In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists' assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists' perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists' assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at https://zenodo.org/records/7833096.

We propose a sphere phantom approach to measure spatially varying directional modulation transfer functions (MTFs) for tomosynthesis imaging systems. Since the reconstructed tomosynthesis images contain significant artifacts, traditional background detrending techniques may not be effective to estimate the background trends accurately, which is essential to acquire sphere only data. A background detrending technique optimized for local volumes with different cone angles is presented. To measure directional MTFs, we calculate plane integrals of ideal sphere phantom and sphere only data. To minimize the effects of the high level of noise in tomosynthesis images, Richardson-Lucy deconvolution with Tikhonov-Miller is used to estimate directional plane spread function (PlSF). Then, directional MTFs are calculated by taking the modulus of the Fourier transform of the directional PlSFs. The measured directional MTFs are compared with the ideal directional MTFs calculated from a simulated point object. Our results show that the proposed method reliably measures directional MTFs along any desired directions, especially near low frequency regions.

We propose a new method to measure directional modulation transfer function (MTF) using sphere phantoms in a cone beam computed tomography (CBCT) system. To measure spatially varying 3-D MTFs, we model FDK reconstruction in local regions and calculate the plane integrals of an ideal sphere phantom and reconstructed sphere phantoms. Then, we modify the Richardson-Lucy (RL) deconvolution method to relax the non-negativity constraint in RL deconvolution and apply it to estimate the directional plane spread functions (PlSFs). Directional MTFs are calculated by taking the modulus of the Fourier transform of the estimated directional PlSFs. To validate the proposed method, we simulate ideal 3-D MTFs and compare them with directional MTFs measured by simulation and experimental data along three major axes. For quantitative evaluation, we compare full-width at half-maximum (FWHM) and full-width at tenth-maximum (FWTM) of measured and ideal directional MTFs. The measured directional MTFs from simulation and experimental data show excellent agreement with the ideal directional MTFs, demonstrating the effectiveness of the proposed method to estimate directional MTFs in a CBCT system.

Computed Tomography (CT) scanners evolved from simple parallel-beam geometry into more complex fan-beam geometry. The rebinning mechanism to convert fan-beam projections to parallel-beam projections is one of the methods simplifying the reconstruction of the CT image. Various interpolation methods result in different numerical presentations and noisy textures in the reconstructed CT images. This paper evaluates three interpolation methods, linear, frequency domain and higher order in rebinning fan-beam data into parallel-beam data in the framework of the FBP algorithm in parallel-beam CTs. The high-contrast spatial resolution, modulation transfer function (MTF), and noise are used to quantify the image quality. The noise is expressed as the noise power spectrum (NPS) and noise variance of the reconstructed image containing only Poisson distributed noise. Our results show that the higher order interpolation, cubic-spline, is the most helpful to improve the image quality in rebinning. We further demonstrate that the rebinning data gives more satisfactory results for assessment of noise variance in comparison with the fan-beam data.