State of the Art in Abdominal CT: The Limits of Iterative Reconstruction Algorithms.

The development and widespread adoption of iterative reconstruction (IR) algorithms for CT have greatly facilitated the contemporary practice of radiation dose reduction during abdominal CT examinations. IR mitigates the increased image noise typically associated with reduced radiation dose levels, thereby maintaining subjective image quality and diagnostic confidence for a variety of clinical tasks. Mounting evidence, however, points to important limitations of this method involving radiologists' ability to perform low-contrast diagnostic tasks, such as the detection of liver metastases or pancreatic masses. Radiologists need to be aware that use of IR can result in a decline of spatial resolution for low-contrast structures and degradation of low-contrast detectability when radiation dose reductions exceed approximately 25%. This article will review the principles of IR algorithm technology, describe the various commercial implementations of IR in CT, and review published studies that have evaluated the ability of IR to preserve diagnostic performance for low-contrast diagnostic tasks. In addition, future developments in CT noise reduction techniques and methods to rigorously evaluate their diagnostic performance will be discussed.

[1]  Armando Manduca,et al.  Observer Performance in the Detection and Classification of Malignant Hepatic Nodules and Masses with CT Image-Space Denoising and Iterative Reconstruction. , 2015, Radiology.

[2]  C. McCollough,et al.  Technical Note: Measuring contrast- and noise-dependent spatial resolution of an iterative reconstruction method in CT using ensemble averaging. , 2015, Medical physics.

[3]  Cynthia H McCollough,et al.  The phantom portion of the American College of Radiology (ACR) computed tomography (CT) accreditation program: practical tips, artifact examples, and pitfalls to avoid. , 2004, Medical physics.

[4]  Atul Padole,et al.  Ultra-low dose abdominal MDCT: using a knowledge-based Iterative Model Reconstruction technique for substantial dose reduction in a prospective clinical study. , 2015, European journal of radiology.

[5]  J M Boone,et al.  Determination of the presampled MTF in computed tomography. , 2001, Medical physics.

[6]  Shuai Leng,et al.  Observer Performance with Varying Radiation Dose and Reconstruction Methods for Detection of Hepatic Metastases. , 2018, Radiology.

[7]  A. Bankier,et al.  Through the Looking Glass revisited: the need for more meaning and less drama in the reporting of dose and dose reduction in CT. , 2012, Radiology.

[8]  Shuai Leng,et al.  Correlation between model observer and human observer performance in CT imaging when lesion location is uncertain. , 2013, Medical physics.

[9]  Matthew A Kupinski,et al.  Correlation between a 2D channelized Hotelling observer and human observers in a low‐contrast detection task with multislice reading in CT , 2017, Medical physics.

[10]  R. Raupach,et al.  Iterative reconstruction algorithm for abdominal multidetector CT at different tube voltages: assessment of diagnostic accuracy, image quality, and radiation dose in a phantom study. , 2011, Radiology.

[11]  Wei Zhou,et al.  A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT. , 2019, Medical physics.

[12]  U.S. National Diagnostic Reference Levels: Closing the Gap. , 2015, Radiology.

[13]  Ke Li,et al.  Prospective Evaluation of Reduced Dose Computed Tomography for the Detection of Low-Contrast Liver Lesions: Direct Comparison with Concurrent Standard Dose Imaging , 2017, European Radiology.

[14]  Adam M Alessio,et al.  CT Detectability of Small Low-Contrast Hypoattenuating Focal Lesions: Iterative Reconstructions versus Filtered Back Projection. , 2018, Radiology.

[15]  H. Brisse,et al.  Comment on: Are the studies on cancer risk from CT scans biased by indication? Elements of answer from a large-scale cohort study in France , 2015, British Journal of Cancer.

[16]  Ehsan Samei,et al.  Effect of Radiation Dose Reduction and Reconstruction Algorithm on Image Noise, Contrast, Resolution, and Detectability of Subtle Hypoattenuating Liver Lesions at Multidetector CT: Filtered Back Projection versus a Commercial Model-based Iterative Reconstruction Algorithm. , 2017, Radiology.

[17]  M F McNitt-Gray,et al.  Application of the noise power spectrum in modern diagnostic MDCT: part I. Measurement of noise power spectra and noise equivalent quanta , 2007, Physics in medicine and biology.

[18]  Arnold M. R. Schilham,et al.  Iterative reconstruction techniques for computed tomography part 2: initial results in dose reduction and image quality , 2013, European Radiology.

[19]  S. Achenbach,et al.  Iterative reconstruction in image space (IRIS) in cardiac computed tomography: initial experience , 2011, The International Journal of Cardiovascular Imaging.

[20]  J. Leipsic,et al.  State of the Art: Iterative CT Reconstruction Techniques. , 2015, Radiology.

[21]  Grace J Gang,et al.  Analysis of Fourier-domain task-based detectability index in tomosynthesis and cone-beam CT in relation to human observer performance. , 2011, Medical physics.

[22]  Armando Manduca,et al.  Methods for clinical evaluation of noise reduction techniques in abdominopelvic CT. , 2014, Radiographics : a review publication of the Radiological Society of North America, Inc.

[23]  A. Sodickson,et al.  Recurrent CT, cumulative radiation exposure, and associated radiation-induced cancer risks from CT of adults. , 2009, Radiology.

[24]  John M Boone,et al.  Radiation exposure from CT scans: how to close our knowledge gaps, monitor and safeguard exposure--proceedings and recommendations of the Radiation Dose Summit, sponsored by NIBIB, February 24-25, 2011. , 2012, Radiology.

[25]  Shuai Leng,et al.  Estimation of Observer Performance for Reduced Radiation Dose Levels in CT: Eliminating Reduced Dose Levels That Are Too Low Is the First Step. , 2017, Academic radiology.

[26]  Yi Zhang,et al.  Degradation of CT Low-Contrast Spatial Resolution Due to the Use of Iterative Reconstruction and Reduced Dose Levels. , 2015, Radiology.

[27]  Berkman Sahiner,et al.  Hypothesis testing in noninferiority and equivalence MRMC ROC studies. , 2012, Academic radiology.

[28]  Guang-Hong Chen,et al.  Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance. , 2014, Medical physics.

[29]  Keith T. Chan,et al.  Standard and reduced radiation dose liver CT images: adaptive statistical iterative reconstruction versus model-based iterative reconstruction-comparison of findings and image quality. , 2014, Radiology.

[30]  Jean-Baptiste Thibault,et al.  A three-dimensional statistical approach to improved image quality for multislice helical CT. , 2007, Medical physics.

[31]  C. McCollough,et al.  Optimal tube potential for radiation dose reduction in pediatric CT: principles, clinical implementations, and pitfalls. , 2011, Radiographics : a review publication of the Radiological Society of North America, Inc.

[32]  C. McCollough,et al.  Relationship between noise, dose, and pitch in cardiac multi-detector row CT. , 2006, Radiographics : a review publication of the Radiological Society of North America, Inc.

[33]  Nancy A Obuchowski,et al.  Effect of reduced radiation exposure and iterative reconstruction on detection of low-contrast low-attenuation lesions in an anthropomorphic liver phantom: an 18-reader study. , 2014, Radiology.

[34]  Grace J Gang,et al.  Task-based detectability in CT image reconstruction by filtered backprojection and penalized likelihood estimation. , 2014, Medical physics.

[35]  Jeffrey A. Fessler,et al.  Spatial resolution properties of penalized-likelihood image reconstruction: space-invariant tomographs , 1996, IEEE Trans. Image Process..

[36]  Ehsan Samei,et al.  Detection of Colorectal Hepatic Metastases Is Superior at Standard Radiation Dose CT versus Reduced Dose CT. , 2019, Radiology.

[37]  Craig K. Abbey,et al.  Inter‐laboratory comparison of channelized hotelling observer computation , 2018, Medical physics.

[38]  Kyle J Myers,et al.  CT image assessment by low contrast signal detectability evaluation with unknown signal location. , 2013, Medical physics.

[39]  D. Miglioretti,et al.  Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. , 2009, Archives of internal medicine.

[40]  Ehsan Samei,et al.  Evaluation of Low-Contrast Detectability of Iterative Reconstruction across Multiple Institutions, CT Scanner Manufacturers, and Radiation Exposure Levels. , 2015, Radiology.

[41]  Bram Stieltjes,et al.  Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages , 2017, European Radiology.

[42]  Joel G Fletcher,et al.  Answers to Common Questions About the Use and Safety of CT Scans. , 2015, Mayo Clinic proceedings.

[43]  K. Stierstorfer,et al.  Weighted FBP--a simple approximate 3D FBP algorithm for multislice spiral CT with good dose usage for arbitrary pitch. , 2004, Physics in medicine and biology.

[44]  Shuai Leng,et al.  Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: impact of radiation dose and reconstruction algorithms. , 2013, Medical physics.

[45]  Frank Dong,et al.  Image Noise, CNR, and Detectability of Low-Contrast, Low-Attenuation Liver Lesions in a Phantom: Effects of Radiation Exposure, Phantom Size, Integrated Circuit Detector, and Iterative Reconstruction. , 2016, Radiology.

[46]  D. Volders,et al.  Model-based iterative reconstruction and adaptive statistical iterative reconstruction techniques in abdominal CT: comparison of image quality in the detection of colorectal liver metastases. , 2013, Radiology.

[47]  Lifeng Yu,et al.  Simulation of CT images reconstructed with different kernels using a convolutional neural network and its implications for efficient CT workflow , 2019, Medical Imaging.

[48]  R. Morin,et al.  U.S. Diagnostic Reference Levels and Achievable Doses for 10 Adult CT Examinations. , 2017, Radiology.

[49]  Patrik Rogalla,et al.  Iterative reconstruction algorithm for CT: can radiation dose be decreased while low-contrast detectability is preserved? , 2013, Radiology.

[50]  Yi Zhang,et al.  Correlation between human and model observer performance for discrimination task in CT , 2014, Physics in medicine and biology.