Although terminology requires continuous consideration of recorded technical terms, extracting these terms manually is difficult, because the number of recorded terms is constantly increasing. Text-mining acquires information from numerous documents, and is capable of extracting technical terms. The purpose of this study is to extract candidate terms using text-mining toward updating the terminology of Japanese society of radiological technology (JSRT). First, the subjects for this study were textbooks published by the JSRT, and morphological analysis was conducted, which is an analysis to break the books up into meaningful words. Additionally, index terms of textbooks were extracted. Second, we observed overlaps between the JSRT technical terms and the terms obtained from the morphological analysis and the indexes of textbooks and the extracted terms were absent in the JSRT terminology. The overlap was 53.6% (3090/5770 terms). The terms, “imaging technology for magnetic resonance” and “information and system in radiological technology” were missing from the JSRT terminology. From these results, it was estimated that half number of the JSRT technical terms were changing with time. This study demonstrated that text mining showed the differences between old and new technical terms.

Purpose: We developed a novel digital phantom-creation tool that will help formulate the standard shooting method for a three-phase dynamic liver study. Here, we present data demonstrating the usefulness of this tool in the assessment of low-contrast detectability and visibility. Methods: We performed a visual evaluation by adding a spherical digital phantom with a diameter of 8 mm and a computed tomography (CT) value difference of 10 Hounsfield unit (HU) to images taken using filtered back projection and seven types of adaptive iterative dose reduction 3D (Weak, Mild, eMild, Standard, eStandard, Strong, and eStrong). We also examined the partial-volume effect by drawing a profile curve using a digital phantom with a CT value difference of 30 HU and a diameter of 5 mm. Furthermore, a digital phantom with two kinds of filters (smoothing and Gaussian) was added to the image of the home-made simulated tumor phantom to visual valuate its visibility in the phantom’s low-contrast module and the digital phantom. Results: Detection sensitivity was significantly decreased in Standard, eStandard, Strong, and eStrong, and the area under the curve also decreased in a similar fashion. We confirmed that the partial-volume effect was due to the different maximum CT values in the profile curve at 4 and 5 mm thickness. The visibility of the low-contrast module and digital phantom was most consistent when using the Gaussian filter. Conclusion: This tool can be used for low-contrast detection ability evaluation.

In this study, computer simulations and experiments were used to verify the accuracy of a two-dimensional image registration program (program) for portal images that we previously developed. The program used a computed radiography cassette system and digitally reconstructed radiography images as planning images for external beam radiation therapy. Using this program, we also investigated the reason two-dimensional automatic image registration images experienced large misregistration in clinical practice using commercial image registration systems. Mutual information and normalized mutual information were used as the registration criteria. To investigate the influence of image background with or without a region of interest (ROI), results of image registrations were compared. Parameters of image registration were defined as translation in the horizontal and vertical directions (x and y, respectively) and rotation (θ) around the axis perpendicular to the x-y plane. There was no significant difference in image registration arising from the difference between mutual information and normalized mutual information. Image registration was improved with a ROI. Regardless of the registration criteria, errors in image registration with a ROI in the experimental study were ≤1.2 mm in directions x and y and ≤1.0 degree in rotation θ. We found that image registration required setting up as close to the planned position as possible.

Purpose: Non-uniformity of a scintillation camera can result in artifacts on planar, projection, and single-photon emission computed tomography (SPECT) images. The purpose of this study was to evaluate the effect of field uniformity on artifact generation. Methods: Using a simulation phantom, we investigated the relationship between non-uniformity of the image and artifacts on planar, projection, and SPECT images. All the non-uniformity images were generated by decreasing the photomultiplier tube sensitivity ranging from 0% to 10%. Quantitative analysis was performed using integral and differential uniformity. We also visually assessed artifact magnitude. Results: Integral and differential uniformity increased with decreasing the photomultiplier tube sensitivity and tended to be higher in SPECT images compared with planar and projection images. For visual assessment, mean scores in SPECT images were higher than in planar and projection images for artifact detection. Conclusions: Our results indicated that decreasing field uniformity is expected to produce artifacts in planar and SPECT images. Also, SPECT images require very high-field uniformity.

Purpose: This study aimed to evaluate the effect of adaptive iterative dose reduction 3D (AIDR 3D) on the computed tomography (CT) image quality by using single energy metal artifact reduction (SEMAR). Materials & Methods: A water phantom (22 cmφ) with the stem for total hip arthroplasty made of titanium was scanned. The volume CT dose index (CTDI_vol) was set to 8.9 and 5.0 mGy. The reconstruction was performed using filtered back projection and AIDR 3D by soft kernel (FC13) and SEMAR. The averaged profile method was used for the quantitative evaluation of artifacts. We placed a rectangular region-of-interest on the artifact part, and obtained the x-direction averaged profile (Profile A). Profile B was obtained using a water phantom without metal. Profiles A and B were normalized as Profiles A′ and B′ using the mean value calculated from Profile B. Based on the standard deviation (SD) calculated from Profile B′, the background variation level was defined as ±2SD, and subtracted from Profile A′ (Profile A″). Finally, the area of Profile A″ was calculated and defined as Artifact_total. Artifact_over, and Artifact_under, respectively, the positive- and negative-side components of Artifact_total. Results: Both Artifact_total and Artifact_under increased according to the strength of AIDR 3D. The variations of Artifact_over and Artifact_under, due to the AIDR 3D strength, were small and large, respectively. Further, in comparison with a high dose, the effect of artifact emphasis increased at low dose. Therefore, it should be noted that stronger AIDR 3D can emphasize the residual metal artifact.

Apparent diffusion coefficient (ADC) values calculated from diffusion-weighted magnetic resonance imaging (DW-MRI) can be used for differentiation of tumors. Clinically, ADC values are used for monitoring treatment response after chemotherapy or radiation. However, it is reported that the threshold of the ADC value differs among institutions. In addition, there are reports regarding the change factor of the ADC value. Slice thickness may induce error in the ADC value by the influence of the partial volume effect in thicker objects, and by the influence of signal-to-noise ratio (SNR) in thinner objects. Therefore, in this study, the effect of slice thickness was examined. The signal body of spherical high-diffusion coefficients of 6, 7.9, and 9.3 mm in diameter was fixed in the low-circumference material of the diffusion coefficient. These phantoms were imaged using DW imaging (DWI) of 1, 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10, 15, and 20 mm slice thickness using the multi-b values. In addition, different SNR were imaged by changing field-of-view and the number of additions. ADC was calculated by DWI of the different b values. As a result, slice thickness showed a peak at 50–65% of the diameter of the signal body. Furthermore, ADC values fluctuated in the slice thickness in front of the peak with a change in SNR. In conclusion, the ADC value was most accurate at a setting of 50–65% of slice thickness for the object diameter.