The purpose of this study was to assess the performance of iterative reconstruction (IR) and deep-learning image reconstruction (DLR) algorithms developed by four CT vendors in terms of image quality. Acquisitions were performed on an image quality phantom at three dose levels (1.8, 6 and 11 mGy) using four CT systems (further referred to as G-CT, P-CT, U-CT, and C-CT). For each CT, raw data were reconstructed using the commonly used soft tissue kernel and level for IR and DLR algorithms. Noise power spectrum and task-based transfer function were computed to assess noise magnitude, noise texture (fav) and spatial resolution, respectively. Detectability indexes (d') were computed to model the detection of two abdominal lesions. Compared to IR, noise magnitude reduction with DLR was similar for all dose levels for G-CT (-21.1 ± 1.5 [standard deviation (SD)] %) and P-CT (-48.4 ± 0.1 [SD] %) but more pronounced at 1.8 mGy and decreased as the dose level increased for U-CT and C-CT. Noise texture was greater with DLR than IR at all dose levels for all CT systems, except for U-CT, which gave similar fav values. For both inserts, spatial resolution was better with DLR than with IR for all CT systems, except for the low-contrast insert with C-CT at 1.8 and 6 mGy and P-CT at 1.8 mGy. For both simulated lesions and all dose levels, d' values were greater with DLR than with IR by 77.5 ± 8.7 (SD) % for C-CT, 33.7 ± 5.6 (SD) % for G-CT, 112.7 ± 4.7 (SD) % for P-CT and from 158.3 % to 546.6 % on average for U-CT. Compared to IR, DLR algorithms reduce the image noise and improve detectability whilst providing similar or better noise texture and spatial resolution.

To introduce and evaluate the feasibility of a novel RF-phase modulated gradient echo (GRE) method for quantitative diffusion MRI, aimed at mitigating geometric distortion and enabling high-resolution 3D quantitative diffusion/T2 mapping as a complementary alternative to conventional DWI. The proposed phase-based diffusion (PBD) method employs RF phase modulation to encode both diffusion and T2 information into the GRE signal phase. A closed-form analytical model enables joint apparent diffusion coefficient (ADC) and T2 mapping via iterative reconstruction. The method's feasibility was evaluated via Bloch equation simulations, phantom experiments, and preliminary in vivo imaging studies. Monte Carlo simulations revealed that PBD provides more accurate median ADC estimates at low signal-to-noise ratios (SNRs) compared to conventional single-shot echo-planar imaging (SS-EPI), although PBD exhibited greater variability. Phantom studies demonstrated good agreement for PBD-derived ADC values (e.g., R2 = 0.99) with reference methods and strong correlation for PBD-derived T2 values (e.g., R2 = 0.89), though the latter showed some systematic bias in phantoms. In vivo results from patients with benign or malignant prostate disease demonstrated the feasibility of the PBD method to provide high-resolution ADC and T2 maps with minimal geometric distortions relative to conventional SS-EPI. PBD provides ADC and T2 maps with improved geometric fidelity in phantoms and in vivo, and offers robust median ADC estimates from noisy data based on simulations. This combination of spatial precision and noise characteristics makes PBD promising for applications such as high-resolution DWI for prostate MRI.

Incremental value of deep learning denoising in low-dose coronary CT angiography in predominantly obese patients.

Part I of this study introduced a new photon-counting detector (PCD-)CT protocol for coronary artery calcium (CAC) scoring using 120 kVp, 75% dose, thin slices, quantum iterative reconstructions (IR) 2, leading to a significant reduction of score variability. The second part evaluated the potential of virtual monoenergetic image (VMI) reconstruction in further reducing score variability with PCD-CT. CAC scoring was performed on PCD-CT with a chest phantom containing nine calcifications using the optimized PCD-CT protocol from Part I. Images were reconstructed at different VMI levels (50-80 keV, 5 keV-steps), with adjusted CAC thresholds to maintain density equivalence to 70 keV. CAC scores, image noise, and calcification detectability were investigated. Results were compared to standard PCD-CT, EID-CT and previously proposed EID-CT protocols. Using 65 keV reconstructions, score variability decreased by 9% compared to the optimized PCD-CT protocol from Part I, by 43% vs. the standard PCD-CT, by 78% vs. the standard EID-CT, and by 69% vs. the proposed EID-CT protocol. Image noise remained within targets, eliminating the risk of false-positives. Calcification detectability was comparable to the optimized PCD-CT protocol (7.1 ± 0.6 vs. 7.1 ± 0.8). Calcium volume and mass scores from the keV-optimized PCD protocol were closer to the physical reference compared to scores from the standard PCD protocol. Score variability and calcification detectability in PCD-CT-based CAC scoring can be further improved when augmenting an optimized PCD-CT protocol at 65 keV. In addition to reducing the radiation dose, this protocol may enable more consistent CAC quantification and seems to perform even better than the proposed, multivendor EID-CT protocol. Question Coronary calcium scoring lacks reproducibility. Adding virtual monoenergetic imaging with adapted thresholds to a pre-optimized photon-counting CT protocol may further improve score variability. Findings A 120 kVp, 75%-dose, thin-slice photon-counting CT protocol at 65 keV achieved the lowest coronary calcium score variability compared to previous protocols. Clinical relevance Minimizing variability in coronary calcium scoring improves the technical reliability of serial measurements. The additional use of virtual monoenergetic imaging further reduces variability in photon-counting CT, supporting a precise and consistent cardiovascular risk assessment.

The recent development of 3D Gaussian splatting (3DGS) has led to great interest in 4D dynamic spatial reconstruction. Existing approaches mainly rely on full-length multi-view videos, while there has been limited exploration of online reconstruction methods that enable on-the-fly training and per-timestep streaming. Current 3DGS-based streaming methods treat the Gaussian primitives uniformly and constantly renew the densified Gaussians. Thus, they overlook the difference between dynamic and static features and neglect the temporal continuity of the scene. To address these limitations, we propose a novel pipeline for iterative streamable 4D dynamic spatial reconstruction. It comprises three stages: a selective inheritance stage that retains priors from previous timesteps to preserve the temporal continuity, a dynamics-aware shift stage that distinguishes dynamic and static primitives and employs distinct strategies to optimize their movements, and an error-guided densification stage that efficiently identifies Gaussians requiring densification to accommodate emerging objects. Our method achieves state-of-the-art performance in online 4D reconstruction, demonstrating compact storage, the fastest on-the-fly training speed, and superior representation quality.

Coronary artery calcium (CAC) scoring is a well-established method for cardiovascular risk assessment but has limited reproducibility. For energy-integrating detector (EID)-CT, a new, multivendor validated protocol has been proposed. This study aimed to investigate the variability of photon-counting detector (PCD)-CT-based CAC scoring and propose a new protocol with decreased variability. A chest phantom containing nine calcifications was scanned on a PCD-CT using various settings: tube voltages (90 kVp, 120 kVp), tube currents (100% to 25% dose), slice thickness (3 mm, 1 mm), quantum iterative reconstruction (IR, 1-4). To evaluate interscan variability, phantoms were scanned five times per protocol with slight translational (5 mm) and rotational (2°) movements. The standard PCD-CT protocol used 120 kVp, 100% dose, 3 mm slices. CAC scores, image noise, and calcification detectability were assessed. Results were compared to the standard PCD-CT, and standard and proposed EID-CT protocols. Compared to the standard PCD-CT protocol, score variability decreased by 37% using a thin-sliced protocol at 120 kVp, 25% dose reduction and IR2. Compared to the proposed EID-CT protocol, variability was 66% lower. The optimized PCD-CT protocol met noise targets, eliminating the risk of false-positives. While 6.0 ± 0.0 and 7.0 ± 0.4 calcifications were detected using the PCD-CT standard and the proposed EID-CT protocol, respectively, 7.1 ± 0.7 calcifications were detected with the optimized PCD-CT protocol. Volume and mass scores were closer to physical reference. A thin-slice, 25%-dose-reduced PCD-CT protocol at 120 kVp improves CAC score reproducibility and outperforms the proposed EID-CT protocol, possibly offering more reproducible CAC quantification at lower radiation doses. Question Coronary artery calcium scoring is used for cardiovascular risk stratification. However, the current standard method lacks score reproducibility. Findings A thin-slice, 25%-dose-reduced photon-counting detector CT protocol at 120 kVp significantly reduces score variability compared to previous protocols, including the proposed energy-integrating detector CT protocol. Clinical relevance Improved reproducibility of coronary artery calcium scoring may enable more consistent cardiovascular risk prediction and provide a robust technical basis for further in vivo studies.

To evaluate the environmental impact associated with CT scanners equipped with deep-learning-based image reconstruction (DLIR) compared with scanners equipped with hybrid-iterative reconstruction (HIR), focusing on electricity consumption, carbon dioxide equivalent (CO₂e) emissions, and iodinated contrast media (ICM) utilization in a high-volume tertiary referral center. In this retrospective single-center study, environmental data were collected over an 18-month period from four CT scanners: two using HIR (Group 1) and two using DLIR (Group 2), including body CT examinations. DLIR-based protocols were implemented with reduced tube voltage (80-100 kV vs 120 kV) and optimized ICM doses. Electricity consumption, CO₂e emissions, and ICM utilization were quantified and compared between groups. Environmental outcomes were analyzed at the scanner level and normalized per examination. A total of 42,300 examinations were analyzed (23,096 in Group 1; 19,204 in Group 2). Electricity consumption was 123,000 kWh for Group 1 and 66,927 kWh for Group 2, corresponding to 30.75 and 16.73 tons of CO₂e emissions, respectively. At the scanner level, this represented a reduction of 28,037 kWh and 7.01 tons of CO₂e per scanner (4.67 tons/year). DLIR-based protocols were associated with an ICM saving of 434 L over 18 months, corresponding to 4.47 tons of avoided CO₂e emissions and 60,730 L of water preserved. Combined CO₂e emissions from electricity and ICM were 49.62 tons in Group 1 and 29.10 tons in Group 2. DLIR-based optimized protocols were associated with improved environmental metrics, supporting their potential contribution to more sustainable radiology practices in high-volume settings. Deep learning-based image reconstruction enables routine body CT protocols with lower tube voltage and reduced ICM dose, supporting a clinically feasible transition toward more sustainable CT practice in high-volume imaging workflows. DLIR was associated with the implementation of lower tube voltage and reduced ICM dose, supporting more sustainable CT imaging based on protocol adaptations. In a high-volume tertiary referral center, deep learning-based image reconstruction was associated with a substantial reduction in electricity consumption and overall CO₂-equivalent emissions compared with hybrid iterative reconstruction. Optimization of contrast media dosing with deep learning-based image reconstruction contributed meaningfully to environmental benefits.

A plug-and-play method for guided multi-contrast MRI reconstruction based on content/style modeling.

Contrast injection protocols used in oncological CT and opportunities for practice optimisation.

Atomic electron tomography (AET) is a powerful technique for determining the three-dimensional atomic structure of matter in real space. However, conventional AET requires numerous projections across a wide angular range. The high dose and prolonged acquisition severely limit its application. Here, we propose an interpretable and universal algorithm for low-dose, fast and tilt-constrained AET: null-space iterative reconstruction (NSIRE), which uses an unsupervised diffusion model to iteratively compute null-space solutions of projection equations, thereby generating tomograms consistent with both projection constraints and atomic potential prior. NSIRE can resolve a wide range of complex materials under 6-12° sparse projection or within ±29° small tilt-range without retraining. Using the NSIRE, we determine the three-dimensional atomic structures of a 4-nm Pt nanoparticle with grain boundaries and a 3-nm PtCo nanoalloy (the smallest one resolved so far), achieving a root-mean-square displacement of <20 pm and high projection consistency, overcoming the scale, dose and time limitations of AET.

The objective was to evaluate the image quality and hepatic lesion conspicuity in a dual-low-dose (radiation and contrast volume) upper abdominal dual-energy CT (DECT) protocol utilizing deep learning image reconstruction (DLIR). After propensity score matching, 92 matched pairs underwent either chest DECT with upper abdominal coverage (LD group) or abdominal DECT (SD group). LD 50-keV images were reconstructed using adaptive statistical iterative reconstruction (LD-50-AR), DLIR-middle (LD-50-DM), and DLIR-high (LD-50-DH) while additional 60-keV images were reconstructed using DLIR-high (LD-60-DH). SD 60-keV images were reconstructed with AR (SD-60-AR). Objective metrics including noise, signal-to-noise (SNR), contrast-to-noise ratio (CNR), and beam-hardening artifact (BHA); subjective scores on overall image quality, diagnostic confidence, anatomic clarity, artifacts, and image noise were evaluated. The noise dose efficiency index (lower values indicate higher dose image quality transfer efficiency) was derived to assess the trade-off between image quality and radiation dose. For low-attenuation liver lesions, lesion-to-liver CNR (LLR) and subjective lesion conspicuity scores were assessed. Compared with SD group, the CTDIvol and contrast volume in the LD group were reduced by 33.27% and 25%. For the LD group, LD-50-DH images showed the highest image quality with 38.1-46.4% lower noise than LD-50-AR, comparable to the SD-60-AR; CNR increased 85.3-105.6%, BHA comparable to SD-60-AR and the highest subjective scores. The low-attenuation hepatic lesions showed good conspicuity. Dual-low-dose abdominal DECT utilizing 50keV and DLIR-high achieved substantial radiation and contrast volume reductions while maintaining comparable noise, lesion conspicuity, and superior image quality relative to SD protocols with 60keV and AR.

To investigate whether a higher image noise reduction improves the diagnostic accuracy of Computer-Aided Detection (CAD) software to detect pulmonary embolism (PE) on CT pulmonary angiography (CTPA). We retrospectively included 238 consecutive CTPAs of patients with suspected PE obtained between 01/09/2014 and 06/03/2014. Hybrid iterative reconstruction (HIR) was performed using either iDose4 level 3 or level 4 with 23% vs. 29% noise reduction, respectively. CAD software marked all potential PE. Two radiologists evaluated CAD markers and classified them as either true positive (TP) or false positive (FP). The reference standard was determined by a consensus reading of two experienced radiologists. In total, 110 scans made use of iDose4 level 3 HIR and 128 scans made use of iDose4 level 4 HIR. PE was present in 34 patients in the iDose4 level 3 group (30.9%) and in 39 patients in the iDose4 level 4 group (30.5%). Sensitivity of CAD software was not significantly different between noise reduction groups level 3 and level 4 (100% and 92.3% respectively, p = 0.24). Specificity was significantly higher in the level 4 group compared to the level 3 group (48.3% vs. 11.8%, p < 0.001). CNR was significantly higher in CT images with level 4 noise reduction (12.0 vs. 9.8, p < 0.001). CAD software produced significantly fewer FP PE markers in images with higher CNR, reconstructed using a higher noise-reduction setting. The noise reduction level 4 images had significantly higher CNR values, indicating that image quality has a substantial impact on the performance of CAD software.

X-ray interferometry provides valuable information in terms of attenuation, small-angle scatter, and differential-phase contrast. This multi-modal contrast can aid in many clinical applications, such as lung diseases and breast cancer. However, standard interferometry has an analyzer grating that can increase the dose requirement to maintain the same image quality as a standard X-ray. We propose the use of super-resolution methods for X-ray grating interferometry without an analyzer, with detectors that fail to meet the Nyquist sampling rate needed for traditional image recovery algorithms. Detector phase steps are used to nominally recover the fringe sampling, followed by iterative recovery of the visibility and object parameters. This method enables Talbot-Lau interferometry without the X-ray absorbing analyzer. Removing the absorbing analyzer grating may improve dose efficiency and reduce system complexity. We demonstrate the use of super-resolution methods to iteratively reconstruct attenuation, differential-phase, and dark-field images using simulations of two-dimensional lung phantoms with lesions. A direct CdTe detector was simulated with pixel sizes of 55, 75, and 150 micron. The simulation results show that the proposed super-resolution iterative reconstruction method for Talbot-Lau Interferometry remains stable under the simulated noise conditions and can recover image parameters in cases where traditional algorithms cannot be used.

To investigate whether a CT pulmonary angiography (CTPA) protocol with reduced radiation dose and deep-learning based image reconstruction (DLIR) is non-inferior in image quality to standard-dose CTPA using iterative reconstruction. A phantom study was conducted to estimate the additional radiation dose reduction enabled by high-strength deep learning-based image reconstruction (DLIR-H) compared to adaptive statistical iterative reconstruction (ASiR-V 90%). Medium and large phantoms were used to simulate different body sizes. Subsequently, we reduced radiation dose of our clinical CTPA protocol and transitioned to DLIR for image reconstruction. We retrospectively analyzed 307 consecutive patients who were examined before (n = 152) and after (n = 155) this clinically driven change in the CTPA protocol. Objective image quality was quantified and subjective image quality was rated by two radiologists. The non-inferiority margin was pre-specified as a < 5% difference in image quality parameters. In the phantom, DLIR-H allowed radiation dose to be reduced by up to 71% with equivalent or higher signal-to-noise-ratio (SNR) compared to standard-dose examinations reconstructed with ASiR-V 90%. In the patient cohort, radiation dose was reduced by 41% (median DLP 116 vs. 68 mGy*cm; effective dose 1.69 vs. 0.99 mSv, p < 0.001). In the modified protocol, median SNR was superior for the central pulmonary artery (13.6 vs. 22.3) and non-inferior for the segmental pulmonary arteries (16.4 vs. 16.8). Subjective image quality averaged over both readers was superior with the modified protocol. Compared to state-of-the-art iterative reconstruction, DLIR allows radiation dose for CTPA to be reduced by an additional 41% with non-inferior image quality. Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-026-56545-y.

While spiral sampling offers SNR advantages for diffusion MRI, its acceleration with simultaneous multislice remains relatively unexplored. This study introduces Laterally Oscillating Trajectory for Undersampling Slices (LOTUS), which is a 3D spiral-like k-space trajectory that aims to minimize g-factor via controlled incoherent aliasing. To aid in validation, we introduce a constrained reconstruction approach that enables robust pseudo-multiple replica g-factor estimation for iterative non-Cartesian reconstructions. Simulated data sampling of a numerical phantom was performed using LOTUS and several acquisition schemes proposed by others to quantitatively compare the resulting image quality when compared to a known ground truth. Diffusion-weighted in vivo brain data from two subjects was acquired with two in-plane acceleration factors (2× and 4×) and two slice acceleration factors (2× and 4×). Estimated g-factor maps and fractional anisotropy maps were calculated to quantitatively and qualitatively compare trajectory performance. For both simulation and in vivo, reconstructions both with and without compressed sensing were utilized. Simulations generally showed decreased g-factor (20%-31%, depending on trajectory, at highest undersampling rate) and improved reconstruction accuracy (mean-square error, structural similarity index, and entropy metrics) for LOTUS compared to the other trajectories. The in vivo acquisitions demonstrated g-factor benefits and qualitative image quality improvements that mirrored the simulation results. For both simulation and in vivo, improvements for LOTUS increased for higher numbers of simultaneous slices. By enabling higher rates of slice acceleration, LOTUS shows promise to decrease scan time, which is especially beneficial for diffusion MRI.

The aim of this study is to evaluate the performance of a novel deep learning image reconstruction (DLIR) algorithm in noise reduction, contrast-to-noise ratio (CNR), and low iodine concentration detection for ultralow-dose computed tomography (CT) imaging. A nine-hole phantom with iodine concentrations (0-40 mg/mL) was scanned at various tube voltages (60-120 kVp). Images were reconstructed using filtered back projection (FBP), iterative reconstruction (IR), and DLIR at different weight levels (10%-90%). Objective metrics (noise, CNR, CT value accuracy via Bland-Altman analysis) and subjective image quality were assessed. At all tube voltages (60-120 kVp), DLIR with medium-to-high weight levels (50%-90%) reduced background noise and increased CNR compared with FBP and IR (p < 0.001). The CNR at a low iodine concentration (1.25 mg/mL) was enhanced, and the DLIR algorithm (weight levels 30%-90%) was able to continuously detect an iodine concentration of 1.25 mg/mL (CNR ≥ 3) at all tube voltages. Under fixed ultralow-dose conditions, DLIR preserved image quality and low-contrast detectability. DLIR (weight levels 90%) reduced background noise by 84.7% compared with FBP and improved CNR (p < 0.001). Bland-Altman analysis confirmed excellent quantitative accuracy for DLIR. The exploratory subjective evaluation was consistent with objective metrics. The DLIR algorithm can enhance image quality in low-dose CT imaging and improve the ability to detect low concentrations of iodine. These findings demonstrate that DLIR maintains image quality and CNR at low iodine concentrations in phantom studies. Clinical implications require further validation. This phantom study shows that the deep learning reconstruction algorithm can still maintain the diagnostic image quality and low-contrast detectability even under ultralow-dose CT (94% dose reduction). These findings support further clinical research to optimize the dosage regimens and potentially reduce the use of iodine contrast agents. Under ultralow-dose conditions (60 kV), DLIR preserved image quality metrics and detectability thresholds in a phantom under ultralow-dose conditions. It significantly suppressed image noise and improved the CNR. The algorithm reliably detected low iodine concentrations (1.25 mg/mL) at all dose levels.

Impact of projection quantity on SPECT/CT image quality assessed with a NEMA body phantom.

To create modular solutions for interactive real-time MRI using reconstruction algorithms implemented in BART. A new protocol for streaming of multidimensional arrays is presented and integrated into BART. The new functionality is demonstrated using examples for cardiac interactive real-time MRI based on radial FLASH, where iterative reconstruction is combined with advanced features such as dynamic coil compression and gradient-delay correction. We analyze the latency of the reconstruction and measure end-to-end latency of the full imaging process. Reconstruction pipelines with iterative reconstruction and advanced functionality were built in a modular way using scripting. Latency measurements demonstrate latency sufficient for interactive real-time MRI, on the order of 30 ms for BART processing and network transfer time, or 200 ms for end-to-end latency including acquisition, vendor processing, and display. With the new streaming capabilities, real-time reconstruction pipelines can be assembled using BART in a flexible way, enabling rapid prototyping of advanced applications such as interactive real-time MRI.

Accurate assessment of regional wall motion abnormalities (RWMA) is essential for diagnosing coronary artery disease. The quality of image reconstruction in gated SPECT myocardial perfusion imaging (MPI) can significantly influence diagnostic accuracy. Methods: We retrospectively analyzed 100 patients who underwent gated SPECT MPI, including 25 normal and 75 abnormal cases stratified into mild, moderate, and severe defects groups. Images were reconstructed using filtered backprojection (FBP) and iterative reconstruction (IR) with varying iteration-subset settings. RWMA was visually assessed using a 20-segment model. Statistical agreement between methods was analyzed. Results: No significant differences were found between FBP and IR in normal and mild MPI groups. However, in moderate and severe cases, IR with 4 iterations × 6 subsets and 4 iterations × 8 subsets reclassified multiple patients into more severe RWMA categories, demonstrating higher diagnostic sensitivity compared with FBP (P < 0.0001). Conclusion: Optimized IR parameters (particularly 4 iterations × 6 subsets and 4 iterations × 8 subsets) improve the detection of RWMA in moderate to severe coronary artery disease compared with FBP. Incorporating IR into routine MPI protocols may enhance clinical decision-making and patient outcomes.

To compare lung nodule volumetric accuracy and precision between photon-counting detector (PCD) computed tomography (CT) and energy-integrating detector (EID) CT using low-dose lung cancer screening protocols, and to optimize reconstruction parameters for lung nodule volumetry. An anthropomorphic chest phantom with 12 artificial lung nodules of varying size, shape, and radiodensity was scanned using EID-CT and PCD-CT with reference and optimized low-dose lung cancer screening protocols. PCD-CT reconstruction parameters (slice thickness, matrix size, kernel, iterative reconstruction, and virtual monoenergetic imaging energy) were varied. Each protocol was scanned 3 times with nodule repositioning. Nodule volumes were independently measured semiautomatically by 2 observers. Interobserver agreement and test-retest reliability were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman plots. Volumetric accuracy and precision were calculated relative to ground-truth volumes. Volumetric accuracy was compared between PCD-CT and EID-CT using one-way analysis of variance, and across PCD-CT reconstructions using univariable linear regression. Volumetric precision was assessed based on the SD of mean volume differences. Noise was compared across scanners and reconstructions using one-way analysis of variance. A total of 1224 nodule measurements demonstrated excellent volumetric interobserver agreement (ICC: 0.99) and test-retest reliability (ICCs of 0.96 for both observers). Volumetric accuracy improved from -16.6% and -15.2% with the reference and optimized EID-CT protocols to -12.5% and -10.2% with the reference and optimized PCD-CT protocols (P < 0.05). Volumetric precision remained comparable between reference and optimized EID-CT (6.3 and 8.2mm3) and PCD-CT (6.4 and 6.2mm3) protocols. On PCD-CT, ultra-thin slices (0.2mm) and an ultra-sharp kernel (Qr76) worsened volumetric accuracy by 2.9% and 2.8%, respectively (P < 0.05). Image noise was lower on PCD-CT than on EID-CT (P < 0.05) and varied significantly across reconstruction settings on PCD-CT. PCD-CT improved lung nodule volumetric accuracy and reduced volume underestimation by up to 6% compared with EID-CT using low-dose screening protocols, while maintaining similar volumetric precision. Ultra-thin slices and an ultra-sharp kernel worsened volumetric accuracy. By reducing volume underestimation, PCD-CT may shift a larger proportion of nodules to higher baseline risk categories, potentially increasing the number of screening participants requiring clinical referral or short-term follow-up CT.