Epiphyseal bone marrow perfusion of the distal femur is a valuable biomarker for knee diseases or injuries, such as anterior cruciate ligament tears and post-traumatic osteoarthritis. Bone marrow perfusion imaging is also of interest in the management of developmental knee diseases, such as osteochondritis dissecans, where the capacity to heal may be related to sufficient perfusion to the site of injury. ASL is well suited for monitoring of knee disease progression, assessment of therapy response, and use in pediatric populations. This study is to evaluate the feasibility and challenges of epiphyseal bone marrow ASL imaging in the distal femur.
Studies were performed on a Siemens 3T MRI scanner using a single-channel transmit and 15-channel receive knee coil under an IRB approved protocol with informed written consent. A series of healthy volunteers and patients with femoral condylar OCD lesions were imaged. Knee bone marrow perfusion imaging was performed using a flow-sensitive alternating inversion recovery (FAIR) method (5) with defined temporal bolus width (6) for ASL and a single-shot fast spin echo (ss-FSE) method for image acquisition, referred to as FAIR ss-FSE (Figure 1). Within an ASL scan, 200 noise images were acquired at the end of the perfusion acquisition by turning off RF pulses to facilitate perfusion SNR analysis (7).
A single oblique transverse slice was utilized for the FAIR ss-FSE acquisition (Figure 1B) with 2 x 2 x 10 mm3 resolution. The major imaging parameters were as follows: TR/TE = 3000/13 ms; partial Fourier = 5/8; labeling time (TI1)/total delay time (TI) = 600/1200 ms; pairs of label and control images = 50; slab thickness/RF duration/interval of proximal saturation = 100 mm/25 ms/50 ms; and total imaging time = ~5 minutes. For some subjects, perfusion imaging using a 1200 ms post-bolus delay (PBD) was also performed to evaluate how the increase of total delay time affects the hyper-intense intravascular artifacts and perfusion signal-to-noise ratio (SNR). Bone marrow blood flow (BMBF) maps were calculated using a simplified single blood-compartment model (8). ROI-based analyses were performed with ROIs manually defined on the perfusion-weighted images (Figure 2). To further reduce the impact of subtraction errors resulting from residual motion and hyper-intense intravascular artifacts, trimmed mean perfusion signals within ROIs were used as the estimates of perfusion signal by excluding the 5% of voxels with the lowest and highest values (9). The spatial and temporal SNRs with a defined ROI were also obtained using the previous approach (7).
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