European Radiology

, Volume 29, Issue 5, pp 2507–2517 | Cite as

Evaluation of a free-breathing respiratory-triggered (Navigator) 3-D T1-weighted (T1W) gradient recalled echo sequence (LAVA) for detection of enhancement in cystic and solid renal masses

  • Wendy Tu
  • Abdulrahman Alzahrani
  • Stephen Currin
  • Cindy Walsh
  • Sabarish Narayanasamy
  • Matthew D. F. McInnes
  • Nicola SchiedaEmail author



To evaluate free-breathing Navigator-triggered 3-D T1-weighted MRI (NAV-LAVA) compared to breath-hold (BH)-LAVA among cystic and solid renal masses.

Materials and methods

With an IRB waiver, 44 patients with 105 renal masses (71 non-enhancing cysts and 14 cystic and 20 solid renal masses) underwent MRI between 2016 and 2017 where BH-LAVA and NAV-LAVA were performed. Subtraction images were generated for BH-LAVA and NAV-LAVA using pre- and 3-min post-gadolinium-enhanced images and were evaluated by two blinded radiologists for overall image quality, image sharpness, motion artifact, and quality of subtraction (using 5-point Likert scales) and presence/absence of enhancement. Percentage signal intensity change (Δ%SI) = ([]/SI.pre-gadolinium)*100, was measured on BH-LAVA and NAV-LAVA. Likert scores were compared using Wilcoxon’s sign-rank test and accuracy for detection of enhancement compared using receiver operator characteristic (ROC) analysis.


Overall image quality (p = 0.002–0.141), image sharpness (p = 0.002–0.031), and motion artifact were better (p = 0.002) comparing BH-LAVA to NAV-LAVA for both radiologists; however, quality of image subtraction did not differ between groups (p = 0.09–0.14). Sensitivity/specificity/area under ROC curve for enhancement in cystic and solid renal masses using subtraction and %SIΔ were (1) BH-LAVA: 64.7%/98.6%/0.82 (radiologist 1), 61.8%/95.8%/0.79 (radiologist 2), and 70.6%/81.7%/0.76 (%SIΔ) versus 2) NAV-LAVA: 58.8%/95.8%/0.79 (radiologist 1, p = 0.16), 58.8%/88.7%/0.73 (radiologist 2, p = 0.37), and 73.5%/76.1%/0.75 (%SIΔ, p = 0.74).


NAV-LAVA showed similar quality of subtraction and ability to detect enhancement compared to BH-LAVA in renal masses albeit with lower image quality, image sharpness, and increased motion artifact. NAV-LAVA may be considered in renal MRI for patients where BH is suboptimal.

Key Points

• Free-breathing Navigator (NAV) 3-D subtraction MRI is comparable to breath-hold (BH) images.

Accuracy for subjective and quantitative diagnosis of enhancement in renal masses on NAV 3-D T1W is comparable to BH MRI.

• NAV 3-D T1W renal MRI is useful in patients who may not be able to adequately BH.


Magnetic resonance imaging Kidney Neoplasms Renal cell carcinoma Image enhancement 


% SI Change

Percentage of signal intensity difference




Breath-hold 3-D T1-weighted MRI






Fast spin echo


Gradient recalled echo


Hounsfield units


Immunoglobulin G4-related disease


Gradient recalled echo sequence


Navigator-triggered 3-D T1-weighted MRI


Free-breathing Navigator


Picture archiving and communication system


Renal cell carcinoma


Repeated K-t-subsampling and artifact-minimization


Region of interest


T1-weighted MRI


T2-weighted MRI


Volumetric interpolated breath-hold examination



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Nicola Schieda, MD.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise: Nicola Schieda, the Ottawa Hospital -University of Ottawa, corresponding author.

Informed consent

Written informed consent was waived by the institutional review board.

Ethical approval

Institutional review board approval was waived under a quality assurance waiver.


• Retrospective

• Cross-sectional study

• Performed at one institution


  1. 1.
    Heilbrun ME, Remer EM, Casalino DD et al (2015) ACR Appropriateness Criteria indeterminate renal mass. J Am Coll Radiol 12:333–341CrossRefGoogle Scholar
  2. 2.
    Krishna S, Murray CA, McInnes MD et al (2017) CT imaging of solid renal masses: pitfalls and solutions. Clin Radiol 72:708–721CrossRefGoogle Scholar
  3. 3.
    Ramamurthy NK, Moosavi B, McInnes MD, Flood TA, Schieda N (2014) Multiparametric MRI of solid renal masses: pearls and pitfalls. Clin Radiol.
  4. 4.
    Israel GM, Bosniak MA (2005) How I do it: evaluating renal masses. Radiology 236:441–450CrossRefGoogle Scholar
  5. 5.
    Bosniak MA (1986) The current radiological approach to renal cysts. Radiology 158:1–10CrossRefGoogle Scholar
  6. 6.
    Bosniak MA (2012) The Bosniak renal cyst classification: 25 years later. Radiology 262:781–785CrossRefGoogle Scholar
  7. 7.
    Dilauro M, Quon M, McInnes MD et al (2016) Comparison of contrast-enhanced multiphase renal protocol CT versus MRI for diagnosis of papillary renal cell carcinoma. AJR Am J Roentgenol 206:319–325CrossRefGoogle Scholar
  8. 8.
    Egbert ND, Caoili EM, Cohan RH et al (2013) Differentiation of papillary renal cell carcinoma subtypes on CT and MRI. AJR Am J Roentgenol 201:347–355CrossRefGoogle Scholar
  9. 9.
    Israel GM, Bosniak MA (2008) Pitfalls in renal mass evaluation and how to avoid them. Radiographics 28:1325–1338CrossRefGoogle Scholar
  10. 10.
    Siddaiah M, Krishna S, McInnes MDF et al (2017) Is ultrasound useful for further evaluation of homogeneously hyperattenuating renal lesions detected on CT? AJR Am J Roentgenol 209:604–610CrossRefGoogle Scholar
  11. 11.
    Schieda N, Dilauro M, Moosavi B et al (2015) MRI evaluation of small (<4cm) solid renal masses: multivariate modeling improves diagnostic accuracy for angiomyolipoma without visible fat compared to univariate analysis. Eur Radiol.
  12. 12.
    Canvasser NE, Kay FU, Xi Y et al (2017) Diagnostic accuracy of multiparametric magnetic resonance imaging to identify clear cell renal cell carcinoma in cT1a renal masses. J Urol 198:780–786CrossRefGoogle Scholar
  13. 13.
    Cornelis F, Tricaud E, Lasserre AS et al (2014) Routinely performed multiparametric magnetic resonance imaging helps to differentiate common subtypes of renal tumours. Eur Radiol 24:1068–1080CrossRefGoogle Scholar
  14. 14.
    Cornelis F, Grenier N (2017) Multiparametric magnetic resonance imaging of solid renal tumors: a practical algorithm. Semin Ultrasound CT MR 38:47–58CrossRefGoogle Scholar
  15. 15.
    Willatt JM, Hussain HK, Chong S et al (2014) MR imaging in the characterization of small renal masses. Abdom Imaging 39:761–769CrossRefGoogle Scholar
  16. 16.
    Ramamurthy NK, Moosavi B, McInnes MD, Flood TA, Schieda N (2015) Multi-parametric (MP) MRI of solid renal masses: pearls and pitfalls. Clin Radiol.
  17. 17.
    Ho VB, Allen SF, Hood MN, Choyke PL (2002) Renal masses: quantitative assessment of enhancement with dynamic MR imaging. Radiology 224:695–700CrossRefGoogle Scholar
  18. 18.
    Hecht EM, Israel GM, Krinsky GA et al (2004) Renal masses: quantitative analysis of enhancement with signal intensity measurements versus qualitative analysis of enhancement with image subtraction for diagnosing malignancy at MR imaging. Radiology 232:373–378CrossRefGoogle Scholar
  19. 19.
    Kim KW, Lee JM, Jeon YS et al (2013) Free-breathing dynamic contrast-enhanced MRI of the abdomen and chest using a radial gradient echo sequence with K-space weighted image contrast (KWIC). Eur Radiol 23:1352–1360CrossRefGoogle Scholar
  20. 20.
    Chandarana H, Block KT, Winfeld MJ et al (2014) Free-breathing contrast-enhanced T1-weighted gradient-echo imaging with radial k-space sampling for paediatric abdominopelvic MRI. Eur Radiol 24:320–326CrossRefGoogle Scholar
  21. 21.
    O'Connor SD, Silverman SG, Ip IK, Maehara CK, Khorasani R (2013) Simple cyst-appearing renal masses at unenhanced CT: can they be presumed to be benign? Radiology 269:793–800CrossRefGoogle Scholar
  22. 22.
    Davarpanah AH, Spektor M, Mathur M, Israel GM (2016) Homogeneous T1 Hyperintense renal lesions with smooth borders: is contrast-enhanced MR imaging needed? Radiology 281:326CrossRefGoogle Scholar
  23. 23.
    Kim CW, Shanbhogue KP, Schreiber-Zinaman J, Deng FM, Rosenkrantz AB (2017) Visual assessment of the intensity and pattern of T1 Hyperintensity on MRI to differentiate hemorrhagic renal cysts from renal cell carcinoma. AJR Am J Roentgenol 208:337–342CrossRefGoogle Scholar
  24. 24.
    Jonisch AI, Rubinowitz AN, Mutalik PG, Israel GM (2007) Can high-attenuation renal cysts be differentiated from renal cell carcinoma at unenhanced CT? Radiology 243:445–450CrossRefGoogle Scholar
  25. 25.
    Israel GM, Hindman N, Bosniak MA (2004) Evaluation of cystic renal masses: comparison of CT and MR imaging by using the Bosniak classification system. Radiology 231:365–371CrossRefGoogle Scholar
  26. 26.
    Schieda N, Avruch L, Flood TA (2014) Small (<1 cm) incidental echogenic renal cortical nodules: chemical shift MRI outperforms CT for confirmatory diagnosis of angiomyolipoma (AML). Insights Imaging.
  27. 27.
    Schieda N, Hodgdon T, El-Khodary M, Flood TA, McInnes MD (2014) Unenhanced CT for the diagnosis of minimal-fat renal angiomyolipoma. AJR Am J Roentgenol 203:1236–1241CrossRefGoogle Scholar
  28. 28.
    Vasanawala SS, Iwadate Y, Church DG, Herfkens RJ, Brau AC (2010) Navigated abdominal T1-W MRI permits free-breathing image acquisition with less motion artifact. Pediatr Radiol 40:340–344CrossRefGoogle Scholar
  29. 29.
    Samji K, Alrashed A, Shabana WM, McInnes MD, Bayram E, Schieda N (2016) Comparison of high-resolution T1W 3D GRE (LAVA) with 2-point Dixon fat/water separation (FLEX) to T1W fast spin echo (FSE) in prostate cancer (PCa). Clin Imaging 40:407–413CrossRefGoogle Scholar
  30. 30.
    Schieda N, Avruch L, Shabana WM, Malone SC (2015) Multi-echo gradient recalled echo imaging of the pelvis for improved depiction of brachytherapy seeds and fiducial markers facilitating radiotherapy planning and treatment of prostatic carcinoma. J Magn Reson Imaging 41:715–720CrossRefGoogle Scholar
  31. 31.
    Ehman RL, Felmlee JP (1989) Adaptive technique for high-definition MR imaging of moving structures. Radiology 173:255–263CrossRefGoogle Scholar
  32. 32.
    Klessen C, Asbach P, Kroencke TJ et al (2005) Magnetic resonance imaging of the upper abdomen using a free-breathing T2-weighted turbo spin echo sequence with navigator triggered prospective acquisition correction. J Magn Reson Imaging 21:576–582CrossRefGoogle Scholar
  33. 33.
    Tyszka JM, Silverman JM (1998) Navigated single-voxel proton spectroscopy of the human liver. Magn Reson Med 39:1–5CrossRefGoogle Scholar
  34. 34.
    Tokuda Y, Kuriyama K, Nakamoto A et al (2009) Evaluation of suspicious nipple discharge by magnetic resonance mammography based on breast imaging reporting and data system magnetic resonance imaging descriptors. J Comput Assist Tomogr 33:58–62CrossRefGoogle Scholar
  35. 35.
    Kim ID, Azuma T, Ido A et al (2006) Navigator-echo-based MR provides high-resolution images and precise volumetry of swine livers without breath holding or injection of contrast media. Liver Transpl 12:72–77CrossRefGoogle Scholar
  36. 36.
    Azevedo RM, de Campos RO, Ramalho M, Herédia V, Dale BM, Semelka RC (2011) Free-breathing 3D T1-weighted gradient-echo sequence with radial data sampling in abdominal MRI: preliminary observations. AJR Am J Roentgenol 197:650–657CrossRefGoogle Scholar
  37. 37.
    Seo N, Park SJ, Kim B et al (2016) Feasibility of free-breathing dynamic contrast-enhanced MRI of the abdomen: a comparison between CAIPIRINHA-VIBE, Radial-VIBE with KWIC reconstruction and conventional VIBE. Br J Radiol 89:20160150CrossRefGoogle Scholar
  38. 38.
    Chu ML, Chang HC, Chung HW et al (2018) Free-breathing abdominal MRI improved by repeated k-t-subsampling and artifact-minimization (ReKAM). Med Phys 45:178–190CrossRefGoogle Scholar
  39. 39.
    Spuentrup E, Katoh M, Buecker A et al (2004) Free-breathing 3D steady-state free precession coronary MR angiography with radial k-space sampling: comparison with cartesian k-space sampling and cartesian gradient-echo coronary MR angiography--pilot study. Radiology 231:581–586CrossRefGoogle Scholar
  40. 40.
    Bamrungchart S, Tantaway EM, Midia EC et al (2013) Free breathing three-dimensional gradient echo-sequence with radial data sampling (radial 3D-GRE) examination of the pancreas: comparison with standard 3D-GRE volumetric interpolated breathhold examination (VIBE). J Magn Reson Imaging 38:1572–1577CrossRefGoogle Scholar
  41. 41.
    Reiner CS, Neville AM, Nazeer HK et al (2013) Contrast-enhanced free-breathing 3D T1-weighted gradient-echo sequence for hepatobiliary MRI in patients with breath-holding difficulties. Eur Radiol 23:3087–3093CrossRefGoogle Scholar
  42. 42.
    Lee SS, Byun JH, Hong HS et al (2007) Image quality and focal lesion detection on T2-weighted MR imaging of the liver: comparison of two high-resolution free-breathing imaging techniques with two breath-hold imaging techniques. J Magn Reson Imaging 26:323–330CrossRefGoogle Scholar
  43. 43.
    Li HH, Zhu H, Yue L et al (2018) Feasibility of free-breathing dynamic contrast-enhanced MRI of gastric cancer using a golden-angle radial stack-of-stars VIBE sequence: comparison with the conventional contrast-enhanced breath-hold 3D VIBE sequence. Eur Radiol 28:1891–1899CrossRefGoogle Scholar
  44. 44.
    Boss A, Schaefer JF, Martirosian P et al (2006) Contrast-enhanced dynamic MR nephrography using the TurboFLASH navigator-gating technique in children. Eur Radiol 16:1509–1518CrossRefGoogle Scholar
  45. 45.
    Riffel P, Zoellner FG, Budjan J et al (2016) “One-stop shop”: free-breathing dynamic contrast-enhanced magnetic resonance imaging of the kidney using iterative reconstruction and continuous golden-angle radial sampling. Invest Radiol 51:714–719CrossRefGoogle Scholar
  46. 46.
    Chandarana H, Feng L, Block TK et al (2013) Free-breathing contrast-enhanced multiphase MRI of the liver using a combination of compressed sensing, parallel imaging, and golden-angle radial sampling. Invest Radiol 48:10–16CrossRefGoogle Scholar
  47. 47.
    Stehning C (2005) Motion correction and volumetric acquisition techniques for coronary magnetic resonance angiography. Accessed 12 May 2018
  48. 48.
    Esses SJ, Lu X, Zhao T et al (2018) Automated image quality evaluation of T2 -weighted liver MRI utilizing deep learning architecture. J Magn Reson Imaging 47:723–728CrossRefGoogle Scholar
  49. 49.
    Ng CS, Wood CG, Silverman PM, Tannir NM, Tamboli P, Sandler CM (2008) Renal cell carcinoma: diagnosis, staging, and surveillance. AJR Am J Roentgenol 191:1220–1232CrossRefGoogle Scholar
  50. 50.
    Pierorazio PM, Hyams ES, Mullins JK, Allaf ME (2012) Active surveillance for small renal masses. Rev Urol 14:13–19Google Scholar
  51. 51.
    Smaldone MC, Kutikov A, Egleston BL et al (2012) Small renal masses progressing to metastases under active surveillance: a systematic review and pooled analysis. Cancer 118:997–1006CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  • Wendy Tu
    • 1
  • Abdulrahman Alzahrani
    • 1
  • Stephen Currin
    • 1
  • Cindy Walsh
    • 1
  • Sabarish Narayanasamy
    • 1
  • Matthew D. F. McInnes
    • 1
  • Nicola Schieda
    • 1
    Email author
  1. 1.The Ottawa HospitalThe University of OttawaOttawaCanada

Personalised recommendations