Magnetic Resonance Imaging and Image Analysis for Assessment et al. (23) showed interpretation of the T2 images of HPMC tablets in terms of dry core, swollen glassy and gel layers. An important issue in completing MRI studies of dosage forms is to assure reasonable dissolution conditions. This could be accomplished by using one of the standard dissolution methods, i.e. usage of dedicated USP Apparatus 4 compatible probe-head (24–27). In our previous research, a dissolution Apparatus 4 integrated with an MRI system was used for imaging of non-compressed HPMC matrix systems during the dissolution process. This arrangement allowed assessment of the parameters related to swelling, diffusion and erosion fronts (5,25,26,28). The concept of three moving fronts, introduced by Gao and Meury (10), for the first time was applied to the analysis of MRI data obtained inside the flow-through cell in 2005 (28). The interface region between dry polymer and gel was identified and marked as “hydrogel formation region” (26), yet there was no justification for this assertion. On the contrary, in the MR micro-imaging study performed in the non-pharmacopoeial flow-through cell by Tajarobi et al. (29), the interface (i.e. swollen glassy) region was not identified. Only two fronts, erosion and swelling front, were assessed. In recent years, pharmaceutical sciences have expanded their interest into new imaging techniques, including MRI (30). However, image processing and analysis are rarely discussed in the pharmaceutical literature. The main problem in the analysis of MR images of hydrating polymeric matrices is proper identification of regions characterized by different physico-chemical properties. In particular, the aim of the study was & & to show a method of MR image processing providing quantitative information on evolution of the system during drug dissolution study under dynamic flow condition (USP Apparatus 4) to verify the results by comparison and discussion with the previous theoretical and experimental studies. MATERIALS AND METHODS Materials Commercially available Controlled Release (CR) quetiapine fumarate (400 mg) tablets were used. In the formulation of dosage form, the following excipients were used: hypromellose (HPMC), lactose monohydrate, microcrystalline cellulose, sodium citrate, and magnesium stearate. MR Imaging An MR research system equipped with MARAN DRX console (Oxford Instruments, UK) and a 4.7-T/310 mm 1067 superconducting magnet (Bruker, Germany) was used for imaging. The MRI study of the tablets was performed inside the homemade probe-head. The probe-head con- sisted of a flow-through cell (US Pharmacopoeia 4), RF resonator based on a saddle type RF coil and positioning holder (25,26). The experiments were performed in 1000 ml circulating distilled water at a flow rate of 10 ml/min, and the temperature was maintained at 37°C. A modified spin-echo, flow-compensated pulse sequence was used to avoid artifacts from flowing dissolution medium. T1 and T2 weighted MR images were collected every 15 min over 12 hrs. The following parameters were applied: FOV = 35 × 35 mm, TE = 19 ms, TR = 625 ms, sampling rate 20 μs, read gradient strength 33.7 mT/m, slice thickness 1 mm, number of averages 4, matrix size 256 × 256. Parameters of the sequence were chosen for imaging of hydrophilic HPMC matrices with incorporated soluble active pharmaceutical ingredient. Echo time (TE) was set to minimum available value, i.e. 19 ms, to maximize the contribution from the less hydrated parts of the matrix. Repetition time (TR) was set short enough (625 ms) to partially suppress bulk fluid (due to its long T1 relaxation time), and in consequence to obtain reasonable gel-bulk fluid contrast. Image Analysis MR signals were acquired during 12 hours with the same optimized acquisition parameters. After the reconstruction, MR images were saved in Analyze format preserving original image intensity, i.e. no scaling or normalization was performed. This allowed the entire dataset of images to be processed together using the same image processing procedures and parameters. The images were analyzed using public domain software Fiji (http://pacific.mpi-cbg.de/wiki/index.php/Fiji), distribution of ImageJ v.1.44 (NIH, http://rsb.info.nih.gov/ij/). Modality of the image intensity histogram of the dosage form region is self-evident (see Figs. 1B and 2); for this reason, histogram-based thresholding was chosen as the method of image segmentation (31,32). However, it was discovered that proper segmentation was impossible when bulk fluid was included in the image. In order to overcome this difficulty, the textural segmentation with some manual corrections was applied to separate and remove bulk fluid. Textural segmentation was performed with the Trainable Segmentation Plugin (33). The Plugin applies several filters (including a median with radius of 1, 2, 4 and 8 pixels, Sobel and Hessian) to the image; the resulting set of images is called “feature stack.” The user selects samples (groups of pixels) and assigns them to a class—in our case, the classes were bulk fluid, dry glassy, swollen glassy and gel. Then the classifier, Fast Random Forest algorithm, is applied to the