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IVIM–DKI for differentiation between prostate cancer and benign prostatic hyperplasia: comparison of 1.5 T vs. 3 T MRI

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Abstract

Objective

To implement an advanced spatial penalty-based reconstruction to constrain the intravoxel incoherent motion (IVIM)–diffusion kurtosis imaging (DKI) model and investigate whether it provides a suitable alternative at 1.5 T to the traditional IVIM–DKI model at 3 T for clinical characterization of prostate cancer (PCa) and benign prostatic hyperplasia (BPH).

Materials and methods

Thirty-two patients with biopsy-proven PCa were recruited for MRI examination (n = 16 scanned at 1.5 T, n = 16 scanned at 3 T). Diffusion-weighted imaging (DWI) with 13 b values (b = 0 to 2000 s/mm2 up to 3 averages, 1.5 T: TR = 5.774 s, TE = 81 ms and 3 T: TR = 4.899 s, TE = 100 ms), T2-weighted, and T1-weighted imaging were used on the 1.5 T and 3 T MRI scanner, respectively. The IVIM–DKI signal was modeled using the traditional IVIM–DKI model and a novel model in which the total variation (TV) penalty function was combined with the traditional model to optimize non-physiological variations. Paired and unpaired t-tests were used to compare intra-scanner and scanner group differences in IVIM–DKI parameters obtained using the novel and the traditional models. Analysis of variance with post hoc test and receiver operating characteristic (ROC) curve analysis were used to assess the ability of parameters obtained using the novel model (at 1.5 T) and the traditional model (at 3 T) to characterize prostate lesions.

Results

IVIM–DKI modeled using novel model with TV spatial penalty function at 1.5 T, produced parameter maps with 50–78% lower coefficient of variation (CV) than traditional model at 3 T. Novel model estimated higher D with lower D*, f and k values at both field strengths compared to traditional model. For scanner differences, the novel model at 1.5 T estimated lower D* and f values as compared to traditional model at 3 T. At 1.5 T, D and f values were significantly lower with k values significantly higher in tumor than BPH and healthy tissue. D (AUC: 0.98), f (AUC: 0.82), and k (AUC: 0.91) parameters estimated using novel model showed high diagnostic performance in cancer lesion detection at 1.5 T.

Discussion

In comparison with the IVIM–DKI model at 3 T, IVIM–DKI signal modeled with the TV penalty function at 1.5 T showed lower estimation errors. The proposed novel model can be utilized for improved detection of prostate lesions.

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Acknowledgements

The authors would like to acknowledge Dr. Seema Kaushal for her support in providing histopathology specimens. Dr. Indrajit Saha, Mrs. Rupsa Bhattacharjee, and Mr. Tejas J. Shah for their support from Philips India Ltd. Dr. Anup Singh, Mr. Dharmesh Singh and support staffs of IIT Delhi, New Delhi and AIIMS Delhi, New Delhi for their immense support in data acquisition and processing. A.V.M was supported with research fellowship, funded by Ministry of Human Resource Development, Government of India.

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Authors and Affiliations

  1. Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India

    Archana Vadiraj Malagi, Esha Baidya Kayal & Amit Mehndiratta

  2. Department of Radio-Diagnosis, All India Institute of Medical Sciences, New Delhi, India

    Arjunlokesh Netaji & Chandan Jyoti Das

  3. Department of Nuclear Magnetic Resonance, All India Institute of Medical Sciences, New Delhi, India

    Virendra Kumar

  4. Department of Physics, Indian Institute of Technology Delhi, New Delhi, India

    Kedar Khare

  5. Sydney Imaging and School of Biomedical Engineering, University of Sydney, Sydney, Australia

    Fernando Calamante

  6. Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India

    Amit Mehndiratta

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  1. Archana Vadiraj Malagi
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Contributions

AVM: study conception and design, acquisition of data, analysis and interpretation of data, drafting of manuscript, critical revision. AN: acquisition of data, analysis and interpretation of data. VK: analysis and interpretation of data, critical revision. EBK: analysis and interpretation of data. KK: analysis and interpretation of data, critical revision. CJD: acquisition of data, analysis and interpretation of data. FC: analysis and interpretation of data, drafting of manuscript, critical revision. AM: study conception and design, analysis and interpretation of data, drafting of manuscript, critical revision.

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Correspondence to Amit Mehndiratta.

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Malagi, A.V., Netaji, A., Kumar, V. et al. IVIM–DKI for differentiation between prostate cancer and benign prostatic hyperplasia: comparison of 1.5 T vs. 3 T MRI. Magn Reson Mater Phy 35, 609–620 (2022). https://doi.org/10.1007/s10334-021-00932-1

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  • DOI: https://doi.org/10.1007/s10334-021-00932-1

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