Nanomedicine is the medical application of nanotechnology. Nanomedicine ranges from the medical applications of nanomaterials and biological devices, to nanoelectronic biosensors, and even possible future applications of molecular nanotechnology such as biological machines. In 2005, a new imaging method called magnetic particle imaging （MPI）was introduced. MPI can visualize the spatial distribution of magnetic nanoparticles（MNPs）with high sensitivity, spatial resolution, and imaging speed. MPI uses the nonlinear response of MNPs to detect their presence in an alternating magnetic field（AMF）. MNPs can induce heat under an AMF, which allows applications to hyperthermia（magnetic hyperthermia）. This paper describes the principle of MPI and applications to nanomedicine including the quantification of pulmonary mucociliary clearance, the prediction of therapeutic effects of magnetic hyperthermia, the monitoring of magnetic targeting, and cellular imaging and tracking for regenerative medicine. The future perspective of MPI is also described.
Deep learning has a great potential in biotechnology and medicine. Development of the method gives usextracting meaningful information on drug discovery and medical care. Conceptual understanding of diseases, effectivenessof drugs, evaluation of recovery, telemedicine etc, could be realized from patient’s medical records, MRI and CT images,pathological images, DNA sequences. In this review, as a basis for aiming at the development of medical applications, weshould outline the nature and potential of deep learning. Difference form natural science has to be emphasized. Artificialintelligence（deep learning）is a method of categorization, expanding “recognition” which is the essence of diagnosis inmedical application.
To obtain two tomograms at two different photon energy ranges simultaneously, we have developed a dualenergy X-ray photon counter consisting of a cadmium telluride（CdTe）detector system, three comparators, two microcomputers（MCs）, and two frequency-voltage converters（FVCs）. X-ray photons are detected using the CdTe detector,and the event pulses are input to three comparators simultaneously to determine threshold energies. At a tube voltage of 100 kV, the three threshold energies are 33, 50 and 60 keV, and two energy ranges are 33-60 and 50-100 keV. X-ray photons in the two ranges are counted using MCs, and the logical pulses from the two MCs are input to two FVCs. In dualenergy computed tomography（DE-CT）, the tube voltage and current were 100 kV and 22 μA, respectively. Two tomograms were obtained simultaneously at two energy ranges. The energy ranges for K-edge CT using iodine and gadolinium media were 33-60 and 50-100 keV, respectively. The maximum count rate was 6.5 kilocounts per second with energies ranging from 10 to 100 keV, and the exposure time for tomography was 19.6 min.
The apparent diffusion coefficient （ADC）obtained from diffusion magnetic resonance imaging （MRI）in brain tissue changes significantly during the cardiac cycle because of water molecule fluctuation. However, these changes （ΔADC）are affected by the regional cerebral blood flow （rCBF）. In this study, we evaluated the relationship between rCBF and ΔADC, and corrected the rCBF effect by using the diffusion data. On a 3.0-T MRI system, ECG-triggered singleshot diffusion echo planar imaging（b=0, 200, 600, and 1000 s/mm2）was performed using sensitivity encoding and halfscan techniques to minimize the bulk motion. Next, the maximum ADC（ADCpeak）and minimum ADC（ADCbot）during the cardiac cycle, and the ΔADC of the frontal white matter were determined in in healthy volunteers（n= 10）. These values were compared with the rCBF obtained using a pseudo-continuous arterial spin labeling technique. Finally, we corrected the ΔADC using the ADCpeak that exhibited the strongest correlation with the rCBF. There was a significant correlation between the ΔADC and rCBF. The ADCpeak with b = 0-200 exhibited the strongest positive correlation of all perfusion-related diffusion values. There was no significant correlation between the ΔADC divided by the ADCpeak with b = 0-200 and the rCBF, indicating the hemodynamic independence of the corrected-ΔADC. The corrected-ΔADC makes it possible to obtain the degree of fluctuation of water molecules hemodynamic-independently in the brain without additional rCBF measurements.
Arteriolar-to-venular diameter ratio（AVR）is measured for decision of retinal arteriolar narrowing, which is one of the findings for hypertensive retinopathy. We have been developing an automated method for measuring AVR to help diagnosis of ophthalmologists. For measuring AVR, arteries and veins must be segmented with high accuracy. Previous methods first extracted blood vessels and then classified arteries and veins by using linear discriminant analysis（LDA）of pixel-based-features. However, previous methods could not segment in cases with close contact between the artery and a vein. Therefore, this paper describes a novel method based on independent extraction of major arteries and veins. Veins were first extracted in the red channel of color retinal image. By removing the veins from the blood vessels extracted in the green channel of color retinal image, the arteries were obtained. Major veins were decided by using LDA proposed in the previous method. Major arteries were decided by using decision tree with three features. The method was applied to 22 retinal images including cases with arteriolar narrowing. As a result, 98% of major veins and 77% of major arteries were correctly identified. The proposed method may be useful in automated measurement of AVR.
This study aimed to validate the quantitation of venous blood flow in gravity magnetic resonance imaging （MRI）using a flow phantom. The phantom consisted of a pressure tube, which simulated a venous vessel, and a programmable pump. The pump produced a steady flow of a 40% v/v glycerin-water solution with different flow velocities （2.5, 5.0, 10, 20, or 40 cm/s）through the simulated vessel. Electrocardiograph-triggered phase-contrast（PC）MRI was performed using a 0.4-T gravity MRI system, and the imaging plane was set to be perpendicular to the flow direction. In total, 17 pairs of magnitude and velocity-mapped phase images per simulated cardiac cycle were reconstructed for each flow velocity. We placed a region of interest in the simulated vessel, determined the flow velocity for each cardiac phase and mean flow rate in all phases, and compared them with the actual flow velocity and rate defined by the pump. Generally,the flow velocities were consistent with the actual ones. Moreover, the mean flow rate highly correlated with the actual rate. PC-MRI in gravity MRI makes it possible to quantify venous blood flow, thereby facilitating the investigations into the effect of gravity on venous blood flow in humans.