European Journal of Cardiovascular Medicine

Computed tomography (CT) has revolutionized thoracic imaging, providing unparalleled anatomical detail for diagnosing a vast spectrum of pulmonary, mediastinal, and pleural pathologies [1]. The widespread utilization of chest CT has contributed substantially to improved detection and characterization of lung nodules, interstitial lung diseases, pulmonary embolism, and thoracic malignancies [2]. However, the ionizing radiation associated with CT examinations raises legitimate concerns regarding potential stochastic effects, particularly cancer induction, especially in patients requiring multiple follow-up studies [3].

The "As Low As Reasonably Achievable" (ALARA) principle mandates optimization of imaging protocols to minimize radiation exposure while maintaining diagnostic efficacy [4]. Traditional approaches to dose reduction, including tube current modulation and voltage optimization, have achieved meaningful reductions but are ultimately constrained by the inherent noise amplification associated with decreased photon flux [5]. Filtered back projection (FBP), the conventional reconstruction algorithm employed since CT's inception, exhibits a direct relationship between radiation dose and image noise, creating a fundamental barrier to further dose reduction [6].

Iterative reconstruction (IR) techniques represent a paradigm shift in CT image reconstruction, offering the potential to decouple the traditional dose-noise relationship [7]. Unlike FBP, which applies a single-pass mathematical filter to raw projection data, IR algorithms employ iterative cycles of forward projection, comparison with measured data, and noise modeling to progressively refine image quality [8]. Contemporary IR implementations range from hybrid approaches that blend iterative noise reduction with FBP to fully model-based iterative reconstruction (MBIR) algorithms that comprehensively model the imaging chain physics [9].

Clinical investigations have demonstrated the efficacy of IR techniques across various anatomical regions and clinical applications. Studies in abdominal CT have reported dose reductions of 40-60% while maintaining diagnostic image quality [10]. Cardiac CT angiography protocols utilizing IR have achieved submillisievert examinations without compromising coronary artery visualization [11]. However, the translation of these findings to chest CT presents unique challenges, given the inherent high-contrast nature of pulmonary parenchyma and the critical importance of detecting subtle ground-glass opacities and small nodules [12].

The emergence of lung cancer screening programs utilizing low-dose CT has accelerated interest in radiation optimization for thoracic imaging [13]. The National Lung Screening Trial demonstrated mortality reduction with low-dose CT screening, establishing dose targets that have become benchmarks for protocol optimization [14]. Nevertheless, questions persist regarding the optimal balance between dose reduction and diagnostic confidence, particularly for characterizing indeterminate pulmonary findings.

Despite accumulating evidence supporting IR-based dose reduction, systematic evaluations comparing multiple dose reduction levels with comprehensive objective and subjective quality assessments remain limited. Furthermore, the impact of aggressive dose reduction on diagnostic confidence for specific clinical tasks, such as nodule detection and ground-glass opacity characterization, requires further elucidation. This study aimed to evaluate the effectiveness of low-dose and ultra-low-dose chest CT protocols utilizing advanced iterative reconstruction techniques, comparing radiation dose parameters and image quality metrics against standard-dose FBP reconstruction.

Study Design and Patient Population

This prospective randomized comparative study was conducted at a tertiary medical center.

Inclusion criteria comprised: (1) age 18 years or older; (2) clinical indication for non-contrast or contrast-enhanced chest CT; (3) ability to maintain breath-hold for scan duration; (4) body mass index (BMI) between 18.5 and 35 kg/m². Exclusion criteria included: (1) pregnancy or suspected pregnancy; (2) BMI exceeding 35 kg/m² due to potential image quality degradation; (3) previous thoracic surgery or radiation therapy; (4) severe respiratory distress precluding adequate breath-hold; (5) inability to provide informed consent.

Sample Size Calculation

Based on preliminary data indicating a mean image noise of 12 HU with standard deviation of 3 HU for standard-dose protocols, sample size calculation determined that 58 patients per group would provide 90% power to detect a 3 HU difference in image noise at alpha level 0.05. Accounting for potential dropouts, 62 patients were enrolled per group, totaling 248 participants.

Randomization and Protocol Assignment

Eligible patients were randomized using computer-generated random number sequences to one of four imaging protocols:

• Group A (Standard-dose FBP): Conventional dose parameters with filtered back projection reconstruction
• Group B (Standard-dose IR): Conventional dose parameters with iterative reconstruction
• Group C (Low-dose IR): 50% dose reduction with iterative reconstruction
• Group D (Ultra-low-dose IR): 75% dose reduction with iterative reconstruction

CT Acquisition Parameters

All examinations were performed on a 128-slice multidetector CT scanner (SOMATOM Definition Edge, Siemens Healthineers). Standard-dose protocols employed 120 kVp with automated tube current modulation (reference mAs: 110). Low-dose protocols utilized reduced reference mAs values (55 mAs for Group C, 28 mAs for Group D) while maintaining automated modulation. All acquisitions employed 0.6 mm collimation, pitch 1.2, and rotation time 0.5 seconds. Images were reconstructed at 1.0 mm slice thickness with 0.7 mm increment.

Image Reconstruction

Group A images were reconstructed using standard FBP kernel (B40f). Groups B, C, and D utilized advanced modeled iterative reconstruction (ADMIRE, Siemens Healthineers) at strength level 3, with equivalent kernel (Br40). All images were transferred to the institutional PACS for analysis.

Radiation Dose Assessment

Volume CT dose index (CTDIvol), dose-length product (DLP), and size-specific dose estimate (SSDE) were recorded from dose reports. Effective dose was calculated by multiplying DLP by the chest-specific conversion factor (k = 0.014 mSv/mGy·cm).

Objective Image Quality Assessment

Quantitative measurements were performed by a single radiologist using standardized regions of interest (ROIs). Image noise was measured as standard deviation of attenuation values within homogeneous ROIs placed in the descending aorta. Signal-to-noise ratio (SNR) was calculated as mean attenuation divided by noise. Contrast-to-noise ratio (CNR) was determined using the difference between aortic and paraspinal muscle attenuation divided by background noise.

Subjective Image Quality Assessment

Two board-certified thoracic radiologists with 12 and 8 years of experience, blinded to acquisition parameters, independently evaluated all examinations. A 5-point Likert scale assessed overall image quality (1=non-diagnostic, 2=poor, 3=acceptable, 4=good, 5=excellent). Additional assessments included visualization of anatomical structures (pulmonary vessels, bronchi, fissures), artifact severity, and diagnostic confidence for nodule detection.

Statistical Analysis

Continuous variables were expressed as mean ± standard deviation. Categorical variables were presented as frequencies and percentages. Between-group comparisons employed one-way ANOVA with Bonferroni post-hoc correction for continuous variables and chi-square test for categorical variables. Inter-reader agreement was assessed using Cohen's kappa coefficient. Statistical significance was defined as p<0.05. Analyses were performed using SPSS version 27.0.

Patient Characteristics

A total of 248 patients completed the study, with 62 patients in each protocol group. Baseline demographic characteristics were comparable across groups, with no statistically significant differences in age, sex distribution, BMI, or clinical indications.

Table 1: Patient Demographics and Clinical Characteristics

BMI: Body Mass Index; ILD: Interstitial Lung Disease; SD: Standard Deviation

Radiation Dose Parameters

Significant dose reductions were achieved in low-dose and ultra-low-dose protocols compared to standard-dose groups. Group D demonstrated 73.9% reduction in effective dose compared to Group A, while Group C achieved 49.5% reduction.

Table 2: Radiation Dose Parameters Across Protocol Groups

CTDIvol: Volume CT Dose Index; DLP: Dose-Length Product; SSDE: Size-Specific Dose Estimate

Image Quality Assessment

Objective image quality metrics demonstrated expected increases in image noise with dose reduction, partially compensated by iterative reconstruction. Subjective quality scores remained diagnostically acceptable across all groups, with inter-reader agreement ranging from substantial to excellent (κ = 0.72-0.86).

Table 3: Objective and Subjective Image Quality Parameters

HU: Hounsfield Units; SNR: Signal-to-Noise Ratio; CNR: Contrast-to-Noise Ratio

Post-hoc analysis revealed that image noise in Group B (standard-dose IR) was significantly lower than Group A (9.6 vs. 12.8 HU; p<0.001), demonstrating the noise reduction capability of IR at equivalent dose levels. Importantly, noise levels in Group C (low-dose IR) were comparable to Group A (13.4 vs. 12.8 HU; p=0.412), indicating successful noise compensation despite 50% dose reduction.

Diagnostic confidence for nodule detection remained high in Group C (91.8%), with no significant difference compared to Group A (94.2%; p=0.284). Group D demonstrated slightly reduced but clinically acceptable confidence (86.4%), with the difference reaching statistical significance compared to Group A (p=0.038).

This prospective comparative study demonstrates that advanced iterative reconstruction techniques enable substantial radiation dose reductions in chest CT imaging while maintaining diagnostically acceptable image quality. The achievement of 50% dose reduction with equivalent image noise and preserved diagnostic confidence has significant implications for clinical practice and radiation protection.

The observed noise reduction with iterative reconstruction at standard dose levels (25% reduction compared to FBP) aligns with published literature characterizing IR algorithm performance [15]. This inherent noise suppression capability forms the foundation for dose reduction strategies, as the "saved" image quality margin can be traded for reduced radiation exposure [16]. Our finding that low-dose IR (Group C) achieved comparable noise levels to standard-dose FBP confirms the practical implementation of this principle in thoracic imaging.

The 73.9% dose reduction achieved in the ultra-low-dose protocol (Group D) approaches the sub-millisievert range, comparable to dose levels employed in lung cancer screening programs [17]. While image noise increased significantly in this group (18.4 HU), subjective quality ratings remained diagnostically acceptable (mean score 3.8/5), with 93.5% of examinations rated as acceptable or better. These findings suggest that ultra-low-dose protocols may be appropriate for specific clinical scenarios, particularly follow-up imaging of known pulmonary nodules or young patients requiring serial examinations [18].

The preservation of diagnostic confidence for nodule detection at 50% dose reduction (91.8% vs. 94.2% for standard-dose) represents a clinically meaningful finding. Pulmonary nodule characterization demands high spatial resolution and low noise for accurate size measurement and morphological assessment [19]. Our results indicate that these critical diagnostic tasks can be accomplished reliably at substantially reduced radiation exposures, supporting broader implementation of dose-optimized protocols.

Subjective image quality assessments revealed hierarchical degradation with increasing dose reduction, yet the magnitude of quality reduction was modest relative to the substantial dose savings achieved. The superior quality ratings for standard-dose IR compared to standard-dose FBP (4.6 vs. 4.4) highlight the image quality enhancement potential of IR algorithms, which may benefit challenging cases requiring optimal visualization [20].

Comparison with previous investigations demonstrates consistent findings across different scanner platforms and IR implementations. Singh et al. reported successful 50% dose reduction in abdominal CT using ASIR-V reconstruction [21]. Gordic et al. achieved similar results in chest CT with ADMIRE, documenting maintained diagnostic accuracy for pulmonary nodule detection [22]. Our study extends these observations by systematically comparing multiple dose reduction levels within a single investigation, providing practical guidance for protocol optimization.

The clinical implementation of low-dose protocols requires consideration of patient-specific factors. Larger body habitus may necessitate modified approaches to maintain diagnostic quality, while pediatric and young adult patients warrant particularly aggressive dose optimization given their increased radiosensitivity and potential for cumulative lifetime exposure [23]. Additionally, specific clinical indications may influence acceptable quality thresholds, with screening examinations tolerating greater noise than diagnostic evaluations of complex pathology [24].

Study limitations include the single-center design and utilization of a single vendor's IR algorithm, potentially limiting generalizability across different scanner platforms. The BMI restriction excluded morbidly obese patients, in whom image quality challenges may be more pronounced. Furthermore, the relatively short-term nature of this study precluded assessment of long-term diagnostic outcomes or cancer detection rates.

Future investigations should explore the integration of artificial intelligence-based reconstruction algorithms, which have demonstrated potential for further dose reduction beyond current IR techniques [25]. Additionally, task-specific optimization approaches tailoring protocols to clinical indications may enable more refined dose-quality trade-offs.

This prospective study establishes that low-dose chest CT protocols utilizing advanced iterative reconstruction techniques achieve radiation dose reductions of 50-75% while maintaining diagnostically acceptable image quality. At 50% dose reduction, image noise levels and diagnostic confidence for pulmonary nodule detection remain equivalent to standard-dose filtered back projection reconstruction. Ultra-low-dose protocols approaching sub-millisievert exposures produce acceptable image quality for appropriate clinical indications. Implementation of iterative reconstruction-based low-dose protocols should be prioritized in clinical practice to minimize patient radiation exposure while preserving diagnostic accuracy. These findings support the broader adoption of dose optimization strategies as standard practice in thoracic CT imaging.

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