the incidence of PCa, this research has international interest and potentially contributes to improve not only the sensitivity of PCa diagnostic but also the knowledge of the tissue micro-changing caused by the tumor development. A part of this project was dedicated to Diffusion MRI application in woman pelvis to image fetuses during gestation. The aim of this work was to develop a fast and reliable protocol for fetal imaging to minimize mother-fetal motion artifact and perfusion effects. The protocol designed for acquisition and post-processing was employed to successfully study fetal brain development during the second and third trimester of gestation, in normal cases and in fetuses affected by ventriculomegaly disease. These preliminary data can contribute to delineate a reference standard to assess the normal progress of sulcation and myelination as well as the normative biometry of the fetal brain, improving the knowledge of brain maturation. Globally, the impact of this research lies in having demonstrated that the sensitivity of DMRI for microstructural changes in body tissue caused by cancer, brain disease or normal condition like brain maturation can be fruitfully utilized in combination with artifact correction methods. Moreover, new strategy of image reconstruction, such as 3D gradient echo, can be successfully employed to perform abdominal imaging, enriching the investigation of in-vivo systems with information on tissue microenvironment and metabolism

Acquisition Parameters Influence Diffusion Metrics Effectiveness in Probing Prostate Tumor and Age-Related Microstructure

: This study aimed to investigate the Diffusion-Tensor-Imaging (DTI) potential in the detection of microstructural changes in prostate cancer (PCa) in relation to the diffusion weight (b-value) and the associated diffusion length lD. Thirty-two patients (age range = 50-87 years) with biopsy-proven PCa underwent Diffusion-Weighted-Imaging (DWI) at 3T, using single non-zero b-value or groups of b-values up to b = 2500 s/mm2. The DTI maps (mean-diffusivity, MD; fractional-anisotropy, FA; axial and radial diffusivity, D// and D┴), visual quality, and the association between DTI-metrics and Gleason Score (GS) and DTI-metrics and age were discussed in relation to diffusion compartments probed by water molecules at different b-values. DTI-metrics differentiated benign from PCa tissue (p ≤ 0.0005), with the best discriminative power versus GS at b-values ≥ 1500 s/mm2, and for b-values range 0-2000 s/mm2, when the lD is comparable to the size of the epithelial compartment. The strongest linear correlations between MD, D//, D┴, and GS were found at b = 2000 s/mm2 and for the range 0-2000 s/mm2. A positive correlation between DTI parameters and age was found in benign tissue. In conclusion, the use of the b-value range 0-2000 s/mm2 and b-value = 2000 s/mm2 improves the contrast and discriminative power of DTI with respect to PCa. The sensitivity of DTI parameters to age-related microstructural changes is worth consideration

A multi-parametric investigation on waterlogged wood using a magnetic resonance imaging clinical scanner

In cultural heritage conservation science, moisture content (MC) is an essential factor to determine. At the same time, it is essential to choose non-destructive and non-invasive approaches for more sustainable investigations and make them safe for the environment and the sample. The question addressed in this work concerns the possibility and the opportunity to investigate waterlogged wood by using nuclear magnetic resonance imaging (MRI) clinical scanners to carry out non-destructive volumetric diagnostics. In this study, MRI, the most important non-invasive medical imaging technique for human tissue analysis, was applied to study archaeological waterlogged wood samples. This type of archaeological material has a very high moisture content (400%–800%), thus, it is an ideal investigative subject for MRI which detects water molecules inside matter. By following this methodology, it was possible to obtain information about water content and conservation status through a T1, T2, and T2* weighted image analysis, without any sampling or handling, and the samples were directly scanned in the water where they were stored. Furthermore, it permited processing 3D reconstruction, which could be an innovative tool for the digitalization of marine archaeological collections. In this work, 16 modern species of wood and a waterlogged archaeological wood sample were studied and investigated using a clinical NMR scanner operating at 3T. The results were compared with X-ray computed tomography (CT) images, as they had already been used for dendrochronology. The comparison highlights the similar, different, and complementary information about moisture content and conservation status in an all-in-one methodology obtainable from both MRI and CT techniques

The rapid spread of SARS-COV-2 Omicron variant in Italy reflected early through wastewater surveillance

The SARS-CoV-2 Omicron variant emerged in South Africa in November 2021, and has later been identified worldwide, raising serious concerns. A real-time RT-PCR assay was designed for the rapid screening of the Omicron variant, targeting characteristic mutations of the spike gene. The assay was used to test 737 sewage samples collected throughout Italy (19/21 Regions) between 11 November and 25 December 2021, with the aim of assessing the spread of the Omicron variant in the country. Positive samples were also tested with a real-time RT-PCR developed by the European Commission, Joint Research Centre (JRC), and through nested RT-PCR followed by Sanger sequencing. Overall, 115 samples tested positive for Omicron SARS-CoV-2 variant. The first occurrence was detected on 7 December, in Veneto, North Italy. Later on, the variant spread extremely fast in three weeks, with prevalence of positive wastewater samples rising from 1.0% (1/104 samples) in the week 5-11 December, to 17.5% (25/143 samples) in the week 12-18, to 65.9% (89/135 samples) in the week 19-25, in line with the increase in cases of infection with the Omicron variant observed during December in Italy. Similarly, the number of Regions/Autonomous Provinces in which the variant was detected increased from one in the first week, to 11 in the second, and to 17 in the last one. The presence of the Omicron variant was confirmed by the JRC real-time RT-PCR in 79.1% (91/115) of the positive samples, and by Sanger sequencing in 66% (64/97) of PCR amplicons. In conclusion, we designed an RT-qPCR assay capable to detect the Omicron variant, which can be successfully used for the purpose of wastewater-based epidemiology. We also described the history of the introduction and diffusion of the Omicron variant in the Italian population and territory, confirming the effectiveness of sewage monitoring as a powerful surveillance tool

Towards a definition of the biophysical bases of transient Anomalous Diffusion (TAD) parameters. Evaluation of tAD, DKI and DTI in normal and cancer prostate tissue with Magnetic Resonance micro-imaging at 9.4 Tesla

Synopsis Since DKI and transient anomalous diffusion imaging (tADI) are based on statistical models, they can be performed without the need of a-priori hypothesis on tissue micro-structures. However, the relation between tissue micro-structure DKI and tADI derived parameters have not been clearly established yet. In this work, we evaluated DKI, tAD and DTI diffusion parameters in normal and high-grade cancer prostate, by MR microimaging at 9.4T with a 70μmx70μm in plane resolution. As prostate tissue is a complex tissue, composed by several micro-compartments that exhibit different diffusion behaviors, it is an ideal tissue to investigate the biophysical features of diffusion parameters. Introduction In this work Diffusion Kurtosis Imaging (DKI) and transient Anomalous Diffusion Imaging (tADI) were performed on ex-vivo prostate specimens, in addition to the conventional DTI. Kurtosis is the fourth-order term of the NMR signal cumulant expansion, it quantifies the signal deviation from the mono-exponential decay, providing a measure of tissue heterogeneity [1]. tADI derived from Continuous Time Random Walk model introduced by Metzler and Klafter [2], a generalization of the basic random walk theory, developed with the purpose to investigate heterogeneous and complex media. tADI allows to measure the γ-parameter that is sensitive to tissue susceptibility differences and multi-compartmentalization [3,4]. Since DKI and tADI are based on statistical models, they can be performed without needing a-priori hypothesis on tissue micro-structures. However, the relation between tissue micro-modifications and DKI- and tADI-derived parameters must be investigated and established. In this preliminary work, we evaluated DKI, tAD and DTI diffusion parameters in normal and high-grade PCa, by MR microimaging at 9.4T and with a resolution of 70x70μm2 in the plane. Prostate tissue is composed by several micro-compartments that exhibit different diffusion behaviors [5]. Therefore, as prostate is a complex tissue, consisting of structures with length scale from 100 μm to less than 1 μm, it is an ideal tissue to investigate the biophysical features of diffusion parameters. Methods Tissue samples were obtained from radical prostatectomy specimens and kept in 4% PBS formaldehyde at 4°C for conservation. An expert uropathologist selected one normal and one cancer sample from 3 patients with high-grade Pca (Gleason Score ≥ 4+4). Acquisition was performed on a Bruker AV400 spectrometer operating at 9.4 T with a micro-imaging probe and maximum gradient strength of 1200mT/m. XWINNMR® and ParaVision® 3.0 software were employed for data acquisition. DWIs were acquired with a Pulsed Gradient Stimulated Echo (TE/TR=14,8/4500 (ms); resolution=70x70x1000μm3; δ/Δ=3/40 (ms); NSA=8), by varing diffusion gradient strengths; 9 b-values from 0 to 5000 s/mm2 were applied along 6 non-collinear directions DTI was performed by FSL 5.0 software, with the b-value range 0-1500 s/mm2; non-Gaussian parameters were calculated by a customized algorithm developed in Matlab R2012b. Mean Kurtosis (MK) and kurtosis-derived mean diffusivity (MDk) were calculated by fitting DWI signal for each diffusion encoding direction in the b-value range 0-2000 s/mm2. Moreover, proxy Kurtosis Fractional Anisotropy (KFA) was calculated as reported in [6]. tAD was performed, as described in [7], by fitting signal in the b-values range 0-5000s/mm2, according the approach of [3]. Region of Interests (ROI) were manually drawn on DW-images in tumoral and normal tissue. Results The glandular structure of prostate is visible in DWIs and diffusion parameters maps of normal samples, except for FA-map; MDk seems to better describe the tissue architecture (Fig. 1 and 2). MD, MDk and Mγ are lower in PCa, while FA, KFA and MK are higher; DTI-parameters show mean values comparable with other ex-vivo studies [5,8]. Discussion and Conclusions Diffusion derived micro-images highlight tissue architecture and reflect structural modifications occurring with tumor. Histopathological evidences showed that Pca with Gleason Score (GS) ≥ 4+4 consists in a solid mass of undifferentiated cells (Fig.3), indeed no glandular structure is recognizable on DWIs or diffusion maps (Fig. 2). MD and MDk are lower in PCa, as a result of malignant cells proliferation that obstructs the almost-free diffusion compartments (acini, ducts), leading to an increase of tissue heterogeneity (K increases) and a reduction of tissue susceptibility differences (Mγ decreases). As a consequence of increasing cell density, FA and KFA are lower in cancer tissue. In conclusion, MR micro-imaging in healthy and cancer prostate tissue allows to investigate diffusion proprieties of micro-structures approaching the cellular scale. As a consequence, micro-imaging technique could be employed to elucidate the biophysical underpinning of non–Gaussian diffusion parameters and in particular of the tAD parameters. Acknowledgements No acknowledgement found. References [1] Jensen J.H. and Helpern J.A., MRI quantification of Non-Gaussian water diffusion by Kurtosis Analysis. NMR Biomed, 2010. [2] Metzler R. and Klafter J., The random walk’s guide to anomalous diffusion: a fractional dynamics approach. Physics Reports, 2000. [3] Palombo M. et al., Spatio-temporal anomalous diffusion in heterogeneous media by nuclear magnetic resonance. J Chem Phys, 2011. [4] Capuani S. et al., Spatio-temporal anomalous diffusion imaging: results in controlled phantoms and in excised human meningiomas. Magn Reson Imaging, 2013. [5] Bourne R.M. et al., Microscopic diffusivity compartmentation in formalin-fixed prostate tissue.Magnetic Resonance in Medicine, 2012. [6] Hansen B. and Jespersen S.N., Kurtosis fractional anisotropy, its contrast and estimation by proxy. Sci. Rep., 2016. [7] Caporale A. et al.,The γ-parameter of Anomalous Diffusion quantified in human brain by MRI depends on local magnetic susceptibility differences, NeuroImage, 2016. [8] Xu J.Q., et al.,. Magnetic resonance diffusion characteristics of histologically defined prostate cancer in humans. Magn Reson Med, 2009

Using Emotional Text Mining to Explore the Cultural Representation of Organ Donation in Spanish and Italian Culture

Spain is considered the world leader in the field of Organ Donation (OD). Italy has adopted the Spanish model in its health system but failed to reach the Spanish primacy. This work investigates the cultural elements influencing the choice to donate in Italy and Spain. We collected from two newspapers of Italy and Spain, all the published articles (2001-2021), containing the respective translation of OD. The two final corpora were analyzed through the Emotional Text Mining methodology. The analysis produced 4 CR for the Italian corpus and 5 CR for the Spanish one. The principal CR for Spain is viewing the OD as a national project, while in Italy, the donation is viewed as a life-saving product. In Italy, the theme of death is viewed as unacceptable. In Spain, the principal context in which donation is discussed is the family. Another difference is related to the developmental process: Italy is rooted in established promotional models, while in Spain, the desire for innovation emerges. Finally, OD is connected to other fields in Spain. In conclusion, this study allowed us to understand deep cultural differences between Spain and Italy in OD. Results ought to be used to improve promotional campaigns to citizens

Mean Kurtosis discriminates between low- and high-risk prostate cancer better than mean diffusivity does

Synopsis This work was finalized to compare the diagnostic potential of Diffusion Tensor and Diffusion Kurtosis Imaging in discriminating between low- and high-risk prostate cancer (Pca). Maps of Mean Diffusivity (MD), apparent Kurtosis (K) and apparent diffusion coefficient (D) were obtained from DWIs of 24 patients with different tumour grade. K maps better highlight differences between periferal PCa, PCa and benign tissue. In particular K discriminates between low- and high-risk PCa with a higher statistical significance compared to that of MD. DKI can improve the accuracy of the current PCa diagnosis providing a useful tool for PCa detection and grading. Purpose Prostate cancer (PCa) is the second most common malignancy and the fifth leading cause of death in men worldwide [1]. Accurate staging is desirable for treatment planning, since high-risk cancer is treated with surgery or radiation, while therapy for low-risk cancer considers active surveillance without invasive treatments. According to the new grading system proposed by the ISUP [2], low risk PCa are characterized by Grade Group (GG)=1,2 while high risk PCa are defined by GG≥ 3. Diffusion Tensor Imaging (DTI) with high b-values (up to 2500s/mm2) has highlighted to provide a good discrimination between low- and high-risk PCa [3]. However, parameters derived from non-Gaussian diffusion model are in principle more sensitive to the microstructural changes in biological tissue than the Gaussian model [4]. Therefore, our aim was to compare the diagnostic performance of diffusional kurtosis imaging (DKI) and the conventional DTI approach in the discrimination between low- and high-risk PCa. Materials and Methods A cohort of 24 patients with different aggressiveness grades (GG=1,2,3,4,5 corresponding to Gleason Score GS=3+3,3+4,4+3,4+4,4+5/5+4) PCa were retrospectively enrolled to be examined by MRI, using a 3T clinical MR scanner (Intera Achieva, Philips Medical Systems, The Nederlands) and a six-channel phased array SENSE torso coil. Each patient underwent the MR examination after two months from the first TRUS-guided biopsy. Diffusion-weighted images were acquired along 6 different diffusion directions with 6 different b-values (0,500,1000,1500,2000,2500 s/mm2), by using a diffusion weighted single shot EPI sequence (TR=3000, TE=67, FOV=150×130×70mm3, acquisition matrix=64×52, reconstruction matrix 96×96, slice thickness STK=3mm, gap=0, NSA=4). The acquisition protocol also included high spatial resolution T2-weighted (T2W) turbo spin echo (TR=3957, TE=150, turbo factor 21, FOV=150×130mm, STK=3mm, gap=0, acquisition matrix256x178, reconstruction matrix=512×512, NSA=6, flip angle=90°). The image pre-processing and the reconstruction of the Mean Diffusivity (MD) parametric maps was performed using FSL 5.0 (FMRIB Software Library v5.0, FMRIB, Oxford, UK). Parametric maps of apparent Kurtosis (K) and apparent diffusion coefficient (D) of the quadratic model were obtained by using an in-house algorithm developed in Matlab (MATLAB R2012b, The Mathworks, Natick, MA). Region of Interests (ROI) in PCa and contralateral benign zone were manually drawn by an expert radiologist, referring to T2W-images, for each subject. The pixels nearest to the PCa ROI edge were considered as peritumoral ROI. One-way ANOVA was performed to test statistical significance of differences in MD, K and D values calculated in PCa belonging to low- and high-grade groups. Moreover, the statistical significance of differences in MD, K and D values between benign and PCa tissue and between benign and peritumoral area were evaluated. The linear correlation between MD, K, D values and the tumour grade was estimated by the Pearson's test. Because low Signal to Noise Ratio (SNR) of DWIs acquired at larger b-values are an obvious drawback for non Gaussian diffusion techniques, we evaluated SNR of DWIs at each b values to investigate about the reliability DKI maps. Results An example of T2, MD, K and D maps are displayed in Fig.1 for a patient with PCa characterized by GG=3. The SNR of b=0 images was approximately equal to 55 in PCa and remained higher than 22 up to b=2500 s/mm2. Statistically significant difference was found between each parameter values (MD, K, D) measured in PCa and benign controlateral zone. However, K had the highest significance (p<0.0001). K showed the highest significance (p<0.001) also in the discrimination between peritumoral and benign regions and peritumoral and PCa. A moderate positive correlation was found between K and GG (r=0.52; p<0.001), while a weak negative correlation was found between both D and MD and GG (r=-0.38, p=0.011; r=-0.36, p=0.016, respectively). Plots of K, D and MD as a function of GG are displayed in Fig.2. K, D and MD significantly discriminate between low-risk (GG=1&2) and high-risk PCa (GG=3,4,5) with p<0.001, p<0.004, p<0.02, respectively. Discussion and conclusions The SNR was higher than 20, which is an acceptable value for considering DKI maps reliable. The diagnostic performance of DKI in discriminating between PCa and benign tissue and in differentiating among PCa characterized by different GG was superior compared to that provided by DTI. Moreover K maps better highlight differences between periferal PCa and benign tissue. In particular K discriminates between low- and high-risk PCa better than Mean diffusivity does (Fig.2). These results confirm that non-Gaussian DKI parameters are more sensitive to tissue microstructural changes, occurring with tumour onset and progression compared to Gaussian parametrs. Therefore this work suggests that DKI could be a useful tool in the diagnosis and grading of PCa to ensure a correct therapy for the patients. References [1] Ferlay, J. et al., Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer, 136 (2015): E359–E386. [2] J.I. Epstein et al. The grading committee, the 2014 International Society of Urological Pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma: definition of grading patterns and proposal for a new grading system. Am. J. Surg. Pathol., 40 (2) (2016), pp. 244–252. [3] Nezzo, M. et al., Mean diffusivity discriminates between prostate cancer with grade group 1&2 and grade groups equal to or greater than 3. European Journal of Radiology , Volume 85 (2016) , Issue 10 , 1794 – 1801. [4] Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010;23:698– 710

Non-Gaussian diffusion NMR discriminates between low- and high-risk prostate cancer.

Prostate cancer (Pca) is the second most common malignancy and the fifth leading cause of death in men worldwide. Current diagnostic methods, based on PSA measurement and biopsy, are limited by low specificity (36%) and invasive procedures; moreover, 30% of tumor-grade were under-estimated[1]. High- grade PCa is treated with more aggressive therapy, including surgery or radiations, than low-grade PCa; discriminating correctly different tumor grade is mandatory to plan patient treatment. Prostate tissue has a glandular structure, composed by several compartments: acini, formed by secretory cells and connected to ducts; the glandular structure is supported by the connective tissue of stroma. PCa development is characterized by microstructural modifications, due to the growth of undifferentiated cells and alteration of cell membranes, that change each compartment volume fraction[2]. The Diffusion-weighted NMR (DW-NMR) imaging is sensible to the tumor modifications, since this technique exploits the diffusion of biological water molecules as an endogenous contrast agent. Diffusion is the stochastic thermally-induced displacement of molecules, that colliding with the structures and barriers they encounter during motion, probe the tissue and reveal its histoarchitecture at microscopic scale, non-invasively. By using a couple of pulsed magnetic-field gradients, the NMR acquired signal, named DW signal, is proportional to the Fourier Transform of molecules motion propagator; if the displacements probability distribution (dpd) is Gaussian-shaped, as it happens in homogeneous media, the signal mono-exponentially decays. Nevertheless, prostate, as any biological systems, is a complex and inhomogeneous media, which exhibits a non-Gaussian diffusion. In order to obtain additional information on tissue microstructures, inaccessible to Gaussian-diffusion NMR technique, we estimate the Kurtosis, i.e. the fourth-order term of the cumulant expansion, that quantifies the deviation of dpd from a Gaussian distribution, providing a measure of tissue heterogeneity[3]. In this work, we compare two NMR technique, Diffusion Tensor Imaging (DTI) and Kurtosis Imaging (DKI), based respectively on Gaussian and non-Gaussian diffusion, to test their diagnostic potential in PCa identification and stadiation. 31 patients with different tumor grades (TG) PCa were enrolled to be examined by a 3T scanner, after two months from the first biopsy. Diffusion-weighted images were acquired with 5 different diffusion weights, i.e. b-values up to 2500s/mm^2. Parametric maps of Mean Diffusivity (MD) and apparent Kurtosis (K) were obtained by using an in-house algorithm developed in Matlab. One-way ANOVA was performed to test statistical significance of differences in MD and K values calculated in benign prostate and in PCa among different TG. The linear correlation between diffusion parameters and the tumor grade was estimated by the Pearson's test.Malignant tissue shows a significantly higher K and lower MD values compared to the healthy tissue (p<10 -4 ). K-values of PCa were positively correlated with TG (r=0.37;p<0.004), while MD-values were negatively correlated with TG (r=-0.31;p=0.02). Both K and MD can significantly discriminate between low- and high-grade PCa; however K showed the highest significance (p K =0.005;p MD =0.015). These results may be explained considering that, in healthy prostate, water diffusion is almost free in acini and ducts, restricted in stroma and highly restricted in secretory cell layer. The histopathological evidences show that tumor progression causes prostate glands to change in size and shape and malignant cells to infiltrate compartments. The overall effect of these modification lead to a decrease of diffusivity and an increase of heterogeneity. Our results demonstrate that non-Gaussian diffusion parameter K is more sensitive to tumor-induced microstructural changes, suggesting that DKI could provide a reliable, non-damaging and less expensive diagnostic exam. Since DKI is a non-invasive technique, it also could be employed to follow-up patients, evaluating therapy response. [1] Ferlay, J. et al., Int. J. Cancer, (2015): E359–E386. [2] Nezzo, M. et al., European Journal of Radiology, (2016); 85:1794-1801. [3] Jensen JH, Helpern JA. NMR Biomed, (2010);23:698-710

Mean Kurtosis discriminates between low- and high-risk prostate cancer better than mean diffusivity does

Synopsis This work was finalized to compare the diagnostic potential of Diffusion Tensor and Diffusion Kurtosis Imaging in discriminating between low- and high-risk prostate cancer (Pca). Maps of Mean Diffusivity (MD), apparent Kurtosis (K) and apparent diffusion coefficient (D) were obtained from DWIs of 24 patients with different tumour grade. K maps better highlight differences between periferal PCa, PCa and benign tissue. In particular K discriminates between low- and high-risk PCa with a higher statistical significance compared to that of MD. DKI can improve the accuracy of the current PCa diagnosis providing a useful tool for PCa detection and grading. Purpose Prostate cancer (PCa) is the second most common malignancy and the fifth leading cause of death in men worldwide [1]. Accurate staging is desirable for treatment planning, since high-risk cancer is treated with surgery or radiation, while therapy for low-risk cancer considers active surveillance without invasive treatments. According to the new grading system proposed by the ISUP [2], low risk PCa are characterized by Grade Group (GG)=1,2 while high risk PCa are defined by GG≥ 3. Diffusion Tensor Imaging (DTI) with high b-values (up to 2500s/mm2) has highlighted to provide a good discrimination between low- and high-risk PCa [3]. However, parameters derived from non-Gaussian diffusion model are in principle more sensitive to the microstructural changes in biological tissue than the Gaussian model [4]. Therefore, our aim was to compare the diagnostic performance of diffusional kurtosis imaging (DKI) and the conventional DTI approach in the discrimination between low- and high-risk PCa. Materials and Methods A cohort of 24 patients with different aggressiveness grades (GG=1,2,3,4,5 corresponding to Gleason Score GS=3+3,3+4,4+3,4+4,4+5/5+4) PCa were retrospectively enrolled to be examined by MRI, using a 3T clinical MR scanner (Intera Achieva, Philips Medical Systems, The Nederlands) and a six-channel phased array SENSE torso coil. Each patient underwent the MR examination after two months from the first TRUS-guided biopsy. Diffusion-weighted images were acquired along 6 different diffusion directions with 6 different b-values (0,500,1000,1500,2000,2500 s/mm2), by using a diffusion weighted single shot EPI sequence (TR=3000, TE=67, FOV=150×130×70mm3, acquisition matrix=64×52, reconstruction matrix 96×96, slice thickness STK=3mm, gap=0, NSA=4). The acquisition protocol also included high spatial resolution T2-weighted (T2W) turbo spin echo (TR=3957, TE=150, turbo factor 21, FOV=150×130mm, STK=3mm, gap=0, acquisition matrix256x178, reconstruction matrix=512×512, NSA=6, flip angle=90°). The image pre-processing and the reconstruction of the Mean Diffusivity (MD) parametric maps was performed using FSL 5.0 (FMRIB Software Library v5.0, FMRIB, Oxford, UK). Parametric maps of apparent Kurtosis (K) and apparent diffusion coefficient (D) of the quadratic model were obtained by using an in-house algorithm developed in Matlab (MATLAB R2012b, The Mathworks, Natick, MA). Region of Interests (ROI) in PCa and contralateral benign zone were manually drawn by an expert radiologist, referring to T2W-images, for each subject. The pixels nearest to the PCa ROI edge were considered as peritumoral ROI. One-way ANOVA was performed to test statistical significance of differences in MD, K and D values calculated in PCa belonging to low- and high-grade groups. Moreover, the statistical significance of differences in MD, K and D values between benign and PCa tissue and between benign and peritumoral area were evaluated. The linear correlation between MD, K, D values and the tumour grade was estimated by the Pearson's test. Because low Signal to Noise Ratio (SNR) of DWIs acquired at larger b-values are an obvious drawback for non Gaussian diffusion techniques, we evaluated SNR of DWIs at each b values to investigate about the reliability DKI maps. Results An example of T2, MD, K and D maps are displayed in Fig.1 for a patient with PCa characterized by GG=3. The SNR of b=0 images was approximately equal to 55 in PCa and remained higher than 22 up to b=2500 s/mm2. Statistically significant difference was found between each parameter values (MD, K, D) measured in PCa and benign controlateral zone. However, K had the highest significance (p<0.0001). K showed the highest significance (p<0.001) also in the discrimination between peritumoral and benign regions and peritumoral and PCa. A moderate positive correlation was found between K and GG (r=0.52; p<0.001), while a weak negative correlation was found between both D and MD and GG (r=-0.38, p=0.011; r=-0.36, p=0.016, respectively). Plots of K, D and MD as a function of GG are displayed in Fig.2. K, D and MD significantly discriminate between low-risk (GG=1&2) and high-risk PCa (GG=3,4,5) with p<0.001, p<0.004, p<0.02, respectively. Discussion and conclusions The SNR was higher than 20, which is an acceptable value for considering DKI maps reliable. The diagnostic performance of DKI in discriminating between PCa and benign tissue and in differentiating among PCa characterized by different GG was superior compared to that provided by DTI. Moreover K maps better highlight differences between periferal PCa and benign tissue. In particular K discriminates between low- and high-risk PCa better than Mean diffusivity does (Fig.2). These results confirm that non-Gaussian DKI parameters are more sensitive to tissue microstructural changes, occurring with tumour onset and progression compared to Gaussian parametrs. Therefore this work suggests that DKI could be a useful tool in the diagnosis and grading of PCa to ensure a correct therapy for the patients. References [1] Ferlay, J. et al., Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer, 136 (2015): E359–E386. [2] J.I. Epstein et al. The grading committee, the 2014 International Society of Urological Pathology (ISUP) consensus conference on gleason grading of prostatic carcinoma: definition of grading patterns and proposal for a new grading system. Am. J. Surg. Pathol., 40 (2) (2016), pp. 244–252. [3] Nezzo, M. et al., Mean diffusivity discriminates between prostate cancer with grade group 1&2 and grade groups equal to or greater than 3. European Journal of Radiology , Volume 85 (2016) , Issue 10 , 1794 – 1801. [4] Jensen JH, Helpern JA. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010;23:698– 710

A Multi-Parametric Investigation on Waterlogged Wood Using a Magnetic Resonance Imaging Clinical Scanner

In cultural heritage conservation science, moisture content (MC) is an essential factor to determine. At the same time, it is essential to choose non-destructive and non-invasive approaches for more sustainable investigations and make them safe for the environment and the sample. The question addressed in this work concerns the possibility and the opportunity to investigate waterlogged wood by using nuclear magnetic resonance imaging (MRI) clinical scanners to carry out non-destructive volumetric diagnostics. In this study, MRI, the most important non-invasive medical imaging technique for human tissue analysis, was applied to study archaeological waterlogged wood samples. This type of archaeological material has a very high moisture content (400%&ndash;800%), thus, it is an ideal investigative subject for MRI which detects water molecules inside matter. By following this methodology, it was possible to obtain information about water content and conservation status through a T1, T2, and T2* weighted image analysis, without any sampling or handling, and the samples were directly scanned in the water where they were stored. Furthermore, it permited processing 3D reconstruction, which could be an innovative tool for the digitalization of marine archaeological collections. In this work, 16 modern species of wood and a waterlogged archaeological wood sample were studied and investigated using a clinical NMR scanner operating at 3T. The results were compared with X-ray computed tomography (CT) images, as they had already been used for dendrochronology. The comparison highlights the similar, different, and complementary information about moisture content and conservation status in an all-in-one methodology obtainable from both MRI and CT techniques