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Salyapongse AM, Szczykutowicz TP. Misinterpretations about CT numbers, material decomposition, and elemental quantification. Eur Radiol 2024:10.1007/s00330-024-10934-x. [PMID: 39033471 DOI: 10.1007/s00330-024-10934-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 05/13/2024] [Accepted: 06/07/2024] [Indexed: 07/23/2024]
Abstract
BACKGROUND Quantitative CT imaging, particularly iodine and calcium quantification, is an important CT-based biomarker. PURPOSE This study quantifies sources of errors in quantitative CT imaging in both single-energy and spectral CT. MATERIALS AND METHODS This work examines the theoretical relationship between CT numbers, linear attenuation coefficient, and material quantification. We derive four understandings: (1) CT numbers are not proportional with element mass in vivo, (2) CT numbers are proportional with element mass only when contained in a voxel of pure water, (3) iodine-water material decomposition is never accurate in vivo, and (4) for error-free material decomposition a voxel must only consist of the basis decomposition vectors. Misinterpretation-based errors are calculated using the National Institute of Standards and Technology (NIST) XCOM database for: tissue chemical compositions, clinical concentrations of hydroxyapatite (HAP), and iodine. Quantification errors are also demonstrated experimentally using phantoms. RESULTS In single-energy CT, misinterpretation-induced errors for HAP density in adipose, muscle, lung, soft tissue, and blood ranged from 0-132%, i.e., a mass error of 0-749 mg/cm3. In spectral CT, errors with iodine in the same tissues resulted in a range of < 0.1-33% error, resulting in a mass error of < 0.1-1.2 mg/mL. CONCLUSION Our work demonstrates material quantification is fundamentally limited when measured in vivo due to measurement conditions differing from assumed and the errors are at or above detection limits for bone mineral density (BMD) and spectral iodine quantification. To define CT-derived biomarkers, the errors we demonstrate should either be avoided or built into uncertainty bounds. CLINICAL RELEVANCE STATEMENT Improving error bounds in quantitative CT biomarkers, specifically in iodine and BMD quantification, could lead to improvements in clinical care aspects based on quantitative CT. KEY POINTS CT numbers are only proportional with element mass only when contained in a voxel of pure water, therefore iodine-water material decomposition is never accurate in vivo. Misinterpretation-induced errors ranged from 0-132% for HAP density and < 0.1-33% in spectral CT with iodine. For error-free material decomposition, a voxel must only consist of the basis decomposition vectors.
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Affiliation(s)
- Aria M Salyapongse
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, USA
- Department of Radiology, University of Wisconsin Madison, Madison, WI, USA
| | - Timothy P Szczykutowicz
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, USA.
- Department of Radiology, University of Wisconsin Madison, Madison, WI, USA.
- Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI, USA.
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Chakravarti S, Uyeda JW. Expanding Role of Dual-Energy CT for Genitourinary Tract Assessment in the Emergency Department, From the AJR Special Series on Emergency Radiology. AJR Am J Roentgenol 2023; 221:720-730. [PMID: 37073900 DOI: 10.2214/ajr.22.27864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Among explored applications of dual-energy CT (DECT) in the abdomen and pelvis, the genitourinary (GU) tract represents an area where accumulated evidence has established the role of DECT to provide useful information that may change management. This review discusses established applications of DECT for GU tract assessment in the emergency department (ED) setting, including characterization of renal stones, evaluation of traumatic injuries and hemorrhage, and characterization of incidental renal and adrenal findings. Use of DECT for such applications can reduce the need for additional multiphase CT or MRI examinations and reduce follow-up imaging recommendations. Emerging applications are also highlighted, including use of low-energy virtual monoenergetic images (VMIs) to improve image quality and potentially reduce contrast media doses and use of high-energy VMIs to mitigate renal mass pseudoenhancement. Finally, implementation of DECT into busy ED radiology practices is presented, weighing the trade-off of additional image acquisition, processing time, and interpretation time against potential additional useful clinical information. Automatic generation of DECT-derived images with direct PACS transfer can facilitate radiologists' adoption of DECT in busy ED environments and minimize impact on interpretation times. Using the described approaches, radiologists can apply DECT technology to improve the quality and efficiency of care in the ED.
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Affiliation(s)
| | - Jennifer W Uyeda
- Department of Emergency Radiology, Brigham and Women's Hospital/Harvard Medical School, 75 Francis St, Boston, MA 02115
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Turrion Gomollon AM, Mergen V, Sartoretti T, Polacin M, Nakhostin D, Puippe G, Alkadhi H, Euler A. Photon-Counting Detector CT Angiography for Endoleak Detection After Endovascular Aortic Repair: Triphasic CT With True Noncontrast Versus Biphasic CT With Virtual Noniodine Imaging. Invest Radiol 2023; 58:816-821. [PMID: 37358359 PMCID: PMC10581441 DOI: 10.1097/rli.0000000000000993] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 04/25/2023] [Indexed: 06/27/2023]
Abstract
OBJECTIVES The aim of this study was to compare image quality and endoleak detection after endovascular abdominal aortic aneurysm repair between a triphasic computed tomography (CT) with true noncontrast (TNC) and a biphasic CT with virtual noniodine (VNI) images on photon-counting detector CT (PCD-CT). MATERIALS AND METHODS Adult patients after endovascular abdominal aortic aneurysm repair who received a triphasic examination (TNC, arterial, venous phase) on a PCD-CT between August 2021 and July 2022 were retrospectively included. Endoleak detection was evaluated by 2 blinded radiologists on 2 different readout sets (triphasic CT with TNC-arterial-venous vs biphasic CT with VNI-arterial-venous). Virtual noniodine images were reconstructed from the venous phase. The radiologic report with additional confirmation by an expert reader served as reference standard for endoleak presence. Sensitivity, specificity, and interreader agreement (Krippendorf α) were calculated. Image noise was assessed subjectively in patients using a 5-point scale and objectively calculating the noise power spectrum in a phantom. RESULTS One hundred ten patients (7 women; age, 76 ± 8 years) with 41 endoleaks were included. Endoleak detection was comparable between both readout sets with a sensitivity and specificity of 0.95/0.84 (TNC) versus 0.95/0.86 (VNI) for reader 1 and 0.88/0.98 (TNC) versus 0.88/0.94 (VNI) for reader 2. Interreader agreement for endoleak detection was substantial (TNC: 0.716, VNI: 0.756). Subjective image noise was comparable between TNC and VNI (4; IQR [4, 5] vs 4; IQR [4, 5], P = 0.44). In the phantom, noise power spectrum peak spatial frequency was similar between TNC and VNI (both f peak = 0.16 mm -1 ). Objective image noise was higher in TNC (12.7 HU) as compared with VNI (11.5 HU). CONCLUSIONS Endoleak detection and image quality were comparable using VNI images in biphasic CT as compared with TNC images in triphasic CT offering the possibility to reduce scan phases and radiation exposure.
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Chew BH, Wong VKF, Halawani A, Lee S, Baek S, Kang H, Koo KC. Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units < 800. Urolithiasis 2023; 51:117. [PMID: 37776331 DOI: 10.1007/s00240-023-01490-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 09/11/2023] [Indexed: 10/02/2023]
Abstract
The correct diagnosis of uric acid (UA) stones has important clinical implications since patients with a high risk of perioperative morbidity may be spared surgical intervention and be offered alkalization therapy. We developed and validated a machine learning (ML)-based model to identify stones on computed tomography (CT) images and simultaneously classify UA stones from non-UA stones. An international, multicenter study was performed on 202 patients who received percutaneous nephrolithotomy for kidney stones with HU < 800. Data from 156 (77.2%) patients were used for model development, while data from 46 (22.8%) patients from a multinational institution were used for external validation. A total of 21,074 kidney and stone contour-annotated CT images were trained with the ResNet-18 Mask R-convolutional neural network algorithm. Finally, this model was concatenated with demographic and clinical data as a fully connected layer for stone classification. Our model was 100% sensitive in detecting kidney stones in each patient, and the delineation of kidney and stone contours was precise within clinically acceptable ranges. The development model provided an accuracy of 99.9%, with 100.0% sensitivity and 98.9% specificity, in distinguishing UA from non-UA stones. On external validation, the model performed with an accuracy of 97.1%, with 89.4% sensitivity and 98.6% specificity. SHAP plots revealed stone density, diabetes mellitus, and urinary pH as the most important features for classification. Our ML-based model accurately identified and delineated kidney stones and classified UA stones from non-UA stones with the highest predictive accuracy reported to date. Our model can be reliably used to select candidates for an earlier-directed alkalization therapy.
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Affiliation(s)
- Ben H Chew
- Department of Urological Sciences, University of British Columbia, Stone Centre at Vancouver General Hospital, Vancouver, BC, Canada
| | - Victor K F Wong
- Department of Urological Sciences, University of British Columbia, Stone Centre at Vancouver General Hospital, Vancouver, BC, Canada
| | | | - Sujin Lee
- Infinyx, AI research team, Daegu, Republic of Korea
| | | | - Hoyong Kang
- Infinyx, AI research team, Daegu, Republic of Korea
| | - Kyo Chul Koo
- Department of Urology, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul, 135-720, Republic of Korea.
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Euler A, Wullschleger S, Sartoretti T, Müller D, Keller EX, Lavrek D, Donati O. Dual-energy CT kidney stone characterization-can diagnostic accuracy be achieved at low radiation dose? Eur Radiol 2023; 33:6238-6244. [PMID: 36988716 PMCID: PMC10415460 DOI: 10.1007/s00330-023-09569-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/11/2023] [Accepted: 02/07/2023] [Indexed: 03/30/2023]
Abstract
OBJECTIVES To assess the accuracy of low-dose dual-energy computed tomography (DECT) to differentiate uric acid from non-uric acid kidney stones in two generations of dual-source DECT with stone composition analysis as the reference standard. METHODS Patients who received a low-dose unenhanced DECT for the detection or follow-up of urolithiasis and stone extraction with stone composition analysis between January 2020 and January 2022 were retrospectively included. Collected stones were characterized using X-ray diffraction. Size, volume, CT attenuation, and stone characterization were assessed using DECT post-processing software. Characterization as uric acid or non-uric acid stones was compared to stone composition analysis as the reference standard. Sensitivity, specificity, and accuracy of stone classification were computed. Dose length product (DLP) and effective dose served as radiation dose estimates. RESULTS A total of 227 stones in 203 patients were analyzed. Stone composition analysis identified 15 uric acid and 212 non-uric acid stones. Mean size and volume were 4.7 mm × 2.8 mm and 114 mm3, respectively. CT attenuation of uric acid stones was significantly lower as compared to non-uric acid stones (p < 0.001). Two hundred twenty-five of 227 kidney stones were correctly classified by DECT. Pooled sensitivity, specificity, and accuracy were 1.0 (95%CI: 0.97, 1.00), 0.93 (95%CI: 0.68, 1.00), and 0.99 (95%CI: 0.97, 1.00), respectively. Eighty-two of 84 stones with a diameter of ≤ 3 mm were correctly classified. Mean DLP was 162 ± 57 mGy*cm and effective dose was 2.43 ± 0.86 mSv. CONCLUSIONS Low-dose dual-source DECT demonstrated high accuracy to discriminate uric acid from non-uric acid stones even at small stone sizes. KEY POINTS • Two hundred twenty-five of 227 stones were correctly classified as uric acid vs. non-uric acid stones by low-dose dual-energy CT with stone composition analysis as the reference standard. • Pooled sensitivity, specificity, and accuracy for stone characterization were 1.0, 0.93, and 0.99, respectively. • Low-dose dual-energy CT for stone characterization was feasible in the majority of small stones < 3 mm.
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Affiliation(s)
- André Euler
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
| | - Sara Wullschleger
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Thomas Sartoretti
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Daniel Müller
- Institute of Clinical Chemistry, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Etienne Xavier Keller
- Department of Urology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Dejan Lavrek
- Department of Urology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Olivio Donati
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland
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Abstract
PURPOSE OF REVIEW Radiological imaging techniques and applications are constantly advancing. This review will examine modern imaging techniques in the diagnosis of urolithiasis and applications for surgical planning. RECENT FINDINGS The diagnosis of urolithiasis may be done via plain film X-ray, ultrasound (US), or contrast tomography (CT) scan. US should be applied in the workup of flank pain in emergency rooms and may reduce unnecessary radiation exposure. Low dose and ultra-low-dose CT remain the diagnostic standard for most populations but remain underutilized. Single and dual-energy CT provide three-dimensional imaging that can predict stone-specific parameters that help clinicians predict stone passage likelihood, identify ideal management techniques, and possibly reduce complications. Machine learning has been increasingly applied to 3-D imaging to support clinicians in these prognostications and treatment selection. SUMMARY The diagnosis and management of urolithiasis are increasingly personalized. Patient and stone characteristics will support clinicians in treatment decision, surgical planning, and counseling.
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Ringl H, Apfaltrer P. Comparison of Four Dual-Energy CT Scanner Technologies for Determining Renal Stone Composition Using a Phantom Approach. Radiology 2022; 304:590-592. [PMID: 35638932 DOI: 10.1148/radiol.220728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Helmut Ringl
- From the Department of Radiology, Klinik Donaustadt, Langobardenstrasse 122, 1220 Vienna, Austria (H.R.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (H.R.); and Division of General Radiology, Department of Radiology, Medical University of Graz, Graz, Austria (P.A.)
| | - Paul Apfaltrer
- From the Department of Radiology, Klinik Donaustadt, Langobardenstrasse 122, 1220 Vienna, Austria (H.R.); Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria (H.R.); and Division of General Radiology, Department of Radiology, Medical University of Graz, Graz, Austria (P.A.)
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Appel E, Thomas C, Steuwe A, Schaarschmidt BM, Brook OR, Aissa J, Hennenlotter J, Antoch G, Boos J. Evaluation of split-filter dual-energy CT for characterization of urinary stones. Br J Radiol 2021; 94:20210084. [PMID: 33989046 PMCID: PMC8553179 DOI: 10.1259/bjr.20210084] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 03/22/2021] [Accepted: 04/26/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE To assess accuracy of dual-energy computed tomography (DECT) to differentiate uric acid from calcium urinary stones in dual-energy split filter vs sequential-spiral vs dual-source acquisition. METHODS Thirty-four urinary stones (volume 89.0 ± 77.4 mm³; 17 calcium stones, 17 uric acid stones) were scanned in a water-filled phantom using a split-filter equipped CT scanner (SOMATOM Definition Edge, Siemens Healthineers, Forchheim, Germany) in split-filter mode at 120 kVp and sequential-spiral mode at 80 and 140 kVp. Additional DE scans were acquired at 80 and 140 kVp (tin filter) with a dual-source CT scanner (SOMATOM Definition FLASH, Siemens Healthineers). Scans were performed with a CTDIvol of 7.3 mGy in all protocols. Urinary stone categorization was based on dual energy ratio (DER) using an automated 3D segmentation. As reference standard, infrared spectroscopy was used to determine urinary stone composition. RESULTS All three DECT techniques significantly differentiated between uric acid and calcium stones by attenuation values and DERs (p < 0.001 for all). Split-filter DECT provided higher DERs for uric acid stones, when compared with dual-source and sequential-spiral DECT, and lower DERs for calcified stones when compared with dual-source DECT (p < 0.001 for both), leading to a decreased accuracy for material differentiation. CONCLUSION Split-filter DECT, sequential-spiral DECT and dual-source DECT all allow for the acquisition of DER to classify urinary stones. ADVANCES IN KNOWLEDGE Split-filter DECT enables the differentiation between uric acid and calcium stones despite decreased spectral separation when compared with dual-source and dual-spiral DECT.
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Affiliation(s)
- Elisabeth Appel
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstrasse 5, D-40225, Düsseldorf, Germany
| | - Christoph Thomas
- Radiologicum Krefeld, Oberdießemer Straße 96, 47805 Krefeld, Germany
| | - Andrea Steuwe
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstrasse 5, D-40225, Düsseldorf, Germany
| | - Benedikt M Schaarschmidt
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg-Essen, D-45147, Essen, Germany
| | - Olga R Brook
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, USA
| | - Joel Aissa
- RIO - Radiologie Institut Oberhausen, Mülheimer Str. 87, 46045 Oberhausen, Germany
| | - Jörg Hennenlotter
- Department of Urology, University Hospital Tübingen, Tübingen, Germany
| | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstrasse 5, D-40225, Düsseldorf, Germany
| | - Johannes Boos
- Radiologie Münster MVZ, Von-Steuben-Str. 10a, 48143 Münster, Germany
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