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Add some HiP-CT docs #18
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# HiP-CT data | ||||||
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HiP-CT stands for hierarchical phase-contrast tomography. | ||||||
For more information on the modality see the [HiP-CT website](https://mecheng.ucl.ac.uk/hip-ct/). | ||||||
Most of the time HiP-CT consists of a single 'low' (but still quite high!) resolution scan of a whole sample, and then one or more 'high' resolution scans of particular volumes of interest within the same sample. | ||||||
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## Data information | ||||||
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There are HiP-CT scans of two donors, S45 and I58. | ||||||
The data are part of the [Pilot human brains dataset](https://lincbrain.org/dandiset/000005) on lincbrain. | ||||||
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Datasets have the name `sub-<donor>_sample-<sample name>_chunk<chunk number>_PC.ome.zarr`. | ||||||
The chunk number is set to `yyxx`, where yy is the resolution of the scan in micrometers, and xx is a scan number that is unique between datasets. | ||||||
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For example, a scan number of `402` is the second 4um scan. | ||||||
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The highest lowest resolution dataset (equivalently, highest chunk number) is the overview dataset that contains an image of the whole sample. | ||||||
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There are then one or more datsets at higher resolution. | ||||||
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## Registration | ||||||
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Each high resolution dataset is registered to the overview dataset using [`hipct-reg`](https://github.com/hipCTProject/hipct-reg). | ||||||
Each transform maps the pixel coordinates of the high resolution zoom datasets to the overview full-sample dataset. | ||||||
The transform is of the form: | ||||||
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$$ | ||||||
T(\mathbf{x}) = s \left ( \mathbf{R} \cdot\mathbf{x} \right ) + \mathbf{t} | ||||||
$$ | ||||||
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Where | ||||||
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- $s$ is a scaling factor | ||||||
- $\mathbf{R}$ is a rotation matrix that rotates about the z-axis. | ||||||
- $\mathbf{t}$ is a translation vector | ||||||
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The rotation matrix takes the form | ||||||
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$$ | ||||||
\mathbf{R} = \begin{pmatrix} | ||||||
\cos(-\theta) & \sin(-\theta) & 0 \\ | ||||||
-\sin(-\theta) & \cos(-\theta) & 0 \\ | ||||||
0 & 0 & 1 \\ | ||||||
\end{pmatrix} | ||||||
$$ | ||||||
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The transform has 5 parameters (1 scaling, 1 rotation, 3 translation). | ||||||
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### Registration accuracy | ||||||
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The HiP-CT registration pipeline is designed to be accurate enough to approximately visually align two datasets. **It is not designed for pixel perfect registration.** We recommend anyone doing analysis relying on the registration between two datasets performs a more accurate registration themselves, using our registration as an initial starting point. | ||||||
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### Registration data | ||||||
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| Dataset | Overview dataset | $\mathbf{t}$ | $s$ | $\theta$ (degrees) | | ||||||
| -------- | -------- | -------- | -------- | -------- | | ||||||
| `sub-I58_sample-blockIC2_chunk-101_PC` | `sub-I58_sample-blockIC2_chunk-401_PC` | (3746.4467746089194, 3660.0610535229666, 1127.3616712249263) | 0.2016106611944697 | -6.496401244495998 | | ||||||
| `sub-I58_sample-blockIC2_chunk-201_PC` | `sub-I58_sample-blockIC2_chunk-401_PC` | (2119.695989239648, 2321.6572801771194, 971.0041825720597) | 0.5181803632402424 | 0.5004912711693281 | |
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window.MathJax = { | ||
tex: { | ||
inlineMath: [["\\(", "\\)"]], | ||
displayMath: [["\\[", "\\]"]], | ||
processEscapes: true, | ||
processEnvironments: true | ||
}, | ||
options: { | ||
ignoreHtmlClass: ".*|", | ||
processHtmlClass: "arithmatex" | ||
} | ||
}; | ||
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document$.subscribe(() => { | ||
MathJax.startup.output.clearCache() | ||
MathJax.typesetClear() | ||
MathJax.texReset() | ||
MathJax.typesetPromise() | ||
}) |
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