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Prostate cancer is the most common cancer among US men. However, prostate imaging is still challenging despite the advances in multi-parametric magnetic resonance imaging (MRI), which provides both morphologic and functional information pertaining to the pathological regions. Along with whole prostate gland segmentation, distinguishing between the central gland (CG) and peripheral zone (PZ) can guide toward differential diagnosis, since the frequency and severity of tumors differ in these regions; however, their boundary is often weak and fuzzy. This work presents a preliminary study on deep learning to automatically delineate the CG and PZ, aiming at evaluating the generalization ability of convolutional neural networks (CNNs) on two multi-centric MRI prostate datasets. Especially, we compared three CNN-based architectures: SegNet, U-Net, and pix2pix. In such a context, the segmentation performances achieved with/without pre-training were compared in 4-fold cross-validation. In general, U-Net outperforms the other methods, especially when training and testing are performed on multiple datasets.

In breast cancer (BC) care, radiotherapy is considered an efficient treatment, prescribed both for controlling localized tumors or as a therapeutic option in case of inoperable, incompletely resected or recurrent tumors. However, approximately 90% of BC-related deaths are due to the metastatic tumor progression. Then, it is strongly desirable to improve tumor radiosensitivity using molecules with synergistic action. The main aim of this study is to develop curcumin-loaded solid nanoparticles (Cur-SLN) in order to increase curcumin bioavailability and to evaluate their radiosensitizing ability in comparison to free curcumin (free-Cur), by using an in vitro approach on BC cell lines. In addition, transcriptomic and metabolomic profiles, induced by Cur-SLN treatments, highlighted networks involved in this radiosensitization ability. The non tumorigenic MCF10A and the tumorigenic MCF7 and MDA-MB-231 BC cell lines were used. Curcumin-loaded solid nanoparticles were prepared using ethanolic precipitation and the loading capacity was evaluated by UV spectrophotometer analysis. Cell survival after treatments was evaluated by clonogenic assay. Dose-response curves were generated testing three concentrations of free-Cur and Cur-SLN in combination with increasing doses of IR (2-9 Gy). IC50 value and Dose Modifying Factor (DMF) was measured to quantify the sensitivity to curcumin and to combined treatments. A multi-"omic" approach was used to explain the Cur-SLN radiosensitizer effect by microarray and metobolomic analysis. We have shown the efficacy of the Cur-SLN formulation as radiosensitizer on three BC cell lines. The DMFs values, calculated at the isoeffect of SF = 50%, showed that the Luminal A MCF7 resulted sensitive to the combined treatments using increasing concentration of vehicled curcumin Cur-SLN (DMF: 1,78 with 10 µM Cur-SLN.) Instead, triple negative MDA-MB-231 cells were more sensitive to free-Cur, although these cells also receive a radiosensitization effect b

Nowadays, uterine fibroids can be treated using Magnetic Resonance guided Focused Ultrasound Surgery (MRgFUS), which is a non-invasive therapy exploiting thermal ablation. In order to measure the Non-Perfused Volume (NPV) for treatment response assessment, the ablated fibroid areas (i.e., Region of Treatment, ROT) are manually contoured by a radiologist. The current operator-dependent methodology could affect the subsequent follow-up phases, due to the lack of result repeatability. In addition, this fully manual procedure is time-consuming, considerably increasing execution times. These critical issues can be addressed only by means of accurate and efficient automated Pattern Recognition approaches. In this contribution, we evaluate two computer-assisted segmentation methods, which we have already developed and validated, for uterine fibroid segmentation in MRgFUS treatments. A quantitative comparison on segmentation accuracy, in terms of area-based and distance-based metrics, was performed. The clinical feasibility of these approaches was assessed from physicians’ perspective, by proposing an integrated solution.

Background and Objectives: Image segmentation represents one of the most challenging issues in medical image analysis to distinguish among different adjacent tissues in a body part. In this context, appropriate image pre-processing tools can improve the result accuracy achieved by computer-assisted segmentation methods. Taking into consideration images with a bimodal intensity distribution, image binarization can be used to classify the input pictorial data into two classes, given a threshold intensity value. Unfortunately, adaptive thresholding techniques for two-class segmentation work properly only for images characterized by bimodal histograms. We aim at overcoming these limitations and automatically determining a suitable optimal threshold for bimodal Magnetic Resonance (MR) images, by designing an intelligent image analysis framework tailored to effectively assist the physicians during their decision-making tasks. Methods: In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. Our framework exploits MedGA as a pre-processing stage. MedGA is an image enhancement method based on Genetic Algorithms that improves the threshold selection, obtained by the efficient Iterative Optimal Threshold Selection algorithm, between the underlying sub-distributions in a nearly bimodal histogram. Results: The results achieved by the proposed evolutionary framework were quantitatively evaluated, showing that the use of MedGA as a pre-processing stage outperforms the conventional image enhancement methods (i.e., histogram equalization, bi-histogram equalization, Gamma transformation, and sigmoid transformation), in terms of both MR image enhancement and segmentation evalu

Stereotactic neuro-radiosurgery is a well-established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft-tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and growth. Therefore, particular attention must be deserved to these hypoxic areas that could lead to recurrent cancers and resistance to therapeutic damage. This article proposes a novel fully automatic method for necrosis extraction (NeXt), using the Fuzzy C-Means algorithm, after the GTV segmentation. This unsupervised Machine Learning technique detects and delineates the necrotic regions also in heterogeneous cancers. The overall processing pipeline is an integrated two-stage segmentation approach useful to support neuro-radiosurgery. NeXt can be exploited for dose escalation, allowing for a more selective strategy to increase radiation dose in hypoxic radioresistant areas. Moreover, NeXt analyzes contrast-enhanced T1-weighted MR images alone and does not require multispectral MRI data, representing a clinically feasible solution. This study considers an MRI dataset composed of 32 brain metastatic cancers, wherein 20 tumors present necroses. The segmentation accuracy of NeXt was evaluated using both spatial overlap-based and distance-based metrics, achieving these average values: Dice similarity coefficient 95.93% ± 4.23% and mean absolute distance 0.225 ± 0.229 (pixels).

Conventional dielectrophoresis (DEP) force on cell and particle is altered in the proximity of the electrodes due to the failure of the dipole approximation. In these conditions an anomalous DEP (aDEP) force rules the particle manipulation. Anyhow, the role of the aDEP is barely considered in the design of DEP devices. Here we analyze, using a multiscale simulation approach, the aDEP effects in micro-fluidic device coupled with interdigitated channel commonly used in continuous mode field flow fractionation dielectrophoretic (FFF-DEP) devices for the separation of circulating tumor cells (MDA) and Lymphocytes (LYM). We study the propagation of an injected density of MDA and LYM respectively and evaluate how the aDEP changes the migrations of the cells.

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

We report a case study demonstrating the value of prone positioning in positron emission tomography (PET) of a woman with diagnosed breast cancer (BC) addressed to lumpectomy. Surgeon required 18F-fluorodeoxiglucose (18F-FDG) PET study for staging and assessment of lymphnode involvement/metastasis prior to lumpectomy: a whole-body supine study and a prone acquisition of breast. Supine study revealed one lesion, while prone study revealed two lesions. Prone PET findings changed diagnosis and therapeutic intervention for patient who was subsequently subjected to quadrantectomy.

A general limitation in PET is represented by the poor spatial resolution of the system. To compensate for this limitation by using iterative reconstruction algorithms it is possible to account for the response of the PET system (Point Spread Function, PSF) in the reconstruction scheme to improve PET image quality and quantitative accuracy. Unfortunately, a common behaviour of iterative reconstruction techniques is the increase of noise as the iterations proceed due to the ill-posed nature of the reconstruction process. On the other hand a high number of iterations is usually needed to recover a significant percentage of the signal and to reach the convergence, especially when including resolution modelling. To solve this dilemma, regularization strategies could be employed to control the noise amplification as the iterations proceed. In this work a new prior for variational Maximum a Posteriori regularization is proposed to be used in a 3D ML-OSEM reconstruction algorithm which also accounts for the PSF of the PET system. The new regularization prior is characterised by a strong smoothing component for regions in the image with a magnitude of the gradient below a given threshold (set to discriminate between background and signal), while preserving edges above the threshold. The new algorithm has been validated on phantom and clinical data. The results showed that the use of the proposed regularization prior allows a better control of the noise, while maintaining high enough signal recovery thanks to the PSF modelling. To obtain the best results using the proposed prior, i.e. the best compromise between noise control and loss of recovered signal, an optimization of the regularization parameters is therefore required

This study aims at evaluating the dependence of 4D-PET data sorting on the number of phases in which the respiratory cycle can be divided. The issue is to find the best compromise to reduce the conflicting effects induced by increasing the number of phases: lesion motion on each set of images decreases, but on the other hand image noise increases. The IQ NEMA 2001 IEC body phantom was used to simulate the movement of neoplastic lesions in the thorax and abdomen, investigating the effect of target size (10-37 mm), lesion to background activity concentrations ratio (4-to-1 and 8-to-1), total acquisition time (3, 6, 12, 20 min) and number of phase partition (1, 2, 4, 6, 8, 10, 13). The phantom was moved in a cranial-caudal direction with an excursion of 25 mm and with a period of 4.0 s. Five parameters associated to lesion volume and activity concentration were considered to assess the capability of the 4D-PET technique to "freeze" the phantom motion. The results for all the parameters showed the capability of the 4D-PET acquisition technique to "freeze" the lesion motion. The division into 6 phases was found to be the best compromise between temporal resolution and image noise for the phase where the "lesions" move faster, whereas the partition into 4 phases could be used if a stable breathing phase is considered

An accurate identification and interpretation of neoplastic lesions by PET is related to PET image quality, and depends on several factors including data processing for image formation. The aim of this work was to assess the influence of data rebinning and reconstruction on lesion detectability and quantification for a high-resolution 3-D PET/CT system, in order to optimize 3-D PET/CT oncological protocols. Oncological 18F-FDG PET studies were Monte Carlo (MC) simulated, based on a normal-weight oncological patient study, varying lesion size, lesion-to-background ratio, statistics, and including or not attenuation and scatter effects. Single slice rebinned (SSR), Fourier rebinned (FORE) and Fully 3-D (3-D) sinograms were considered. Reconstruction was carried out using ordered subset expectation maximization (OSEM) and attenuation weighting OSEM (AWOSEM). Human observers evaluated images in terms of clinical parameters characteristic of lesion detectability and quantification: lesion number and lesion size. By comparison with the known activity map (input for MC simulations), identified lesions were classified as true and false positive; the true positive fraction (TPF) and sensitivity were derived, as indices of lesion detectability. % maximum number of lesions identified with erroneous size (MAXls) and % errors on lesion-to-background ratio (LBR) were calculated as quantitative indices for lesion characterization. The results show that, when images corrected for scatter and attenuation are considered, 2-D rebinning allows more lesions to be identified than 3-D, with the best detectability found using FORE + AWOSEM: in this case, sensitivity was found equal to 0.43, higher than with SSR + OSEM (0.30), SSR+AWOSEM(0.23), FORE+OSEM(0.35) and 3-D (0.18 for both OSEM and AWOSEM). Quantitatively, smaller MAXls and smaller LBRs were found using FORE + AWOSEM, in comparison to 3-D+AWOSEM (p = 0125 for both parameters); FORE + OSEM presented the smallest LBRs. Our findings

We investigated whether observation of actions reproduced in three-dimensional virtual reality would engage perceptual and visuomotor brain processes different from those induced by the observation of real hand actions. Participants were asked to passively observe grasping actions of geometrical objects made by a real hand or by hand reconstructions of different quality in 3D virtual reality as well as on a 2D TV screen. We found that only real actions in natural environment activated a visuospatial network including the right posterior parietal cortex. Observation of virtual-reality hand actions engaged prevalent visual perceptual processes within lateral and mesial occipital regions. Thus, only perception of actions in reality maps onto existing action representations, whereas virtual-reality conditions do not access the full motor knowledge available to the central nervous system

We present an enhancement of the OSEM (ordered set expectation maximization) algorithm for 3D PET reconstruction, which we call the inter-update Metz filtered OSEM (IMF-OSEM). The IMF-OSEM algorithm incorporates filtering action into the image updating process in order to improve the quality of the reconstruction. With this technique, the multiplicative correction image--ordinarily used to update image estimates in plain OSEM--is applied to a Metz-filtered version of the image estimate at certain intervals. In addition, we present a software implementation that employs several high-speed features to accelerate reconstruction. These features include, firstly, forward and back projection functions which make full use of symmetry as well as a fast incremental computation technique. Secondly, the software has the capability of running in parallel mode on several processors. The parallelization approach employed yields a significant speed-up, which is nearly independent of the amount of data. Together, these features lead to reasonable reconstruction times even when using large image arrays and non-axially compressed projection data. The performance of IMF-OSEM was tested on phantom data acquired on the GE Advance scanner. Our results demonstrate that an appropriate choice of Metz filter parameters can improve the contrast-noise balance of certain regions of interest relative to both plain and post-filtered OSEM, and to the GE commercial reprojection algorithm software

In this study we evaluated pre- (PR-R) and post-reconstruction (PO-R) count-dependent Metz filters for PET brain studies in order to increase signal-to-noise ratio. A set of studies using a 3D Hoffman brain phantom was performed at various count levels, and a reference image set was created from extremely high count images. Several combinations of PR-R and PO-R filtering were considered to find the optimal means of processing, including: Hann filter alone; PR-R Metz filter without or with a PO-R low pass filter; and PO-R Metz filter without or with a PR-R low pass filter. A formula was established to correlate the optimal PO-R Metz filter order with the net counts. Resolution [full width at half-maximum (FWHM) and fill width at tenth maximum (FWTM)], normalized residual mean square differences (NRMSD) between the ideal and the processed images, noise reduction and contrast were used as parameters for the evaluation of the different filter combinations. Resolution is decreased by all filter combinations that can effectively control noise; however, FWTM increases less than FWHM. NRMSD indicates that the use of Hann and (optimal) PO-R Metz filter is the most powerful combination from among those tested. A close correlation (r = 0.969) was found between the net counts and the optimal order of the PO-R Metz filter. At the count levels of clinical studies the PO-R Metz filter was found to control noise much more effectively and enhance the contrast when compared to the routinely used Hann filter alone, and produced images of better quality