The Use of Model-based Iterative Reconstruction to Optimize Chest CT Examinations for Diagnosing Lung Metastases in Patients with Sarcoma

Rationale and Objectives

This phantom study aimed to evaluate low-dose (LD) chest computed tomography (CT) protocols using model-based iterative reconstruction (MBIR) for diagnosing lung metastases in patients with sarcoma.

Materials and Methods

An adult female anthropomorphic phantom was scanned with a 64-slice CT using four LD protocols and a standard-dose protocol. Absorbed organ doses were measured with 10 metal-oxide-semiconductor field-effect transistor dosimeters. Furthermore, Monte Carlo simulations were performed to estimate organ and effective doses. Image quality in terms of image noise, contrast, and resolution was measured from the CT images reconstructed with conventional filtered back projection, adaptive statistical iterative reconstruction, and MBIR algorithms. All the results were compared to the performance of the standard-dose protocol.

Results

Mean absorbed organ and effective doses were reduced by approximately 95% with the LD protocol (100-kVp tube voltage and a fixed 10-mA tube current) compared to the standard-dose protocol (120-kVp tube voltage and tube current modulation) while yielding an acceptable image quality for diagnosing round-shaped lung metastases. The effective doses ranged from 0.16 to 2.83 mSv in the studied protocols. The image noise, contrast, and resolution were maintained or improved when comparing the image quality of LD protocols using MBIR to the performance of the standard-dose chest CT protocol using filtered back projection. The small round-shaped lung metastases were delineated at levels comparable to the used protocols.

Conclusions

Radiation exposure in patients can be reduced significantly by using LD chest CT protocols and MBIR algorithm while maintaining image quality for detecting round-shaped lung metastases.

Introduction

Soft tissue sarcoma is a cancer that originates in the soft tissues of the body, such as muscles, tendons, ligaments, cartilage, fat tissue, lymph and blood vessels, or nerves. Tumors are often located in the limbs, head and neck, chest, or abdomen; the lungs are the most common site of metastatic disease in soft tissue sarcoma . Therefore, patients at high risk of metastases are usually evaluated with chest computed tomography (CT) scans that can typically show round-shaped sarcoma metastases. Dose reduction in CT has become a major objective in optimizing radiological examinations. This finding is due to increased CT use in diagnosing diseases in the chest and other body areas and CT’s significant role in the accumulated radiation dose of the population. In accordance with current knowledge, the likelihood of presenting with stochastic adverse effects (eg, cancer) is assumed to increase linearly with radiation dose .

According to the commonly accepted ALARA (as low as reasonably achievable) principle, examinations using ionizing radiation should be performed with a radiation dose that is as low as reasonably achievable while maintaining sufficient image quality for diagnosis. Optimization strategies for chest CT include the use of automatic tube current modulation (TCM), lowered tube voltage, adaptive beam collimation in helical scans, and partial scanning (organ dose modulation); these strategies have all been used to reduce radiation exposure to tissues and to optimize image quality . One of the most promising CT optimization techniques is the continuously developing iterative reconstruction algorithms that aim to overcome the limitations of the traditional reconstruction method of filtered back projection (FBP) for image quality and diagnostic dose efficiency. The iterative reconstruction algorithms include statistical (hybrid) and model-based iterative reconstruction (MBIR) techniques. The former only models the noise statistics, whereas the latter uses a more complex system of prediction models, including modeling of optical factors, such as x-ray tube and detector responses, in addition to voxel projections, x-ray beam spectra, and noise statistics . Several studies have reported significant dose reduction capabilities of iterative reconstruction methods (especially with MBIR) in the chest and other body areas while maintaining or improving diagnostic image quality . However, some researchers have warned about the possibilities of missing clinically significant lesions with low-dose (LD) abdominal and chest CT protocols using iterative reconstruction techniques . The image noise magnitude and texture of the iteratively reconstructed images depend on the scanned tissue, and noise magnitude may behave differently at tissue boundaries compared to uniform regions .

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Materials and Methods

Dose Measurements and Simulations

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TABLE 1

Scanning Parameters and Dose Indices (CTDI vol and DLP) of the Used Low-dose and Standard-dose Protocols

Protocol Tube Voltage (kVp) Tube Current/GE NI Rotation Time (s) CTDI vol (mGy) DLP (mGy⋅cm) Low dose 1 100 Fixed 10 mA 0.4 0.20 6.33 Low dose 2 100 NI = 50 0.4 0.31 9.91 Low dose 3 120 Fixed 10 mA 0.4 0.31 10.05 Low dose 4 120 NI = 40 0.4 0.49 15.70 Standard dose 120 NI = 15 0.5 3.58 115.08

CTDI vol , volume computed tomography dose index, DLP, dose-length product; NI, noise index.

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Image Quality Measurements and Analysis

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Results

Organ Doses and Effective Doses

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TABLE 2

Organ Doses and Effective Doses Determined From the Monte Carlo Simulations for Different LD and Standard-dose Protocols

ICRP 103 Tissue Weighting Factor LD 1 (100 kVp, 10 mA) LD 2 (100 kVp), NI = 50 LD 3 (120 kVp, 10 mA) LD 4 (120 kVp), NI = 40 Standard Dose (120 kVp), NI = 15 Organ/Tissue_W T_ Equivalent Dose (mSv) Active bone marrow 0.12 0.0566 0.0882 0.0896 0.1395 1.0249 Colon 0.12 0.0073 0.0105 0.0130 0.0188 0.1444 Lung 0.12 0.2778 0.4270 0.4585 0.7057 5.1819 Stomach 0.12 0.2632 0.3737 0.4357 0.6190 4.7250 Breast 0.12 0.2683 0.3741 0.4316 0.6015 4.4266 Gonads 0.08 0.0006 0.0009 0.0012 0.0016 0.0127 Bladder 0.04 0.0012 0.0018 0.0023 0.0034 0.0259 Esophagus 0.04 0.2572 0.4046 0.4330 0.6824 4.9770 Liver 0.04 0.2654 0.3752 0.4379 0.6191 4.7167 Thyroid 0.04 0.2592 0.4382 0.4146 0.7032 4.9756 Bone surface 0.01 0.1134 0.1799 0.1799 0.2852 2.0858 Brain 0.01 0.0038 0.0065 0.0069 0.0118 0.0838 Salivary glands 0.01 0.0245 0.0418 0.0409 0.0698 0.5017 Skin 0.01 0.0930 0.1470 0.1496 0.2361 1.7316 Adrenals 0.0092 0.2245 0.3233 0.3822 0.5482 4.2295 Extrathoracic region 0.0092 0.0208 0.0351 0.0351 0.0598 0.4231 Gallbladder 0.0092 0.2525 0.3625 0.4218 0.6057 4.6587 Heart 0.0092 0.2944 0.4521 0.4911 0.7542 5.5098 Kidneys 0.0092 0.1206 0.1742 0.2044 0.2950 2.2723 Lymphatic nodes 0.0092 0.1238 0.1987 0.2068 0.3313 2.4233 Muscle 0.0092 0.0917 0.1457 0.1509 0.2396 1.7566 Oral mucosa 0.0092 0.0165 0.0282 0.0288 0.0485 0.3476 Pancreas 0.0092 0.1801 0.2594 0.3027 0.4365 3.3613 Small intestine 0.0092 0.0223 0.0322 0.0380 0.0550 0.4236 Spleen 0.0092 0.2408 0.3470 0.4020 0.5808 4.4665 Thymus 0.0092 0.3119 0.5305 0.5138 0.8726 6.2617 Uterus 0.0092 0.0007 0.0010 0.0013 0.0020 0.0149 Effective dose, E (mSv) ∑ W T = 1 0.16 0.23 0.26 0.38 2.83

ICRP, International Commission on Radiological Protection; LD, low dose; NI, noise index.

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Image Quality Measurements and Analysis

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Discussion

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Conclusions

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Acknowledgments

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Supplementary Data

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Figure S1

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Figure S2

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Figure S3

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Figure S4

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The Use of Model-based Iterative Reconstruction to Optimize Chest CT Examinations for Diagnosing Lung Metastases in Patients with Sarcoma

Rationale and Objectives

Materials and Methods

Results

Conclusions

Introduction

Materials and Methods

Dose Measurements and Simulations

Image Quality Measurements and Analysis

Results

Organ Doses and Effective Doses

Image Quality Measurements and Analysis

Discussion

Conclusions

Acknowledgments

Supplementary Data

References

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