# Contactless medical equipment AI big data risk control and quasi thinking iterative planning

$$mathrm{Assume }delta =center_frequency/imagein{g}_{frequency}, andomega =left(TRotimes TEright), mathrm{MR}=mathrm{Image Definition} .$$

Image definition formula, diagonal matrix signal transmission and reception form. Therefore, to some extent, sometimes its magnetic resonance signals can be received and interpreted6.

$${A}^{left(x,y,zright)}to frac{delta }{omega }times {Matrixleft[begin{array}{ccc}{E}_{x}& & \ & {S}_{y}& \ & & {M}_{z}end{array}right]},and {A}^{left(x,y,zright)}to Imag{e}_{Definite}$$

(4)

The general formula of MRI image definition is as follows:

begin{aligned} & left( {A_{{left( {x,y,z} right)}}^{MR} ,overline{{A_{{left( {x,y, z} right)}}^{MR} }} } right)^{{H_{ij} Q_{i} H_{ji}^{H} }} = \ & quad mathop sum limits_ {i = 1}^{k} frac{{varvec{delta}}}{{omega_{i} }} times logleft| {I + R^{ – 1} times H_{ij} times Matrixleft[ {begin{array}{*{20}c} {E_{x} } & {} & {} \ {} & {S_{y} } & {} \ {} & {} & {M_{z} } \ end{array} } right]_{i}^{Q} left( {A_{{}}^{E,S,M} ,overline{{A_{{}}^{E,S,M} }} } right) times H_{ji}^{H} } right|,and \ & quad R^{ – 1} interference signal, \ & quad E_{x} = Excitations_number,S_{y} = Spacing _between_slices,M_{z} = Magnet_field_strength, \ & quad omega_{i} = left( {TR otimes TE} right) \ end{aligned}

(5)

Therefore, the image definition of MR is directly related to the interference signal (({R}^{-1})). It is also related to the performance of MR machine, that is, whether it is high-end MR. The image of high-dimensional signal (information polar coordinates) of MR DISCOVERY MR750w is as follows, and reference to Figs. 14, 15.

({omega }_{i}=left(TRotimes TEright)) is a constraint parameter. (1/{omega }_{i}) controls the stability morphological characteristics of high-dimensional information distribution boundary, and its image is as above. The core energy and sub core energy structure Q of MR,({Q}_{core}=Eleft{{X}_{k}{X}_{k}^{H}right})

begin{aligned} & Q_{core}^{{}} left( {A_{{}}^{{X_{E} ,X_{S} ,X_{M} }} ,overline{{ A_{{}}^{{X_{E} ,X_{S} ,X_{M} }} }} } right) = Matrixleft[ {begin{array}{*{20}c} {E_{{X_{E} }}^{k} otimes X_{k}^{H} } & {} & {} \ {} & {E_{{X_{S} }}^{k} otimes X_{k}^{H} } & {} \ {} & {} & {E_{{X_{M} }}^{k} otimes X_{k}^{H} } \ end{array} } right]_{i}^{Q} ,and E_{{X_{E} }}^{k} otimes X_{k}^{H} ,E_{{X_{S} }}^{k} otimes X_ {k}^{H} ,E_{{X_{M} }}^{k} \ & quad otimes X_{k}^{H} {text{Sub core energy structure}} \ end {aligned}

(6)

The simplified general formula for MR image definition is as follows:

begin{aligned} & left( {A_{{left( {x,y,z} right)}}^{core} ,overline{{A_{{left( {x,y, z} right)}}^{core} }} } right)_{MR}^{{H_{ij} Q_{i} H_{ji}^{H} }} = mathop sum limits_{ i = 1}^{k} frac{delta }{{omega_{i} }} times logleft| {I + R^{ – 1} times H_{ij} times Q_{core}^{{}} left( {A_{{}}^{{X_{E} ,X_{S} ,X_{ M} }} ,overline{{A_{{}}^{{X_{E} ,X_{S} ,X_{M} }} }} } right) times H_{ji}^{H} } right| \ & quad ,and R^{ – 1} Interference signal,omega_{i} = left( {TR otimes TE} right) \ end{aligned}

(7)

### When the ({{varvec{R}}}^{-1}) interference signal is strengthened, the clarity of MR image decreases and the comprehensive evaluation index decreases

The MR parameter is related to the machine parameter (upomega =left(mathrm{TR}otimes mathrm{TE}right)), excitations_number, spacing_between_slices, Magnet_field_strength, SAR. Reference to Figs. 16, 17 and 18.

### When ({mathbf{R}}^{-1}) interference signals decrease, MR image clarity increases and comprehensive evaluation index increases

Comprehensive evaluation indexes: 69.730%, 62.940%, 74.716%, its core boundary is 40.01%, and the image is more scientific. And reference to Figs. 19, 20, 21.

### MR peak SAR RF (similar to CT exposure time high-dimensional data heavy core clustering mathematical model)

If SAR > 11.2 then MR stops, when SAR drops down, start MR again. MR does not need to set the domain value, because AI Mathematical model risk control can dynamically find the domain value and boundary of various internal indicators of MR machine. This is the advantage of AI system, and adopts the most cutting-edge and advanced original innovative mathematics to combine with AI. Medical equipment management is characterized by high professionalism, high compliance requirements, diverse types and uses, scattered applicable standards and regulations, and large time and space span of equipment management7.

AI Mathematical model risk control can automatically and dynamically find the domain values ​​and boundaries of various medical equipment indexes, such as the domain values ​​and boundaries of CT’s heat capacity and internal indexes of the machine. And reference to Figs. 22, 23.

AI Mathematical model risk control automatically and dynamically finds the index domain values ​​and boundaries of various medical equipment. Such as MR peakSAR RF, image definition, internal index domain value and boundary of the machine. And reference to Figs. 24, 25.