Usefulness of the spectral shaping dual-source computed tomography imaging technique in posterior corrective fusion for adolescent idiopathic scoliosis.

PURPOSE: Since childhood exposure to radiation has been demonstrated to increase cancer risk with increase in radiation dose, reduced radiation exposure during computed tomography (CT) evaluation is desired for adolescent idiopathic scoliosis (AIS). Therefore, this retrospective study aimed to investigate the radiation dose of dual-source CT using a spectral shaping technique and the accuracy of the thoracic pedicle screw (TPS) placement for posterior spinal fusion (PSF) in patients with AIS. METHODS: Fifty-nine female patients with thoracic AIS who underwent PSF using CT-guided TPSs were included and divided into two groups comprised of 23 patients who underwent dual-source CT (DSCT) with a tin filter (DSCT group) and 36 who underwent conventional multislice CT (MSCT group). We assessed the CT radiation dose using the CT dose index (CTDIvol), effective dose (ED), and accuracy of TPS insertion according to the established Neo's classification. RESULTS: The DSCT and MSCT groups differed significantly (p < 0.001) in the mean CTDIvol (0.76 vs. 3.31 mGy, respectively) and ED (0.77 vs. 3.47 mSv, respectively). Although the correction rate of the main thoracic curve in the DSCT group was lower (65.7% vs. 71.2%) (p = 0.0126), the TPS accuracy (Grades 0-1) was similar in both groups (381 screws [88.8%] vs. 600 screws [88.4%], respectively) (p = 0.8133). No patient required replacement of malpositioned screws. CONCLUSION: Spectral shaping DSCT with a tube-based tin filter allowed a 75% radiation dose reduction while achieving TPS insertion accuracy similar to procedures based on conventional CT without spectral shaping.

[Method for Measuring Noise Power Spectrum Depending on X-ray Tube Angle in Spiral Orbit of Helical CT Scan].

PURPOSE: The noise power spectrum (NPS) in computed tomography (CT) images potentially varies with the X-ray tube angle in a spiral orbit of the helical scan. The purpose of this study was to propose a method for measuring the NPS for each angle of the X-ray tube. METHODS: Images of the water phantom were acquired using a helical scan. As a conventional method, we measured the two-dimensional (2D) NPS from each image and averaged them; the obtained 2D-NPS was referred to as NPSconventional. In the proposed method, we made the X-ray tube angle θ (0°≤θ<360°) to correspond to the image according to each slice position of the images that located within the travel distance of the CT scan table per 360° rotation of the X-ray tube. We obtained the 2D-NPS from each image and assigned the θ (0°, 30°, 60°, 90°, 120°, 150°, 180°); the obtained 2D-NPS was referred to as NPSsθ. The NPSsθ was compared to the NPSconventional. Also, we investigated the dependency of the NPSsθ on the θ. RESULTS: The NPSconventional was found to be isotropic, and in contrast, the NPSsθ was anisotropic. The NPSsθ showed a continuously rotational change while increasing the θ. There was an excellent correlation (R2>0.999) between the rotation angle of NPSθ and the θ. CONCLUSION: The proposed method was demonstrated to be effective for evaluating anisotropic noise characteristics depending on the X-ray tube angle.

Development of a new body weight estimation method using head CT scout images.

BACKGROUND: Imaging examinations are crucial for diagnosing acute ischemic stroke, and knowledge of a patient's body weight is necessary for safe examination. To perform examinations safely and rapidly, estimating body weight using head computed tomography (CT) scout images can be useful. OBJECTIVE: This study aims to develop a new method for estimating body weight using head CT scout images for contrast-enhanced CT examinations in patients with acute ischemic stroke. METHODS: This study investigates three weight estimation techniques. The first utilizes total pixel values from head CT scout images. The second one employs the Xception model, which was trained using 216 images with leave-one-out cross-validation. The third one is an average of the first two estimates. Our primary focus is the weight estimated from this third new method. RESULTS: The third new method, an average of the first two weight estimation methods, demonstrates moderate accuracy with a 95% confidence interval of ±14.7 kg. The first method, using only total pixel values, has a wider interval of ±20.6 kg, while the second method, a deep learning approach, results in a 95% interval of ±16.3 kg. CONCLUSIONS: The presented new method is a potentially valuable support tool for medical staff, such as doctors and nurses, in estimating weight during emergency examinations for patients with acute conditions such as stroke when obtaining accurate weight measurements is not easily feasible.

Method for measuring noise-power spectrum independent of the effect of extracting the region of interest from a noise image.

This study aimed to evaluate the impact of region of interest (ROI) size on noise-power spectrum (NPS) measurement in computed tomography (CT) images and to propose a novel method for measuring NPS independent of ROI size. The NPS was measured using the conventional method with an ROI of size P × P pixels in a uniform region in the CT image; the NPS is referred to as NPSR=P. NPSsR=256, 128, 64, 32, 16, and 8 were obtained and compared to assess their dependency on ROI size. In the proposed method, the true NPS was numerically modeled as an NPSmodel, with adjustable parameters, and a noise image with the property of the NPSmodel was generated. From the generated noise image, the NPS was measured using the conventional method with a P × P pixel ROI size; the obtained NPS was referred to as NPS'R=P. The adjustable parameters of the NPSmodel were optimized such that NPS'R=P was most similar to NPSR=P. When NPS'R=P was almost equivalent to NPSR=P, the NPSmodel was considered the true NPS. NPSsR=256, 128, 64, 32, 16, and 8 obtained using the conventional method were dependent on the ROI size. Conversely, the NPSs (optimized NPSsmodel) measured using the proposed method were not dependent on the ROI size, even when a much smaller ROI (P = 16 or 8) was used. The proposed method for NPS measurement was confirmed to be precise, independent of the ROI size, and useful for measuring local NPSs using a small ROI.

[A Study of Longitudinal NPS Measurement in CT Images Based on the Central Cross-section Theorem].

PURPOSE: Various approaches in noise power spectrum (NPS) analysis are currently used for measuring a patient's longitudinal (z-direction) NPS from three-dimensional (3D) CT volume data. The purpose of this study was to clarify the relationship between those NPSs and 3D-NPS based on the central slice theorem. METHODS: We defined the 3D-NPS(fx, fy, fz) that was calculated by 3D Fourier transform (FT) from 3D noise data (3D-Noise(x, y, z), x-y scan plane). Here, fx, fy and fz are spatial frequencies corresponding to the axes of x, y and z, respectively. Based on the central slice theorem, we described three relationships as follows. (1) The fz-directional NPS calculated from the 3D-Noise(x=0, y=0, z) is equal to the profile obtained by projecting 3D-NPS(fx, fy, fz) in fx- and fy-directions. (2) The fz-directional NPS calculated from the profile obtained by projecting 3D-Noise(x=0, y, z) in the y-direction is equal to the profile at fy=0 in the data obtained by projecting 3D-NPS(fx, fy, fz) in the fx-direction. (3) The fz-directional NPS calculated from the profile obtained by projecting 3D-Noise(x, y, z) in x and y-directions is equal to the profile of 3D-NPS(fx=0, fy=0, fz). To verify them, we compared the NPSs measured from actual 3D noise data that were obtained using a cylindrical water phantom. RESULTS: In each relationship (1)-(3), the fz-directional NPS matched the profile obtained from the 3D-NPS(fx, fy, fz). CONCLUSION: Based on the central slice theorem, we clarified the relationships between fz-directional NPSs and 3D-NPS. We should understand them and then consider which method should be used for fz-directional NPS measurement.

[A Study of 3D-NPS Analysis in CT Images Based on the Central Cross-section Theorem].

PURPOSE: The noise power spectrum (NPS) of a CT scanner is commonly measured from a single noise image. However, since CT images are three-dimensional (3D) volume data, they have 3D noise characteristics (3D-NPS). In this study, we clarify the relationship among NPSs measured by various approaches in NPS analysis based on the central slice theorem. Its validity is verified by the NPS measurements using actual 3D noise data. METHODS: We defined the NPSz-projection(fx, fy) that was calculated by the 2D Fourier transform (FT) from the 2D projection of 3D noise data in the patient longitudinal direction, the 3D-NPS(fx, fy, fz) that was calculated by the 3D-FT from the 3D noise data, and the 2D-NPS(fx, fy) that was calculated by the 2D-FT from a single noise image; fx, fy, and fz are spatial frequencies corresponding to the axes of x, y, and z in the reconstructed CT volume, respectively. Based on the central slice theorem, we described that the NPSz-projection(fx, fy=0) was equal to the 3D-NPS(fx, fy=0, fz=0), and the NPS(2D-NPS(fx, fy=0)) was different from the 3D-NPS(fx, fy=0, fz=0). To verify them, we compared the NPSs calculated from actual 3D noise data that were obtained using a cylindrical water phantom. RESULTS: The 3D-NPS(fx, fy=0, fz=0) matched the NPSz-projection(fx, fy=0) and was different from the 2D-NPS(fx, fy=0). CONCLUSION: Based on the central slice theorem, we clarified the relationship among NPSs measured by various approaches in NPS analysis; it is important to understand this and then select an appropriate noise data handling and NPS measurement method.

Method for determining slice sensitivity profile of iterative reconstruction CT images using low-contrast sphere phantom.

A novel method for measuring the slice sensitivity profile (SSP) of computed tomography (CT) images reconstructed using an iterative reconstruction (IR) algorithm is proposed herein. A phantom that included a low-contrast spherical object was scanned and consecutive cross-sectional images were reconstructed. The mean CT values in a region including the sphere were measured for all images and plotted as a function of slice position along the longitudinal [Formula: see text] direction to yield a mean CT value profile [Formula: see text]. Next, we numerically generated an object function corresponding to the sphere and obtained the mean CT value profile [Formula: see text]. Subsequently, the SSP was modeled as a product of the Gaussian and cosine functions. We convolved [Formula: see text] with the modeled SSP to obtain [Formula: see text]. The difference between [Formula: see text] and [Formula: see text] was evaluated using the root mean square error (RMSE), which was minimized via optimization of the SSP model parameters. To validate the methodology, we first used filtered back projection (FBP) images to compare the SSPs determined using the proposed and standard coin methods. Subsequently, the proposed method was applied to measure the SSPs of four types of IR algorithms in two scanners. The SSPs of the FBP images determined using the proposed and coin methods showed good agreement. Additionally, in the SSP measurements using the proposed method, [Formula: see text] agreed well with [Formula: see text] for every IR algorithm. The RMSEs for all measurements were less than 0.7 HU, indicating the accuracy of the SSPs. Thus, the proposed method is effective for obtaining valid SSPs.

[Central Slice Theorem-based Relationship between 1D-NPS Obtained by the Slit Method and 2D-NPS for CT Images].

PURPOSE: The method using a numerical slit (slit method) is used commonly to obtain the one-dimensional (1D) noise power spectrum (NPS) in computed tomography. However, the relationship between the 1D-NPS obtained by the slit method and the original two-dimensional (2D) NPS derived by the 2D Fourier transformation has not been elucidated clearly. The purpose of this study was to clarify their relationship based on the well-known central slice theorem (projection slice theorem) and validate it using computer simulation analysis. METHODS: With the application of the central slice theorem, we described that the 1D-NPS obtained by the slit method was equal to the central slice (profile) in the 2D-NPS when we set the slit length to the maximum (i.e. the matrix size of the noise image). To verify this, we generated computer-simulated noise images with the known 2D-NPS (true 2D-NPS). From those images, we obtained the 1D-NPS that was obtained by the slit method and compared it with the central slice in the true 2D-NPS. RESULTS: When we set the slit length to the maximum, the 1D-NPS obtained by the slit method showed good agreement with the central slice in the true 2D-NPS. CONCLUSION: We clarified the relationship between the 1D-NPS obtained by the slit method and the 2D-NPS using a theoretical approach and the computer simulation. We had to maximize the slit length to achieve the accurate measurement of the 1D-NPS using the slit method.

Technical Note: A simple method for measuring the slice sensitivity profile of iteratively reconstructed CT images using a non-slanted edge plane.

PURPOSE: A method for measuring the slice sensitivity profile (SSP) of computed tomography (CT) images reconstructed with iterative reconstruction (IR) algorithms was reported by the AAPM Task Group 233 (TG233). In this method, the phantom plane edge is slightly slanted with respect to the scan plane to obtain a composite oversampled edge-spread function (ESF). However, it is expected that a fine-sampled ESF can be obtained directly from images reconstructed with a small slice increment without slanting the edge plane. This study aimed to investigate the validity of using a non-slanted edge plane. METHODS: In the proposed non-slanted edge method, the phantom was positioned so that the plane edge was perpendicular to the longitudinal z-axis, and images were reconstructed with a 1-mm slice thickness and 0.1-mm increment. The mean CT value was obtained in each slice and plotted as a function of slice position along the z-axis, thereby generating the ESF. The SSP was calculated from the ESF by differentiation. In the TG 233-recommended slanted edge method, the SSP was obtained by following the procedure described in the TG233 report. To validate the methodology, we first used filtered back projection (FBP) images to compare SSPs obtained using the non-slanted edge method, slanted edge method, and a standard method using a high-contrast thin object (coin). Next, for two types of IR algorithms, we compared the SSPs obtained using the non-slanted and slanted edge methods. RESULTS: For the FBP images, the SSP measured using the non-slanted edge method agreed well with SSPs measured using the coin and slanted edge methods. For the IR images, the SSPs measured using the non-slanted and slanted edge methods showed good agreement. CONCLUSIONS: The non-slanted edge method was demonstrated to be valid. The simplicity and practicality of the method allows routine and accurate determination of the SSP.

Proton dose calculation based on converting dual-energy CT data to stopping power ratio (DEEDZ-SPR): a beam-hardening assessment.

To achieve an accurate stopping power ratio (SPR) prediction in particle therapy treatment planning, we previously proposed a simple conversion to the SPR from dual-energy (DE) computed tomography (CT) data via electron density and effective atomic number (Z eff) calibration (DEEDZ-SPR). This study was conducted to carry out an initial implementation of the DEEDZ-SPR conversion method with a clinical treatment planning system (TPS; VQA, Hitachi Ltd., Tokyo) for proton beam therapy. Consequently, this paper presents a proton therapy plan for an anthropomorphic phantom to evaluate the stability of the dose calculations obtained by the DEEDZ-SPR conversion against the variation of the calibration phantom size. Dual-energy x-ray CT images were acquired using a dual-source CT (DSCT) scanner. A single-energy CT (SECT) scan using the same DSCT scanner was also performed to compare the DEEDZ-SPR conversion with the SECT-based SPR (SECT-SPR) conversion. The scanner-specific parameters necessary for the SPR calibration were obtained from the CT images of tissue substitutes in a calibration phantom. Two calibration phantoms with different sizes (a 33 cm diameter phantom and an 18 cm diameter phantom) were used for the SPR calibrations to investigate the beam-hardening effect on dosimetric uncertainties. Each set of calibrated SPR data was applied to the proton therapy plan designed using the VQA TPS with a pencil beam algorithm for the anthropomorphic phantom. The treatment plans with the SECT-SPR conversion exhibited discrepancies between the dose distributions and the dose-volume histograms (DVHs) of the 33 cm and 18 cm phantom calibrations. In contrast, the corresponding dose distributions and the DVHs obtained using the DEEDZ-SPR conversion method coincided almost perfectly with each other. The DEEDZ-SPR conversion appears to be a promising method for providing proton dose plans that are stable against the size variations of the calibration phantom and the patient.

Initial implementation of the conversion from the energy-subtracted CT number to electron density in tissue inhomogeneity corrections: an anthropomorphic phantom study of radiotherapy treatment planning.

PURPOSE: To achieve accurate tissue inhomogeneity corrections in radiotherapy treatment planning, the authors had previously proposed a novel conversion of the energy-subtracted computed tomography (CT) number to an electron density (ΔHU-ρ(e) conversion), which provides a single linear relationship between ΔHU and ρ(e) over a wide range of ρ(e). The purpose of this study is to present an initial implementation of the ΔHU-ρ(e) conversion method for a treatment planning system (TPS). In this paper, two example radiotherapy plans are used to evaluate the reliability of dose calculations in the ΔHU-ρ(e) conversion method. METHODS: CT images were acquired using a clinical dual-source CT (DSCT) scanner operated in the dual-energy mode with two tube potential pairs and an additional tin (Sn) filter for the high-kV tube (80-140 kV/Sn and 100-140 kV/Sn). Single-energy CT using the same DSCT scanner was also performed at 120 kV to compare the ΔHU-ρ(e) conversion method with a conventional conversion from a CT number to ρ(e) (Hounsfield units, HU-ρ(e) conversion). Lookup tables for ρ(e) calibration were obtained from the CT image acquisitions for tissue substitutes in an electron density phantom (EDP). To investigate the beam-hardening effect on dosimetric uncertainties, two EDPs with different sizes (a body EDP and a head EDP) were used for the ρ(e) calibration. Each acquired lookup table was applied to two radiotherapy plans designed using the XiO TPS with the superposition algorithm for an anthropomorphic phantom. The first radiotherapy plan was for an oral cavity tumor and the second was for a lung tumor. RESULTS: In both treatment plans, the performance of the ΔHU-ρ(e) conversion was superior to that of the conventional HU-ρ(e) conversion in terms of the reliability of dose calculations. Especially, for the oral tumor plan, which dealt with dentition and bony structures, treatment planning with the HU-ρ(e) conversion exhibited apparent discrepancies between the dose distributions and dose-volume histograms (DVHs) of the body-EDP and head-EDP calibrations. In contrast, the dose distributions and DVHs of the body-EDP and head-EDP calibrations coincided with each other almost perfectly in the ΔHU-ρ(e) conversion for 100-140 kV/Sn. The difference between the V100's (the mean planning target volume receiving 100% of the prescribed dose; a DVH parameter) of the body-EDP and head-EDP calibrations could be reduced to less than 1% using the ΔHU-ρ(e) conversion, but exceeded 11% for the HU-ρ(e) conversion. CONCLUSIONS: The ΔHU-ρ(e) conversion can be implemented for currently available TPS's without any modifications or extensions. The ΔHU-ρ(e) conversion appears to be a promising method for providing an accurate and reliable inhomogeneity correction in treatment planning for any ill-conditioned scans that include (i) the use of a calibration EDP that is nonequivalent to the patient's body tissues, (ii) a mismatch between the size of the patient and the calibration EDP, or (iii) a large quantity of high-density and high-atomic-number tissue structures.

Analysis of decrease in lung perfusion blood volume with occlusive and non-occlusive pulmonary embolisms.

PURPOSE: The aim of this study was to determine if lung perfusion blood volume (lung PBV) with non-occlusive pulmonary embolism (PE) differs quantitatively and visually from that with occlusive PE and to investigate if lung PBV with non-occlusive PE remains the same as that without PE. MATERIALS AND METHODS: Totally, 108 patients suspected of having acute PE underwent pulmonary dual-energy computed tomography angiography (DECTA) between April 2011 and January 2012. Presence of PE on DECTA was evaluated by one radiologist. Two radiologists visually evaluated the PE distribution (segmental or subsegmental) and its nature (occlusive or non-occlusive) on DECTA and classified perfusion in lung PBV as "decreased," "slightly decreased," and "preserved". Two radiologists used a lung PBV application to set a region of interest (ROI) in the center of the lesion and measured HU values of an iodine map. In the same slice as the ROI of the lesion and close to the lesion, another ROI was set in the normal perfusion area without PE, and HUs were measured. The proportion of lesions was compared between the occlusive and non-occlusive groups. HUs were compared among the occlusive, non-occlusive, and corresponding normal groups. RESULTS: Twenty-five patients had 80 segmental or subsegmental lesions. There were 37 and 43 lesions in the occlusive and non-occlusive groups, respectively. The proportion of decreased lesions was 73.0% (27/37) in the occlusive group, while that of preserved lesions in the non-occlusive group was 76.7% (33/43). There was a significant difference in the proportion of lesions (P<0.001) between the two groups. HUs of the iodine map were significantly higher in the non-occlusive group than in the occlusive group (33.8 ± 8.2 HU vs. 11.9 ± 6.1 HU, P<0.001). There was no significant difference in HUs for the entire lesion between the non-occlusive (33.8 ± 8.2 HU) and corresponding normal group (34.5 ± 6.8 HU; P=0.294). CONCLUSION: Iodine perfusion tended to be visually and quantitatively preserved in lungs with nonocclusive PE. Lung PBV is required to evaluate pulmonary blood flow.

Reduction of dark-band-like metal artifacts caused by dental implant bodies using hypothetical monoenergetic imaging after dual-energy computed tomography.

OBJECTIVE: The aim of this study was to evaluate the usefulness of hypothetical monoenergetic images after dual-energy computed tomography (DECT) for assessment of the bone encircling dental implant bodies. STUDY DESIGN: Seventy-two axial images of implantation sites clipped out from image data scanned using DECT in dual-energy mode were used. Subjective assessment on reduction of dark-band-like artifacts (R-DBAs) and diagnosability of adjacent bone condition (D-ABC) in 3 sets of DECT images-a fused image set (DE120) and 2 sets of hypothetical monoenergetic images (ME100, ME190)-was performed and the results were statistically analyzed. RESULTS: With regards to R-DBAs and D-ABC, significant differences among DE120, ME100, and ME190 were observed. The ME100 and ME190 images revealed more artifact reduction and diagnosability than those of DE120. CONCLUSIONS: DECT imaging followed by hypothetical monoenergetic image construction can cause R-DBAs and increase D-ABC and may be potentially used for the evaluation of postoperative changes in the bone encircling implant bodies.

Conversion of the energy-subtracted CT number to electron density based on a single linear relationship: an experimental verification using a clinical dual-source CT scanner.

In radiotherapy treatment planning, the conversion of the computed tomography (CT) number to electron density is one of the main processes that determine the accuracy of patient dose calculations. However, in general, the CT number and electron density of tissues cannot be interrelated using a simple one-to-one correspondence. This study aims to experimentally verify the clinical feasibility of an existing novel conversion method proposed by the author of this note, which converts the energy-subtracted CT number (ΔHU) to the relative electron density (ρe) via a single linear relationship by using a dual-energy CT (DECT). The ΔHU-ρe conversion was performed using a clinical second-generation dual-source CT scanner operated in the dual-energy mode with tube potentials of 80 kV and 140 kV with and without an additional tin filter. The ΔHU-ρe calibration line was obtained from the DECT image acquisition for tissue substitutes in an electron density phantom. In addition, the effect of object size on ΔHU-ρe conversion was also experimentally investigated. The plot of the measured ΔHU versus nominal ρe values exhibited a single linear relationship over a wide ρe range from 0.00 (air) to 2.35 (aluminum). The ΔHU-ρe conversion performed with the tin filter yielded a lower dose and more reliable ρe values that were less affected by the object-size variation when compared to the corresponding values obtained for the case without the tin filter.

Trigeminal neuralgia associated with the specific bridging pattern of transverse pontine vein: diagnostic value of three-dimensional multifusion volumetric imaging.

OBJECTIVES: We report the specific bridging pattern of a transverse pontine vein (TPV) associated with trigeminal neuralgia (TN), which was evaluated by 3-dimensional (3D) multifusion volumetric imaging (MFVI). METHODS: In 3 cases with TN (V1 or V1-2 territory), constructive interference in steady state (CISS) imaging confirmed no arterial compression but indicated a vein draining into Meckel's cave. Virtual endoscopic (VE) analysis for CISS images and 3D MFVI (in 2 cases) including venous information was obtained by a multidetector row computed tomography (MDCT) system. Additionally, we investigated the bridging pattern of veins around Meckel's cave on 3D MFVI of 50 cerebellopontine angle (CPA) regions without any lesions. RESULTS: In all 3 patients, VE of CISS or 3D MFVI identified a bridging vein from the TPV causing the focal deformity of the trigeminal nerve near Meckel's cave. All those patients achieved a pain-free state after surgically coagulating and cutting the vein. In investigating 3D MFVI of 50 CPA regions, this type of the bridging vein was found in 4 (8%) including the presented 2 cases. CONCLUSIONS: The specific bridging pattern of the TPV draining into Meckel's cave can be associated with TN. The 3D MFVI analysis using venous information obtained by MDCT was useful to evaluate surgical anatomy including the offending vein which can be missed.