Roberto Franchini

Optimal Enhancement Configuration of Silica Nanoparticles for Ultrasound Imaging and Automatic Detection at Conventional Diagnostic Frequencies

Objectives:To experimentally investigate the acoustical behavior of silica nanoparticles within conventional diagnostic ultrasound fields and to determine a suitable configuration, in terms of particle size and concentration, for their employment as targetable contrast agents. We also assessed the effectiveness of a novel method for automatic detection of targeted silica nanoparticles for future tissue typing applications. Materials and Methods:Silica nanospheres of variable size (160, 330, and 660 nm in diameter) and concentration (1010–1013 part/mL) were dispersed in different custom-designed agarose-based gel samples and imaged at 7.5 MHz with a conventional echograph linked to a research platform for radiofrequency signal acquisition. Off-line analysis included evaluation of backscattered ultrasound amplitude, image brightness, and nanoparticle automatic detection through radiofrequency signal processing. Results:Amplitude of nanoparticle-backscattered signals linearly increased with particle number concentration, but image brightness did not show the same trend, because the logarithmic compression caused the reaching of a “plateau” where brightness remained almost constant for further increments in particle concentration. On the other hand, both backscatter amplitude and image brightness showed significant increments when particle diameter was increased. Taking into account particle size constraints for tumor targeting (pore size of tumor endothelium and trapping effects because of reticulo-endothelial system limit the dimension of effectively employable particles to less than 380 nm), a suitable compromise is represented by the employment of 330-nm silica nanospheres at a concentration of about 1 to 2 × 1011 part/mL. These particles, in fact, showed the best combination of number concentration and diameter value to obtain an effective enhancement on conventional echographic images. Furthermore, also the sensitivity of the developed method for automatic nanoparticle detection had a maximum (72.8%) with 330-nm particles, whereas it was lower with both bigger and smaller particles (being equal to 64.1% and 17.5%, respectively). Conclusions:Silica nanoparticles at a diameter of about 330 nm are very promising contrast agents for ultrasound imaging and specific tumor targeting at conventional diagnostic frequencies, being in particular automatically detectable with high sensitivity already at low doses. Future studies will be carried out to assess the acoustic behavior of nanoparticles with different geometries/sizes and to improve sensitivity of the automatic detection algorithm.

Hepatic vessel segmentation for 3D planning of liver surgery experimental evaluation of a new fully automatic algorithm.

RATIONALE AND OBJECTIVES The aim of this study was to identify the optimal parameter configuration of a new algorithm for fully automatic segmentation of hepatic vessels, evaluating its accuracy in view of its use in a computer system for three-dimensional (3D) planning of liver surgery. MATERIALS AND METHODS A phantom reproduction of a human liver with vessels up to the fourth subsegment order, corresponding to a minimum diameter of 0.2 mm, was realized through stereolithography, exploiting a 3D model derived from a real human computed tomographic data set. Algorithm parameter configuration was experimentally optimized, and the maximum achievable segmentation accuracy was quantified for both single two-dimensional slices and 3D reconstruction of the vessel network, through an analytic comparison of the automatic 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 the range 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 and manually segmented vessel sections, with an average difference of 0.06 mm(2). The average values of corresponding false-positive and false-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 computed tomographic volume images was proposed and experimentally assessed on a liver model, showing unprecedented sensitivity in vessel delineation. This automatic segmentation algorithm is promising for supporting liver surgery planning and for guiding intraoperative resections.

Fully Automatic Segmentations of Liver and Hepatic Tumors From 3-D Computed Tomography Abdominal Images: Comparative Evaluation of Two Automatic Methods

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 tumors. Twenty-five 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 tumors 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 tumors: graph-cut algorithm detected 48 tumors with a DSC of 88.65%, while active contour algorithm detected only 44 tumors 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 initialization method here presented resulted suitable and reliable for two different segmentation techniques and could be further extended.

A novel ultrasound methodology for estimating spine mineral density.

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.

In Vitro Evaluation and Theoretical Modeling of the Dissolution Behavior of a Microbubble Contrast Agent for Ultrasound Imaging

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 Kabalnovs model for unshelled bubbles, with an extended lifetime due to shell effect. Kabalnovs 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.

A quantitative and automatic echographic method for real-time localization of endovascular devices

Current imaging methods for catheter position monitoring during minimally invasive surgery do not provide an effective support to surgeons, often resulting in the choice of more invasive procedures. This study was conducted to demonstrate the feasibility of non-ionizing monitoring of endovascular devices through embedded quantitative ultrasound (QUS) methods, providing catheter self-localization with respect to selected anatomical structures. QUS-based algorithms for real-time automatic tracking of device position were developed and validated on in vitro and ex vivo phantoms. A trans-esophageal ultrasound probe was adapted to simulate an endovascular device equipped with an intravascular ultrasound probe. B-mode images were acquired and processed in real time by means of a new algorithm for accurate measurement of device position. After off-line verification, automatic position calculation was found to be correct in 96% and 94% of computed frames in the in vitro and ex vivo phantoms, respectively. The average errors of distance measurements (bias ± 2SD) in a 41-step 10-cm-long parabolic pathway were 0.76 ± 3.75 mm or 0.52 ± 3.20 mm, depending on algorithm implementations. Our results showed the effectiveness of QUS-based tracking algorithms for real-time automatic calculation and display of endovascular system position. The method, validated for the case of an endoclamp balloon catheter, can be easily extended to most endovascular surgical systems.

Advanced spectral analyses for real-time automatic echographic tissue-typing of simulated tumor masses at different compression stages

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%.

Effectiveness of Functionalized Nanosystems for Multimodal Molecular Sensing and Imaging in Medicine

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.

A new automatic phase mask filter for high-resolution brain venography at 3 T: theoretical background and experimental validation.

To improve vessel contrast in high-resolution susceptibility-based brain venography, an automatic phase contrast enhancing procedure is proposed, based on a new phase mask filter suitable for maximizing contrast of venous MR signals. The effectiveness of the new approach was assessed both on digital phantoms and on acquired MR human brain images, and then compared with venographic results of phase masking methods in recent literature. The digital phantom consisted of a simulated MR dataset with given signal-to-noise ratios (SNRs), while real human data were collected by scanning healthy volunteers with a 3.0-T MR system and a 3D gradient echo pulse sequence. The new phase mask (NM) was more effective than the conventional mask (CM) both on the digital phantoms and on the acquired MR images. A quantitative comparison based on phantom venograms indicates how this phase enhancement can lead to a significant increase in the contrast-to-noise ratio (CNR) for all considered phase values as well as for all vessel sizes of clinical interest. Likewise, the in vivo brain venograms reveal a better depiction of the smallest venous vessels and the enhancement of many details undetectable in conventional venograms.

Fully automatic 3D segmentation measurements of human liver vessels from contrast-enhanced CT

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 mm2. 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.