We developed a text-data based learning tool that integrates image processing and displaying by Excel. Knowledge required for programing this tool is limited to using absolute, relative, and composite cell references and learning approximately 20 mathematical functions available in Excel. The new tool is capable of resolution translation, geometric transformation, spatial-filter processing, Radon transform, Fourier transform, convolutions, correlations, deconvolutions, wavelet transform, mutual information, and simulation of proton density-, T1-, and T2-weighted MR images. The processed images of 128×128 pixels or 256×256 pixels are observed directly within Excel worksheets without using any particular image display software. The results of image processing using this tool were compared with those using C language and the new tool was judged to have sufficient accuracy to be practically useful. The images displayed on Excel worksheets were compared with images using binary-data display software. This comparison indicated that the image quality of the Excel worksheets was nearly equal to the latter in visual impressions. Since image processing is performed by using text-data, the process is visible and facilitates making contrasts by using mathematical equations within the program. We concluded that the newly developed tool is adequate as a computer-assisted learning tool for use in medical image processing.

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Medical imaging is one of the major tools that have enriched medical science, disease detection and treatment. Computed tomography (CT) is the most widely used imaging modality in clinical practice for cancer detection, oncologic diagnosis, and treatment guidance. Recent advances in CT imaging technologies allow the high-throughput extraction of informative imaging features to quantify the differences that oncologic tissues exhibit. The development of computer-aided detection/diagnosis (CADe/CADx) systems based on imaging biomakers associated with disease probabilities may become increasingly an attractive field to support clinicians in detecting early-stage diseases and determining risk-adaptive treatments. Three-dimensional visualization for CADe/CADx systems may have a large impact as various imaging modalities are routinely used in clinical practice to improve medical decision-support. In this article, we present some examples of 3D visualization for CADe/CADx systems of thoracic CT images.

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Medical images are being recently reconstructed using several data sets. In x-ray and computed tomography (CT), 3-dimensional (3D) images are reconstructed using 2-dimensional (2D) projection data. Research regarding the image reconstruction method has been actively conducted. Further, 3D images include patient information and are used as diagnostic tools. Therefore, it is necessary to acquire a projection technique. There are several algorithms of the projection method. In this paper, we reviewed three methods. The first method is based on mathematical formulae, the second integrates pixel values at regular intervals along the x-ray track, and the third integrates pixel values multiplied by distance. We confirmed the usefulness of the projection method for reconstructing images.

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Imaging techniques to obtain a tomographic image in nuclear medicine such as PET and SPECT are widely used. It is necessary to interpreting all of the tomographic images obtained in order to accurately evaluate the individual lesion, whereas three dimensional display is often useful in order to overview and evaluate the feature of the entire lesion or disease such as the position, size and abnormal pattern. In Japan, the use of three dimensional image analysis workstation with an application of the co-registration and image fusion between the functional images such as PET or SPECT and anatomical images such as CT or MRI has been generalized. In addition, multimodality imaging system such as a PET/CT and SPECT/CT has been widespread. Therefore, it is expected to improve the diagnostic accuracy using three dimensionally image fusion to functional images with poor anatomical information. In this commentary, as an example of a three dimensional display that are commonly used in nuclear medicine examination in Japan, brain regions, cardiac region and bone and tumor region will be introduced separately.

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It has been than 15 years since medical image analysis is used clinical scene. We provide 3D image for doctors to recognize easily by reconstructed CT or MRI images. It is introduced a recently technique for 3D medical image, for example, volume rendering, CPR, colon analysis, CT-SPECT fusion image.

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The three-dimensional representation is used by cerebral blood flow SPECT and myocardial SPECT. It is used with the statistical image analysis software (3D-SSP, eZIS) in cerebral blood flow SPECT, and regional cerebral blood flow decrease can be detected. It is used with the heart function analysis software (QGS, QPS, QBS) in myocardial SPECT, and is effective for the cardiac wall motion evaluation. SPECT/CT and PET/CT are used in clinical. The three-dimensional fusion image that overlaps CT- volume rendering image with SPECT (PET) image can be constructed with an easy and high quality by SPECT/CT (PET/CT). It is effective for identification of accumulation part of radiopharmaceutical.

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Recently, MSCT acquisition of volume data becomes easier. For computer processing technology has advanced, was the performance of the workstation also be improved. Therefore, three-dimensional representation of the whole body is also made possible.Three-dimensional display, is usually doing the diagnosis in only axial image, it is useful to understand the structure that traveling to the cranio-caudal direction. When surgical care is necessary, an examination for CT is conducted for the purpose of a metastasis search. Use the data obtained this time, it becomes possible to provide surgical support image. I make what kind of image to use a clinical on the site three-dimensional image, and it is necessary to understand it what you want to know.

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The recent improvements in magnetic resonance (MR) hardware, especially 3T devices, scanning protocols, and three dimensional (3D) volumetric reconstruction software have facilitated great expansion of the role of 3D imaging. With the volumetric data, various post processing image reconstructions could readily be done on the 3D workstation. The reconstructions allow better demonstration of anatomic details, which help improve lesion localization and surgical planning. Particularly, in patients who are planned for surgery such as tumors assessment or pre-transplant evaluation, the 3D reconstructions provide not only better tumors extent, tumors volume evaluation and an excellent anatomical guide map, but also tumors relations with the blood vessels.
In this review, we explain the various 3D reconstruction techniques used in MRI and demonstrate the value of 3D imaging in preoperative evaluation, diagnosis of coronary diseases, vascular diseases and each abdominal disease.

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Time-consuming Monte Carlo dose calculation becomes feasible owing to the development of computer technology. However, the recent development is due to emergence of the multi-core high performance computers. Therefore, parallel computing becomes a key to achieve good performance of software programs. A Monte Carlo simulation code PHITS contains two parallel computing functions, the distributed-memory parallelization using protocols of message passing interface (MPI) and the shared-memory parallelization using open multi-processing (OpenMP) directives. Users can choose the two functions according to their needs. This paper gives the explanation of the two functions with their advantages and disadvantages. Some test applications are also provided to show their performance using a typical multi-core high performance workstation.

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