Home Reconstructions Using RIF in Motion Mapping Technique Have Substantially Less Arrhythmogenic Artifacts in Dual-source Coronary CTA
Post
Cancel

Reconstructions Using RIF in Motion Mapping Technique Have Substantially Less Arrhythmogenic Artifacts in Dual-source Coronary CTA

Highlights

  • Artifacts by cardiac arrhythmias often hinder radiological evaluation in cardiac CT.

  • RIMM technique (RIF in motion mapping) considerably eliminates artifacts.

  • This enhanced visualization by RIMM precludes further invasive diagnostic procedures.

Rationale and Objectives

Particularly for patients with heart arrhythmias, conventional BestSystole (BS) and BestDiastole (BD) reconstruction techniques in computed tomography (CT) frequently show artifacts that hinder the readability of the coronary tree. To address this problem, this paper presents an alternative reconstruction method that combines the technique “reconstructions with identical filling” (RIF) with motion mapping: This new technique is called “RIF in motion mapping” (RIMM). This study compares the diagnostic quality of images generated with RIMM to that of the other reconstruction techniques.

Materials and Methods

Get Radiology Tree app to read full this article<

Results

Get Radiology Tree app to read full this article<

Conclusions

Get Radiology Tree app to read full this article<

Introduction

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Materials and Methods

RIMM Concept

Get Radiology Tree app to read full this article<

Patients

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Imaging Protocol

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Image Reconstruction

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Visual Assessment of Image Quality

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

TABLE 1

Likert Scale Was Used for Stratifying Image Quality Into Non-diagnostic (Likert 1 and 2) and Diagnostic Datasets (Likert 3–5)

Likert Value Interpretation 0 Segment anatomically not existing 1 Non-diagnostic image quality Segment existing due to reference (conventional coronary angiography (CCA) or cCTA), but not identifiable because of severe artifacts 2 Segment existing and identifiable, but not evaluable due to artifacts, representing image with non-diagnostic quality. 3 Diagnostic image quality Moderate image quality: vessel wall and plaques adequately defined, artifacts not influencing diagnostic performance 4 Good image quality: vessel wall and plaques well but not entirely sharply defined; minor artifacts allowed 5 Excellent image quality: vessel wall and plaques sharply delineated; no artifacts

cCTA, coronary computed tomography angiography.

Get Radiology Tree app to read full this article<

Statistical Analysis

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Results

Cohort Analysis

Get Radiology Tree app to read full this article<

TABLE 2

Summary of Patient Characteristics

Entire Cohort ( n = 23) Subgroups SR ( n = 14) ARR ( n = 9)P cCTA ( n = 12) TAVR ( n = 11)P Demographics Age (y) 71.0(±12) 70.0(±14) 77.2(±10) 0.04 62.8(±12) 80.0(±7) 0.001 Gender(male) 10(43%) 22(40%) 9(47%) n.s. 16(46%) 15(39%) n.s. Heart rate(HR) HR mean(bpm) 69.2(±14) 67.0(±14) 75.7(±15) 0.026 65.9(±11) 72.2(±17) n.s. HR submax(bpm) 76.4(±21) 69.5(±16) 96.6(±22) <0.001 70.4(±14) 81.9(±25) 0.01 Risk factors Arterial hypertension 15(88%) 40(73%) 18(95%) n.s. 23(31%) 35(47%) 0.024 Diabetes 5(30%) 6(11%) 4(21%) n.s. 3(4%) 7(10%) n.s. Hypercholesterolemia 5(30%) 22(40%) 8(42%) n.s. 12(16%) 17(23%) n.s. Obesity 4(24%) 10(18%) 4(21%) n.s. 4(5%) 10(14%) n.s. Smoking 3(18%) 11(20%) 6(32%) n.s. 8(11%) 9(12%) n.s. Family history of CAD 5(15%) 9(12%) 2(3%) n.s. 10(14%) 1(2%) 0.003

ARR, arrhythmic; CAD, coronary artery disease; cCTA, coronary computed tomography angiography; HR, heart rate; SR, sinus rhythm; TAVR, transcatheter aortic valve replacement. Significant differences between subgroups are denoted with P values.

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Per Segment Analysis

Get Radiology Tree app to read full this article<

TABLE 3

Results on Per Segment, Per Vessel, and Per Patient Analysis with Regard to the Entire Cohort and Subgroups

Entire Cohort ( n = 23) SR ( n = 14) ARR ( n = 9) cCTA ( n = 12) TAVR ( n = 11) RIMM RIF BD_BS_ RIMM RIF BD_BS_ RIMM RIF BD_BS_ RIMM RIF BD_BS_ RIMM RIF BD_BS_ Per segment AHA-segments ( n = 345) 228(66%) 199(58%) 174(50%) 128(37%) 162(77%) 133(63%) 142(68%) 49(23%) 66(49%) 66(49%) 32(23%) 79(58%) 133(74%) 115(63%) 110(61%) 64(36%) 95(58%) 84(51%) 55(33%) 64(39%) Per vessel LAD(AHA 5, 6, 7, 8) 15(65%) 13(57%) 8(35%) 6(26%) 11(79%) 8(57%) 7(50%) 2(14%) 4(44%) 5(55%) 1(11%) 4(44%) 10(83%) 7(58%) 6(50%) 3(25%) 5(45%) 6(55%) 2(18%) 3(27%) LCX(AHA 11, 13, 15) 12(52%) 9(39%) 7(30%) 7(30%) 4(29%) 5(36%) 7(50%) 2(14%) 2(22%) 4(44%) 0(0%) 5(55%) 9(75%) 4(33%) 6(50%) 4(33%) 3(27%) 5(45%) 1(9%) 3(27%) RCA(AHA 1, 2 ,3) 12(52%) 7(30%) 6(26%) 5(22%) 10(71%) 5(36%) 6(43%) 1(7%) 2(22%) 2(22%) 0(0%) 4(44%) 8(67%) 4(33%) 5(42%) 2(17%) 4(36%) 3(27%) 1(9%) 3(27%) Per patient 10 of 15 17(74%) 12(52%) 9(39%) 6(26%) 14(100%) 9(60%) 9(60%) 2(14%) 3(33%) 3(33%) 0(0%) 4(44%) 11(92%) 8(67%) 7(58%) 3(25%) 6(55%) 4(36%) 2(18%) 3(27%)

AHA, American Heart Association; ARR, arrhythmic subgroup; BD, BestDiastole; BS, BestSystole; cCTA, coronary computed tomography angiography; LAD, left anterior descending; LCX, left circumflex artery; RCA, right coronary artery; RIF, reconstructions with identical filling; RIMM, RIF in motion mapping; SR, sinus rhythm; TAVR, transcatheter aortic valve replacement.

In case of significant differences, P values are provided.

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Figure 1, Distribution of diagnostic segments with regard to reconstruction type, showing number on the ordinate and diagnostic Likert groups on the abscissa. RIMM and RIF scores were highest in Likert groups 4 and 5. (RIF, reconstructions with identical filling; RIMM, RIF in motion mapping).

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Per Vessel Analysis

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Per Patient Analysis

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Figure 2, Example of the performance of RIMM reconstruction: a 61-year-old male patient with atrial fibrillation and a heart rate variation from 37 bpm to 91 bpm. No premedication was administered. Curved multi-plane reformation and motion maps with corresponding reconstruction timings BS, BD, RIF, and RIMM with regard to RCA are shown. Proximal vessel segment is not existing in BD, identifiable in BS and RIF, and diagnostically evaluable in RIMM reconstruction. BD, BestDiastole; BS, BestSystole; RCA, right coronary artery; RIF, reconstructions with identical filling; RIMM, RIF in motion mapping.

Get Radiology Tree app to read full this article<

Discussion

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Limitations

Get Radiology Tree app to read full this article<

Conclusions

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

References

  • 1. Achenbach S., Barkhausen J., Beer M., et. al.: Konsensusempfehlungen der DRG/DGK/DGPK zum Einsatz der Herzbildgebung mit Computertomographie und Magnetresonanztomographie. Der Kardiol 2012; 6: pp. 105-125.

  • 2. Taylor A.J., Cerqueira M., Hodgson J.M., et. al.: ACCF/SCCT/ACR/AHA/ASE/ASNC/NASCI/SCAI/SCMR 2010 appropriate use criteria for cardiac computed tomography. A report of the American College of Cardiology Foundation Appropriate Use Criteria Task Force, the Society of Cardiovascular Computed Tomography, the American College of Radiology, the American Heart Association, the American Society of Echocardiography, the American Society of Nuclear Cardiology, the North American Society for Cardiovascular Imaging, the Society for Cardiovascular Angiography and Interventions, and the Society for Cardiovascular Magnetic Resonance. J Cardiovasc Comput Tomogr 2010; 4: pp. e401-e433.

  • 3. Go A.S., Hylek E.M., Phillips K.A., et. al.: Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA 2001; 285: pp. 2370-2375.

  • 4. Lloyd-Jones D.M., Wang T.J., Leip E.P., et. al.: Lifetime risk for development of atrial fibrillation: the Framingham Heart Study. Circulation 2004; 110: pp. 1042-1046.

  • 5. Camm A.J., Kirchhof P., Lip G.Y., et. al.: Guidelines for the management of atrial fibrillation: the Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC). Eur Heart J 2010; 31: pp. 2369-2429.

  • 6. Seifarth H., Puesken M., Wienbeck S., et. al.: Automatic selection of optimal systolic and diastolic reconstruction windows for dual-source CT coronary angiography. Eur Radiol 2009; 19: pp. 1645-1652.

  • 7. Cademartiri F., Mollet N.R., Runza G., et. al.: Improving diagnostic accuracy of MDCT coronary angiography in patients with mild heart rhythm irregularities using ECG editing. AJR Am J Roentgenol 2006; 186: pp. 634-638.

  • 8. Lesser J.R., Flygenring B.J., Knickelbine T., et. al.: Practical approaches to overcoming artifacts in coronary CT angiography. J Cardiovasc Comput Tomogr 2009; 3: pp. 4-15.

  • 9. Reinartz S.D., Diefenbach B.S., Allmendinger T., et. al.: Reconstructions with identical filling (RIF) of the heart: a physiological approach to image reconstruction in coronary CT angiography. Eur Radiol 2012; 22: pp. 2670-2678.

  • 10. Austen W.G., Edwards J.E., Frye R.L., et. al.: A reporting system on patients evaluated for coronary artery disease. Report of the Ad Hoc Committee for Grading of Coronary Artery Disease, Council on Cardiovascular Surgery, American Heart Association. Circulation 1975; 51: pp. 5-40.

  • 11. Achenbach S., Manolopoulos M., Schuhback A., et. al.: Influence of heart rate and phase of the cardiac cycle on the occurrence of motion artifact in dual-source CT angiography of the coronary arteries. J Cardiovasc Comput Tomogr 2012; 6: pp. 91-98.

  • 12. Akgoz A., Akata D., Hazirolan T., et. al.: Optimal reconstruction interval in dual source CT coronary angiography: a single-center experience in 285 patients. Diagn Interv Radiol 2014; 20: pp. 399-406.

  • 13. Erol B., Karcaaltincaba M., Cay N., et. al.: Effectiveness best R-R reconstruction interval determination software for the evaluation of dual-source coronary CT angiography examinations. J Comput Assist Tomogr 2011; 35: pp. 229-234.

  • 14. Srichai M.B., Barreto M., Lim R.P., et. al.: Prospective-triggered sequential dual-source end-systolic coronary CT angiography for patients with atrial fibrillation: a feasibility study. J Cardiovasc Comput Tomogr 2013; 7: pp. 102-109.

This post is licensed under CC BY 4.0 by the author.