Vessel wall MRI with intravascular (IV) detectors can produce superior local signal-to-noise ratios (SNR) [1] and high resolution (<200μm) T₁, T₂, and proton density (PD) maps [2]. Combined with machine learning-based automatic atherosclerotic lesion classification, up to 97% area-under-curve (AUC) receiver operating characteristic (ROC) analyses is potentially achievable [2]. However, long acquisition times are potentially limiting for clinical T₁, T₂ and PD mapping, and increase the likelihood of motion artifacts. Recently, spectroscopy with linear algebraic modeling (SLAM) was developed for MRS and CEST to reduce scan-times up to 120-fold by delivering compartment-average signals reconstructed from a small subset of those k-space acquisitions having the highest SNR [3-4]. Here, for the first time, SLAM is applied to yield compartment-average T₁, T₂ and PD measures up to 10-fold faster than a standard “MIX” sequence [5]. The phase and amplitude of the sensitivity profiles of the IV coils as receivers, are incorporated into the SLAM reconstruction to compensate for intra-compartmental non-uniformity. SLAM measurements in human vessel specimens are compared with those from conventional full k-space reconstruction acquired at 3T.IV MRI was performed on freshly autopsied human iliac artery specimens immersed in saline and placed in a Philips 3T Achieva scanner with a loopless antenna in the lumen. Reference data were acquired with an axial 2D MIX-turbo spin-echo (TSE) sequence, interleaved with inversion recovery (IR; scan-time=2.3 min; 8 echoes; voxel=0.3x0.3x5mm3; FOV=55mm; TSE-factor=16, TI=370ms, TRSE/TRIR=760/1920ms, TE1/TE2/TE3/TE4= 25/65/105/145ms). The T₁, T₂ and PD values of the compartments were computed as in [5]. Compartment-average T₁, T₂ and PD were measured in compartments that were manually segmented from anatomical images (fat=F; lesion=L; vessel fluid contents=W1; smooth vessel-wall muscle=SM, surrounding tissue=W2) and reconstructed using: (i) the full k-space data set; and (ii) SLAM applied to the central k-space data with enough outer k-space data discarded to achieve acceleration factors of R≤10. During reconstruction, the magnitude and phase of the IV coil’s sensitivity were corrected by 1/r (r=distance from the coil center; Fig.1a), and an azimuthal phase map linearly varying from 0-2π (Fig.1b), respectively. Compartment average T₁, T₂ and PD values from SLAM were compared with conventional Fourier transform (FT) MRI.Magnitude and phase inhomogeneities of IV-MRI were successfully eliminated by the sensitivity profile corrections (Fig.1c-d). A segmented FOV from a diseased vessel (myelodysplastic syndrome) is exemplified in Fig.2. The 10-fold accelerated SLAM parametric maps show high consistency with the regular FT-MIX maps in Fig.3. SLAM results for the vessel wall are plotted against R in Fig. 4, with error-bands indicating compartment mean ±standard deviations (SD) measured in the FT-MIX maps. For R≤10, SLAM T₁, T₂ and PD measurements in all compartments fall within the mean±SD of the FT results. In both lesion compartments (L1, L2) and F, and with R=10, errors in the three parameters are ≤ 0.5%(±4%) compared to the FT mean. Even in W1 and W2, where the SD of T₁ and PD are ≥30% in the FT maps, SLAM T₁ and PD agree with the FT means within ≤6%±6%.SLAM reconstruction can be combined with IV MRI to produce accurate compartment average T₁, T₂, and PD values at least R=10 times faster. In principle, R can be increased until the number of phase-encoding steps equals the number of compartments, which in Fig. 2 would yield R=30. While the SLAM assumption of signal uniformity within compartments [3] is adequately satisfied by applying simple sensitivity profile corrections, these may not suffice for large R in tiny compartments with large phase variations. Nevertheless, SLAM offers a means of dramatically reducing scan-times in high-resolution multi-parametric IV-MRI to ≤10 sec, even in regions of low SNR. In vivo SLAM IV-MRI measurements of T₁, T₂ and PD could enable fast and reliable high-resolution characterization of vessel disease, and in conjunction with automated disease classification [2], permit automatic real-time high-resolution detection of cardiovascular disease.