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

A novel approach for fMRI data analysis based on the combination of sparse approximation and affinity propagation clustering

Clustering analysis has been widely used to detect the functional connectivity from functional magnetic resonance imaging (fMRI) data. However, it has some limitations such as enormous computer memory requirement, and difficulty in estimating the number of clusters. In this study, in order to effectually resolve the deficiencies mentioned above, we have proposed a novel approach (SAAPC) for fMRI data analysis, which combines sparsity, an effective assumption for analyzing fMRI signal, with affinity propagation clustering (APC). The SAAPC method is composed of three parts: to obtain the sparse approximation coefficients set through wavelet packet decomposition and sparsity measuring and selection, which contributes a lot in the brain functional connectivity detection accuracy; to implement a split APC algorithm, which is put forward in this paper to overcome the computer memory shortage problem and to reduce the time cost in basic APC; to reconstruct the source signal by unmixing the mixed fMRI data using the time courses which are derived from the ultimate exemplars. In the task-related experiments, we can see that SAAPC is more accurate to detect the functional networks than basic APC, and it significantly reduces the time cost relative to basic APC. In addition, in the resting-state data experiments, the SAAPC method can successfully identify typical resting-state networks from the resting-state data set, while this performance is seldom reported by the classical cluster method and the basic APC method. This proposed clustering analysis method is expected to have wide applicability.

Comparison of multiecho postprocessing schemes for SWI with use of linear and nonlinear mask functions

BACKGROUND AND PURPOSE

SWI is an MR technique conventionally implemented with single-echo gradient-echo data. The purpose of this study was to compare single-echo SWI processing and 2 multiecho SWI processing schemes: postaverage, where an SWI image is created for each echo and then averaged to create a single volume; and frequency-based, where a SWI image is generated from an average frequency image. Linear and nonlinear mask functions were investigated for all 3 processing schemes.

MATERIALS AND METHODS

Comprehensive optimizations were performed. Single and multigradient-echo data were acquired at 3T in 10 volunteers. Contrast-to-noise ratio was measured in various structures. Visibilities of the same structures were ranked in different SWI images by trained raters.

RESULTS

When image evaluation was based on measurements of contrast-to-noise ratio, the nonlinear mask and frequency-based scheme were superior. However, when image evaluation was based on ranks of qualitative visibility, the linear mask and postaverage scheme were superior. Although the nonlinear mask and frequency-based scheme allow increased contrast of paramagnetic perturbers such as the globus pallidus, periventricular veins, red nucleus, and subthalamic nucleus, they do not necessarily increase the information content of the image; rather, they result in a harsh contrast that is visually unpleasing to radiologists and wherein more subtle structure is relatively less apparent.

CONCLUSIONS

Linearly masked postaverage SWI is the recommended implementation of multiecho SWI for radiologic use; however, nonlinearly masked frequency-based SWI may have use in computer-based segmentation or registration.

Combined diffusion-weighted, blood oxygen level-dependent, and dynamic contrast-enhanced MRI for characterization and differentiation of renal cell carcinoma

PURPOSE

To investigate a multiparametric magnetic resonance imaging (MRI) approach comprising diffusion-weighted imaging (DWI), blood oxygen-dependent (BOLD), and dynamic contrast-enhanced (DCE) MRI for characterization and differentiation of primary renal cell carcinoma (RCC).

MATERIAL AND METHODS

Fourteen patients with clear-cell carcinoma and four patients with papillary RCC were examined with DWI, BOLD MRI, and DCE MRI at 1.5T. The apparent diffusion coefficient (ADC) was calculated with a monoexponential decay. The spin-dephasing rate R2* was derived from parametric R2* maps. DCE-MRI was analyzed using a two-compartment exchange model allowing separation of perfusion (plasma flow [FP] and plasma volume [VP]), permeability (permeability surface area product [PS]), and extravascular extracellular volume (VE). Statistical analysis was performed with Wilcoxon signed-rank test, Pearson's correlation coefficient, and receiver operating characteristic curve analysis.

RESULTS

Clear-cell RCC showed higher ADC and lower R2* compared to papillary subtypes, but differences were not significant. FP of clear-cell subtypes was significantly higher than in papillary RCC. Perfusion parameters showed moderate but significant inverse correlation with R2*. VE showed moderate inverse correlation with ADC. Fp and Vp showed best sensitivity for histological differentiation.

CONCLUSION

Multiparametric MRI comprising DWI, BOLD, and DCE MRI is feasible for assessment of primary RCC. BOLD moderately correlates to DCE MRI-derived perfusion. ADC shows moderate correlation to the extracellular volume, but does not correlate to tumor oxygenation or perfusion. In this preliminary study DCE-MRI appeared superior to BOLD and DWI for histological differentiation.

Theranostic applications: Non-ionizing cellular and molecular imaging through innovative nanosystems for early diagnosis and therapy

Modern medicine is expanding the possibilities of receiving "personalized" diagnosis and therapies, providing minimal invasiveness, technological solutions based on non-ionizing radiation, early detection of pathologies with the main objectives of being operator independent and with low cost to society. Our research activities aim to strongly contribute to these trends by improving the capabilities of current diagnostic imaging systems, which are of key importance in possibly providing both optimal diagnosis and therapies to patients. In medical diagnostics, cellular imaging aims to develop new methods and technologies for the detection of specific metabolic processes in living organisms, in order to accurately identify and discriminate normal from pathological tissues. In fact, most diseases have a "molecular basis" that detected through these new diagnostic methodologies can provide enormous benefits to medicine. Nowadays, this possibility is mainly related to the use of Positron Emission Tomography, with an exposure to ionizing radiation for patients and operators and with extremely high medical diagnostics costs. The future possible development of non-ionizing cellular imaging based on techniques such as Nuclear Magnetic Resonance or Ultrasound, would represent an important step towards modern and personalized therapies. During the last decade, the field of nanotechnology has made important progress and a wide range of organic and inorganic nanomaterials are now available with an incredible number of further combinations with other compounds for cellular targeting. The availability of these new advanced nanosystems allows new scenarios in diagnostic methodologies which are potentially capable of providing morphological and functional information together with metabolic and cellular indications.

Theranostic applications: Non-ionizing cellular and molecular imaging through innovative nanosystems for early diagnosis and therapy

Modern medicine is expanding the possibilities of receiving “personalized” diagnosis and therapies, providing minimal invasiveness, technological solutions based on non-ionizing radiation, early detection of pathologies with the main objectives of being operator independent and with low cost to society. Our research activities aim to strongly contribute to these trends by improving the capabilities of current diagnostic imaging systems, which are of key importance in possibly providing both optimal diagnosis and therapies to patients. In medical diagnostics, cellular imaging aims to develop new methods and technologies for the detection of specific metabolic processes in living organisms, in order to accurately identify and discriminate normal from pathological tissues. In fact, most diseases have a “molecular basis” that detected through these new diagnostic methodologies can provide enormous benefits to medicine. Nowadays, this possibility is mainly related to the use of Positron Emission Tomography, with an exposure to ionizing radiation for patients and operators and with extremely high medical diagnostics costs. The future possible development of non-ionizing cellular imaging based on techniques such as Nuclear Magnetic Resonance or Ultrasound, would represent an important step towards modern and personalized therapies. During the last decade, the field of nanotechnology has made important progress and a wide range of organic and inorganic nanomaterials are now available with an incredible number of further combinations with other compounds for cellular targeting. The availability of these new advanced nanosystems allows new scenarios in diagnostic methodologies which are potentially capable of providing morphological and functional information together with metabolic and cellular indications.