Latest advancements in imaging techniques in OA

Abstract

The osteoarthritis (OA) research community has been advocating a shift from radiography-based screening criteria and outcome measures in OA clinical trials to a magnetic resonance imaging (MRI)-based definition of eligibility and endpoint. For conventional morphological MRI, various semiquantitative evaluation tools are available. We have lately witnessed a remarkable technological advance in MRI techniques, including compositional/physiologic imaging and automated quantitative analyses of articular and periarticular structures. More recently, additional technologies were introduced, including positron emission tomography (PET)-MRI, weight-bearing computed tomography (CT), photon-counting spectral CT, shear wave elastography, contrast-enhanced ultrasound, multiscale X-ray phase contrast imaging, and spectroscopic photoacoustic imaging of cartilage. On top of these, we now live in an era in which artificial intelligence is increasingly utilized in medicine. Osteoarthritis imaging is no exception. Successful implementation of artificial intelligence (AI) will hopefully improve the workflow of radiologists, as well as the level of precision and reproducibility in the interpretation of images.

Keywords: MRI; Osteoarthritis; PET-MRI; compositional MRI; imaging; weight-bearing CT.

Figure 1.

Phenotypic characterization. (a) The cartilage/meniscus phenotype is characterized by severe meniscal damage depicted in this example as partial meniscal maceration of the medial meniscal body (arrowhead) and commonly associated with severe cartilage loss (arrows point to diffuse superficial cartilage damage of the medial tibia and femur). Different definitions of a cartilage-meniscus phenotype have been proposed depending on the amount of cartilage damage and meniscal involvement. (b) Bone phenotype. A large bone marrow lesion (BML) is present in the medial central subregion of the medial femur (grade 3, arrows). The size of the BML defines this knee as a subchondral bone phenotype. (c) The inflammatory phenotype is characterized by severe joint effusion-synovitis (asterisk). (d) So-called Hoffa-synovitis, a nonspecific surrogate of whole knee synovitis, is another manifestation of inflammation and is considered for the classification of an inflammatory structural phenotype. (e) The atrophic phenotype is characterized by severe cartilage loss without relevant osteophyte formation. It can be diagnosed by radiography. Anterior-posterior radiograph shows severe medial joint space narrowing (arrows) defining this knee as Kellgren–Lawrence grade 3. The discrepancy between joint space narrowing (a surrogate for cartilage and meniscal damage, and meniscal extrusion) defines this knee as having an atrophic phenotype. (f) The hypertrophic phenotype is characterized by large osteophytes with only minor cartilage loss. Of the five suggested phenotypes also the hypertrophic phenotype can be diagnosed by X-ray, acknowledging that joint space narrowing is a composite structural manifestation of disease. The coronal intermediate-weighted image shows large osteophytes at the medial and lateral femoral joint margin (arrows) and moderate-sized osteophytes at the medial and lateral tibia (arrowheads).

Figure 2.

T_1ρ and T₂ maps reconstructed from prospectively under-sampled data using a novel deep learning-based reconstruction framework (termed as ‘SuperMAP’). SuperMAP incorporates batchwise training with the entire image as the backward cycle (model-data) for consistency, and directly converts a series of under-sampled (both in k-space and parameter-space) T2- and T_1ρ-weighted images into T₂ and T_1ρ maps, bypassing the conventional exponential fitting procedure. SuperMAP exploits both spatial and temporal information and generates T_1ρ (a) and T₂ (d) maps with high acceleration factors (AF = 16). Furthermore, the deep learning model were trained to simultaneously reconstruct T_1ρ (b) and T₂ (e) maps from combined T_1ρ and T₂ acquisition within a single scan with higher acceleration factors (AF = 21.33). T₂ and T_1ρ maps generated from both methods had small errors (g, h) (i, j) and high agreement to reference maps (c, f). Such techniques will allow T_1ρ and T₂ imaging of the whole knee within less than 1 min. Source: Figure from Zhou et al. with permission.

Figure 3.

T2-weighted FSE MRI (Left) and [¹⁸ F]NaF PET-MRI fusion images of a 23-year-old male subject 2.0 years after ACL tear and reconstructive surgery as well as partial meniscectomy. Increased [¹⁸ F]NaF uptake, indicative of bone metabolism, is seen in the ACLR in areas of BML (Purple Arrow) and adjacent meniscal damage (Green Arrow) as well as several regions that appear unremarkable on MRI (Blue arrows). Cartilage morphology and subchondral bone uptake in the contralateral leg appears unremarkable.

Figure 4.

(a) Coronal proton-density-weighted MRI depicting medial meniscal boundary in line with the tibial plateau and no signs of extrusion of the meniscal body (arrows). The root of the posterior horn of the medial meniscus appears thinned but without evidence of a definite tear. (b) Weight-bearing CT arthrography (WBCTa) demonstrates substantial extrusion of the medial meniscus beyond the tibial plateau (arrow). Weight-bearing imaging also reveals a complete tear of the root of the posterior tibial attachment of the medial meniscus with a wide gap (double headed arrow). WBCTa added clinical value by visualizing the cause of the patient’s persistent pain and informed the patient’s care plan.