The purpose of this article is to describe the recent development of imaging techniques in MRI. After the brief review of the basics of MR imaging, new techniques such as simultaneous multi‐slice techniques, compressed sensing （CS） MRI and MR fingerprinting are discussed.

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Magnetic resonance imaging （MRI） can provide valuable information based on the various contrast mechanisms. To analyze information derived from MRI data, image processing techniques are applied as preprocessing. The aims of this article are to introduce widely used image processing techniques and to promote interests for further study and research in image processing for MRI.

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Computational analyses （i.e., Voxel‐based morphometry （VBM）, boundary shift integral, tensor‐based morphometry, and atlas‐based volumetry） with three‐dimensional T1‐weighted image （3D‐T1WI） were used for brain volume estimation. VBM serve as a good tool for assessment of brain volume and can be easily used to study different pathologies（e.g., Alzheimer's disease （AD）, epilepsy, diabetes, and panic disorder, etc.）. To diagnose brain disease, it is important to improve understanding of correlation between cognitive function and local brain atrophy in normal aging. Previous studies report that brain shrinkage occurs disproportionately throughout the whole brain. There has been a rapid increase in research with VBM into clinical disease. However, there are numerous controversial issues associated with VBM, including signal intensity non‐uniformity and image distortion of 3D‐T1WI. Non‐parametric non‐uniform intensity normalization （N3） is the most used method, and reduces estimation error caused by signal intensity non‐uniformity in VBM study. Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra （DARTEL） has recently attracted attention as a group‐wise registration algorithm for forming a template for spatial normalization, and reduces estimation error caused by image distortion in VBM study. However, estimation errors caused by intensity non‐uniformity and image distortion are not completely eliminated. VBM users must pay attention to changes of 3D‐T1WI quality （intensity non‐uniformity, image distortion, signal‐to‐noise ratio, and contrast）.

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Our current study concerns T₁ measurement and mapping for dynamic contrast‐enhanced magnetic resonance imaging （DCE‐MRI） in clinical settings. The methodology requires rapid, accurate, and reproducible measurements in order to calculate various pharmacokinetic kinetic parameters. We are focusing on methodology, application, and future development of T₁ mapping for DCE‐MRI. The current study explains the methodology of three T₁ measurement methods; variable flip angle （VFA）, variable reputation time （VTR）, and Look‐Locker （LL）, respectively. Moreover, for future studies, we discuss the possibility of compressed sensing （CS） and MR fingerprinting （MRF） on T₁ mapping. The physical methodology of T₁ mapping for hemodynamic analysis will become more and more important in clinical and practical settings.

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Deep vein thrombosis （DVT） subsequently develops pulmonary embolism, a life threatening cardiovascular disease. Although clinical studies have shown the feasibility of identifying DVT with magnetic resonance （MR） imaging, thrombus age‐related MR changes and MR findings reflecting response to thrombolysis have not been adequately characterized. Recent studies demonstrated that MR direct thrombus imaging can reliably detect organizing process and predict thrombolytic response. In this article, we focus on technological advances in MR imaging for qualitative assessment of DVT.

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With electrocardiography‐triggered single‐shot diffusion echo‐planar imaging, apparent diffusion coefficient（ADC）of white matter significantly changes during the cardiac cycle even if bulk motion effect is minimized. ADC change during the cardiac cycle is synchronized with intracranial volume change; water molecules in white matter fluctuate because of the driving force of intracranial volume loading, rather than bulk motion. In addition, maximum change in ADC during the cardiac cycle（ΔADC）reveals the degree of fluctuation of water molecules and its relationship with the biomechanical properties of intracranial tissue. In this review, we describe the MRI technique used to detect water fluctuation in the brain and its clinical application for idiopathic normal pressure hydrocephalus patients.

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Arterial spin labeling （ASL） is a means of perfusion assessments in magnetic resonance imaging （MRI）. Since ASL uses arterial blood water as an endogenous tracer, it is no need to use contrast agent or irradiation. Therefore, these features make ASL complete non‐invasive and easily accepted in routine clinical practice. Recent prevalence of 3T clinical MRI system has dramatically increased the quality of ASL image along together with the development of ASL techniques. In this paper, we have described the overview of ASL techniques such as labeling scheme, post‐label delay, background signal control, and intra vascular signal suppression. Finally, we have presented some clinical cases with ASL application from our own experience.

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Magnetic resonance imaging（MRI）has been the most important method for assessing knee cartilage in the past few decades. MRI has the merit of facilitating, through sequence selection and operations, visualization of the cartilage as areas of signal hyperintensity, as well as visualization with increased contrast between cartilage and synovial fluid. The objective of the present manuscript was to introduce the characteristics and diagnostic applications of sequences for observing the morphology of knee cartilage that are commonly used in routine MRI of the knee.

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The purpose of our study was to compare temporal changes in the apparent diffusion coefficient （ADC） of the brain parenchyma and ventricular system independent of water restriction over the cardiac cycle among 14 patients with idiopathic normal‐pressure hydrocephalus （iNPH）, 9 patients with atrophic ventricular dilation （VD）, and 8 healthy control subjects. On a 1.5‐T magnetic resonance imaging system, electrocardiogram‐triggered single‐shot diffusion echo planar imaging was performed with sensitivity encoding and half‐scan techniques to minimize the bulk motion. Then, an ADC image of each cardiac phase was calculated. The normalized‐ΔADC image was calculated by using the following equation from all cardiac‐phase ADC images （20 phases） :Normalized‐ΔADC＝（ADC_max－ADC_min）/ADC_min, where ADC_max and ADC_min represent the maximum and minimum ADC during the cardiac cycle, respectively. We assessed the mean normalized‐ΔADC, maximum change in ADC（ΔADC）, and mean ADC of the cardiac phases at which the ADC change was stable （ADC_stable） in the frontal white matter and the three ventricular regions. No significant differences were observed in the normalized‐ΔADCs of healthy subjects among the brain regions, whereas significant differences were observed in ΔADC and ADC_stable between the frontal white matter and each ventricular region. The mean normalized‐ΔADCs of the third ventricle and frontal white matter were significantly higher in the iNPH group than in the control and atrophic VD groups. Normalized‐ΔADC analysis in the frontal white matter and third ventricle may provide more detailed information on intracranial conditions in iNPH, which may be useful for determining a diagnosis.

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Quantitative susceptibility mapping（QSM）of tissue using MRI phase data has recently received increasing scientific and clinical attention. QSM requires deconvolution of the phase data with dipole kernel. However this problem is ill‐posed. To overcome this challenge, we proposed a new method that replaces the magic angle domain in k‐space by the corresponding domain calculated from a 3‐D magnitude image with a binary susceptibility distribution. We successfully applied the method to a numerical head model and showed improved results compared with a previous method. The proposed method yielded a better solution for the RMSE, error, standard deviation, and SNR.

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In order to select the appropriate form of surgery, exact analysis of the invasive region of the tumour using breast MR images has become an important role in the diagnosis. However, many images are obtained for the identification of the tumour; the diagnostic ability of the physician may be decreased. The main purpose of this study was to propose a novel method for automated analysis of the region of the tumour using dynamic contrast‐enhanced breast MR images. For this method, T1‐weighted MR images of pre‐, early‐, and post‐contrast enhanced breast MR images were introduced. Early‐ and late‐subtraction images were obtained by subtracting early‐ and late‐contrast‐enhanced images, respectively, from the pre‐contrast ones. And then, tumor enhanced images were obtained using subtraction images. Subsequently, using the level‐set method, which was a type of dynamic contour extraction, the outline of the tumour in the tumour‐extracted images was obtained. We compared the tumour size listed in the interpretation report by a physician and analysed the results obtained from the proposed method. The mean absolute error of the size of tumours in all cases was less than 3.0 mm. These results indicate than propose method may be useful for the analysis of breast tumors.

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Dual‐energy photon counting was performed using an energy‐selecting device （ESD） and a detector, consisting of a cerium‐doped yttrium aluminum perovskite [YAP（Ce）] crystal and a multipixel photon counter （MPPC）. The ESD is used to determine a low‐energy‐channel range for CT and consists of two comparators and a microcomputer （MC）. The two threshold channels in proportion to energies are determined using low and high‐energy comparators, respectively. The MC in the ESD produces a single logical pulse when only a logical pulse from the low‐energy comparator is input to the MC. To determine the high‐energy‐channel range for CT, logical pulses from the high‐energy comparator are input to the MC outside the ESD. Logical pulses from the two MCs are input to frequency‐voltage converters（FVCs）to convert count rates into voltages. The output voltages from the two FVCs are sent to a personal computer through an analog‐digital converter to reconstruct tomograms. Dual‐energy computed tomography was accomplished at a tube voltage of 60 kV and a maximum count rate of 67 kilo‐counts per second, and two‐different‐energy tomograms were obtained simultaneously.

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In this study, we propose an improved method to detect abnormal regions in maxillary sinuses by accurate determination of regions of interest （ROIs） based on the anatomical information. The ROIs are set based on the automated search of a hard palate and the average widths of tooth crowns. Preferably, dental panoramic radiographs are obtained with the Frankfort plane aligned horizontally. In order to manage the ROI setting in images with inappropriate positioning, the ROI locations are individually adjusted on the basis of the relationship between the reference line and the alveolar line. The proposed method was evaluated with two databases, DB‐1 and DB‐2, consisting of 59 and 39 images. Using the proposed method, the average concordance rate （Jaccard Index） was improved by more than 10%. The areas under the ROC curves for the sinusitis detection were comparable （0.86 and 0.82 for the two databases） with the previous method. As a result, it is expected that the reliability of the computer output was increased with the improved ROI determination.

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