TEND-MSIM(1)

💖 TEND-MSIM(1) 💖

okay bestie, welcome to tend-msim, a User Command and honestly? a whole vibe. she simulates DW images from an image of models, and she is so good at it. pull up a chair, grab a strawberry 🍓, and let's get into it.

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✨ NAME ✨

tend-msim — simulate DW images from an image of models 🧚

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🌸 SYNOPSIS 🌸

tend msim [-sigma <sigma>] [-seed <seed>] -g <grad list> [-b0
<b0 image>] [-i <model image>] -m <model> [-ib0 <bool>] [-b <b>] [-kvp
<bool>] [-t <type>] [-o <nout>]

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🦋 DESCRIPTION 🦋

So like, what she does: you hand her an image of models, and she simulates DW images from it. No cap. The output will be in the same form as the input to tend-estim(1) — which is lowkey iconic, because it means you can just... chain them together. slay.

The B-matrices (that's -B) can be the output from tend-bmat(1), or you can give the gradients directly (that's -g). one of these is required, bestie — you cannot skip this part, it's giving mandatory.

Also, heads up: the input tensor image (that's -i) is the basis of the output per-axis fields and image orientation. she runs the whole show.

💀 NOTE: this includes the measurement frame used in the input tensor image, which implies that the given gradients or B-matrices are already expressed in that measurement frame. so like... don't come at her with gradients in some other frame, she will not convert them for you. periodt.

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🎀 OPTIONS 🎀

• -sigma <sigma> — the Gaussian/Rician noise parameter, a double, and the default is “0.0”. so by default she's noise-free, squeaky clean ✨
• -seed <seed> — the seed value for the RNG that creates the noise, an int, default “42”. yes, the answer to everything. iconic behavior honestly.
• -g <grad list> — the gradient list, one row per diffusion-weighted image. this is the "give me the gradients directly" route 🫶
• -b0 <b0 image> — a reference non-diffusion-weighted (“B0”) image. you might need this if it isn't already part of the model parameter image you gave her. she's not gonna invent it, bestie.
• -m <model> — the model with which to simulate DWIs. this must be specified if it isn't indicated by the first axis in the input model image. type: “string”.
• -ib0 <bool> — insert a non-DW B0 image at the beginning of the experiment specification (super useful if your gradient list doesn't already have one!) — and therefore also insert a B0 image at the beginning of the output simulated DWIs. type “bool”, default “false”.
• -b <b> — b value for the simulated scan. “double”, default “1000”.
• -kvp <bool> — generate key/value pairs in the NRRD header corresponding to the input b-value and gradients. “bool”, default “true”. she does this by default, she's thoughtful like that 💅
• -t <type> — output type of the DWIs. default: “float”.
• -o <nout> — output dwis, a string, default “-”.

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🌈 SEE ALSO 🌈

tend(1), tend-bmat(1), tend-estim(1) — go read them, iykyk 💜

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and that's the whole page, bestie!! 🎀 this one's from April 2021, and it lives in the User Commands section of the manual — the TEND-MSIM(1) girlies stay winning. go simulate something gorgeous. mwah 💖✨🦋