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. 2022 Feb;8(1):e002078.
doi: 10.1136/rmdopen-2021-002078.

Fractal analysis of perfusion imaging in synovitis: a novel imaging biomarker for grading inflammatory activity based on assessing angiogenesis

Florian Michallek  1 , Sevtap Tugce Ulas  2 , Denis Poddubnyy  3 , Fabian Proft  3 , Udo Schneider  4 , Kay-Geert A Hermann  2 , Marc Dewey  2 , Torsten Diekhoff  2
Affiliations

Fractal analysis of perfusion imaging in synovitis: a novel imaging biomarker for grading inflammatory activity based on assessing angiogenesis

Florian Michallek et al. RMD Open. 2022 Feb.

Abstract

Objectives: The mutual and intertwined dependence of inflammation and angiogenesis in synovitis is widely acknowledged. However, no clinically established tool for objective and quantitative assessment of angiogenesis is routinely available. This study establishes fractal analysis as a novel method to quantitatively assess inflammatory activity based on angiogenesis in synovitis.

Methods: First, we established a pathophysiological framework for synovitis including fractal analysis of software perfusion phantoms, which allowed to derive explainability with a known and controllable reference standard for vascular structure. Second, we acquired MRI datasets of patients with suspected rheumatoid arthritis of the hand, and three imaging experts independently assessed synovitis analogue to Rheumatoid Arthritis MRI Scoring (RAMRIS) criteria. Finally, we performed fractal analysis of dynamic first-pass perfusion MRI in vivo to evaluate angiogenesis in relation to inflammatory activity with RAMRIS as reference standard.

Results: Fractal dimension (FD) achieved highly significant discriminability for different degrees of inflammatory activity (p<0.01) in software phantoms with known ground-truth of angiogenic structure. FD indicated increasingly chaotic perfusion patterns with increasing grades of inflammatory activity (Spearman's ρ=0.94, p<0.001). In 36 clinical patients, fractal analysis quantitatively and objectively discriminated individual RAMRIS scores (p≤0.05). Area under the receiver-operating curve was 0.84 (95% CI 0.7 to 0.89) for fractal analysis when considering RAMRIS as ground-truth. Fractal analysis additionally identified angiogenesis in cases where RAMRIS underestimated inflammatory activity.

Conclusions: Based on angiogenesis and perfusion pathophysiology, fractal analysis non-invasively enables comprehensive, objective and quantitative characterisation of inflammatory angiogenesis with subjective and qualitative RAMRIS as reference standard. Further studies are required to establish the clinical value of fractal analysis for diagnosis, prognostication and therapy monitoring in inflammatory arthritis.

Keywords: Magnetic Resonance Imaging; arthritis; synovitis.

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Conflict of interest statement

Competing interests: FM holds a US patent (USPTO: 10,991,109, Patent 2021) on fractal analysis of perfusion imaging and has filed a patent application on the same topic at the European Patent Office (PCT/EP2016/071551), each together with Marc Dewey. FM receives grant support from the German Research Foundation (DFG, grant number Ml 2272/1-1 (392304398)), which covers 50% of his position. FP has received research grants from Lilly, Novartis and UCB. FP received speaker and consultancy fees from AbbVie, AMGEN, Bristol-Myers Squibb, Hexal, Janssen, MSD, Novartis, Pfizer, Roche und UCB. K-GAH has received lecture fees from AbbVie, MSD, Novartis, and Pfizer. MD holds a United States patent (USPTO: 10,991,109, Patent 2021) on fractal analysis of perfusion imaging and has filed a patent application on the same topic at the European Patent Office (PCT/EP2016/071551), each together with Florian Michallek. MD receives grant support from the German Research Foundation for this project (DFG, grant number 392304398). MD has received grant support from the FP7 Program of the European Commission for the randomised multicentre DISCHARGE trial (603266-2, HEALTH-2012.2.4.-2). He also received grant support from German Research Foundation (DFG) in the Heisenberg Programme (DE 1361/14-1), graduate program on quantitative biomedical imaging (BIOQIC, GRK 2260/1), the Priority Programme Radiomics for the investigation of coronary plaque and coronary flow (DE 1361/19-1 [428222922] and 20-1 [428223139] in SPP 2177/1). He also received funding from the Berlin University Alliance (GC_SC_PC 27) and from the Digital Health Accelerator of the Berlin Institute of Health. MD is European Society of Radiology (ESR) Research Chair (2019–2022) and the opinions expressed in this article are the author’s own and do not represent the view of ESR. Per the guiding principles of ESR, the work as Research Chair is on a voluntary basis and only remuneration of travel expenses occurs. MD is also the editor of Cardiac CT, published by Springer Nature, and offers hands-on courses on CT imaging (www.ct-kurs.de). Institutional master research agreements exist with Siemens, General Electric, Philips and Canon. The terms of these arrangements are managed by the legal department of Charité–Universitätsmedizin Berlin. TD has received personal fees from MSD, Novartis and Canon MS.

Figures

Figure 1
Figure 1
Pathophysiological framework illustrated with in silico models. (A) Vascular tree model of non-inflamed, synovial host tissue with placeholders for four angiogenic nests. (B) Vascular tree models of angiogenic nests with alterations in vascular structure representing varying degrees of inflammatory activity. To simulate the changes in vascular structure and perfusion patterns induced by inflammation, vessel architecture in angiogenic nests is shifted from an optimality perspective: in healthy tissue, vascular structure is designed to minimise the work needed to generate, perfuse and maintain the vascular network. In inflammation, however, endothelial surface is increased to provide Gateways for migration of inflammatory cells into the inflamed tissue by altering vascular structure and increasing vascular density as well as perfusion rate. (C) Assembled vascular tree model of host and angiogenic nests together with the corresponding grey-level-encoded perfusion model in full resolution. Note the depiction of perfusion territories as a function of vascular scale. Before fractal analysis was performed, resolution was reduced by a factor of 0.01.
Figure 2
Figure 2
Fractal analysis of in silico phantoms. (A) The first row shows a model host tree with inserted angiogenic nests simulating low, intermediate and high inflammatory activity. Perfusion phantoms calculated based on vascular models are shown in the second row. Fractal analysis of perfusion phantoms yields maps of the fractal dimension (FD) encoded in the given colour scale, which are presented in the third row. (B) Boxplot of FD versus degree of simulated inflammatory activity. FD was significantly different between the three groups of inflammatory activity (n=10 per group).
Figure 3
Figure 3
Fractal analysis of MRI first-pass perfusion. (A) Fractal dimension (FD) maps of the third metacarpophalangeal joint with first-pass perfusion MRI in five cases with different RAMRIS scores are presented in the first row. The second and third rows show the corresponding unenhanced T1-weighted (T1w) and contrast-enhanced T1-weighted, fat-saturated (T1w fs CE) sequences. The patient on the far right (0*) had a RAMRIS score of 0 but with an elevated fractal dimension indicating an angiogenic nest consistent with mild synovitis. (B) Boxplot of FD vs RAMRIS for all 216 joints (blue dots). Differences in median FD between the groups were statistically significant as indicated by the p values given above. RAMRIS, Rheumatoid Arthritis MRI Scoring.
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