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Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.

Natural phenomena and mechanisms have always intrigued humans, inspiring the design of effective solutions for real-world problems. Indeed, fascinating processes occur in nature, giving rise to an ever-increasing scientific interest. In everyday life, the amount of heterogeneous biomedical data is increasing more and more thanks to the advances in image acquisition modalities and high-throughput technologies. The automated analysis of these large-scale datasets creates new compelling challenges for data-driven and model-based computational methods. The application of intelligent algorithms, which mimic natural phenomena, is emerging as an effective paradigm for tackling complex problems, by considering the unique challenges and opportunities pertaining to biomedical images. Therefore, the principal contribution of computer science research in life sciences concerns the proper combination of diverse and heterogeneous datasets-i.e., medical imaging modalities (considering also radiomics approaches), Electronic Health Record engines, multi-omics studies, and real-time monitoring-to provide a comprehensive clinical knowledge. In this paper, the state-of-the-art of nature-inspired medical image analysis methods is surveyed, aiming at establishing a common platform for beneficial exchanges among computer scientists and clinicians. In particular, this review focuses on the main natureinspired computational techniques applied to medical image analysis tasks, namely: physical processes, bio-inspired mathematical models, Evolutionary Computation, Swarm Intelligence, and neural computation. These frameworks, tightly coupled with Clinical Decision Support Systems, can be suitably applied to every phase of the clinical workflow. We show that the proper combination of quantitative imaging and healthcare informatics enables an in-depth understanding of molecular processes that can guide towards personalised patient care.

Parkinson's disease (PD) is characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the appearance of α-synuclein insoluble aggregates known as Lewy bodies. Neurodegeneration is accompanied by neuroinflammation mediated by cytokines and chemokines produced by the activated microglia. Several studies demonstrated that such an inflammatory process is an early event, and contributes to oxidative stress and mitochondrial dysfunctions. α-synuclein fibrillization and aggregation activate microglia and contribute to disease onset and progression. Mutations in different genes exacerbate the inflammatory phenotype in the monogenic compared to sporadic forms of PD. Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) with selected radiopharmaceuticals allow in vivo imaging of molecular modifications in the brain of living subjects. Several publications showed a reduction of dopaminergic terminals and dopamine (DA) content in the basal ganglia, starting from the early stages of the disease. Moreover, non-dopaminergic neuronal pathways are also affected, as shown by in vivo studies with serotonergic and glutamatergic radiotracers. The role played by the immune system during illness progression could be investigated with PET ligands that target the microglia/macrophage Translocator protein (TSPO) receptor. These agents have been used in PD patients and rodent models, although often without attempting correlations with other molecular or functional parameters. For example, neurodegeneration and brain plasticity can be monitored using the metabolic marker 2-Deoxy-2-[18F]fluoroglucose ([18F]-FDG), while oxidative stress can be probed using the copper-labeled diacetyl-bis(N-methyl-thiosemicarbazone) ([Cu]-ATSM) radioligand, whose striatal-specific binding ratio in PD patients seems to correlate with a disease rating scale and motor scores. Also, structural and functional modifications during disease progression may be evaluated by Magnetic Resonance Imaging (MRI), using different parameters as iron content or cerebral volume. In this review article, we propose an overview of in vivo clinical and non-clinical imaging research on neuroinflammation as an emerging marker of early PD. We also discuss how multimodal-imaging approaches could provide more insights into the role of the inflammatory process and related events in PD development.

Prostate canceris the most common cancer among US men. However, prostateimaging 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. Alongwith whole prostate gland segmentation, distinguishingbetween the centralgland (CG) and peripheralzone (PZ) can guidetoward differential diagnosis, sincethe frequency andseverity of tumors differin these regions; however, theirboundary is often weakand fuzzy. This work presentsa 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.

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 evaluation metrics. CONCLUSIONS: Thanks to this framework, MR image segmentation accuracy is considerably increased, allowing for measurement repeatability in clinical workflows. The proposed computational solution could be well-suited for other clinical contexts requiring MR image analysis and segmentation, aiming at providing useful insights for differential diagnosis and prognosis.

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since tumor's frequency and severity differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks' adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training., 44 pages, 6 figures, Accepted to Neurocomputing, Co-first authors: Leonardo Rundo and Changhee Han

The preclinical β-Cube PET scanner features a design with monolithic crystal detectors and DOI estimation. Images are reconstructed with a 3D list mode algorithm that should preserve the full spatial resolution of the system. This design is supposed to provide isotropic spatial resolution in the whole field of view. To test this claim we measured the effective reconstructed spatial resolution using a micro Derenzo phantom (Diameter 40 mm), using 3 isotopes with different positron ranges: 18F, 11C, 68Ga. The phantom was first acquired to measure the transverse resolution positioned in center of the field of view. Then it was shifted radially to the most extreme position allowed and scanned twice: once with the smallest rod in the outermost position, then rotated by 180°. For the 18F acquisition the phantom was also acquired with its axis orthogonal to that of the scanner, to measure the axial resolution. Resolution was quantified using the modulation transfer function. The maximum spatial resolution was observed with 18F, while with 68Ga only the 4.7 mm rods were clearly distinguishable. Modulation transfer function analysis confirms that the spatial resolution is identical along all 3 axes. The resolution loss with the radial distance from the center is minimal.

Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40% thresholding method, region growing method, and fuzzy c-means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real-time performance satisfying the physician requirements in a radiotherapy environment.

Background: There is extensive scientific evidence that radiation therapy (RT) is a crucial treatment, either alone or in combination with other treatment modalities, for many types of cancer, including breast cancer (BC). BC is a heterogeneous disease at both clinical and molecular levels, presenting distinct subtypes linked to the hormone receptor (HR) status and associated with different clinical outcomes. The aim of this study was to assess the molecular changes induced by high doses of ionizing radiation (IR) on immortalized and primary BC cell lines grouped according to Human epidermal growth factor receptor (HER2), estrogen, and progesterone receptors, to study how HR status influences the radiation response. Our genomic approach using in vitro and ex-vivo models (e.g., primary cells) is a necessary first step for a translational study to describe the common driven radio-resistance features associated with HR status. This information will eventually allow clinicians to prescribe more personalized total doses or associated targeted therapies for specific tumor subtypes, thus enhancing cancer radio-sensitivity. Methods: Nontumorigenic (MCF10A) and BC (MCF7 and MDA-MB-231) immortalized cell lines, as well as healthy (HMEC) and BC (BCpc7 and BCpcEMT) primary cultures, were divided into low grade, high grade, and healthy groups according to their HR status. At 24 h post-treatment, the gene expression profiles induced by two doses of IR treatment with 9 and 23 Gy were analyzed by cDNA microarray technology to select and compare the differential gene and pathway expressions among the experimental groups. Results: We present a descriptive report of the substantial alterations in gene expression levels and pathways after IR treatment in both immortalized and primary cell cultures. Overall, the IR-induced gene expression profiles and pathways appear to be cell-line dependent. The data suggest that some specific gene and pathway signatures seem to be linked to HR status. Conclusions: Genomic biomarkers and gene-signatures of specific tumor subtypes, selected according to their HR status and molecular features, could facilitate personalized biological-driven RT treatment planning alone and in combination with targeted therapies.

Despite of the development of advanced segmentation techniques, achieving accurate and reproducible gross tumor volume (GTV) segmentation results is still an important challenge in neuro-radiosurgery. Nowadays, magnetic resonance imaging (MRI) is the most prominent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a minimally invasive technology for dealing with inaccessible or insufficiently treated tumors with traditional surgery or radiotherapy. During a treatment planning phase, the GTV is generally contoured by experienced neurosurgeons and radiation oncologists using fully manual segmentation procedures on MR images. Unfortunately, this operative methodology is definitely time-expensive and operator-dependent. Delineation result repeatability, in terms of both intra- and inter-operator reliability, can be achieved only by using computer-assisted approaches. In this paper a novel semi-automatic seeded image segmentation method, based on a cellular automata model, for MRI brain cancer detection and delineation is proposed. This approach, called GTVcut, employs an adaptive seed selection strategy and helps to segment the GTV, by identifying the target volume to be treated using the Gamma Knife device. The accuracy of GTVcut was evaluated on a dataset composed of 32 brain cancers, using both spatial overlap-based and distance-based metrics. The achieved experimental results are very reproducible, showing the effectiveness and the clinical feasibility of the proposed approach.

Uterine fibroids are benign tumors that can affect female patients during reproductive years. Magnetic resonance-guided focused ultrasound (MRgFUS) represents a noninvasive approach that uses thermal ablation principles to treat symptomatic fibroids. During traditional treatment planning, uterus, fibroids, and surrounding organs at risk must be manually marked on MR images by an operator. After treatment, an operator must segment, again manually, treated areas to evaluate the non-perfused volume (NPV) inside the fibroids. Both pre- and post-treatment procedures are time-consuming and operator-dependent. This paper presents a novel method, based on an advanced direct region detection model, for fibroid segmentation in MR images to address MRgFUS post-treatment segmentation issues. An incremental procedure is proposed: split-and-merge algorithm results are employed as multiple seed-region selections by an adaptive region growing procedure. The proposed approach segments multiple fibroids with different pixel intensity, even in the same MR image. The method was evaluated using area-based and distance-based metrics and was compared with other similar works in the literature. Segmentation results, performed on 14 patients, demonstrated the effectiveness of the proposed approach showing a sensitivity of 84.05 %, a specificity of 92.84%, and a speedup factor of 1.56× with respect to classic region growing implementations (average values).

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 (18)F-fluorodeoxiglucose ((18)F-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.

An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment.

Prostate imaging is a very critical issue in the clinical practice, especially for diagnosis, therapy, and staging of prostate cancer. Magnetic Resonance Imaging (MRI) can provide both morphologic and complementary functional information of tumor region. Manual detection and segmentation of prostate gland and carcinoma on multispectral MRI data is not easily practicable in the clinical routine because of the long times required by experienced radiologists to analyze several types of imaging data. In this paper, a fully automatic image segmentation method, exploiting an unsupervised Fuzzy C-Means (FCM) clustering technique for multispectral T1-weighted and T2-weighted MRI data processing, is proposed. This approach enables prostate segmentation and automatic gland volume calculation. Segmentation trials have been performed on a dataset composed of 7 patients affected by prostate cancer, using both area-based and distance-based metrics for its evaluation. The achieved experimental results are encouraging, showing good segmentation accuracy.

The goal of radiation therapy (RT) is to deliver the therapeutic dose to target tissues minimizing the risks of normal tissue complication. Nowadays, technological advances in radiation delivery and the introduction of particle therapies have strongly limited the amount of dose distributed to normal tissues and enhanced the tumor killing capacity. Here, we discuss about the state of art in RT treatment modalities, tumor sensitivity and radiation toxicity. A special insight is dedicated to the role of tumor heterogeneity, cancer stem cells (CSCs) and hypoxia. In addition, in this review we provide an overview of the most recently studies evaluating the potential role of “omic” biomarkers for a personalized biological-driven treatment planning, useful to maximize the radiotherapy success without normal tissues complications.

In recent years, high-intensity focused ultrasound (HIFU) has emerged as a new and promising non-invasive and non-ionizing ablative technique for the treatment of localized solid tumors. Extensive pre-clinical and clinical studies have evidenced that, in addition to direct destruction of the primary tumor, HIFU-thermoablation may elicit long-term systemic host anti-tumor immunity. In particular, an important consequence of HIFU treatment includes the release of tumor-associated antigens (TAAs), the secretion of immuno-suppressing factors by cancer cells and the induction of cytotoxic T lymphocyte (CTL) activity. Radiation therapy (RT) is the main treatment modality used for many types of tumors and about 50% of all cancer patients receive RT, often used in combination with surgery and chemotherapy. It is well known that RT can modulate anti-tumor immune responses, modifying micro-environment and stimulating inflammatory factors that can greatly affect cell invasion, bystander effects, radiation tissue complications (such as fibrosis), genomic instability and thus, intrinsic cellular radio-sensitivity. To date, various combined therapeutic strategies (such as immuno-therapy) have been performed in order to enhance RT success in treating locally advanced and recurrent tumors. Recent works suggested the combined use of HIFU and RT treatments to increase the tumor cell radio-sensitivity, in order to synergize the effects reaching the maximum results with minimal doses of ionizing radiation (IR). Here, we highlight the opposite immuno-modulation roles of RT and HIFU, providing scientific reasons to test, by experimental approaches, the use of HIFU immune-stimulatory capacity to improve tumor radio-sensitivity, to reduce the RT induced inflammatory response and to decrease the dose-correlated side effects in normal tissues.

The aim of this study is to combine Biological Target Volume (BTV) segmentation and Gross Target Volume (GTV) segmentation in stereotactic neurosurgery.Our goal is to enhance Clinical Target Volume (CTV) definition, including metabolic and morphologic information, for treatment planning and patient follow-up.We propose a fully automatic approach for multimodal PET and MR image segmentation. This method is based on the Random Walker (RW) and Fuzzy C-Means clustering (FCM) algorithms. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is presented, considering volume-based, overlap-based and spatial distance-based metrics. Statistics was also included to measure correlation. Finally, a five-point Likert scale was used to provide a qualitative evaluation about the clinical value of the proposed multimodal approach. Background and objectives: Nowadays, clinical practice in Gamma Knife treatments is generally based on MRI anatomical information alone. However, the joint use of MRI and PET images can be useful for considering both anatomical and metabolic information about the lesion to be treated. In this paper we present a co-segmentation method to integrate the segmented Biological Target Volume (BTV), using [11C]-Methionine-PET (MET-PET) images, and the segmented Gross Target Volume (GTV), on the respective co-registered MR images. The resulting volume gives enhanced brain tumor information to be used in stereotactic neuro-radiosurgery treatment planning. GTV often does not match entirely with BTV, which provides metabolic information about brain lesions. For this reason, PET imaging is valuable and it could be used to provide complementary information useful for treatment planning. In this way, BTV can be used to modify GTV, enhancing Clinical Target Volume (CTV) delineation.Methods: A novel fully automatic multimodal PET/MRI segmentation method for Leksell Gamma Knife treatments is proposed. This approach improves and combines two computer-assisted and operator-independent single modality methods, previously developed and validated, to segment BTV and GTV from PET and MR images, respectively. In addition, the GTV is utilized to combine the superior contrast of PET images with the higher spatial resolution of MRI, obtaining a new BTV, called BTVMRI. A total of 19 brain metastatic tumors, undergone stereotactic neuro-radiosurgery, were retrospectively analyzed. A framework for the evaluation of multimodal PET/MRI segmentation is also presented. Overlap-based and spatial distance-based metrics were considered to quantify similarity concerning PET and MRI segmentation approaches. Statistics was also included to measure correlation among the different segmentation processes. Since it is not possible to define a gold-standard CTV according to both MRI and PET images without treatment response assessment, the feasibility and the clinical value of BTV integration in Gamma Knife treatment planning were considered. Therefore, a qualitative evaluation was carried out by three experienced clinicians.Results: The achieved experimental results showed that GTV and BTV segmentations are statistically correlated (Spearman's rank correlation coefficient: 0.898) but they have low similarity degree (average Dice Similarity Coefficient: 61.8714.64). Therefore, volume measurements as well as evaluation metrics values demonstrated that MRI and PET convey different but complementary imaging information. GTV and BTV could be combined to enhance treatment planning. In more than 50% of cases the CTV was strongly or moderately conditioned by metabolic imaging. Especially, BTVMRI enhanced the CTV more accurately than BTV in 25% of cases.Conclusions: The proposed fully automatic multimodal PET/MRI segmentation method is a valid operator-independent methodology helping the clinicians to define a CTV that includes both metabolic and morphologic information. BTVMRI and GTV should be considered for a comprehensive treatment planning. Display Omitted

Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T2w) MRI anatomical data processing, is proposed. This approach, using an unsupervised Machine Learning technique, helps to segment the prostate gland effectively. A total of 21 patients with suspicion of prostate cancer were enrolled in this study. Volume-based metrics, spatial overlap-based metrics and spatial distance-based metrics were used to quantitatively evaluate the accuracy of the obtained segmentation results with respect to the gold-standard boundaries delineated manually by an expert radiologist. The proposed multispectral segmentation method was compared with the same processing pipeline applied on either T2w or T1w MR images alone. The multispectral approach considerably outperforms the monoparametric ones, achieving an average Dice Similarity Coefficient 90.77 ± 1.75, with respect to 81.90 ± 6.49 and 82.55 ± 4.93 by processing T2w and T1w imaging alone, respectively. Combining T2w and T1w MR image structural information significantly enhances prostate gland segmentation by exploiting the uniform gray appearance of the prostate on T1w MRI.

Background The current methodology for the Surviving Fraction (SF) measurement in clonogenic assay, which is a technique to study the anti-proliferative effect of treatments on cell cultures, involves manual counting of cell colony forming units. This procedure is operator-dependent and error-prone. Moreover, the identification of the exact colony number is often not feasible due to the high growth rate leading to the adjacent colony merging. As a matter of fact, conventional assessment does not deal with the colony size, which is generally correlated with the delivered radiation dose or the administered cytotoxic agent. Method Considering that the Area Covered by Colony (ACC) is proportional to the colony number and size as well as to the growth rate, we propose a novel fully automatic approach exploiting Circle Hough Transform, to automatically detect the wells in the plate, and local adaptive thresholding, which calculates the percentage of ACC for the SF quantification. This measurement relies just on this covering percentage and does not consider the colony number, preventing inconsistencies due to intra- and inter-operator variability. Results To evaluate the accuracy of the proposed approach, we compared the SFs obtained by our automatic ACC-based method against the conventional counting procedure. The achieved results (r = 0.9791 and r = 0.9682 on MCF7 and MCF10A cells, respectively) showed values highly correlated with the measurements using the traditional approach based on colony number alone. Conclusions The proposed computer-assisted methodology could be integrated in laboratory practice as an expert system for the SF evaluation in clonogenic assays.

A new prior for variational Maximum a Posteriori regularization is proposed to be used in a 3D One-Step-Late (OSL) reconstruction algorithm accounting also for the Point Spread Function (PSF) of the PET system. The new regularization prior strongly smoothes background regions, while preserving transitions. A detectability index is proposed to optimize the prior. The new algorithm has been compared with different reconstruction algorithms such as 3D-OSEM. +. PSF, 3D-OSEM. +. PSF. +. post-filtering and 3D-OSL with a Gauss-Total Variation (GTV) prior. The proposed regularization allows controlling noise, while maintaining good signal recovery; compared to the other algorithms it demonstrates a very good compromise between an improved quantitation and good image quality. © 2014 Elsevier Ltd.

Summary Radiotherapy techniques deliver ionizing radiations (X-rays, photons, electrons, protons, etc.) inside cancerous tissues to kill the abnormal cells. Radiotherapy-related activities such as the optimization of the therapeutic radiation dose to patients, workers' radioprotection, linear accelerator commissioning, quality assurance processes and technical innovations of linear accelerators are strongly based on the ability to predict the dose distribution. Monte Carlo based simulations are so far the most accurate tool for the calculation of dosimetric parameters, with the drawback of requiring extensive computing resources to achieve statistically meaningful results in a reasonable time frame. In the last years, advanced cancer treatment clinical and research communities have used e-Infrastructures to support their activities. The present paper reports on the development of a computing facility for helping clinical researchers in using modern R&E networking and distributed computing and storage resources for a new radiotherapy technique: the Intra-Operative Electron Radiotherapy. The web application developed addresses some technical and clinical needs as the design of linear accelerators' collimation systems and the optimization of the patient therapeutic dose distribution. Copyright © 2014 John Wiley & Sons, Ltd.

Purpose The regional quantification of amyloid burden is crucial for the clinical diagnosis of Alzheimer’s disease [1]. The best method to evaluate regional amyloid deposition in PET is through the use MR imaging for brain space normalization. However, since MR imaging is not always available in the clinical practice, a MR-less methodology is needed in order to compute semi-quantitative and analyze regional amyloid burden. Methods Forty-four patients with clinical evidence of dementia, underwent 18F-Florbetaben PET (FBB-PET), FDG-PET, neuropsychological assessment and cerebrospinal fluid analysis. We implemented a methodology that uses SPM12 to import and normalize the FBB-PET images in Montreal Neurological Institute (MNI) space without MRI, and the Automated Anatomical Labeling (AAL) atlas [2] in order to extract regional uptake from normalized FBB-PET. SUVR has been computed by using cerebellum as control region (‘ Cerebelum _ 4 _ 5 ’ of AAL atlas). We then computed Receiver Operating Characteristic (ROC) curve in order to find best thresholds for identifying clinical subgroups in our patients. Results Semi-quantitative evaluation of FBB-PET images and ROC analysis stated that SUVR value of 1,006 in the bilateral inferior frontal cortex and a SUVR of 1.03 in the precuneus region were the best cutoff (AUC 0.883 and 0.826, respectively). Box-Plot analysis showed a trend distribution of elevated SUVR levels in bilateral frontal cortex, angular girus, occipital, parietal, precuneus and paracentral lobule, among AD patients. Conclusion MR-less methodology based on Tissue Probability Map and AAL atlas provide regional quantification of amyloid burden. The ROC analysis is able to retrieve useful thresholds for the classification of AD versus non-AD thus providing a tool in clinical practice. We will perform the study on a larger sample in order to confirm the results.

Respiratory and cardiac motions represent important sources of image degradation in both PET and computed tomography (CT) studies that need to be taken into account and compensated to improve image quality and quantitative accuracy. This review describes the hardware needed to perform respiratory and cardiac gating with PET and PET/CT systems. In particular, most of the proposed motion-tracking devices for the management of respiratory, cardiac, and multidimensional movements are described and compared. Some advanced applications in PET and PET/CT made possible by the gating technology are considered and analyzed.

Magnetic Resonance Imaging has gained popularity in breast cancer diagnosis since the introduction of contrast media, and recent developments in MRI have demonstrated a new potential use in diagnosis. Indeed, the application of in vivo spectroscopy to mammary tissue has revealed that the spectral appearance of choline could be a marker of malignancy, but early diagnosis and unambiguous breast cancer characterization could benefit by a standardized protocol for the simultaneous use of Dynamic Contrast Enhancement MRI and Magnetic Resonance Spectroscopy and combined interpretation of associated markers. A total of 29 female patients took part in the study. The combined protocol was performed on a General Electric Signa HDtx 1.5 Tesla, and the DCE data analysis was performed through an evaluation of the ROI signal intensity over time. The MRS data analysis evaluated choline concentration and the signal to noise ratio of the choline peak. Sensitivity, specificity and accuracy were assessed by the Receiver Operating Characteristic methodology for Dynamic Contrast Enhancement, Magnetic Resonance Spectroscopy and their linear combination. We performed a linear combination of Dynamic Contrast Enhancement MRI and 1H-Magnetic Resonance Spectroscopy by distribution free approach to obtain a high level diagnostic index. Combining the results of the two diagnostic tests has resulted in a new, very effective, diagnostic index able to discriminate between patients with and without malignant disease.

Aim: A small number of studies evaluated the detection rate of lesions from bladder carcinoma (BC) of 18F-FDG PET/CT in the restaging process. However, the prognostic role of FDG PET/CT still remains unclear. The aim of the present study was to evaluate the accuracy, the effect upon treatment decision, and the prognostic value of FDG PET/CT in patients with suspected recurrent BC. Materials and Methods: Forty-one patients affected by BC underwent FDG PET/CT for restaging purpose. The diagnostic accuracy of visually interpreted FDG PET/CT was assessed compared to histology (n = 8), other diagnostic imaging modalities (contrast-enhanced CT in 38/41 patients and MRI in 15/41) and clinical follow-up (n = 41). Semiquantitative PET values (SUVmax, SUVmean, SUL, MTV, TLG) were calculated using a graph-based method. Progression-free survival (PFS) and overall survival (OS) were assessed by using Kaplan-Meier curves. The risk of progression (hazard ratio, HR) was computed by Cox regression analysis by considering all the available variables. Results: PET was considered positive in 21 of 41 patients. Of these, recurrent BC was confirmed in 20 (95%). Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of FDG PET/CT were 87%, 94%, 95%, 85%, 90%. AUC was 0.9 (95 %IC 0.8-1). Bayesian positive and negative likelihood ratios were 14.5 and 0.13, respectively. FDG PET/CT findings modified the therapeutic approach in 16 patients (modified therapy in 10 PET-positive patients, watch-and-wait in six PET-negative patients). PFS was significantly longer in patients with negative scan vs. those with pathological findings (85% vs. 24%, p < 0.05; HR = 12.4; p = 0.001). Moreover, an unremarkable study was associated with a longer OS (88% vs. 47% after 2years and 87% vs. 25% after 3 years, respectively, p < 0.05). Standardized uptake value (SUV)max > 6 and total lesion glycolysis (TLG) > 8.5 were recognized as the most accurate thresholds to predict PFS (2-year PFS 62% for SUVmax < 6 vs. 15% for SUVmax > 6, p = 0.018; 2-year PFS 66% for TLG < 8.5 vs. 18% for TLG > 8.5, p = 0.09). Conclusion: A very good diagnostic performance for FDG PET/CT was confirmed in patients with suspected recurrent BC. FDG PET/CT allowed for a change in treatment decision in about 40% of cases and showed an important prognostic value in assessing PFS and OS.

// Daniela Gaglio 1, 2, 6, 8 , Silvia Valtorta 1, 2, 3, 4 , Marilena Ripamonti 1, 2 , Marcella Bonanomi 2 , Chiara Damiani 2 , Sergio Todde 5 , Alfredo Simone Negri 6 , Francesca Sanvito 7 , Fabrizia Mastroianni 2 , Antonella Di Campli 8 , Gabriele Turacchio 8 , Giuseppe Di Grigoli 1, 2, 3 , Sara Belloli 1, 2, 3 , Alberto Luini 8 , Maria Carla Gilardi 1, 2 , Anna Maria Colangelo 2, 9 , Lilia Alberghina 2, 9, * , Rosa Maria Moresco 2, 3, 4, * 1 Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Italy 2 SYSBIO.IT, Centre of Systems Biology, Milano, Italy 3 Experimental Imaging Center, IRCCS San Raffaele Scientific Institute, Milan, Italy 4 Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy 5 Tecnomed Foundation of University of Milano-Bicocca, Monza, Italy 6 Department of Agricultural and Environmental Sciences - Production, Landscape, Agroenergy University of Milan, Milan, Italy 7 Mouse Histopathology Unit, Department of Pathology, IRCCS San Raffaele Scientific Institute, Milan, Italy 8 Institute of Protein Biochemistry, National Research Council, Naples, Italy 9 Department of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy * These authors have contributed equally to this work Correspondence to: Daniela Gaglio, email: daniela.gaglio@ibfm.cnr.it Rosa Maria Moresco, email: moresco.rosamaria@hsr.it Keywords: tumor, metabolic rewiring, PET-imaging, metabolomics-mass-spectrometry, oncogenic-K-ras Received: February 02, 2016 Accepted: June 17, 2016 Published: July 07, 2016 ABSTRACT Oncogenic K-ras is capable to control tumor growth and progression by rewiring cancer metabolism. In vitro NIH-Ras cells convert glucose to lactate and use glutamine to sustain anabolic processes, but their in vivo environmental adaptation and multiple metabolic pathways activation ability is poorly understood. Here, we show that NIH-Ras cancer cells and tumors are able to coordinate nutrient utilization to support aggressive cell proliferation and survival. Using PET imaging and metabolomics-mass spectrometry, we identified the activation of multiple metabolic pathways such as: glycolysis, autophagy recycling mechanism, glutamine and serine/glycine metabolism, both under physiological and under stress conditions. Finally, differential responses between in vitro and in vivo systems emphasize the advantageous and uncontrolled nature of the in vivo environment, which has a pivotal role in controlling the responses to therapy.

EHR 2.0 is an evolution of current Electronic Health Records (EHR) which provides an intelligent support for all healthcare providers and patients in the National Health System with the benefit of improving the quality of care and increasing patient involvement in the health care and prevention process. At the center of the EHR 2.0 the paper puts the Virtual Health Record (VHR), an intelligent resource in network that contains all the digital information available about the patient health. How the main services of the VHR were derived from the analysis of some innovative healthcare scenarios is presented.

Background: Misalignment between positron emission tomography (PET) and computed tomography (CT) data is known to generate artifactual defects in cardiac PET images due to imprecise attenuation correction (AC). In this work, the use of a maximum likelihood attenuation and activity (MLAA) algorithm is proposed to avoid such artifacts in time-of-flight (TOF) PET. Methods: MLAA was implemented and tested using a thorax/heart phantom and retrospectively on fourteen 13N-ammonia PET/CT perfusion studies. Global and local misalignments between PET and CT data were generated by shifting matched CT images or using CT data representative of the end-inspiration phase. PET images were reconstructed with MLAA and a 3D-ordered-subsets-expectation-maximization (OSEM)-TOF algorithm. Images obtained with 3D-OSEM-TOF and matched CT were used as references. These images were compared (qualitatively and semi-quantitatively) with those reconstructed with 3D-OSEM-TOF and MLAA for which a misaligned CT was used, respectively, for AC and initialization. Results: Phantom experiment proved the capability of MLAA to converge toward the correct emission and attenuation distributions using, as input, only PET emission data, but convergence was very slow. Initializing MLAA with phantom CT images markedly improved convergence speed. In patient studies, when shifted or end-inspiration CT images were used for AC, 3D-OSEM-TOF reconstructions showed artifacts of increasing severity, size, and frequency with increasing mismatch. Such artifacts were absent in the corresponding MLAA images. Conclusion: The proposed implementation of the MLAA algorithm is a feasible and robust technique to avoid AC mismatch artifacts in cardiac PET studies provided that a CT of the source is available, even if poorly aligned.

MR Imaging is being increasingly used in radiation treatment planning as well as for staging and assessing tumor response. Leksell Gamma Knife (R) is a device for stereotactic neuro-radiosurgery to deal with inaccessible or insufficiently treated lesions with traditional surgery or radiotherapy. The target to be treated with radiation beams is currently contoured through slice-by-slice manual segmentation on MR images. This procedure is time consuming and operator-dependent. Segmentation result repeatability may be ensured only by using automatic/semi-automatic methods with the clinicians supporting the planning phase. In this paper a semi-automatic segmentation method, based on an unsupervised Fuzzy C-Means clustering technique, is proposed. The presented approach allows for the target segmentation and its volume calculation. Segmentation tests on 5 MRI series were performed, using both area-based and distance-based metrics. The following average values have been obtained: DS = 95.10, JC = 90.82, TPF = 95.86, FNF = 2.18, MAD = 0.302, MAXD = 1.260, H = 1.636.

Gross Tumor Volume (GTV) segmentation on medical images is an open issue in neuro-radiosurgery. Magnetic Resonance Imaging (MRI) is the most promi-nent modality in radiation therapy for soft-tissue anatomical districts. Gamma Knife stereotactic neuro-radiosurgery is a mini-invasive technique used to deal with inaccessible or insufficiently treated tumors. During the planning phase, the GTV is usually contoured by radiation oncologists using a manual segmentation procedure on MR images. This methodology is certainly time-consuming and op-erator-dependent. Delineation result repeatability, in terms of both intra- and inter-operator reliability, is only obtained by using computer-assisted approaches. In this paper a novel semi-automatic segmentation method, based on Cellular Au-tomata, is proposed. The developed approach allows for the GTV segmentation and computes the lesion volume to be treated. The method was evaluated on 10 brain cancers, using both area-based and distance-based metrics.

Background A new transcranial focused ultrasound device has been developed that can induce hyperthermia in a large tissue volume. The purpose of this work is to investigate theoretically how glioblastoma multiforme (GBM) can be effectively treated by combining the fast hyperthermia generated by this focused ultrasound device with external beam radiotherapy. Methods/Design To investigate the effect of tumor growth, we have developed a mathematical description of GBM proliferation and diffusion in the context of reaction–diffusion theory. In addition, we have formulated equations describing the impact of radiotherapy and heat on GBM in the reaction–diffusion equation, including tumor regrowth by stem cells. This formulation has been used to predict the effectiveness of the combination treatment for a realistic focused ultrasound heating scenario. Our results show that patient survival could be significantly improved by this combined treatment modality. Discussion High priority should be given to experiments to validate the therapeutic benefit predicted by our model. Electronic supplementary material The online version of this article (doi:10.1186/s40349-016-0078-3) contains supplementary material, which is available to authorized users.

The aim of this chapter is to investigate quantitation and data analysis issues, in the context of a SWOT analysis applied to hybrid brain imaging. In particular, the strengths (S), weaknesses (W), opportunities (O) and threats (T) of PET/MRI with respect to PET/CT are considered. Strengths are found in (a) the high soft tissue contrast typical of MR imaging, allowing an improvement in PET quantitation by using MRI to guide PET reconstruction, to guide correction for partial volume effect and to non-invasively generate accurate input functions and (b) the amount of information on brain functioning and brain disease which can be obtained by PET/MRI multimodal and multi-parametric imaging. Weaknesses are recognized in the complexity of MRI (with respect to CT), making attenuation correction of PET data difficult and requiring long acquisition times and complex workflows. Main opportunity is found in the possibility to combine multimodal and multi-parametric data with advanced image processing methods for the identification and quantitation of biomarkers. Finally, as for PET/MRI technology, threats are the high costs, niche markets and slow translation from research to clinical application.

In vivo molecular imaging techniques is increasingly used in the management of oncological patients, allowing different aspects of oncological pathologies to be assessed (e.g. metabolism, hypoxia) non invasively. The possibility to extract indexes of disease from in vivo biomedical images and to associate them with their biological drivers opens new pro-spective on the role of in vivo molecular imaging and expedites the translation of novel biomarkers from the bench to the clinical environment. In this work we investigate the relationship between 18F-FDG uptake measured by Body- Weight Standardized Uptake Value (SUVBW) as index of cell glucose metabolism, and histological indices for gastric and gastro-oesophageal cancer. For this purpose, Partial Volume Effect Correction (PVC) has been properly compen- sated prior to the measurement of the PET index (PVC-SUVsub>BW). The correlation of 18F-FDG PVC-SUVBW with histol- ogy data was evaluated by bivariate and multivariate statistical analysis. Although obtained in a limited number of pa- tients, our results suggest that correlations can be found when PVC is applied to SUVBW and that 18F-FDG PET can provide information on biological characteristics of gastric and gastro-oesophageal cancer lesions.

Purpose The aim of this work was the assessment of the physical performance of the new hybrid PET∕CT system: Discovery-690. Methods The Discovery-690 combines a lutetium-yttrium-orthosilicate (LYSO) block detector designed PET tomograph with a 64-slice CT scanner. The system is further characterized by a dedicated powerful computing platform implementing fully 3D-PET iterative reconstruction algorithms. These algorithms can account for time of flight (TOF) information and∕or a 3D model of the PET point spread function (PSF). PET physical performance was measured following NEMA NU-2-2007 procedures. Furthermore, specific tests were used: (i) to measure the energy and timing resolution of the PET system and (ii) to evaluate image quality, by using phantoms representing different clinical conditions (e.g., brain and whole body). Data processing and reconstructions were performed as required by standard procedures. Further reconstructions were carried out to evaluate the performance of the new reconstruction algorithms. In particular, four algorithms were considered for the reconstruction of the PET data: (i) HD = standard configuration, without TOF and PSF, (ii) TOF = HD + TOF, (iii) PSF = HD + PSF, and (iv) TOFPSF = HD + TOF + PSF. Results The transverse (axial) spatial resolution values were 4.70 (4.74) mm and 5.06 (5.55) mm at 1 cm and 10 cm off axis, respectively. Sensitivity (average between 0 and 10 cm) was 7.5 cps∕kBq. The noise equivalent count rate (NECR) peak was 139.1 kcps at 29.0 kBq∕ml. The scatter fraction at the NECR peak was 37%. The correction accuracy for the dead time losses and random event counts had a maximum absolute error below the NECR peak of 2.09%. The average energy and timing resolution were 12.4% and 544.3 ps, respectively. PET image quality was evaluated with the NEMA IEC Body phantom by using four reconstruction algorithms (HD, TOF, PSF, and TOFPSF), as previously described. The hot contrast (after 3 iterations and for a lesion∕background activity ratio of 4:1) for the spheres of 10, 13, 17, and 22 mm was (HD) 29.8, 45.4, 55.4, and 68.1%; (TOF) 39.9, 53.5, 62.7, and 72.2%; (PSF) 28.3, 47.3, 60.4, and 71.8%; (TOFPSF) 43.8, 62.9, 70.6, and 76.4%. The cold contrast for the spheres of 28 and 37 mm was (HD) 62.4 and 65.2%; (TOF) 77.1 and 81.4%; (PSF) 62.0 and 65.2%; (TOFPSF) 77.3 and 81.6%. Similar hot and cold contrast trends were found during the analyses of other phantoms representing different clinical conditions (brain and whole body). Nevertheless, the authors observed a predominant role of either TOF or PSF, depending on the specific characteristics and dimensions of the phantoms. Conclusions Discovery-690 shows very good PET physical performance for all the standard NEMA NU-2-2007 measurements. Furthermore, the new reconstruction algorithms available for PET data (TOF and PSF) allow further improvements of the D-690 image quality performance both qualitatively and quantitatively.

Purpose: The aim of this study was to assess the performance measurements of the new PET/CT system Discovery-600 (D-600, GEMS, Milwaukee, WI). Methods: Performance measures were obtained with the National Electrical Manufacturers Association (NEMA) NU 2-2007 procedures. Results: The transverse (axial) spatial resolution FWHMs were 4.9 (5.6) mm and 5.6 (6.4) mm at 1 and 10 cm off axis, respectively. The sensitivity (average at 0 and 10 cm) was 9.6 cps/kBq. The scatter fraction was 36.6% (low energy threshold: 425 keV). The NEC peak rate (k=1) was 75.2 kcps at 12.9 kBq/cc. The hot contrasts for 10, 13, 17, and 22 mm spheres were 41%, 51%, 62%, and 73% and the cold contrasts for 28 and 37 mm spheres were 68% and 72%. Conclusions: The Discovery-600 has good performance for the NEMA NU 2-2007 parameters, particularly in improved sensitivity compared to the scanners of the same Discovery family, D-ST and D-STE.

Purpose The aim of this paper is to develop an operator independent method for biological tumour volume (BTV) delineation from Positron Emission Tomography (PET) images. BTV delineation is challenging because of the low spatial resolution and high noise level in PET images. In addition, BTV varies substantially depending on the method used to segment. Manual delineation is widely-used, but it is strongly user dependent. Methods The proposed method starts with the automatic identification of the PET slice with maximum Standardized Uptake Value (SUV). Then, a user- independent mask is obtained by a rough pre-segmentation step and it is used to perform the local active contour segmentation on the next slices. When the proposed method finds a slice where the mean SUV on the interior of the delineation contour is greater or equal than the mean SUV on the exterior of the delineation contour, the segmentation is automatically stopped. The algorithm is evaluated on four datasets of synthetic lesions considering different ratios between lesion and background radioactivity concentrations. In this way, the actual lesion volumes are known and the segmentation algorithm is evaluated under different contrast ratio scenarios. In addition, BTVs of 25 patient studies have been manually delineated and compared to the proposed method in order to assess its applicability in a clinical environment. Results In phantom experiments, dice similarity coefficient (DSC) rate and true positive volume fraction (TPVF) rate greater than 90% were observed in synthetic lesions with a diameter greater than 17 mm. In clinical cases, TPVF and DSC were 91.00 ± 7.33% and 85.98 ± 3.40%, respectively. Conclusion Our method produces accurate segmentation results in phantom studies. In addition, it is feasible in clinical context and shows good accuracy in realistic conditions, reducing any user interaction.

The paper presents the prototype of a Personal Health System (PHS) realized during the Smart Health 2.0 Project. This implementation, called 'LiSE', represents an evolution with respect to the Citizen's Personal Notebook, introduced and specified by the EHR Italian regulations. The PHR provides some features that simplify the relationships between key players in NHS, like health care workers and patients. The PHR tools, such as the clinical dossier, the virtual medical assistant, or the GP/patient communication system, make the patient 'closer' to the health system, improving the own knowledge about her/his illness and, consequently, making him/her part active and conscious in the care processes. The PHS is able to enrich the EHR with information on the patient's health state and, at the same time, to take advantage of information extracted from the EHR content and supplied in a language and a format accessible to the patient. The proposed PHS, equipped with its own business logic to support patient empowerment, is the key element for the construction of a health care model based on a patient-centered vision of the healthcare.

Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as to lessen the time and cost of clinical trials. Magnetic Resonance (MR)-related biomarkers have been recently identified by the use of machine learning methods for the in vivo differential diagnosis of AD. However, the vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will (MCIc) or not convert (MCInc) to AD. Morphological T1-weighted MRIs of 137 AD, 76 MCIc, 134 MCInc, and 162 healthy controls (CN) selected from the Alzheimer's disease neuroimaging initiative (ADNI) cohort, were used by an optimized machine learning algorithm. Voxels influencing the classification between these AD-related pre-clinical phases involved hippocampus, entorhinal cortex, basal ganglia, gyrus rectus, precuneus, and cerebellum, all critical regions known to be strongly involved in the pathophysiological mechanisms of AD. Classification accuracy was 76% AD vs. CN, 72% MCIc vs. CN, 66% MCIc vs. MCInc (nested 20-fold cross validation). Our data encourage the application of computer-based diagnosis in clinical practice of AD opening new prospective in the early management of AD patients.

The aim of the present study was to evaluate the changes in cervical cancer glucose metabolism for different levels of cellular differentiation. The metabolic activity was measured by standardized uptake value (SUV), SUV normalized to lean body mass, metabolic tumor volume and total lesion glycolysis using fluorine-18 fluorodeoxyglucose positron emission tomography (PET). A correlation study of these values could be used to facilitate therapeutic choice and to improve clinical practice and outcome. This study considered 32 patients with diagnosed cervical cancers, at different International Federation of Gynecology and Obstetrics stages. Glucose metabolism was assessed by PET examination, and histological specimens were examined to determine their initial grade of differentiation. A correlation study of these values was evaluated. Histological examination showed that all cases were of squamous cell carcinoma. Regarding the differentiation of the tumor, 19 well- to moderately-differentiated tumors and 13 poorly-differentiated tumors were determined. Negative findings for correlations between metabolic parameters and initial grade of histological differentiation were found, and considering that histological grade has been shown to have no consistent prognostic value in cervical cancer treatment, PET imaging could play a significant role in cervical cancer prognosis.

Nowadays, radiation treatment is beginning to intensively use MRI thanks to its greater ability to discriminate healthy and diseased soft-tissues. Leksell Gamma Knife® is a radio-surgical device, used to treat different brain lesions, which are often inaccessible for conventional surgery, such as benign or malignant tumors. Currently, the target to be treated with radiation therapy is contoured with slice-by-slice manual segmentation on MR datasets. This approach makes the segmentation procedure time consuming and operator-dependent. The repeatability of the tumor boundary delineation may be ensured only by using automatic or semiautomatic methods, supporting clinicians in the treatment planning phase. This article proposes a semiautomatic segmentation method, based on the unsupervised Fuzzy C-Means clustering algorithm. Our approach helps segment the target and automatically calculates the lesion volume. To evaluate the performance of the proposed approach, segmentation tests on 15 MR datasets were performed, using both area-based and distance-based metrics, obtaining the following average values: Similarity Index = 95.59%, Jaccard Index = 91.86%, Sensitivity = 97.39%, Specificity = 94.30%, Mean Absolute Distance = 0.246[pixels], Maximum Distance = 1.050[pixels], and Hausdorff Distance = 1.365[pixels]. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 213–225, 2015

PurposeMagnetic Resonance guided Focused UltraSound (MRgFUS) represents a non-invasive surgical approach that uses thermal ablation to treat uterine fibroids. After the MRgFUS treatment, an operator must manually segment the treated fibroid areas to evaluate the NonPerfused Volume (NPV). This manual approach is operator-dependent, introducing issues of result reproducibility, which could lead to errors in the subsequent follow-up phase. Moreover, manual segmentation is time-consuming, and can have a negative impact on the optimization of both machine-time and operator-time. MethodTo address these issues, in this paper a novel fully automatic method based on the unsupervised Fuzzy C-Means clustering and iterative optimal threshold selection algorithms for uterus and fibroid segmentation is proposed. The developed method could be used to enhance the current manual methodology performed by healthcare operators for post-operative NPV evaluation in uterine fibroid MRgFUS treatments. ResultsThe proposed method was tested on 15 MR datasets of 15 different patients with uterine fibroids and evaluated using area-based and distance-based metrics. A comparison of extracted volume was also performed. Average values for fibroid (ROT) segmentation are SDI=88.67%, JI=80.70%, SE=89.79%, SP=88.73%, MAD=2.200 pixels], MAXD=6.233 pixels] and HD=2.988 pixels]. Moreover, to make a quantitative evaluation of this method, our experimental results were compared with similar literature approaches. ConclusionsThe proposed method provides a practical approach for the automatic evaluation of the boundary and volume of ablated fibroid regions, without any external user input. The achieved segmentation results show the validity and the effectiveness of the proposed solution. Display Omitted The proposed method performs a fully automatic uterus and fibroids segmentation.We provide the boundary and volume of ablated fibroid regions and uterus.Our approach is based on FCM and iterative optimal threshold selection algorithms.This solution could optimize the current operative MRgFUS methodology.Our approach can improve the follow-up of patients undergone MRgFUS treatments.

Lesion volume delineation of Positron Emission Tomography images is challenging because of the low spatial resolution and high noise level. Aim of this work is the development of an operator independent segmentation method of metabolic images. For this purpose, an algorithm for the biological tumor volume delineation based on random walks on graphs has been used. Twenty-four cerebral tumors are segmented to evaluate the functional follow-up after Gamma Knife radiotherapy treatment. Experimental results show that the segmentation algorithm is accurate and has real-time performance. In addition, it can reflect metabolic changes useful to evaluate radiotherapy response in treated patients.

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 /sup 18/F-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 (MAX/sub ls/) and % errors on lesion-to-background ratio (/spl epsiv/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 MAX/sub ls/ and smaller /spl epsiv/LBRs were found using FORE+AWOSEM, in comparison to 3-D+AWOSEM (p=0.125 for both parameters); FORE+OSEM presented the smallest /spl epsiv/LBRs. Our findings, from one single simulated patient with lesions in the abdomen, suggest that, in the considered case, FORE is more suitable for lesion detectability and quantification than 3-D, FORE+AWOSEM presenting better performance for lesion detectability and spatial characterization, FORE+OSEM for the quantification of lesion activity concentration.

This work presents a method for CT and PET image registration, and multi-modal analysis, to optimize radiotherapy planning in lung cancer treatment. The method relies on an image registration technique based on fiducial external markers to realign, spatially, PET images with the CT spatial reference system. The method was set up for clinical use in radiotherapy, allowing minimal modifications to be introduced in the management of patients undergoing radiation treatment. The accuracy of the registration technique was evaluated on patient studies in terms of Target Registration Error and was found to be less than 6.40 mm. The method was applied in the treatment planning of five patients affected by non-small-cell lung cancer, revealing the usefulness of PET/CT integration in delineating the extension of both the tumor mass and the tissues involved in the neoplastic process. Moreover, the functional information provided by PET often led to alterations in the treatment planning, changing the size and/or direction of radiation portals. The proposed method for PET/CT integration has been confirmed as being useful for optimizing radiotherapy planning in lung cancer treatment.

Characterisation of the physical performance of the new integrated PET/CT system Discovery ST (GE Medical Systems) has been performed following the NEMA NU 2-1994 (N-94) and the NEMA NU 2-2001 (N-01) standards in both 2D and 3D acquisition configuration. The Discovery ST combines a four or eight multi-slice helical CT scanner with a PET tomograph which consists of 10,080 BGO crystals arranged in 24 rings. The crystal dimensions are 6.3 x 6.3 x 30 mm(3) and they are organised in blocks of 6 x 6 crystals, coupled to a single photomultiplier tube with four anodes. The 24 rings of the PET system allow 47 images to be obtained, spaced by 3.27 mm, and covering an axial field of view of 157 mm. The low- and high-energy thresholds are set to 375 and 650 keV, respectively. The coincidence time window is set to 11.7 ns. Using the NEMA N-94 standard, the main results were: (1) the average (radial and tangential) transverse spatial resolution (FWHM) at 1, 10 and 20 cm off axis was 6.28 mm, 7.09 mm and 7.45 mm in 2D, and 6.68 mm, 7.72 mm and 8.13 mm in 3D; (2) the sensitivity for true events was 8,567 cps/kBq/cc in 2D and 36,649 cps/kBq/cc in 3D; (3) the scatter fraction was 15% in 2D and 30% in 3D; (4) the peak true events rate, the true events rate at 50% of the system dead-time and the true events rate when equal to the random events rate were 750 kcps at 189.81 kBq/cc, 744 kcps at 186.48 kBq/cc and 686 kcps at 150.59 kBq/cc, respectively, in 2D, and 922 kcps at 44.03 kBq/cc, 834 kcps at 53.28 kBq/cc and 921 kcps at 44.03 kBq/cc in 3D; (5) the noise equivalent count (NEC) peak rate was 270 kcps at 34.38 kBq/cc in 3D, with random coincidences estimated by delayed events. Using the NEMA N-01 standards the main results were: (1) the average transverse and axial spatial resolution (FWHM) at 1 cm and 10 cm off axis was 6.28 (4.56) mm and 6.88 (6.11) mm in 2D, and 6.29 (5.68) mm and 6.82 (6.05) mm in 3D; (2) the average sensitivity for the two radial positions (r=0 cm and r=10 cm) was 1.93 cps/kBq in 2D and 9.12 cps/kBq in 3D; (3) the scatter fraction was 19% in 2D and 45% in 3D; (4) the NEC peak rate was 54 kcps at 46.99 kBq/cc in 2D and 45.5 kcps at 10.84 kBq/cc in 3D, when random coincidences were estimated by using k=2 in the NEC formula, while the NEC peak rate was 81 kcps at 64.43 kBq/cc and 66 kcps at 14.86 kBq/cc in 2D and 3D, respectively, when random coincidences were estimated by using k=1 in the NEC formula. The new integrated PET-CT system Discovery ST has good overall performances in both 2D and 3D, with in particular a high sensitivity and a very good 3D NEC response.