Ex vivo tissue imaging for radiology-pathology correlation: a pilot study with a small bore 7-T MRI in a rare pigmented ganglioglioma exhibiting complex MR signal characteristics associated with melanin and hemosiderin

Tensor image registration library: Deformable registration of stand-alone histology images to whole-brain post-mortem MRI data

BACKGROUND

Accurate registration between microscopy and MRI data is necessary for validating imaging biomarkers against neuropathology, and to disentangle complex signal dependencies in microstructural MRI. Existing registration methods often rely on serial histological sampling or significant manual input, providing limited scope to work with a large number of stand-alone histology sections. Here we present a customisable pipeline to assist the registration of stand-alone histology sections to whole-brain MRI data.

METHODS

Our pipeline registers stained histology sections to whole-brain post-mortem MRI in 4 stages, with the help of two photographic intermediaries: a block face image (to undistort histology sections) and coronal brain slab photographs (to insert them into MRI space). Each registration stage is implemented as a configurable stand-alone Python script using our novel platform, Tensor Image Registration Library (TIRL), which provides flexibility for wider adaptation. We report our experience of registering 87 PLP-stained histology sections from 14 subjects and perform various experiments to assess the accuracy and robustness of each stage of the pipeline.

RESULTS

All 87 histology sections were successfully registered to MRI. Histology-to-block registration (Stage 1) achieved 0.2-0.4 mm accuracy, better than commonly used existing methods. Block-to-slice matching (Stage 2) showed great robustness in automatically identifying and inserting small tissue blocks into whole brain slices with 0.2 mm accuracy. Simulations demonstrated sub-voxel level accuracy (0.13 mm) of the slice-to-volume registration (Stage 3) algorithm, which was observed in over 200 actual brain slice registrations, compensating 3D slice deformations up to 6.5 mm. Stage 4 combined the previous stages and generated refined pixelwise aligned multi-modal histology-MRI stacks.

CONCLUSIONS

Our open-source pipeline provides robust automation tools for registering stand-alone histology sections to MRI data with sub-voxel level precision, and the underlying framework makes it readily adaptable to a diverse range of microscopy-MRI studies.

Ultrasound and magnetic resonance imaging for the detection of blood: An ex-vivo study

OBJECTIVES

Early detection of bleeding into a joint is crucial in patients with haemophilia. This study was designed to evaluate the sensitivity of ultrasonography (USG) and magnetic resonance imaging (MRI) to detect the presence of blood in small concentrations in a simulated model to mimic joint bleeding.

MATERIALS AND METHODS

Different concentrations of blood in plasma, varying from 0.1% to 45%, were collected in 10-ml plastic syringes and imaged using 12 and 18 MHz USG transducers and with 1.5T and 3T MRI scanners, at different intervals of time following dilution. The images were scored for the presence of blood by four experienced radiologists who were blinded to the concentration of blood.

RESULTS

Within the first 2 h, the 18 MHz transducer was able to detect blood consistently up to 0.5%, whereas the 12 MHz transducer could consistently identify blood up to 1.4%. After the first 12 h, both transducers were able to detect blood up to 0.5% concentration. However, at concentrations below 0.5%, there was discordance in the ability to detect blood, with both transducers. There was no correlation between the signal intensities of MRI images and concentration of blood, at different time intervals, irrespective of the magnetic field strength.

CONCLUSIONS

Detection of blood using the USG is dependent on variables such as the concentration of blood, frequency of the transducer used and timing of the imaging. As the concentration of blood decreases below 0.5%, the discordance between the observers increases, implying that the detection limit of USG affects its reliability at lower concentrations of blood. Caution is urged while interpreting USG imaging studies for the detection of blood in symptomatic joints.

Matching ex vivo MRI With Iron Histology: Pearls and Pitfalls

Iron levels in the brain can be estimated using newly developed specific magnetic resonance imaging (MRI) sequences. This technique has several applications, especially in neurodegenerative disorders like Alzheimer's disease or Parkinson's disease. Coupling ex vivo MRI with histology allows neuroscientists to better understand what they see in the images. Iron is one of the most extensively studied elements, both by MRI and using histological or physical techniques. Researchers were initially only able to make visual comparisons between MRI images and different types of iron staining, but the emergence of specific MRI sequences like R2* or quantitative susceptibility mapping meant that quantification became possible, requiring correlations with physical techniques. Today, with advances in MRI and image post-processing, it is possible to look for MRI/histology correlations by matching the two sorts of images. For the result to be acceptable, the choice of methodology is crucial, as there are hidden pitfalls every step of the way. In order to review the advantages and limitations of ex vivo MRI correlation with iron-based histology, we reviewed all the relevant articles dealing with the topic in humans. We provide separate assessments of qualitative and quantitative studies, and after summarizing the significant results, we emphasize all the pitfalls that may be encountered.