This review was carried out as per the “Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA)” guidelines. ²³

A comprehensive literature search was performed using databases such as “PubMed, Scopus, Web of Science, Cochrane Library, and Embase” to find the relevant original studies ( Table 1). The MeSH terms such as “Deep Learning Image Reconstruction” “Radiation dose” “Image quality”, “Head and Chest Computed Tomography” were used ( Table 2). The search was limited to the English language including both adult and paediatric populations of Head and Chest CT examinations.

Articles were screened considering the Participant’s Intervention Comparison and Outcome (PICO) methodology. Case studies, case reports, conference abstracts, letters, editorial reviews, meta-analyses, or surveys were not included. The title and abstract of all the articles were independently and blindly screened by the two researchers. The articles that described a comparison of DLIR algorithms with the IR technique or FBP were included in the final review. The exclusion criteria were phantom studies, physics-based performance of DLIR, other language than English, articles with no comparison of DLIR with FBP, HIR/MBIR, and articles with no Hounsfield Unit (HU), Contrast to Noise Ratio (CNR), Signal to Noise Ratio (SNR).

Data from each article was assessed independently by two researchers and any differences were solved by the third researcher.

To evaluate the quality of all the included articles, the custom-made Quality Assessment (QA) scale was used. ²⁴ The list of all the questions for the quality assessment (underlying data). A score of 1 was given if the answer to the question was “yes” and each study was assigned a score ranging from 0 to 18. Based on the total scores obtained by each study, the studies were classified into three quality levels: Low- quality studies (score of 6 or lower), Moderate-quality studies (score between 7 and 11), High-quality studies (score of 12 or more).

The search in PubMed, Scopus, Web of Science, Cochrane Library and Embase resulted in 196 studies. 101 duplicates were removed. The title and abstract of 95 studies were assessed and 76 studies were excluded as they did not meet the inclusion criteria. A total of 19 reports were sought for retrieval. A total of the full text of 19 articles were assessed for eligibility. Among them, 4 articles were excluded (3 studies were excluded due to no comparison of DLIR with FBP and HIR/MBIR, and 1 article were excluded due to lack of HU, CNR, and SNR). Finally, 15 articles were included in the systematic review.

CT imaging has increased recently with the advancement in CT technology. The studies included in the review covered different countries such as China (n = 8), Japan (n = 1), France (n = 1), Korea (n = 3), Netherland (n =1), Sweden (n = 1). The RD data and IQ parameters were collected from different CT vendors such as General Electric (GE) Health care (128, 256, 512-slice, and dual-energy CT), Siemens Healthineers (256-slice), Canon Medical system (320 and 640-slice). A total sample size of 1292 was collected from the included studies. 4 studies used prospective data collection, and 11 studies used retrospective data collection. The characteristics of the study and the outcomes of each study are summarized in Table 3.

The results of the quality assessment are summarized in Table 4. All studies compared DLR to hybrid iterative reconstruction techniques. 5 studies compared DLIR with IR and FBP algorithms. A total of 14 studies were rated as high and 1 study as moderate quality.

CT head examination: We summarized the percentage IN and RD reduction for various CT examinations in Table 5. A total of five studies in CT brain that used DLIR showed a reduction in IN (18-52%) compared with IR and FBP. ^{25 – 29} In the brain, a study compared SD with IR (CTDIvol-70.8 mGy & EF-2.8±0.2 mSv) and LD with DLIR (CTDIvol-53.0 mGy & ED-2.1±0.1 mSv) protocol in adult CT brain and noticed a 25% reduction in RD. ²⁷ Another study was done with LD DLIR (CTDIvol-18.18 mGy, DLP- 269.43 mGy.cm) protocol of CT brain. ²⁹

CT chest examination: A total of nine studies from chest CT that used DLIR showed a reduction in IN (9-50%) compared with IR and FBP. ^{30 – 39} Four studies compared LD with DLIR and SD with IR for chest CT and observed a reduction (62-97%) in RD. ^{31 , 37 – 39} Another 4 studies done with LD chest CT with DLIR [CTDI vol (1.07-2.38 mGy.cm), DLP (79.69-08.7 mGy.cm), SSDE (0.69-1.07 mGy), ED (0.98-1.03 mSv)]. ^{30 , 32 , 34 , 35} One study done with Quarter low dose (QLD) CT Chest using DLIR showed 75% reduction in radiation dose compared to IR. ³³

Our review noted that for CT Brain examination, DLIR (Medium and High) showed reduced IN (18-52%), improved IQ (GM-WM differentiation) with better detection of cerebral lesions, and reduced RD (25%). In the Pediatric CT brain, a study by Sun et al. noted that higher strength DLIR reduced image noise and noted better detection of cerebral lesions in 0.625 mm compared to 5 mm slice thickness. The thinner sections of DLIR-H were able to identify micro-hemorrhages of less than 3 mm. ²⁹ Nagayama et al. demonstrated a 25% reduction in RD with LD CT-DLIR (120 kVp, 280 mA) compared to SD -IR (120 kVp, 350 mA) and also observed that DLIR had the highest sensitivity in lesion detection (2.9±0.2) compared to MBIR (1.9±0.5) and HIR (1.2±0.4) in adult CT brain. ²⁷ Studies by Oostveen et al. and Nagayama et al. showed reduced reconstruction times 44 sec; 24±1 sec compared to MBIR (176 s & 319±17 secs) for Non-contrast CT brain. ^{27 – 28} Studies by Alagic et al. and Sun et al. reported CTDIvol and DLP of 46.96±0.49 mGy; 847.84±2.25 mGy.cm and 18.18±2.82 mGy; 269.3±57.95 mGy.cm in adult and pediatric CT Head respectively. ^{25 , 29}

Our review noted that LDCT of the chest with DLIR showed higher image contrast and lower IN (9-56%) and reduced RD (62-97%) compared with FBP and IR. Zhao et al. compared LDCT with DLIR (120kvp, 30 mAs) and HRCT with HIR (120kVp, Automatic tube current modulation) for the patients with interstitial lung disease (ILD) and noted that LDCT DLIR showed better visualization of honeycombing and assessment of bronchiectasis. ³⁹ Kim et al. noted that DLIR-H yielded higher scores in determining the prominence of the lungs main structures of the lungs. ³⁴ Jiang et al. noted that ULD-CT with DLIR under or overestimated the long diameter and sub-solid nodules compared with CECT Thorax. DLIR-H overestimated the solid and calcified nodules while underestimating the long diameter and amount of sub-nodules. ³¹ Wang et al. noted LDCT with DLIR provides higher scores for assessing pulmonary lesions except for sub-solid nodules or ground glass opacity nodules (GGN) compared to SD with HIR, whereas GGN greater than 4 mm can be picked up on LDCT DLIR images. ³⁸ Tian et al. reported that DLIR-H appeared to be slightly smoothed and DLIR M provides higher structures on visualization of smoother structures. ³⁶ Ferri et al. reported DLIR reconstruction series provided the smallest volume of emphysema compared with Adaptive statistical iterative reconstruction-V (ASIR-V) and FBP and also observed the increase in strength of DLIR led to a decrease in the size of emphysema. ³⁰

The study has a few limitations. Firstly, we did not include phantom studies. Secondly, we did not perform meta-analysis due to heterogeneity in terms of scanners and protocols used for head and chest examinations. The adoption of DLIR algorithms holds promise for improving IQ, reducing RD, and mitigating IN in Head and Chest CT examinations compared to traditional IR and FBP techniques. Healthcare providers may consider incorporating DLIR into their imaging protocols to enhance patient care by reducing radiation risks while maintaining diagnostic accuracy. Furthermore, future research efforts should focus on optimizing DLIR algorithms, investigating their long-term effects on patient outcomes, and evaluating cost-effectiveness compared to conventional reconstruction methods. Additional studies exploring the application of DLIR in other anatomical regions and patient populations could further expand its utility and impact on healthcare delivery.

The study did not involve any human participants and only systematic review was conducted; hence the written informed consent and Institutional ethical committee approval (IEC) was not required.