Cartilage compositional MRI-a narrative review of technical development and clinical applications over the past three decades.

Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.

Efficient phase-cycling strategy for high-resolution 3D gradient-echo quantitative parameter mapping.

Magnetization-prepared (MP) gradient-echo (GRE) sequences suffer from signal contaminations from T1 recovery during the readout train, which can be eliminated by paired RF phase cycling (PC) at the cost of doubling the scan time. The objective of this study was to develop and validate a novel unpaired PC strategy to eliminate the time penalty for high-resolution quantitative parameter mapping in 3D MP-GRE sequences. Based on the observation that the contaminating T1 recovery signal along the GRE readout train is independent of magnetization preparation, its impact can be eliminated using a novel curve-fitting approach with complex-valued data without needing paired PC acquisitions. Four new unpaired PC schemes were compared with two traditional paired PC schemes in both phantom and in vivo human knee studies at 3 T using a MP angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS) T1ρ mapping sequence. In the phantom study, all methods resulted in consistent T1ρ measurements (∆T1ρ < 0.5%) at the center slice when B0 /B1 values were uniform. Results were not consistent when off-center slices with nonideal B0 /B1 were included. Two unpaired PC schemes had comparable or significantly improved quantitative accuracy and scan-rescan reproducibility compared with the paired PC schemes. There was no significant T1ρ quantitative variability increase or spatial fidelity loss using the new unpaired PC schemes. Unpaired PC schemes also had different T1ρ spectral responses at different B0 frequency offsets, which can potentially be exploited to reduce sensitivity to B0 field inhomogeneities. The human knee study results were consistent with the phantom study findings. In conclusion, an unpaired PC strategy potentially allows more accurate quantitative parameter mapping with halved scan time compared with the paired PC approach to eliminate signal contaminations from T1 recovery. It therefore offers additional flexibility in SNR optimization, spatial resolution improvement, and choice of imaging sampling points to obtain more accurate quantitative parameter mapping.

Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models.

Bone marrow edema-like lesions (BMEL) in the knee have been linked to the symptoms and progression of osteoarthritis (OA), a highly prevalent disease with profound public health implications. Manual and semi-automatic segmentations of BMELs in magnetic resonance images (MRI) have been used to quantify the significance of BMELs. However, their utilization is hampered by the labor-intensive and time-consuming nature of the process as well as by annotator bias, especially since BMELs exhibit various sizes and irregular shapes with diffuse signal that lead to poor intra- and inter-rater reliability. In this study, we propose a novel unsupervised method for fully automated segmentation of BMELs that leverages conditional diffusion models, multiple MRI sequences that have different contrast of BMELs, and anomaly detection that do not rely on costly and error-prone annotations. We also analyze BMEL segmentation annotations from multiple experts, reporting intra-/inter-rater variability and setting better benchmarks for BMEL segmentation performance.

Assessment of ultrashort echo time (UTE) T2* mapping at 3T for the whole knee: repeatability, the effects of fat suppression, and knee position.

Background: Knee tissues such as tendon, ligament and meniscus have short T2* relaxation times and tend to show little to no signal in conventional magnetic resonance acquisitions. An ultrashort echo time (UTE) technique offers a unique tool to probe fast-decaying signals in these tissues. Clinically relevant factors should be evaluated to quantify the sensitivity needed to distinguish diseased from control tissues. Therefore, the objectives of this study were to (I) quantify the repeatability of UTE-T2* relaxation time values, and (II) evaluate the effects of fat suppression and (III) knee positioning on UTE-T2* relaxation time quantification. Methods: A dual-echo, three-dimensional center-out radially sampling UTE and conventional gradient echo sequences were utilized to image gadolinium phantoms, one ex-vivo specimen, and five in-vivo subjects on a clinical 3T scanner. Scan-rescan images from the phantom and in-vivo experiments were used to evaluate the repeatability of T2* relaxation time values. Fat suppressed and non-suppressed images were acquired for phantoms and the ex-vivo specimen to evaluate the effect of fat suppression on T2* relaxation time quantifications. The effect of knee positioning was evaluated by imaging in-vivo subjects in extended and flexed positions within the knee coil and comparing T2* relaxation times quantified from tissues in each position. Results: Phantom and in-vivo measurements demonstrated repeatable T2* mapping, where the percent difference between T2* relaxation time quantified from scan-rescan images was less than 8% for the phantom and knee tissues. The coefficient of variation across fat suppressed and non-suppressed images was less than 5% for the phantoms and ex-vivo knee tissues, showing that fat suppression had a minimal effect on T2* relaxation time quantification. Knee position introduced variability to T2* quantification of the anterior cruciate ligament, posterior cruciate ligament, and patellar tendon, with percent differences exceeding 20%, but the meniscus showed a percent difference less than 10%. Conclusions: The 3D radial UTE sequence presented in this study could potentially be used to detect clinically relevant changes in mean T2* relaxation time, however, reproducibility of these values is impacted by knee position consistency between scans.

Laser in situ keratomileusis for hyperopia with the LADARVision 4000 with centration on the coaxially sighted corneal light reflex.

PURPOSE: To analyze the visual acuity, contrast sensitivity, and target deviations in patients who had laser in situ keratomileusis (LASIK) for primary hyperopia with the ablation centered on the coaxially sighted corneal light reflex. SETTING: University-based refractive surgery practice. METHODS: Retrospective review comprised 37 consecutive patients (61 eyes) who had LASIK for hyperopia with the LADARVision 4000 excimer laser (Alcon Laboratories). Preoperative and 3-month postoperative visual acuity and contrast sensitivity, as well as the target deviation, were assessed for each eye. The change in best spectacle-corrected visual acuity (BSCVA), best spectacle-corrected contrast sensitivity (BSCCS), and target deviation from the intended correction were analyzed. RESULTS: Postoperatively, the uncorrected visual acuity (UCVA) was 20/20 or better in 44.4% of eyes. The mean deviation from target was +0.25 diopter (D) +/- 0.82 (SD), with 65.6% of eyes within +/-0.50 D of target. None eye lost 2 or more lines of BSCVA. A loss of 3 or more patches of BSCCS were seen in 6.6% of the eyes and a loss of 4 or more patches, in 1.6%. CONCLUSION: Hyperopic LASIK with LADARVision 4000 with the ablation zone centered on the coaxially sighted corneal light reflex did not adversely affect BSCVA and BSCCS.