Every biomedical imaging technique exploitsdifferent physical principles and can provide peculiarinformation, which is often unachievable with differenttechniques and can be further enhanced by the employment ofsuitable contrast agents (CAs). However, each imaging techniquetypically requires its own specific CAs, with correspondingincrements of procedure duration, costs and invasiveness for thepatients, who should undergo two injections. In the last years,great effort has been addressed toward the development ofmultimodal CAs that can be real-time detected by differenttechniques. In this context, we developed a new type of bimodalnanoparticles (NPs), consisting of silica nanospheres (NSs)covered by an outer shell of smaller superparamagnetic NPs, tobe used as dual-mode imaging CAs for ultrasound and magneticresonance imaging techniques. Aim of the present study was toevaluate the echographic detectability of these bimodal NPsthrough a recently developed algorithm that was originallyimplemented to detect pure silica NSs. In particular, weperformed a series of "in vitro" experiments on custom-designedtissue-mimicking phantoms, focused on a specific objective ofdirect clinical interest: the detection of multimodal NPs with adiameter of about 330 nm at a low and biocompatible volumeconcentration (0.2%). The obtained results demonstrated thepossibility of deleting the US echoes coming from structuresother than NPs with high effectiveness, therefore enhancing thebrightness of nanosized contrast agents in the final diagnosticimages. The effectiveness of the proposed method shows verypromising perspectives for future clinical applications.

Aim of this work was to carry out a first clinical validation of a new ultrasound (US)-based approach to bone densitometry of lumbar spine. A total of 290 female patients were enrolled for this study (45-75 years of age, body mass index (BMI)<40 kg/m(2)) and all of them underwent two different diagnostic investigations: a lumbar DXA (dual-energy X-ray absorptiometry) and an US scan of the same vertebras, performed with an echographic device configured for the acquisition of both echographic images and unfiltered radiofrequency signals. US data analysis was carried out through an innovative algorithm, whose main features include: a) measurements are always performed on a specific region of interest of the vertebra, identified on the basis of both morphologic and spectral characteristics; b) analysis takes into account patient BMI; c) the algorithm is integrated with a reference database containing model acquisitions for different combinations of patient age, sex and BMI. Accuracy of final algorithm output, represented by the same diagnostic parameters of a DXA investigation, was evaluated through a direct comparison with DXA results. For 84.5% of the patients US diagnosis (osteoporotic, osteopenic, healthy) coincided with the corresponding DXA one and this accuracy level was not appreciably influenced by patient age nor by BMI. The proposed approach represents the first US method for osteoporosis diagnosis which is directly applicable on spine and has the potential to be effectively used for population mass screenings.

Vertebral morphometry is a commonclinically-used method for vertebral fracture detection andclassification, based on height measurements of vertebralbodies in radiographic images. This method is quantitativeand does not require specific operator skills, but its actualaccuracy is affected by errors made during the timeconsumingmanual or semi-automatic measurements. In thispaper, we propose an innovative fully automatic approach tovertebral morphometry. A novel algorithm, based on a localphase symmetry measure and an "Active Shape Model",was implemented and tested on lateral X-ray radiographs of50 patients. Thoracic and lumbar vertebral bodies in eachimage were independently segmented and measured by boththe automatic algorithm and an experienced radiologist,whose manually-obtained results were assumed as theground truth. The algorithm showed reasonably low errorrates regarding both vertebral localization and morphometricmeasurements with a sensitivity of 86.5% and a perfectspecificity of 100%, because no false positive were present.Furthermore, its performance did not appreciably worsen onpoor quality images, emphasizing a significant potential fora prompt translation into clinical routine.

We investigated the possible clinical feasibility and accuracy of an innovative ultrasound (US) method for diagnosis of osteoporosis of the spine. A total of 342 female patients (aged 51-60 y) underwent spinal dual X-ray absorptiometry and abdominal echographic scanning of the lumbar spine. Recruited patients were subdivided into a reference database used for US spectral model construction and a study population for repeatability and accuracy evaluation. US images and radiofrequency signals were analyzed via a new fully automatic algorithm that performed a series of spectral and statistical analyses, providing a novel diagnostic parameter called the osteoporosis score (O.S.). If dual X-ray absorptiometry is assumed to be the gold standard reference, the accuracy of O.S.-based diagnoses was 91.1%, with k = 0.859 (p < 0.0001). Significant correlations were also found between O.S.-estimated bone mineral densities and corresponding dual X-ray absorptiometry values, with r(2) values up to 0.73 and a root mean square error of 6.3%-9.3%. The results obtained suggest that the proposed method has the potential for future routine application in US-based diagnosis of osteoporosis. (C) 2015 World Federation for Ultrasound in Medicine & Biology.

Prototypal software algorithms for advanced spectral analysis of echographic images were developed to perform automatic detection of simulated tumor masses at two different pathological stages. Previously published works documented the possibility of characterizing macroscopic variation of mechanical properties of tissues through elastographic techniques, using different imaging modalities, including ultrasound (US); however, the accuracy of US-based elastography remains affected by the variable manual modality of the applied compression and several attempts are under investigation to overcome this limitation. Quantitative US (QUS), such as Fourier- and wavelet-based analyses of the RF signal associated with the US images, has been developed to perform a microscopic-scale tissue-type imaging offering new solutions for operator-independent examinations. Because materials able to reproduce the harmonic behavior of human liver can be realized, in this study, tissue-mimicking structures were US imaged and the related RF signals were analyzed using wavelet transform through an in-house-developed algorithm for tissue characterization. The classification performance and reliability of the procedure were evaluated on two different tumor stiffnesses (40 and 130 kPa) and with two different applied compression levels (0 and 3.5 N). Our results demonstrated that spectral components associated with different levels of tissue stiffness within the medium exist and can be mapped onto the original US images independently of the applied compressive forces. This wavelet-based analysis was able to identify different tissue stiffness with satisfactory average sensitivity and specificity: respectively, 72.01% ± 1.70% and 81.28% ± 2.02%.

Quantitative ultrasound (QUS) methods forosteoporosis diagnosis potentially provide information aboutthe bone quality and its elastic properties. In this context, anovel ultrasound-based method for spinal and femoraldensitometry was developed by our research group. In orderto maximize its accuracy, it is very important to properlydetect the bone interfaces that will be analyzed as regions ofinterest (ROIs). A fully automatic segmentation algorithmwas developed to select lumbar vertebral interfaces inechographic images and its actual accuracy was assessed inthe present work by means of a visual checking carried outby an expert operator. Abdominal US scans of lumbar spine(from L1 to L4) were performed on 100 female subjects(60.5±3.0 years old) with different ranges of body massindex (BMI) (25.8±4.6 kg/m2). During each US scan, 100frames of radiofrequency (RF) data were stored on a PChard disk for offline analysis. The operator scanned eachvertebra, moving the probe to the next vertebra after 20seconds. For each acquired RF data frame, the implementedalgorithm generated a sectorial echographic image and, if avertebral interface was detected, it was highlighted on thesaved image. The validation procedure was performed by anexpert operator previously trained to detect the "optimal"vertebral interfaces for osteoporosis diagnosis. Resultsshowed that the segmentation algorithm had a highspecificity (93.4%), which reached its maximum on subjectswith BMI < 25 kg/m2 (94.2%), thus avoiding the selectionof false vertebral interfaces and allowing a good accuracy ofosteoporosis diagnosis.

Aim of this study was to perform a detailed clinical validation of a novel fully automatic method for vertebralmorphometry. About 80 spine lateral radiographs were evaluated both automatically, by the proposed algorithm, andmanually, by an experienced radiologist. The following metrics were used for algorithm performance assessment:sensitivity and specificity in vertebra detection; errors in the localisation of characteristic points of vertebral border;errors in the measurement of six diagnostic parameters; level of agreement and correlation between manual andautomatic morphometric measurements; overall accuracy of automatic diagnoses with respect to manual ones.Obtained results showed a very good performance in vertebra detection (sensitivity = 89.1% and specificity = 100.0%).Average errors in the localisation of vertebral characteristic points were always smaller than 3 mm (range 0.85-2.79mm),causing relative errors in diagnostic parameter values ranging from -5.01 to +6.10%. Bland-Altman analysis documenteda mean error in automatic measurements of diagnostic ratios of 0.01 ± 0.18 (bias ± 2 SDs), while Pearson's correlationcoefficient resulted r = 0.71 (p < 0.001). Finally, an optimal diagnostic coincidence (92.8%) was found between automaticand manual diagnoses. Therefore, the adopted method has a potential for an effective employment in clinical routinefor reliable diagnosis of vertebral fractures.

Aim of this work was to study the dissolution behaviour of the phospholipid-shelled perfluorobutanemicrobubbles of an experimental contrast agent for echographic imaging through an innovative methodologybased on time-scheduled size distribution measurements. Two different contrast handling procedures wereemployed and temporal evolution of corresponding microbubble populations was monitored for several hours.Dissolution behaviour of shelled microbubbles resulted to be qualitatively analogous to that theoreticallypredicted for unshelled perfluorobutane bubbles, with a much longer lifetime due to the shell effect. In particular,mean microbubble diameter, initially around 2 ?m, first increased to more than 2.6 ?m and then graduallyreduced to less than 1.7 ?m, with corresponding variations of the effectively employable ultrasound frequenciesfor imaging purposes. We also demonstrated that excess lipid material is diffused as submicron particleshedding. Finally, we discussed the implications of these results for diagnostic and therapeutic applicationsinvolving the studied microbubbles.

Multimodal contrast agents (CAs) allow theenhancement of medical images acquired through differenttechniques by employing a single contrast injection, withsignificant benefits for diagnostic outcome. The present study isfocused on the characterization of the magnetic behavior of anovel CA class, consisting of silica (Si) nanoparticles (NPs)covered by either superparamagnetic iron oxide (SPIO) NPs orFePt-IO nanocrystals and designed to be detected through bothultrasound and magnetic resonance imaging (MRI). The usemultimodal nanoparticles as negative MRI contrast agents couldopen up new perspectives for the development of novel tools fornanomedicine, combining different non-ionizing techniques fortargeted imaging of specific diseased cells. In this work, wesimulated the MRI signal of a blood vessel in presence of the newbimodal CAs and compared it with the response of thesuperparamagnetic NPs alone. The performed numericalsimulations showed that the magnetic response of the novel nanocomposites,in terms of signal magnitude, was similar to that ofthe conventional superparamagnetic NPs for values of echo time(TE) shorter than 0.4 ms, while for longer TE values it was evenbetter, showing a stronger vessel enhancement leading to aneasier detection of the smaller vessels. Therefore, the testedbimodal NPs have the potential for an effective employment asMRI CAs.

Successful employment of multimodal molecular imaging for cancer targeting entails the development of safe nanoparticle contrast agents (NPCAs), detects at least by two nonionizing imaging techniques. This paper presents a quantitative assessment of the effectiveness of both pure silica nanospheres (SiNSs) and composite silica/superparamagnetic NPCAs as scatterers for low-frequency diagnostic ultrasound (US) (3 MHz) in very low range of concentrations (1.5-5 mg/mL). Iron oxide (IO) and FePt-IO nanocrystals are employed for SiNS magnetic coating. Different samples of NPCA-containing agarose gel are US imaged through a commercially available system and acquired data are processed through a dedicated prototypal platform to extract image backscatter information and perform evaluation of the image gray level. The pure silica NPCAs confirms recent reports for higher concentrations at higher frequencies. The FePt-IO- coated NPCAs show similar behavior, although with lower values of image backscatter, with a marked effectiveness peak for 330-nm SiNSs, particularly useful for tumor targeting purposes. Finally, the IO-coated SiNSs presented a marked lowering of US enhancement potential and a peak efficiency for a particle diameter of 660 nm. The extent of US backscatter reduction is found to be a function of the number of magnetic nanoparticles per mL of NPCA-containing gel and decreased with increasing NPCA concentrations. These results broadened our knowledge of dual-mode molecular imaging of deep tumors, employing US, and magnetic resonance techniques for the accurate, safe and early detection of cancer cells located in internal organs.

An adaptive initialization method was developed to produce fully automatic processing frameworks based on graph-cut and gradient flow active contour algorithms. This method was applied to abdominal Computed Tomography (CT) images for segmentation of liver tissue and hepatic tumours. 25 anonymized datasets were randomly collected from several radiology centres without specific request on acquisition parameter settings nor patient clinical situation as inclusion criteria. Resulting automatic segmentations of liver tissue and tumours were compared to their reference standard delineations manually performed by a specialist. Segmentation accuracy has been assessed through the following evaluation framework: dice similarity coefficient (DSC), false negative ratio (FNR), false positive ratio (FPR) and processing time. Regarding liver surfaces, graph-cuts achieved a DSC of 95.49% (FPR=2.35% and FNR=5.10%), while active contours reached a DSC of 96.17% (FPR=3.35% and FNR=3.87%). The analyzed datasets presented 52 tumours: graph-cut algorithm detected 48 tumours with a DSC of 88.65%, while active contour algorithm detected only 44 tumours with a DSC of 87.10%. In addition, in terms of time performances, less time was requested for graph-cut algorithm with respect to active contour one. The implemented initialization method allows fully automatic segmentation leading to superior overall performances of graph-cut algorithm in terms of accuracy and processing time. The initialisation method here presented resulted suitable and reliable for two different segmentation techniques and could be further extended.

Aim of the present work was to evaluate the performance of a novel fully automatic algorithm for 3D segmentation and volumetric reconstruction of liver vessel network from contrast-enhanced computed tomography (CECT) datasets acquired during routine clinical activity. Three anonymized CECT datasets were randomly collected and were automatically analyzed by the new vessel segmentation algorithm, whose parameter configuration had been previously optimized on a phantom model. The same datasets were also manually segmented by an experienced operator that was blind with respect to algorithm outcome. Automatic segmentation accuracy was quantitatively assessed for both single 2D slices and 3D reconstruction of the vessel network, accounting manual segmentation results as the reference "ground truth". Adopted evaluation framework included the following two groups of calculations: 1) for 3D vessel network, sensitivity in vessel detection was quantified as a function of both vessel diameter and vessel order; 2) for vessel images on 2D slices, dice similarity coefficient (DSC), false positive ratio (FPR), false negative ratio (FNR), Bland-Altman plots and Pearson correlation coefficients were used to judge the correctness of single pixel classifications. Automatic segmentation resulted in a 3D vessel detection sensitivity of 100% for vessels larger than 1 mm in diameter, 64.6% for vessels in the range 0.5-1.0 mm and 27.8% for smaller vessels. An average area overlap of 99.1% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.53 mm(2). The corresponding average values of FPR and FNR were 1.8% and 1.6%, respectively. Therefore, the tested method showed significant robustness and accuracy in automatic extraction of the liver vessel tree from CECT datasets. Although further verification studies on larger patient populations are required, the described algorithm has an exciting potential for supporting liver surgery planning and intraoperative resection guidance.

Rationale and Objectives: The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automaticsegmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liversurgery.Materials and Methods: A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding toa minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographicdata set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracywas quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of theautomatic segmentation performed on contrast-enhanced computed tomographic phantom images with actual model features.Results: The optimal algorithm configuration resulted in a vessel detection sensitivity of 100% for vessels > 1 mm in diameter, 50% in therange 0.5 to 1 mm, and 14% in the range 0.2 to 0.5 mm. An average area overlap of 94.9% was obtained between automatically andmanually segmented vessel sections, with an average difference of 0.06 mm2. The average values of corresponding false-positive andfalse-negative ratios were 7.7% and 2.3%, respectively.Conclusions: A robust and accurate algorithm for automatic extraction of the hepatic vessel tree from contrast-enhanced computedtomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vesseldelineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperativeresections.

Recent literature has reported increasing interest in using contrast agents for ultrasound imaging, in the form of shelled gas microbubbles, for innovative advanced purposes such as noninvasive targeted imaging and drug delivery. Effectiveness of such agents is time-dependent and is determined by microbubble dissolution behavior, a complex phenomenon whose knowledge is still limited. In the present study, we monitored the microbubbles of an experimental phospholipid-shelled perfluorobutane contrast agent through time-scheduled size distribution measurements. The diameter-time curve we obtained for shelled perfluorobutane microbubbles showed a rapid diameter increment up to about 1.4 times the initial value, followed by a slow decrement towards bubble disappearance. This behavior is qualitatively similar to the one theoretically predicted by Kabalnov's model for unshelled bubbles, with an extended lifetime due to shell effect. Kabalnov's model, devised for spontaneous dissolution of unshelled microbubbles, was consequently modified in order to get a proper prediction of experimental results also in the case of encapsulated bubbles. A theoretical diameter-time curve was then derived from this new model and fitted to our experimental data points, to estimate microbubble surface tension and to determine the value of an empirical parameter accounting for the shell effect. The proposed model has the potential to predict the dissolution behavior of all kinds of microbubble contrast agents for ultrasound imaging and the adopted experimental methodology represents a new and simple way to estimate microbubble surface tension, essential also for predicting microbubble oscillation performance.

The knowledge of ultrasound contrast agent (UCA) behaviour is continuously improving, mainly thanks to "invitro" measurements performed by means of specific phantoms, mimicking the acoustic properties of severalhuman body districts. For such purposes, it is necessary to develop experimental setups able to minimisechemical and physical effects due to environmental conditions. In this paper we discuss the design of a newtissue-mimicking phantom, specifically evaluating the sound-absorption properties of three synthetic materials(Polyurethane, Airex©, ethylene vinyl acetate (EVA©)) laid on the bottom of the phantom. Our goal is toestablish the best material to use in order to minimise the artefacts within the tissue-mimicking matrix.Polyurethane showed the best sound-absorbent behaviour for every tested ultrasound frequency, so itsemployment in covering the bottom of tissue-mimicking phantoms is suggested in order to allow experimentalinvestigations of acoustic properties of different UCAs without additional aspects due to environmentalboundary conditions.

In recent years the understanding of the behaviour of currently available ultrasound contrast agents (UCAs), in the form of gas-filled microbubbles encapsulated in elastic shells, has significantly improved thanks to "ad hoc" designed "in vitro" studies. However, in several studies there has been a tendency to use high UCA concentrations, potentially reducing the safety of microbubbles in clinical applications. In this study we investigated a possible strategy to improve microbubble safety by reducing the injection dose and employing low ultrasound intensities. We measured the achievable contrast enhancement insonifying microbubbles at different low concentrations (range 0.01-0.10 ¼L/mL) using a very low mechanical index (MI=0.08). Our results, based on the use of advanced techniques for signal processing and spectrum analysis, showed that UCA backscatter strongly depends on microbubble concentration also in the considered low range, providing useful indications towards the definition of an optimal low contrast dose, effectively employable at low MIs.

Aim of this work was to investigate the effect ofultrasound incident frequency on the echographic contrastenhancement power of an experimental drug delivery agent,halloysite clay nanotubes (HNTs), and to determine a suitableconfiguration in terms of both insonification frequency andparticle concentration for an effective employment as targetedcontrast agent. Various HNT concentrations (range 0.25-3.00mg/mL) were dispersed in custom-designed tissue-mimickingphantoms and exposed to different ultrasound frequencies (7-11MHz) through a conventional clinically-available echographicdevice. Off-line analysis included the evaluation of bothamplitude of backscattered ultrasound signals and imagebrightness. Amplitude of HNT-backscattered signals showed alinear increase with particle concentration, while imagebrightness enhancement was limited by logarithmic compressioneffects. On the other hand, backscatter amplitude showedsignificant increments with increasing ultrasound frequency upto 10 MHz, then showing a concentration-dependent behaviorwithout further enhancements. Overall, the most effectiveresponse was found when a 10-MHz ultrasound frequency wasemployed to insonify HNTs at a concentration of 1.5 mg/mL. Inconclusion, the present study optimized the combination ofincident ultrasound frequency and HNT concentration, in orderto obtain an echographic image enhancement suitable for medicalapplications. Future dedicated studies will assess the feasibility ofautomatic detection of HNTs within echographic images andtheir possible employment as theranostic agents.

Aim of this study was to perform a detailed clinical validation of a new fully automatic algorithm for vertebral interface segmentation in echographic images. Abdominal echographic scans of lumbar vertebrae L1-L4 were carried out on 150 female subjects with variable age and body mass index (BMI). Acquired datasets were automatically processed by the algorithm and the accuracy of the obtained segmentations was then evaluated by three independent experienced operators. Obtained results showed a very good specificity in vertebra detection (93.3%), coupled with a reasonable sensitivity (68.1%), representing a suitable compromise between the detection of a sufficient number of vertebrae for reliable diagnoses and the limitation of the corresponding computation time. Importantly, there was only a minimum presence of 'false vertebrae' detected (2.8%), resulting in a very low influence on subsequent diagnostic analyses. Furthermore, the algorithm was specifically tuned to provide an improved sensitivity (up to 73.1%) with increasing patient BMI, to keep a suitable number of correctly detected vertebrae even when the acquisition was intrinsically more difficult because of the augmented thickness of abdominal soft tissues. The proposed algorithm will represent an essential added value for developing echographic methods for the diagnosis of osteoporosis on lumbar vertebrae.