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Objective: Despite recent advancements in quantum computing, the limited number of available qubits has hindered progress in CT reconstruction. This study investigates the feasibility of utilizing quantum annealing-based computed tomography (QACT) with current quantum bit levels. Approach: The QACT algorithm aims to precisely solve quadratic unconstrained binary optimization (QUBO) problems. Furthermore, a novel approach is proposed to reconstruct images by approximating real numbers using the variational method. This approach allows for accurate CT image reconstruction using a small number of qubits. The study examines the impact of projection data quantity and noise on various image sizes ranging from 4x4 to 24x24 pixels. The reconstructed results are compared against conventional reconstruction algorithms, namely maximum likelihood expectation maximization (MLEM) and filtered back projection (FBP). Main result: By employing the variational approach and utilizing two qubits for each pixel of the image, accurate reconstruction was achieved with an adequate number of projections. Under conditions of abundant projections and lower noise levels, the image quality in QACT outperformed that of MLEM and FBP. However, in situations with limited projection data and in the presence of noise, the image quality in QACT was inferior to that in MLEM. Significance: This study developed the QACT reconstruction algorithm using the variational approach for real-number reconstruction. Remarkably, only 2 qubits were required for each pixel representation, demonstrating their sufficiency for accurate reconstruction., Comment: 17 pages, 9 figures. This is the pre-peer reviewed version of the following article

In recent years, deep-learning-based image processing has emerged as a valuable tool for medical imaging owing to its high performance. However, the quality of deep-learning-based methods heavily relies on the amount of training data; the high cost of acquiring a large dataset is a limitation to their utilization in medical fields. Herein, based on deep learning, we developed a computed tomography (CT) modality conversion method requiring only a few unsupervised images. The proposed method is based on CycleGAN with several extensions tailored for CT images, which aims at preserving the structure in the processed images and reducing the amount of training data. This method was applied to realize the conversion of megavoltage computed tomography (MVCT) to kilovoltage computed tomography (kVCT) images. Training was conducted using several datasets acquired from patients with head and neck cancer. The size of the datasets ranged from 16 slices (two patients) to 2745 slices (137 patients) for MVCT and 2824 slices (98 patients) for kVCT. The required size of the training data was found to be as small as a few hundred slices. By statistical and visual evaluations, the quality improvement and structure preservation of the MVCT images converted by the proposed model were investigated. As a clinical benefit, it was observed by medical doctors that the converted images enhanced the precision of contouring. We developed an MVCT to kVCT conversion model based on deep learning, which can be trained using only a few hundred unpaired images. The stability of the model against changes in data size was demonstrated. This study promotes the reliable use of deep learning in clinical medicine by partially answering commonly asked questions, such as "Is our data sufficient?" and "How much data should we acquire?", Comment: 3+27 pages, 13 figures, version published in Medical Physics

Statistical iterative reconstruction is expected to improve the image quality of megavoltage computed tomography (MVCT). However, one of the challenges of iterative reconstruction is its large computational cost. The purpose of this work is to develop a fast iterative reconstruction algorithm by combining several iterative techniques and by optimizing reconstruction parameters. Megavolt projection data was acquired from a TomoTherapy system and reconstructed using our statistical iterative reconstruction. Total variation was used as the regularization term and the weight of the regularization term was determined by evaluating signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and visual assessment of spatial resolution using Gammex and Cheese phantoms. Gradient decent with an adaptive convergence parameter, ordered subset expectation maximization (OSEM), and CPU/GPU parallelization were applied in order to accelerate the present reconstruction algorithm. The SNR and CNR of the iterative reconstruction were several times better than that of filtered back projection (FBP). The GPU parallelization code combined with the OSEM algorithm reconstructed an image several hundred times faster than a CPU calculation. With 500 iterations, which provided good convergence, our method produced a 512$\times$512 pixel image within a few seconds. The image quality of the present algorithm was much better than that of FBP for patient data. An image from the iterative reconstruction in TomoTherapy can be obtained within few seconds by fine-tuning the parameters. The iterative reconstruction with GPU was fast enough for clinical use, and largely improve the MVCT images., Comment: 11 pages, 4 figures

We present a relativistic chiral mean field (RCMF) model, which is a method for the proper treatment of pion-exchange interaction in the nuclear many-body problem. There the dominant term of the pionic correlation is expressed in two-particle two-hole (2p-2h) states with particle-holes having pionic quantum number, J^{pi}. The charge-and-parity-projected relativistic mean field (CPPRMF) model developed so far treats surface properties of pionic correlation in 2p-2h states with J^{pi} = 0^{-} (spherical ansatz). We extend the CPPRMF model by taking 2p-2h states with higher spin quantum numbers, J^{pi} = 1^{+}, 2^{-}, 3^{+}, ... to describe the full strength of the pionic correlation in the intermediate range (r > 0.5 fm). We apply the RCMF model to the ^{4}He nucleus as a pilot calculation for the study of medium and heavy nuclei. We study the behavior of energy convergence with the pionic quantum number, J^{pi}, and find convergence around J^{pi}_{max} = 6^{-}. We include further the effect of the short-range repulsion in terms of the unitary correlation operator method (UCOM) for the central part of the pion-exchange interaction. The energy contribution of about 50% of the net two-body interaction comes from the tensor part and 20% comes from the spin-spin central part of the pion-exchange interaction., Comment: 22 pages, 12 figures

We study the effect of one-loop corrections from nucleon together with those from boson in the massless chiral sigma model, where we perform the Coleman-Weinberg renormalization procedure. This renormalization procedure has a mechanism of spontaneous symmetry breaking due to radiative corrections in $\phi^4$ theory. We apply it to the system of nucleon and bosons with chiral symmetry where the negative-mass term of bosons does not exist. Spontaneous chiral symmetry breaking is derived from the contribution of nucleon and boson loops which generates the masses of nucleon, scalar meson, and vector meson dynamically. We find that the renormalization scale plays an important role for the breaking of the symmetry between fermion (nucleon) and boson, and eventually for the chiral symmetry at the same time. In addition, we find that the naturalness restores by means of the introduction of the vacuum fluctuation from both nucleon and boson. Finally we obtain a stable effective potential with the effect of Dirac sea in the chiral model for the first time., Comment: 18 pages, 7 figures

We study the relativistic Hartree approach with the exact treatment of the vacuum polarization in the Walecka sigma-omega model. The contribution from the vacuum polarization of nucleon-antinucleon field to the source term of the meson fields is evaluated by performing the energy integrals of the Dirac Green function along the imaginary axis. With the present method of the vacuum polarization in finite system, the total binding energies and charge radii of 16O and 40Ca can be reproduced. On the other hand, the level-splittings in the single-particle level, in particular the spin-orbit splittings, are not described nicely because the inclusion of vacuum effect provides a large effective mass with small meson fields. We also show that the derivative expansion of the effective action which has been used to calculate the vacuum contribution for finite nuclei gives a fairly good approximation., Comment: 15 pages, 8 figures

The nuclear-polarization (NP) energies with the collective model commonly employed in the NP calculations for hydrogenlike heavy ions are found to have serious gauge violations when the ladder and cross diagrams only are taken into account. Using the equivalence of charge-current density with a schematic microscopic model, the NP energy shifts with the collective model are gauge invariantly evaluated for the $1s_{1/2}$ states in $^{208}_{~82}$Pb$^{81+}$ and $^{238}_{~92}$U$^{91+}$., Comment: 4 pageges, 1 figures

We calculate nuclear-polarization energy shifts for hydrogenlike $^{208}_{~82}$Pb$^{81+}$. A retarded transverse part as well as the Coulomb part is taken into account as the electromagnetic interaction between an electron and the nucleus. With a finite charge distribution for the nuclear ground state and the random-phase approximation to describe the nuclear excitations, we obtain nuclear polarization energy of the $1s_{1/2}$ state as --38.2 (--37.0) meV in the Feynman (Coulomb) gauge. For the $2s_{1/2}$, $2p_{1/2}$ and $2p_{3/2}$ states, they are --6.7 (--6.4), --0.2 (--0.2) and +0.0 (+0.0) meV, respectively. The seagull term in the two-photon exchange diagrams is shown to be quite important to obtain the gauge invariance of the nuclear polarization energies., Comment: 26 pages, 5 figures