Simulated Low-Dose Computed Tomography in Oncological Patients: A Feasibility Study

A phantom study of the performance of model-based iterative reconstruction in low-dose chest and abdominal CT: When are benefits maximized?

INTRODUCTION

The aim of this study was to assess and compare the effects of CT image reconstruction techniques on low-dose CT image quality using phantoms.

METHODS

Anthropomorphic torso and spatial/contrast-resolution phantoms were scanned at decreasing tube currents between 400 and 10 mA. CT thorax and abdomen/pelvis series were reconstructed with filtered back projection (FBP) alone, combined 40% adaptive statistical iterative reconstruction & FBP (ASIR40), and model-based iterative reconstruction (MBIR) [(resolution-preference 05 (RP05) and RP20 in the thorax and RP05 and noise-reduction 05 (NR05) in the abdomen)]. Two readers rated image quality quantitatively and qualitatively.

RESULTS

In thoracic CT, objective image noise on MBIR RP05 data sets outperformed FBP at 200, 100, 50 and 10 mA and outperformed ASIR40 at 50 and 10 mA (p < 0.001). MBIR RP20 outperformed FBP at 50 and 10 mA and outperformed ASIR40 at 10 mA (p < 0.001). Compared with both FBP and ASIR40, MBIR RP05 demonstrated significantly better signal-to-noise ratio (SNR) at 10 mA. In abdomino-pelvic CT, MBIR RP05 and NR05 outperformed FBP and ASIR at all tube current levels for objective image noise. NR05 demonstrated greater SNR at 200, 100, 50 and 10 mA and RP05 demonstrated greater SNR at 50 and 10 mA compared with both FBP and ASIR. MBIR images demonstrated better subjective image quality scores. Spatial resolution, low-contrast detectability and contrast-to-noise ratio (CNR) were comparable between image reconstruction techniques.

CONCLUSION

CTs reconstructed with MBIR have lower image noise and improved image quality compared with FBP and ASIR. These effects increase with reduced radiation exposure confirming optimal use for low-dose CT imaging.

Determination of a suitable low-dose abdominopelvic CT protocol using model-based iterative reconstruction through cadaveric study

INTRODUCTION

Cadaveric studies provide a means of safely assessing new technologies and optimizing scanning prior to clinical validation. Reducing radiation exposure in a clinical setting can entail incremental dose reductions to avoid missing important clinical findings. The use of cadavers allows assessment of the impact of more substantial dose reductions on image quality. Our aim was to identify a suitable low-dose abdominopelvic CT protocol for subsequent clinical validation.

METHODS

Five human cadavers were scanned at one conventional dose and three low-dose settings. All scans were reconstructed using three different reconstruction algorithms: filtered back projection (FBP), hybrid iterative reconstruction (60% FBP and 40% adaptive statistical iterative reconstruction (ASIR40)), and model-based iterative reconstruction (MBIR). Two readers rated the image quality both quantitatively and qualitatively.

RESULTS

Model-based iterative reconstruction images had significantly better objective image noise and higher qualitative scores compared with both FBP and ASIR40 images at all dose levels. The greatest absolute noise reduction, between MBIR and FBP, of 34.3 HU (equating to a 68% reduction) was at the lowest dose level. MBIR reduced image noise and improved image quality even in CT images acquired with a mean radiation dose reduction of 62% compared with conventional dose studies reconstructed with ASIR40, with lower levels of objective image noise, superior diagnostic acceptability and contrast resolution, and comparable subjective image noise and streak artefact scores.

CONCLUSION

This cadaveric study demonstrates that MBIR reduces image noise and improves image quality in abdominopelvic CT images acquired with dose reductions of up to 62%.

Managing Incidental Lung Nodules in Patients With a History of Oncologic Disease: A Survey of Thoracic Radiologists

PURPOSE

The aim of this study was to analyze the impact that a clinical history of an oncologic disease may have on the management decisions for incidentally detected lung nodules on chest computed tomographic (CT) examinations.

MATERIALS AND METHODS

An electronic survey was sent to all 796 members of the Society of Thoracic Radiology regarding criteria for the management of incidentally detected lung nodules in oncologic patients, as well as recommendations for nodule follow-up. Nodule characteristics and clinical parameters used by respondents were analyzed. Differences between variables were examined using the χ test.

RESULTS

Of the 796 Society of Thoracic Radiology members, 178 (22.36%) replied. Most respondents were subspecialized in cardiothoracic imaging (92.70%) and practiced in an "academic or teaching hospital setting" (75.28%) with a "dedicated oncology center" (94.03%). "History of oncologic disease" was the most important factor (98.87%) for management decisions. In patients with such a history, respondents most commonly used "experience and common sense" (56.74%) and reported "all incidentally found lung nodules" (65.73%, P<0.0001). "Size" and "shape" were the 2 most important nodule characteristics (33.61% and 27.05%, respectively) used to consider a nodule "clinically relevant," and "size" (44.07%) was also the most important nodule characteristic prompting recommendation for short-term CT follow-up. Follow-up CT examinations in oncologic patients were recommended by 75.84% of respondents.

CONCLUSIONS

In patients with a history of oncologic disease, radiologists tend to report every detected nodule and to routinely recommend follow-up CT examinations. Although most radiologists rely on "experience and common sense" in managing these nodules, greater standardization of lung nodule management in oncologic patients is needed, ideally through guidelines tailored to this patient population.

Iterative reconstruction algorithm for CT: can radiation dose be decreased while low-contrast detectability is preserved?

PURPOSE

To compare the low-contrast detectability and image quality of computed tomography (CT) at different radiation dose levels reconstructed with iterative reconstruction (IR) and filtered back projection (FBP).

MATERIALS AND METHODS

A custom liver phantom with 12 simulated hypoattenuating tumors (diameters of 5, 10, 15, and 20 mm; tumor-to-liver contrast values of -10, -20, and -40 HU) was designed. The phantom was scanned with a standard abdominal CT protocol with a volume CT dose index of 21.6 mGy (equivalent 100% dose) and four low-dose protocols (20%, 40%, 60%, and 80% of the standard protocol dose). CT data sets were reconstructed with IR and FBP. Image noise was measured, and the tumors' contrast-to-noise ratios (CNRs) were calculated. Tumor detection was independently assessed by three radiologists who were blinded to the CT technique used. A total of 840 simulated tumors were presented to the radiologists. Statistical analyses included analysis of variance.

RESULTS

IR yielded an image noise reduction of 43.9%-63.9% and a CNR increase of 74.1%-180% compared with FBP at the same dose level (P < .001). The overall sensitivity for tumor detection was 64.7%-85.3% for IR and 66.3%-85.7% for FBP at the 20%-100% doses, respectively. There was no significant difference in the sensitivity for tumor detection between IR and FBP at the same dose level (P = .99). The sensitivity of the protocol at the 20% dose with FBP and IR was significantly lower than that of the protocol at the 100% dose with FBP and IR (P = .019).

CONCLUSION

As the radiation dose at CT decreases, the IR algorithm does not preserve the low-contrast detectability.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13122349/-/DC1.

Pulmonary kinematics from image data: a review

The effects of certain lung pathologies include alterations in lung physiology negatively affecting pulmonary compliance. Current approaches to diagnosis and treatment assessment of lung disease commonly rely on pulmonary function testing. Such testing is limited to global measures of lung function, neglecting regional measurements, which are critical for early diagnosis and localization of disease. Increased accessibility to medical image acquisition strategies with high spatiotemporal resolution coupled with the development of sophisticated intensity-based and geometric registration techniques has resulted in the recent exploration of modeling pulmonary motion for calculating local measures of deformation. In this review, the authors provide a broad overview of such research efforts for the estimation of pulmonary deformation. This includes discussion of various techniques, current trends in validation approaches, and the public availability of software and data resources.