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Marathu KK, Vahedifard F, Kocak M, Liu X, Adepoju JO, Bowker RM, Supanich M, Cosme-Cruz RM, Byrd S. Fetal MRI Analysis of Corpus Callosal Abnormalities: Classification, and Associated Anomalies. Diagnostics (Basel) 2024; 14:430. [PMID: 38396468 PMCID: PMC10887608 DOI: 10.3390/diagnostics14040430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/09/2024] [Accepted: 02/10/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Corpus callosal abnormalities (CCA) are midline developmental brain malformations and are usually associated with a wide spectrum of other neurological and non-neurological abnormalities. The study aims to highlight the diagnostic role of fetal MRI to characterize heterogeneous corpus callosal abnormalities using the latest classification system. It also helps to identify associated anomalies, which have prognostic implications for the postnatal outcome. METHODS In this study, retrospective data from antenatal women who underwent fetal MRI between January 2014 and July 2023 at Rush University Medical Center were evaluated for CCA and classified based on structural morphology. Patients were further assessed for associated neurological and non-neurological anomalies. RESULTS The most frequent class of CCA was complete agenesis (79.1%), followed by hypoplasia (12.5%), dysplasia (4.2%), and hypoplasia with dysplasia (4.2%). Among them, 17% had isolated CCA, while the majority (83%) had complex forms of CCA associated with other CNS and non-CNS anomalies. Out of the complex CCA cases, 58% were associated with other CNS anomalies, while 8% were associated with non-CNS anomalies. 17% of cases had both. CONCLUSION The use of fetal MRI is valuable in the classification of abnormalities of the corpus callosum after the confirmation of a suspected diagnosis on prenatal ultrasound. This technique is an invaluable method for distinguishing between isolated and complex forms of CCA, especially in cases of apparent isolated CCA. The use of diffusion-weighted imaging or diffusion tensor imaging in fetal neuroimaging is expected to provide further insights into white matter abnormalities in fetuses diagnosed with CCA in the future.
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Affiliation(s)
- Kranthi K. Marathu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 60612, USA; (F.V.); (M.K.); (X.L.); (J.O.A.); (S.B.)
| | - Farzan Vahedifard
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 60612, USA; (F.V.); (M.K.); (X.L.); (J.O.A.); (S.B.)
| | - Mehmet Kocak
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 60612, USA; (F.V.); (M.K.); (X.L.); (J.O.A.); (S.B.)
| | - Xuchu Liu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 60612, USA; (F.V.); (M.K.); (X.L.); (J.O.A.); (S.B.)
| | - Jubril O. Adepoju
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 60612, USA; (F.V.); (M.K.); (X.L.); (J.O.A.); (S.B.)
| | - Rakhee M. Bowker
- Department of Pediatrics, Division of Neonatology, Rush Medical College, Chicago, IL 60612, USA;
| | - Mark Supanich
- Department of Radiology and Nuclear Medicine, Division for Diagnostic Medical Physics, Rush University Medical Center, Chicago, IL 60612, USA;
| | - Rosario M. Cosme-Cruz
- Department of Psychiatry and Behavioral Sciences, Rush Medical College, Chicago, IL 60612, USA;
| | - Sharon Byrd
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 60612, USA; (F.V.); (M.K.); (X.L.); (J.O.A.); (S.B.)
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Vahedifard F, Liu X, Adepoju JO, Zhao S, Ai HA, Marathu KK, Supanich M, Byrd SE, Deng J. Automatic Localization of the Pons and Vermis on Fetal Brain MR Imaging Using a U-Net Deep Learning Model. AJNR Am J Neuroradiol 2023; 44:1191-1200. [PMID: 37652583 PMCID: PMC10549940 DOI: 10.3174/ajnr.a7978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/02/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND AND PURPOSE An MRI of the fetus can enhance the identification of perinatal developmental disorders, which improves the accuracy of ultrasound. Manual MRI measurements require training, time, and intra-variability concerns. Pediatric neuroradiologists are also in short supply. Our purpose was developing a deep learning model and pipeline for automatically identifying anatomic landmarks on the pons and vermis in fetal brain MR imaging and suggesting suitable images for measuring the pons and vermis. MATERIALS AND METHODS We retrospectively used 55 pregnant patients who underwent fetal brain MR imaging with a HASTE protocol. Pediatric neuroradiologists selected them for landmark annotation on sagittal single-shot T2-weighted images, and the clinically reliable method was used as the criterion standard for the measurement of the pons and vermis. A U-Net-based deep learning model was developed to automatically identify fetal brain anatomic landmarks, including the 2 anterior-posterior landmarks of the pons and 2 anterior-posterior and 2 superior-inferior landmarks of the vermis. Four-fold cross-validation was performed to test the accuracy of the model using randomly divided and sorted gestational age-divided data sets. A confidence score of model prediction was generated for each testing case. RESULTS Overall, 85% of the testing results showed a ≥90% confidence, with a mean error of <2.22 mm, providing overall better estimation results with fewer errors and higher confidence scores. The anterior and posterior pons and anterior vermis showed better estimation (which means fewer errors in landmark localization) and accuracy and a higher confidence level than other landmarks. We also developed a graphic user interface for clinical use. CONCLUSIONS This deep learning-facilitated pipeline practically shortens the time spent on selecting good-quality fetal brain images and performing anatomic measurements for radiologists.
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Affiliation(s)
- Farzan Vahedifard
- From the Department of Diagnostic Radiology and Nuclear Medicine (F.V., X.L., J.O.A., K.K.M., S.E.B.), Rush Medical College, Chicago, Illinois
| | - Xuchu Liu
- From the Department of Diagnostic Radiology and Nuclear Medicine (F.V., X.L., J.O.A., K.K.M., S.E.B.), Rush Medical College, Chicago, Illinois
| | - Jubril O Adepoju
- From the Department of Diagnostic Radiology and Nuclear Medicine (F.V., X.L., J.O.A., K.K.M., S.E.B.), Rush Medical College, Chicago, Illinois
| | - Shiqiao Zhao
- Department of Biostatistics (S.Z.), Yale School of Public Health, New Haven, Connecticut
| | - H Asher Ai
- Division for Diagnostic Medical Physics (H.A.A., M.S.), Department of Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois
| | - Kranthi K Marathu
- From the Department of Diagnostic Radiology and Nuclear Medicine (F.V., X.L., J.O.A., K.K.M., S.E.B.), Rush Medical College, Chicago, Illinois
| | - Mark Supanich
- Division for Diagnostic Medical Physics (H.A.A., M.S.), Department of Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois
| | - Sharon E Byrd
- From the Department of Diagnostic Radiology and Nuclear Medicine (F.V., X.L., J.O.A., K.K.M., S.E.B.), Rush Medical College, Chicago, Illinois
| | - Jie Deng
- Department of Radiation Oncology (J.D.), Division of Medical Physics & Engineering, University of Texas Southwestern Medical Center, Dallas, Texas
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Supanich M, Siewerdsen J, Fahrig R, Farahani K, Gang GJ, Helm P, Jans J, Jones K, Koenig T, Kuhls-Gilcrist A, Lin M, Riddell C, Ritschl L, Schafer S, Schueler B, Silver M, Timmer J, Trousset Y, Zhang J. AAPM Task Group Report 238: 3D C-arms with volumetric imaging capability. Med Phys 2023; 50:e904-e945. [PMID: 36710257 DOI: 10.1002/mp.16245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 12/21/2022] [Accepted: 01/09/2023] [Indexed: 01/31/2023] Open
Abstract
This report reviews the image acquisition and reconstruction characteristics of C-arm Cone Beam Computed Tomography (C-arm CBCT) systems and provides guidance on quality control of C-arm systems with this volumetric imaging capability. The concepts of 3D image reconstruction, geometric calibration, image quality, and dosimetry covered in this report are also pertinent to CBCT for Image-Guided Radiation Therapy (IGRT). However, IGRT systems introduce a number of additional considerations, such as geometric alignment of the imaging at treatment isocenter, which are beyond the scope of the charge to the task group and the report. Section 1 provides an introduction to C-arm CBCT systems and reviews a variety of clinical applications. Section 2 briefly presents nomenclature specific or unique to these systems. A short review of C-arm fluoroscopy quality control (QC) in relation to 3D C-arm imaging is given in Section 3. Section 4 discusses system calibration, including geometric calibration and uniformity calibration. A review of the unique approaches and challenges to 3D reconstruction of data sets acquired by C-arm CBCT systems is give in Section 5. Sections 6 and 7 go in greater depth to address the performance assessment of C-arm CBCT units. First, Section 6 describes testing approaches and phantoms that may be used to evaluate image quality (spatial resolution and image noise and artifacts) and identifies several factors that affect image quality. Section 7 describes both free-in-air and in-phantom approaches to evaluating radiation dose indices. The methodologies described for assessing image quality and radiation dose may be used for annual constancy assessment and comparisons among different systems to help medical physicists determine when a system is not operating as expected. Baseline measurements taken either at installation or after a full preventative maintenance service call can also provide valuable data to help determine whether the performance of the system is acceptable. Collecting image quality and radiation dose data on existing phantoms used for CT image quality and radiation dose assessment, or on newly developed phantoms, will inform the development of performance criteria and standards. Phantom images are also useful for identifying and evaluating artifacts. In particular, comparing baseline data with those from current phantom images can reveal the need for system calibration before image artifacts are detected in clinical practice. Examples of artifacts are provided in Sections 4, 5, and 6.
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Affiliation(s)
- Mark Supanich
- Rush University Medical Center, Chicago, Illinois, USA
| | | | | | | | | | - Pat Helm
- Medtronic Inc., Minneapolis, Minnesota, USA
| | | | - Kyle Jones
- University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | | | - MingDe Lin
- Yale University, New Haven, Connecticut, USA
| | | | | | | | | | - Mike Silver
- Canon Medical Systems USA, Long Beach, California, USA
| | | | | | - Jie Zhang
- University of Kentucky, Lexington, Kentucky
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Vahedifard F, Adepoju JO, Supanich M, Ai HA, Liu X, Kocak M, Marathu KK, Byrd SE. Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging. World J Clin Cases 2023; 11:3725-3735. [PMID: 37383127 PMCID: PMC10294149 DOI: 10.12998/wjcc.v11.i16.3725] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/30/2023] [Accepted: 05/06/2023] [Indexed: 06/02/2023] Open
Abstract
Central nervous system abnormalities in fetuses are fairly common, happening in 0.1% to 0.2% of live births and in 3% to 6% of stillbirths. So initial detection and categorization of fetal Brain abnormalities are critical. Manually detecting and segmenting fetal brain magnetic resonance imaging (MRI) could be time-consuming, and susceptible to interpreter experience. Artificial intelligence (AI) algorithms and machine learning approaches have a high potential for assisting in the early detection of these problems, improving the diagnosis process and follow-up procedures. The use of AI and machine learning techniques in fetal brain MRI was the subject of this narrative review paper. Using AI, anatomic fetal brain MRI processing has investigated models to predict specific landmarks and segmentation automatically. All gestation age weeks (17-38 wk) and different AI models (mainly Convolutional Neural Network and U-Net) have been used. Some models' accuracy achieved 95% and more. AI could help preprocess and post-process fetal images and reconstruct images. Also, AI can be used for gestational age prediction (with one-week accuracy), fetal brain extraction, fetal brain segmentation, and placenta detection. Some fetal brain linear measurements, such as Cerebral and Bone Biparietal Diameter, have been suggested. Classification of brain pathology was studied using diagonal quadratic discriminates analysis, K-nearest neighbor, random forest, naive Bayes, and radial basis function neural network classifiers. Deep learning methods will become more powerful as more large-scale, labeled datasets become available. Having shared fetal brain MRI datasets is crucial because there aren not many fetal brain pictures available. Also, physicians should be aware of AI's function in fetal brain MRI, particularly neuroradiologists, general radiologists, and perinatologists.
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Affiliation(s)
- Farzan Vahedifard
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Jubril O Adepoju
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Mark Supanich
- Division for Diagnostic Medical Physics, Department of Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL 606012, United States
| | - Hua Asher Ai
- Division for Diagnostic Medical Physics, Department of Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL 606012, United States
| | - Xuchu Liu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Mehmet Kocak
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Kranthi K Marathu
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
| | - Sharon E Byrd
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Medical College, Chicago, IL 606012, United States
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Shah P, Supanich M. Chest CT-Derived Muscle Metrics for Sarcopenia: Choosing the Right Target. Ann Surg Oncol 2021; 29:1511-1512. [PMID: 34775548 DOI: 10.1245/s10434-021-11034-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 10/21/2021] [Indexed: 11/18/2022]
Affiliation(s)
- Palmi Shah
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, USA.
| | - Mark Supanich
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, IL, USA
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6
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Liu J, Kocak M, Supanich M, Deng J. Motion artifacts reduction in brain MRI by means of a deep residual network with densely connected multi-resolution blocks (DRN-DCMB). Magn Reson Imaging 2020; 71:69-79. [PMID: 32428549 DOI: 10.1016/j.mri.2020.05.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 04/17/2020] [Accepted: 05/11/2020] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving image quality in MRI. METHODS We developed a deep residual network with densely connected multi-resolution blocks (DRN-DCMB) model to reduce the motion artifacts in T1 weighted (T1W) spin echo images acquired on different imaging planes before and after contrast injection. The DRN-DCMB network consisted of multiple multi-resolution blocks connected with dense connections in a feedforward manner. A single residual unit was used to connect the input and output of the entire network with one shortcut connection to predict a residual image (i.e. artifact image). The model was trained with five motion-free T1W image stacks (pre-contrast axial and sagittal, and post-contrast axial, coronal, and sagittal images) with simulated motion artifacts. RESULTS In other 86 testing image stacks with simulated artifacts, our DRN-DCMB model outperformed other state-of-the-art deep learning models with significantly higher structural similarity index (SSIM) and improvement in signal-to-noise ratio (ISNR). The DRN-DCMB model was also applied to 121 testing image stacks appeared with various degrees of real motion artifacts. The acquired images and processed images by the DRN-DCMB model were randomly mixed, and image quality was blindly evaluated by a neuroradiologist. The DRN-DCMB model significantly improved the overall image quality, reduced the severity of the motion artifacts, and improved the image sharpness, while kept the image contrast. CONCLUSION Our DRN-DCMB model provided an effective method for reducing motion artifacts and improving the overall clinical image quality of brain MRI.
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Affiliation(s)
- Junchi Liu
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, 10 W 35th St, Chicago, IL 60616, USA
| | - Mehmet Kocak
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, 1653 W. Congress Pkwy, Chicago, IL 60612, USA
| | - Mark Supanich
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, 1653 W. Congress Pkwy, Chicago, IL 60612, USA
| | - Jie Deng
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, 1653 W. Congress Pkwy, Chicago, IL 60612, USA.
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Arslan B, Padela MT, Madassery S, Masrani A, Tasse J, Supanich M, Ahmed O. Combination Ipsilateral Lobar and Segmental Radioembolization Using Glass Yttrium-90 Microspheres for Treatment of Multifocal Hepatic Malignancies. J Vasc Interv Radiol 2018; 29:1110-1116. [DOI: 10.1016/j.jvir.2018.04.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 04/02/2018] [Accepted: 04/03/2018] [Indexed: 12/21/2022] Open
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Gavrielides MA, Berman BP, Supanich M, Schultz K, Li Q, Petrick N, Zeng R, Siegelman J. Quantitative assessment of nonsolid pulmonary nodule volume with computed tomography in a phantom study. Quant Imaging Med Surg 2017; 7:623-635. [PMID: 29312867 DOI: 10.21037/qims.2017.12.07] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background To assess the volumetric measurement of small (≤1 cm) nonsolid nodules with computed tomography (CT), focusing on the interaction of state of the art iterative reconstruction (IR) methods and dose with nodule densities, sizes, and shapes. Methods Twelve synthetic nodules [5 and 10 mm in diameter, densities of -800, -630 and -10 Hounsfield units (HU), spherical and spiculated shapes] were scanned within an anthropomorphic phantom. Dose [computed tomography scan dose index (CTDIvol)] ranged from standard (4.1 mGy) to below screening levels (0.3 mGy). Data was reconstructed using filtered back-projection and two state-of-the-art IR methods (adaptive and model-based). Measurements were extracted with a previously validated matched filter-based estimator. Analysis of accuracy and precision was based on evaluation of percent bias (PB) and the repeatability coefficient (RC) respectively. Results Density had the most important effect on measurement error followed by the interaction of density with nodule size. The nonsolid -630 HU nodules had high accuracy and precision at levels comparable to solid (-10 HU) nonsolid, regardless of reconstruction method and with CTDIvol as low as 0.6 mGy. PB was <5% and <11% for the 10- and 5-mm in nominal diameter -630 HU nodules respectively, and RC was <5% and <12% for the same nodules. For nonsolid -800 HU nodules, PB increased to <11% and <30% for the 10- and 5-mm nodules respectively, whereas RC increased slightly overall but varied widely across dose and reconstruction algorithms for the 5-mm nodules. Model-based IR improved measurement accuracy for the 5-mm, low-density (-800, -630 HU) nodules. For other nodules the effect of reconstruction method was small. Dose did not affect volumetric accuracy and only affected slightly the precision of 5-mm nonsolid nodules. Conclusions Reasonable values of both accuracy and precision were achieved for volumetric measurements of all 10-mm nonsolid nodules, and for the 5-mm nodules with -630 HU or higher density, when derived from scans acquired with below screening dose levels as low as 0.6 mGy and regardless of reconstruction algorithm.
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Affiliation(s)
- Marios A Gavrielides
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, , Office of In Vitro Diagnostics and Radiological Health, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Benjamin P Berman
- Division of Radiological Health, Office of In Vitro Diagnostics and Radiological Health, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Mark Supanich
- Department of Diagnostic Radiology and Nuclear Medicine, Rush University Medical Center, Chicago, Illinois, USA
| | - Kurt Schultz
- Toshiba Medical Research Institute USA, Inc., Center for Medical Research and Development, Illinois, USA
| | - Qin Li
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, , Office of In Vitro Diagnostics and Radiological Health, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Nicholas Petrick
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, , Office of In Vitro Diagnostics and Radiological Health, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rongping Zeng
- Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, , Office of In Vitro Diagnostics and Radiological Health, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jenifer Siegelman
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachussetts, USA
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Supanich M. TU-G-213-02: IEC Subcommittee 62B (Diagnostic Imaging Equipment): Recent and Active Projects. Med Phys 2015. [DOI: 10.1118/1.4925734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Supanich M, Wang J. TU-PIS-Exhibit Hall-1: Tools for Collecting and Analyzing Patient Dose Index Information from Imaging Equipment. Med Phys 2015. [DOI: 10.1118/1.4925716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Supanich M. TU-G-213-00: The International Electrotechnical Commission (IEC): What Is It and Why Should Medical Physicists Care? Med Phys 2015. [DOI: 10.1118/1.4925732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Supanich M, Dong F, Andersson J, Pavlicek W, Bolch W, Fetterly K. WE-A-18A-01: TG246 On Patient Dose From Diagnostic Radiation. Med Phys 2014. [DOI: 10.1118/1.4889367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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13
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Supanich M, Siegelman J. SU-F-18C-08: A Validation Study of a Commercially Available Software Package's Absorbed Dose Estimates in a Physical Phantom. Med Phys 2014. [DOI: 10.1118/1.4889093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Supanich M, Bevins N. SU-E-I-48: The Behavior of AEC in Scan Regions Outside the Localizer Radiograph FOV: An In Phantom Study of CT Systems From Four Vendors. Med Phys 2014. [DOI: 10.1118/1.4887997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Supanich M, Chu J, Wehmeyer A. MO-F-16A-04: Case Study: Estimation of Peak Skin Dose Following a Physician Reported “High Dose” Case and Sentinel Event Considerations. Med Phys 2014. [DOI: 10.1118/1.4889175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Supanich M, Vanderhoek M. SU-E-I-12: Assessment of Absorbed Dose and Image Quality Using Cone Beam CT On An X-Ray Angiography System. Med Phys 2013. [DOI: 10.1118/1.4814114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Becker S, Leng S, Supanich M. WE-C-105-01: Preparing for the ABR Diagnostic Exam. Med Phys 2013. [DOI: 10.1118/1.4815522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Flynn M, Supanich M. MO-A-110-01: CR/DR Image Noise - Part 1. Med Phys 2011. [DOI: 10.1118/1.3612893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Olariu E, Supanich M, Bakalyar D. WE-C-110-05: The Influence of Beam Filtration and Collimation on CT Number Accuracy in the ACR CT Accreditation Process. Med Phys 2011. [DOI: 10.1118/1.3613344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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Supanich M, Tao Y, Nett B, Pulfer K, Hsieh J, Turski P, Mistretta C, Rowley H, Chen GH. Radiation dose reduction in time-resolved CT angiography using highly constrained back projection reconstruction. Phys Med Biol 2009; 54:4575-93. [PMID: 19567941 DOI: 10.1088/0031-9155/54/14/013] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Recently dynamic, time-resolved three-dimensional computed tomography angiography (CTA) has been introduced to the neurological imaging community. However, the radiation dose delivered to patients in time-resolved CTA protocol is a high and potential risk associated with the ionizing radiation dose. Thus, minimizing the radiation dose is highly desirable for time-resolved CTA. In order to reduce the radiation dose delivered during dynamic, contrast-enhanced CT applications, we introduce here the CT formulation of HighlY constrained back PRojection (HYPR) imaging. We explore the radiation dose reduction approaches of both acquiring a reduced number of projections for each image and lowering the tube current used during acquisition. We then apply HYPR image reconstruction to produce image sets at a reduced patient dose and with low image noise. Numerical phantom experiments and retrospective analysis of in vivo canine studies are used to assess the accuracy and quality of HYPR reduced dose image sets and validate our approach. Experimental results demonstrated that a factor of 6-8 times radiation dose reduction is possible when the HYPR algorithm is applied to time-resolved CTA exams.
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Affiliation(s)
- Mark Supanich
- Department of Medical Physics, University of Wisconsin in Madison, 1111 Highland Avenue, Madison, WI, 53705, USA
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Supanich M, Chen G, Rowley H, Hsieh J, Mistretta C. TH-D-330A-09: Factor of Ten Dose Reduction in CT Perfusion Imaging. Med Phys 2006. [DOI: 10.1118/1.2241900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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