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.

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.

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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.

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.

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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.

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.